From 6644a4e0bd1580bc9d024ad99984ddba7f351eb1 Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Mon, 15 Dec 2025 15:33:59 -0500 Subject: [PATCH 1/7] Personalive implementation in scope. --- .../src/components/InputAndControlsPanel.tsx | 193 ++- frontend/src/data/pipelines.ts | 18 + frontend/src/lib/api.ts | 38 + frontend/src/pages/StreamPage.tsx | 94 +- frontend/src/types/index.ts | 4 +- pyproject.toml | 6 + src/scope/core/pipelines/__init__.py | 10 + .../core/pipelines/personalive/__init__.py | 5 + .../pipelines/personalive/blocks/__init__.py | 1 + .../personalive/components/__init__.py | 7 + .../personalive/components/face_detector.py | 199 +++ .../core/pipelines/personalive/docs/README.md | 124 ++ .../core/pipelines/personalive/model.yaml | 70 + .../modules/FAN_feature_extractor.py | 334 ++++ .../modules/FAN_temporal_feature_extractor.py | 231 +++ .../pipelines/personalive/modules/__init__.py | 30 + .../personalive/modules/attention.py | 463 ++++++ .../pipelines/personalive/modules/camera.py | 73 + .../personalive/modules/convnextv2.py | 215 +++ .../personalive/modules/liveportrait_util.py | 492 ++++++ .../personalive/modules/motion_encoder.py | 42 + .../personalive/modules/motion_extractor.py | 212 +++ .../personalive/modules/motion_module.py | 472 ++++++ .../modules/mutual_self_attention.py | 463 ++++++ .../personalive/modules/pose_guider.py | 57 + .../pipelines/personalive/modules/resnet.py | 255 +++ .../personalive/modules/scheduler_ddim.py | 550 ++++++ .../personalive/modules/transformer_2d.py | 395 +++++ .../personalive/modules/transformer_3d.py | 182 ++ .../personalive/modules/unet_2d_blocks.py | 1073 ++++++++++++ .../personalive/modules/unet_2d_condition.py | 1311 +++++++++++++++ .../pipelines/personalive/modules/unet_3d.py | 717 ++++++++ .../personalive/modules/unet_3d_blocks.py | 1119 +++++++++++++ .../pipelines/personalive/modules/util.py | 313 ++++ .../core/pipelines/personalive/pipeline.py | 656 ++++++++ src/scope/core/pipelines/registry.py | 2 + src/scope/core/pipelines/schema.py | 44 + src/scope/server/app.py | 65 + src/scope/server/download_models.py | 77 +- src/scope/server/models_config.py | 13 + src/scope/server/patch_xformers.py | 85 + src/scope/server/pipeline_manager.py | 32 + src/scope/server/schema.py | 36 + uv.lock | 1469 ++++++++++++++--- 44 files changed, 11969 insertions(+), 278 deletions(-) create mode 100644 src/scope/core/pipelines/personalive/__init__.py create mode 100644 src/scope/core/pipelines/personalive/blocks/__init__.py create mode 100644 src/scope/core/pipelines/personalive/components/__init__.py create mode 100644 src/scope/core/pipelines/personalive/components/face_detector.py create mode 100644 src/scope/core/pipelines/personalive/docs/README.md create mode 100644 src/scope/core/pipelines/personalive/model.yaml create mode 100644 src/scope/core/pipelines/personalive/modules/FAN_feature_extractor.py create mode 100644 src/scope/core/pipelines/personalive/modules/FAN_temporal_feature_extractor.py create mode 100644 src/scope/core/pipelines/personalive/modules/__init__.py create mode 100644 src/scope/core/pipelines/personalive/modules/attention.py create mode 100644 src/scope/core/pipelines/personalive/modules/camera.py create mode 100644 src/scope/core/pipelines/personalive/modules/convnextv2.py create mode 100644 src/scope/core/pipelines/personalive/modules/liveportrait_util.py create mode 100644 src/scope/core/pipelines/personalive/modules/motion_encoder.py create mode 100644 src/scope/core/pipelines/personalive/modules/motion_extractor.py create mode 100644 src/scope/core/pipelines/personalive/modules/motion_module.py create mode 100644 src/scope/core/pipelines/personalive/modules/mutual_self_attention.py create mode 100644 src/scope/core/pipelines/personalive/modules/pose_guider.py create mode 100644 src/scope/core/pipelines/personalive/modules/resnet.py create mode 100644 src/scope/core/pipelines/personalive/modules/scheduler_ddim.py create mode 100644 src/scope/core/pipelines/personalive/modules/transformer_2d.py create mode 100644 src/scope/core/pipelines/personalive/modules/transformer_3d.py create mode 100644 src/scope/core/pipelines/personalive/modules/unet_2d_blocks.py create mode 100644 src/scope/core/pipelines/personalive/modules/unet_2d_condition.py create mode 100644 src/scope/core/pipelines/personalive/modules/unet_3d.py create mode 100644 src/scope/core/pipelines/personalive/modules/unet_3d_blocks.py create mode 100644 src/scope/core/pipelines/personalive/modules/util.py create mode 100644 src/scope/core/pipelines/personalive/pipeline.py create mode 100644 src/scope/server/patch_xformers.py diff --git a/frontend/src/components/InputAndControlsPanel.tsx b/frontend/src/components/InputAndControlsPanel.tsx index a6cbbcb1..176bdef2 100644 --- a/frontend/src/components/InputAndControlsPanel.tsx +++ b/frontend/src/components/InputAndControlsPanel.tsx @@ -15,10 +15,11 @@ import { LabelWithTooltip } from "./ui/label-with-tooltip"; import type { VideoSourceMode } from "../hooks/useVideoSource"; import type { PromptItem, PromptTransition } from "../lib/api"; import type { InputMode } from "../types"; -import { pipelineIsMultiMode } from "../data/pipelines"; +import { pipelineIsMultiMode, pipelineRequiresReferenceImage } from "../data/pipelines"; import { PromptInput } from "./PromptInput"; import { TimelinePromptEditor } from "./TimelinePromptEditor"; import type { TimelinePrompt } from "./PromptTimeline"; +import { ImageIcon } from "lucide-react"; interface InputAndControlsPanelProps { className?: string; @@ -61,6 +62,10 @@ interface InputAndControlsPanelProps { onInputModeChange: (mode: InputMode) => void; // Whether Spout is available (server-side detection for native Windows, not WSL) spoutAvailable?: boolean; + // PersonaLive reference image + referenceImageUrl?: string | null; + onReferenceImageUpload?: (file: File) => void; + isUploadingReference?: boolean; } export function InputAndControlsPanel({ @@ -101,6 +106,9 @@ export function InputAndControlsPanel({ inputMode, onInputModeChange, spoutAvailable = false, + referenceImageUrl = null, + onReferenceImageUpload, + isUploadingReference = false, }: InputAndControlsPanelProps) { // Helper function to determine if playhead is at the end of timeline const isAtEndOfTimeline = () => { @@ -117,6 +125,20 @@ export function InputAndControlsPanel({ // Check if this pipeline supports multiple input modes const isMultiMode = pipelineIsMultiMode(pipelineId); + // Check if this pipeline requires a reference image (PersonaLive) + const needsReferenceImage = pipelineRequiresReferenceImage(pipelineId); + + const handleReferenceImageUpload = ( + event: React.ChangeEvent + ) => { + const file = event.target.files?.[0]; + if (file && onReferenceImageUpload) { + onReferenceImageUpload(file); + } + // Reset the input value so the same file can be selected again + event.target.value = ""; + }; + useEffect(() => { if (videoRef.current && localStream) { videoRef.current.srcObject = localStream; @@ -170,6 +192,56 @@ export function InputAndControlsPanel({ )} + {/* Reference Image upload - only show for PersonaLive */} + {needsReferenceImage && ( +
+

Reference Portrait

+
+ {referenceImageUrl ? ( + Reference portrait + ) : ( +
+ + Upload a portrait image +
+ )} + + +
+ {isUploadingReference && ( +

+ Uploading reference image... +

+ )} + {!referenceImageUrl && ( +

+ This pipeline animates this portrait using your webcam/video as the + driving source. +

+ )} +
+ )} + {/* Video Source toggle - only show when in video input mode */} {inputMode === "video" && (
@@ -280,67 +352,70 @@ export function InputAndControlsPanel({
)} -
- {(() => { - // The Input can have two states: Append (default) and Edit (when a prompt is selected and the video is paused) - const isEditMode = selectedTimelinePrompt && isVideoPaused; + {/* Prompts section - hide for PersonaLive (it uses image conditioning, not text) */} + {!needsReferenceImage && ( +
+ {(() => { + // The Input can have two states: Append (default) and Edit (when a prompt is selected and the video is paused) + const isEditMode = selectedTimelinePrompt && isVideoPaused; + + return ( +
+
+

Prompts

+ {isEditMode && ( + + Editing + + )} +
- return ( -
-
-

Prompts

- {isEditMode && ( - - Editing - + {selectedTimelinePrompt ? ( + p.id === selectedTimelinePrompt.id + )} + /> + ) : ( + )}
- - {selectedTimelinePrompt ? ( - p.id === selectedTimelinePrompt.id - )} - /> - ) : ( - - )} -
- ); - })()} -
+ ); + })()} +
+ )} ); diff --git a/frontend/src/data/pipelines.ts b/frontend/src/data/pipelines.ts index 8ab68cde..e380950e 100644 --- a/frontend/src/data/pipelines.ts +++ b/frontend/src/data/pipelines.ts @@ -18,6 +18,7 @@ export interface PipelineInfo { defaultTemporalInterpolationMethod?: "linear" | "slerp"; // Default method for temporal interpolation defaultTemporalInterpolationSteps?: number; // Default number of steps for temporal interpolation supportsLoRA?: boolean; // Whether this pipeline supports LoRA adapters + requiresReferenceImage?: boolean; // Whether this pipeline requires a reference image (e.g. PersonaLive) // Multi-mode support supportedModes: InputMode[]; @@ -98,6 +99,19 @@ export const PIPELINES: Record = { supportedModes: ["video"], defaultMode: "video", }, + personalive: { + name: "PersonaLive", + docsUrl: "https://github.com/GVCLab/PersonaLive", + about: + "Real-time portrait animation pipeline from GVCLab. Animates a reference portrait image using driving video frames to transfer expressions and head movements.", + estimatedVram: 12, + requiresModels: true, + // Video-only pipeline - requires reference image + driving video + supportedModes: ["video"], + defaultMode: "video", + // PersonaLive requires a reference image to be uploaded first + requiresReferenceImage: true, + }, }; export function pipelineSupportsLoRA(pipelineId: string): boolean { @@ -123,3 +137,7 @@ export function getPipelineDefaultMode(pipelineId: string): InputMode { export function getDefaultPromptForMode(mode: InputMode): string { return DEFAULT_PROMPTS[mode]; } + +export function pipelineRequiresReferenceImage(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.requiresReferenceImage === true; +} diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index 9e657d44..9c1305e7 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -63,6 +63,12 @@ export interface KreaRealtimeVideoLoadParams extends PipelineLoadParams { lora_merge_mode?: "permanent_merge" | "runtime_peft"; } +export interface PersonaLiveLoadParams extends PipelineLoadParams { + height?: number; + width?: number; + seed?: number; +} + export interface PipelineLoadRequest { pipeline_id?: string; load_params?: @@ -70,6 +76,7 @@ export interface PipelineLoadRequest { | StreamDiffusionV2LoadParams | LongLiveLoadParams | KreaRealtimeVideoLoadParams + | PersonaLiveLoadParams | null; } @@ -298,6 +305,37 @@ export const listLoRAFiles = async (): Promise => { return result; }; +// PersonaLive reference image upload +export interface PersonaLiveReferenceResponse { + success: boolean; + message: string; +} + +export const uploadPersonaLiveReference = async ( + imageFile: File | Blob +): Promise => { + // Read file as array buffer + const arrayBuffer = await imageFile.arrayBuffer(); + + const response = await fetch("/api/v1/personalive/reference", { + method: "POST", + headers: { + "Content-Type": imageFile.type || "image/jpeg", + }, + body: arrayBuffer, + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error( + `PersonaLive reference upload failed: ${response.status} ${response.statusText}: ${errorText}` + ); + } + + const result = await response.json(); + return result; +}; + // Pipeline schema types - matches output of get_schema_with_metadata() export interface PipelineSchemaProperty { type?: string; diff --git a/frontend/src/pages/StreamPage.tsx b/frontend/src/pages/StreamPage.tsx index 212c07b3..0996119b 100644 --- a/frontend/src/pages/StreamPage.tsx +++ b/frontend/src/pages/StreamPage.tsx @@ -16,6 +16,7 @@ import { PIPELINES, getPipelineDefaultMode, getDefaultPromptForMode, + pipelineRequiresReferenceImage, } from "../data/pipelines"; import type { InputMode, @@ -24,7 +25,11 @@ import type { LoraMergeStrategy, } from "../types"; import type { PromptItem, PromptTransition } from "../lib/api"; -import { checkModelStatus, downloadPipelineModels } from "../lib/api"; +import { + checkModelStatus, + downloadPipelineModels, + uploadPersonaLiveReference, +} from "../lib/api"; import { sendLoRAScaleUpdates } from "../utils/loraHelpers"; // Delay before resetting video reinitialization flag (ms) @@ -102,6 +107,13 @@ export function StreamPage() { null ); + // PersonaLive reference image state + const [referenceImage, setReferenceImage] = useState(null); + const [referenceImageUrl, setReferenceImageUrl] = useState( + null + ); + const [isUploadingReference, setIsUploadingReference] = useState(false); + // Ref to access timeline functions const timelineRef = useRef<{ getCurrentTimelinePrompt: () => string; @@ -231,6 +243,9 @@ export function StreamPage() { stopStream(); } + // Clear reference image when switching pipelines + clearReferenceImage(); + const newPipeline = PIPELINES[pipelineId]; const modeToUse = newPipeline?.defaultMode || "text"; const currentMode = settings.inputMode || "text"; @@ -473,6 +488,25 @@ export function StreamPage() { }); }; + // Handle reference image upload for PersonaLive + const handleReferenceImageUpload = (file: File) => { + // Clean up old URL + if (referenceImageUrl) { + URL.revokeObjectURL(referenceImageUrl); + } + setReferenceImage(file); + setReferenceImageUrl(URL.createObjectURL(file)); + }; + + // Clear reference image (used when switching pipelines) + const clearReferenceImage = () => { + if (referenceImageUrl) { + URL.revokeObjectURL(referenceImageUrl); + } + setReferenceImage(null); + setReferenceImageUrl(null); + }; + // Sync spoutReceiver.enabled with mode changes const handleModeChange = (newMode: typeof mode) => { // When switching to spout mode, enable spout input @@ -686,6 +720,22 @@ export function StreamPage() { console.log( `Loading with resolution: ${resolution.width}x${resolution.height}, seed: ${loadParams.seed}, quantization: ${loadParams.quantization}, lora_merge_mode: ${loadParams.lora_merge_mode}` ); + } else if (pipelineIdToUse === "personalive" && resolution) { + // PersonaLive requires a reference image + if (!referenceImage) { + console.error( + "PersonaLive requires a reference image. Please upload one first." + ); + return false; + } + loadParams = { + height: resolution.height, + width: resolution.width, + seed: settings.seed ?? 42, + }; + console.log( + `Loading PersonaLive with resolution: ${resolution.width}x${resolution.height}, seed: ${loadParams.seed}` + ); } const loadSuccess = await loadPipeline( @@ -697,6 +747,22 @@ export function StreamPage() { return false; } + // For PersonaLive, upload reference image after pipeline is loaded + if (pipelineIdToUse === "personalive" && referenceImage) { + try { + setIsUploadingReference(true); + console.log("Uploading PersonaLive reference image..."); + const result = await uploadPersonaLiveReference(referenceImage); + console.log("Reference image uploaded:", result.message); + } catch (error) { + console.error("Failed to upload reference image:", error); + setIsUploadingReference(false); + return false; + } finally { + setIsUploadingReference(false); + } + } + // Check video requirements based on input mode const needsVideoInput = currentMode === "video"; const isSpoutMode = mode === "spout" && settings.spoutReceiver?.enabled; @@ -729,7 +795,11 @@ export function StreamPage() { }; // Common parameters for pipelines that support prompts - if (pipelineIdToUse !== "passthrough") { + // PersonaLive and Passthrough don't use text prompts + if ( + pipelineIdToUse !== "passthrough" && + pipelineIdToUse !== "personalive" + ) { initialParameters.prompts = promptItems; initialParameters.prompt_interpolation_method = interpolationMethod; initialParameters.denoising_step_list = settings.denoisingSteps || [ @@ -799,11 +869,17 @@ export function StreamPage() { isConnecting={isConnecting} isPipelineLoading={isPipelineLoading} canStartStream={ - settings.inputMode === "text" - ? !isInitializing - : mode === "spout" - ? !isInitializing // Spout mode doesn't need local stream - : !!localStream && !isInitializing + // PersonaLive requires reference image + pipelineRequiresReferenceImage(settings.pipelineId) + ? !!referenceImage && + (mode === "spout" + ? !isInitializing + : !!localStream && !isInitializing) + : settings.inputMode === "text" + ? !isInitializing + : mode === "spout" + ? !isInitializing // Spout mode doesn't need local stream + : !!localStream && !isInitializing } onStartStream={handleStartStream} onStopStream={stopStream} @@ -834,6 +910,9 @@ export function StreamPage() { } onInputModeChange={handleInputModeChange} spoutAvailable={spoutAvailable} + referenceImageUrl={referenceImageUrl} + onReferenceImageUpload={handleReferenceImageUpload} + isUploadingReference={isUploadingReference} />
@@ -947,6 +1026,7 @@ export function StreamPage() { }} disabled={ settings.pipelineId === "passthrough" || + settings.pipelineId === "personalive" || isPipelineLoading || isConnecting || showDownloadDialog diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index 95bf3e1a..ac4c0c74 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -3,7 +3,8 @@ export type PipelineId = | "passthrough" | "longlive" | "krea-realtime-video" - | "reward-forcing"; + | "reward-forcing" + | "personalive"; // Input mode for pipeline operation export type InputMode = "text" | "video"; @@ -87,6 +88,7 @@ export interface PipelineInfo { defaultTemporalInterpolationMethod?: "linear" | "slerp"; defaultTemporalInterpolationSteps?: number; supportsLoRA?: boolean; + requiresReferenceImage?: boolean; // Whether this pipeline requires a reference image (e.g. PersonaLive) // Multi-mode support supportedModes: InputMode[]; diff --git a/pyproject.toml b/pyproject.toml index 307642f9..edcd08b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,12 +55,18 @@ dependencies = [ "triton-windows==3.4.0.post21; sys_platform == 'win32'", "SpoutGL>=0.1.1; sys_platform == 'win32'", "PyOpenGL>=3.1.10; sys_platform == 'win32'", + # PersonaLive dependencies + "mediapipe>=0.10.11", + "decord>=0.6.0", + "xformers>=0.0.32.post2", + "scikit-image>=0.22.0", ] [project.scripts] daydream-scope = "scope.server.app:main" build = "scope.server.build:main" download_models = "scope.server.download_models:main" +patch-xformers = "scope.server.patch_xformers:main" [project.urls] Homepage = "https://github.com/daydreamlive/scope" diff --git a/src/scope/core/pipelines/__init__.py b/src/scope/core/pipelines/__init__.py index 9ea63303..02ecf5c1 100644 --- a/src/scope/core/pipelines/__init__.py +++ b/src/scope/core/pipelines/__init__.py @@ -24,6 +24,10 @@ def __getattr__(name): from .passthrough.pipeline import PassthroughPipeline return PassthroughPipeline + elif name == "PersonaLivePipeline": + from .personalive.pipeline import PersonaLivePipeline + + return PersonaLivePipeline # Config classes elif name == "BasePipelineConfig": from .schema import BasePipelineConfig @@ -45,6 +49,10 @@ def __getattr__(name): from .schema import PassthroughConfig return PassthroughConfig + elif name == "PersonaLiveConfig": + from .schema import PersonaLiveConfig + + return PersonaLiveConfig raise AttributeError(f"module {__name__!r} has no attribute {name!r}") @@ -55,10 +63,12 @@ def __getattr__(name): "RewardForcingPipeline", "StreamDiffusionV2Pipeline", "PassthroughPipeline", + "PersonaLivePipeline", # Config classes "BasePipelineConfig", "LongLiveConfig", "StreamDiffusionV2Config", "KreaRealtimeVideoConfig", "PassthroughConfig", + "PersonaLiveConfig", ] diff --git a/src/scope/core/pipelines/personalive/__init__.py b/src/scope/core/pipelines/personalive/__init__.py new file mode 100644 index 00000000..f3995ef9 --- /dev/null +++ b/src/scope/core/pipelines/personalive/__init__.py @@ -0,0 +1,5 @@ +"""PersonaLive pipeline for real-time portrait animation.""" + +from .pipeline import PersonaLivePipeline + +__all__ = ["PersonaLivePipeline"] diff --git a/src/scope/core/pipelines/personalive/blocks/__init__.py b/src/scope/core/pipelines/personalive/blocks/__init__.py new file mode 100644 index 00000000..fdb7b942 --- /dev/null +++ b/src/scope/core/pipelines/personalive/blocks/__init__.py @@ -0,0 +1 @@ +"""PersonaLive modular blocks for pipeline composition.""" diff --git a/src/scope/core/pipelines/personalive/components/__init__.py b/src/scope/core/pipelines/personalive/components/__init__.py new file mode 100644 index 00000000..941b0908 --- /dev/null +++ b/src/scope/core/pipelines/personalive/components/__init__.py @@ -0,0 +1,7 @@ +"""PersonaLive model component wrappers.""" + +from .face_detector import FaceDetector + +__all__ = [ + "FaceDetector", +] diff --git a/src/scope/core/pipelines/personalive/components/face_detector.py b/src/scope/core/pipelines/personalive/components/face_detector.py new file mode 100644 index 00000000..9724c95c --- /dev/null +++ b/src/scope/core/pipelines/personalive/components/face_detector.py @@ -0,0 +1,199 @@ +"""Face detection component using MediaPipe for PersonaLive pipeline.""" + +import logging +from typing import Tuple + +import numpy as np +import torch + +logger = logging.getLogger(__name__) + +try: + import mediapipe as mp + MEDIAPIPE_AVAILABLE = True +except ImportError: + MEDIAPIPE_AVAILABLE = False + logger.warning("MediaPipe not available. Face detection will be disabled.") + + +class FaceDetector: + """Face detector using MediaPipe Face Mesh. + + Extracts face regions from images for motion encoding. + """ + + def __init__(self, static_image_mode: bool = True, max_num_faces: int = 1): + """Initialize the face detector. + + Args: + static_image_mode: If True, treats input as unrelated images (no tracking). + max_num_faces: Maximum number of faces to detect. + """ + if not MEDIAPIPE_AVAILABLE: + raise RuntimeError( + "MediaPipe is required for PersonaLive face detection. " + "Install with: pip install mediapipe" + ) + + self.mp_face_mesh = mp.solutions.face_mesh + self.face_mesh = self.mp_face_mesh.FaceMesh( + static_image_mode=static_image_mode, + max_num_faces=max_num_faces, + min_detection_confidence=0.5, + min_tracking_confidence=0.5, + ) + + def detect_face_landmarks(self, image: np.ndarray) -> list | None: + """Detect face landmarks in an image. + + Args: + image: RGB image as numpy array (H, W, 3), uint8. + + Returns: + List of face landmarks or None if no face detected. + """ + results = self.face_mesh.process(image) + if results.multi_face_landmarks: + return results.multi_face_landmarks + return None + + def get_face_bounding_box( + self, + image: np.ndarray, + padding_ratio: float = 0.2 + ) -> Tuple[int, int, int, int] | None: + """Get bounding box for the detected face. + + Args: + image: RGB image as numpy array (H, W, 3). + padding_ratio: Extra padding around the face box. + + Returns: + Tuple of (left, top, right, bottom) or None if no face. + """ + landmarks = self.detect_face_landmarks(image) + if landmarks is None: + return None + + h, w = image.shape[:2] + face_landmarks = landmarks[0] + + # Get all landmark coordinates + xs = [lm.x * w for lm in face_landmarks.landmark] + ys = [lm.y * h for lm in face_landmarks.landmark] + + # Compute bounding box + left = min(xs) + right = max(xs) + top = min(ys) + bottom = max(ys) + + # Add padding + box_w = right - left + box_h = bottom - top + pad_w = box_w * padding_ratio + pad_h = box_h * padding_ratio + + left = max(0, int(left - pad_w)) + top = max(0, int(top - pad_h)) + right = min(w, int(right + pad_w)) + bottom = min(h, int(bottom + pad_h)) + + return (left, top, right, bottom) + + def crop_face( + self, + image: np.ndarray, + target_size: Tuple[int, int] = (512, 512), + padding_ratio: float = 0.2, + ) -> np.ndarray | None: + """Crop and resize face from image. + + Args: + image: RGB image as numpy array (H, W, 3). + target_size: Size to resize the cropped face to (W, H). + padding_ratio: Extra padding around the face box. + + Returns: + Cropped and resized face image, or None if no face detected. + """ + from PIL import Image + + bbox = self.get_face_bounding_box(image, padding_ratio) + if bbox is None: + return None + + left, top, right, bottom = bbox + face_patch = image[top:bottom, left:right] + + # Resize to target size + face_pil = Image.fromarray(face_patch) + face_pil = face_pil.resize(target_size, Image.BILINEAR) + + return np.array(face_pil) + + def crop_face_from_pil(self, pil_image, target_size: Tuple[int, int] = (512, 512)): + """Crop face from a PIL image. + + Args: + pil_image: PIL Image in RGB mode. + target_size: Size to resize the cropped face to (W, H). + + Returns: + PIL Image of cropped face, or None if no face detected. + """ + from PIL import Image + + image_np = np.array(pil_image) + face_np = self.crop_face(image_np, target_size) + + if face_np is None: + return None + + return Image.fromarray(face_np) + + def crop_face_tensor( + self, + image_tensor: torch.Tensor, + boxes: Tuple[int, int, int, int], + target_size: Tuple[int, int] = (224, 224), + ) -> torch.Tensor: + """Crop face region from a tensor using precomputed box. + + Args: + image_tensor: Image tensor of shape (C, H, W) or (B, C, H, W). + boxes: Bounding box as (left, top, right, bottom). + target_size: Output size (H, W). + + Returns: + Cropped and resized face tensor. + """ + import torch.nn.functional as F + + left, top, right, bottom = map(int, boxes) + + if image_tensor.dim() == 3: + # (C, H, W) -> crop + face_patch = image_tensor[:, top:bottom, left:right] + face_patch = face_patch.unsqueeze(0) # Add batch dim for interpolate + else: + # (B, C, H, W) + face_patch = image_tensor[:, :, top:bottom, left:right] + + # Resize + face_patch = F.interpolate( + face_patch, + size=target_size, + mode="bilinear", + align_corners=False, + ) + + return face_patch + + def close(self): + """Release resources.""" + if hasattr(self, 'face_mesh') and self.face_mesh: + self.face_mesh.close() + + def __del__(self): + self.close() diff --git a/src/scope/core/pipelines/personalive/docs/README.md b/src/scope/core/pipelines/personalive/docs/README.md new file mode 100644 index 00000000..da470be1 --- /dev/null +++ b/src/scope/core/pipelines/personalive/docs/README.md @@ -0,0 +1,124 @@ +# PersonaLive Pipeline + +PersonaLive is a real-time portrait animation pipeline that animates a reference portrait image using driving video frames. + +## Overview + +PersonaLive takes: +1. A **reference image** - A portrait photograph of the person to animate +2. **Driving video frames** - Video frames that provide the motion/expression to transfer + +And outputs animated video frames of the reference person following the expressions and movements from the driving video. + +## Installation + +### 1. Download Models + +```bash +# Download PersonaLive models +download_models --pipeline personalive +``` + +This downloads from [huggingface.co/huaichang/PersonaLive](https://huggingface.co/huaichang/PersonaLive), which includes: +- PersonaLive custom weights (denoising_unet, reference_unet, motion_encoder, etc.) +- sd-image-variations-diffusers base model +- sd-vae-ft-mse VAE + +### 2. Install Dependencies + +Make sure you have the required dependencies installed: +- `mediapipe>=0.10.11` - For face detection and keypoint extraction +- `decord>=0.6.0` - For video decoding +- `xformers>=0.0.28` - For memory-efficient attention + +## Usage + +### API Usage + +1. **Load the pipeline**: + +```bash +POST /api/v1/pipeline/load +Content-Type: application/json + +{ + "pipeline_id": "personalive", + "load_params": { + "height": 512, + "width": 512, + "seed": 42 + } +} +``` + +2. **Upload reference image**: + +```bash +POST /api/v1/personalive/reference +Content-Type: image/jpeg + + +``` + +3. **Start WebRTC stream**: + +Connect via WebRTC and send driving video frames. The output will be animated frames of the reference portrait. + +### Workflow + +``` +1. Load PersonaLive pipeline +2. Upload reference portrait image +3. Connect WebRTC stream with camera/video input +4. Receive animated output frames +``` + +## Technical Details + +### Architecture + +PersonaLive uses a multi-component architecture: + +- **Reference UNet (2D)**: Extracts features from the reference image +- **Denoising UNet (3D)**: Generates animated frames with temporal consistency +- **Motion Encoder**: Extracts motion features from driving video faces +- **Pose Encoder (MotionExtractor)**: Extracts keypoints from faces +- **Pose Guider**: Converts keypoints to conditioning features +- **VAE**: Encodes/decodes latent representations +- **Image Encoder (CLIP)**: Extracts semantic features from reference image + +### Processing Flow + +1. **Reference Fusion** (once at start): + - Encode reference image with CLIP + - Encode reference image with VAE + - Run Reference UNet to cache attention features + - Extract reference face keypoints + +2. **Frame Processing** (for each driving frame batch): + - Extract face keypoints from driving frames + - Compute motion features from cropped face regions + - Generate pose condition embeddings + - Denoise latents with temporal attention + - Decode output frames from latents + +### Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `height` | 512 | Output video height | +| `width` | 512 | Output video width | +| `seed` | 42 | Random seed for reproducibility | +| `temporal_window_size` | 4 | Number of frames processed together | +| `temporal_adaptive_step` | 4 | Temporal denoising step | +| `num_inference_steps` | 4 | Denoising steps (typically 4 for real-time) | + +## References + +- [PersonaLive Paper](https://arxiv.org/abs/2506.09887) +- [GitHub Repository](https://github.com/GVCLab/PersonaLive) +- [HuggingFace Models](https://huggingface.co/huaichang/PersonaLive) + +## Credits + +Based on the PersonaLive implementation by GVCLab. diff --git a/src/scope/core/pipelines/personalive/model.yaml b/src/scope/core/pipelines/personalive/model.yaml new file mode 100644 index 00000000..520642fb --- /dev/null +++ b/src/scope/core/pipelines/personalive/model.yaml @@ -0,0 +1,70 @@ +# PersonaLive model configuration +# Based on https://github.com/GVCLab/PersonaLive + +# Base model paths (relative to models_dir) +pretrained_base_model_path: "sd-image-variations-diffusers" +image_encoder_path: "sd-image-variations-diffusers/image_encoder" +vae_model_path: "sd-vae-ft-mse" + +# PersonaLive custom weights +personalive_weights_path: "PersonaLive/pretrained_weights/personalive" + +# Inference configuration +dtype: "fp16" +batch_size: 1 +temporal_window_size: 4 +temporal_adaptive_step: 4 +num_inference_steps: 4 +seed: 42 + +# Resolution +reference_image_height: 512 +reference_image_width: 512 +output_height: 512 +output_width: 512 + +# Scheduler configuration +noise_scheduler_kwargs: + beta_start: 0.00085 + beta_end: 0.02 + beta_schedule: "scaled_linear" + clip_sample: false + steps_offset: 1 + prediction_type: "epsilon" + timestep_spacing: "trailing" + +# UNet additional kwargs for 3D UNet +unet_additional_kwargs: + use_inflated_groupnorm: true + unet_use_cross_frame_attention: false + unet_use_temporal_attention: false + use_motion_module: true + motion_module_resolutions: + - 1 + - 2 + - 4 + - 8 + motion_module_mid_block: true + motion_module_decoder_only: false + motion_module_type: "Vanilla" + motion_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + cross_attention_dim: 16 + attention_block_types: + - "Spatial_Cross" + - "Spatial_Cross" + temporal_position_encoding: false + temporal_position_encoding_max_len: 32 + temporal_attention_dim_div: 1 + use_temporal_module: true + temporal_module_type: "Vanilla" + temporal_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + attention_block_types: + - "Temporal_Self" + - "Temporal_Self" + temporal_position_encoding: true + temporal_position_encoding_max_len: 32 + temporal_attention_dim_div: 1 diff --git a/src/scope/core/pipelines/personalive/modules/FAN_feature_extractor.py b/src/scope/core/pipelines/personalive/modules/FAN_feature_extractor.py new file mode 100644 index 00000000..1fca87de --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/FAN_feature_extractor.py @@ -0,0 +1,334 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Dict, Union +from diffusers.models.attention_processor import AttentionProcessor + + +def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, + stride=strd, padding=padding, bias=bias) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, out_planes): + super(ConvBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = conv3x3(in_planes, int(out_planes / 2)) + self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) + self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) + self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) + self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) + + if in_planes != out_planes: + self.downsample = nn.Sequential( + nn.BatchNorm2d(in_planes, eps=1e-4), + nn.ReLU(True), + nn.Conv2d(in_planes, out_planes, + kernel_size=1, stride=1, bias=False), + ) + else: + self.downsample = None + + def forward(self, x): + residual = x + + out1 = self.bn1(x) + out1 = F.relu(out1, True) + out1 = self.conv1(out1) + + out2 = self.bn2(out1) + out2 = F.relu(out2, True) + out2 = self.conv2(out2) + + out3 = self.bn3(out2) + out3 = F.relu(out3, True) + out3 = self.conv3(out3) + + out3 = torch.cat((out1, out2, out3), 1) + + if self.downsample is not None: + residual = self.downsample(residual) + + out3 += residual + + return out3 + + +class HourGlass(nn.Module): + def __init__(self, num_modules, depth, num_features): + super(HourGlass, self).__init__() + self.num_modules = num_modules + self.depth = depth + self.features = num_features + self.dropout = nn.Dropout(0.5) + + self._generate_network(self.depth) + + def _generate_network(self, level): + self.add_module('b1_' + str(level), ConvBlock(256, 256)) + + self.add_module('b2_' + str(level), ConvBlock(256, 256)) + + if level > 1: + self._generate_network(level - 1) + else: + self.add_module('b2_plus_' + str(level), ConvBlock(256, 256)) + + self.add_module('b3_' + str(level), ConvBlock(256, 256)) + + def _forward(self, level, inp): + # Upper branch + up1 = inp + up1 = self._modules['b1_' + str(level)](up1) + up1 = self.dropout(up1) + # Lower branch + low1 = F.max_pool2d(inp, 2, stride=2) + low1 = self._modules['b2_' + str(level)](low1) + + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = low1 + low2 = self._modules['b2_plus_' + str(level)](low2) + + low3 = low2 + low3 = self._modules['b3_' + str(level)](low3) + up1size = up1.size() + rescale_size = (up1size[2], up1size[3]) + up2 = F.upsample(low3, size=rescale_size, mode='bilinear') + + return up1 + up2 + + def forward(self, x): + return self._forward(self.depth, x) + + +class FAN_use(nn.Module): + def __init__(self): + super(FAN_use, self).__init__() + self.num_modules = 1 + + # Base part + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.conv2 = ConvBlock(64, 128) + self.conv3 = ConvBlock(128, 128) + self.conv4 = ConvBlock(128, 256) + + # Stacking part + hg_module = 0 + self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) + self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) + self.add_module('conv_last' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('l' + str(hg_module), nn.Conv2d(256, 68, kernel_size=1, stride=1, padding=0)) + self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) + + if hg_module < self.num_modules - 1: + self.add_module('bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('al' + str(hg_module), nn.Conv2d(68, 256, kernel_size=1, stride=1, padding=0)) + + self.avgpool = nn.MaxPool2d((2, 2), 2) + self.conv6 = nn.Conv2d(68, 1, 3, 2, 1) + self.fc = nn.Linear(28 * 28, 512) + self.bn5 = nn.BatchNorm2d(68) + self.relu = nn.ReLU(True) + + def forward(self, x, return_featmap=False): + x = F.relu(self.bn1(self.conv1(x)), True) # 112 + x = F.max_pool2d(self.conv2(x), 2) # 56 # [B, 128, 112, 112] + x = self.conv3(x) + x = self.conv4(x) # [B, 256, 56, 56] + + previous = x + + i = 0 + hg = self._modules['m' + str(i)](previous) + + ll = hg + ll = self._modules['top_m_' + str(i)](ll) + + ll = self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll)) # [B, 256, 56, 56] + if return_featmap: + return ll + tmp_out = self._modules['l' + str(i)](F.relu(ll)) + + net = self.relu(self.bn5(tmp_out)) # [B, 68, 56, 56] + net = self.conv6(net) # 28 # [B, 1, 28, 28] + net = net.view(-1, net.shape[-2] * net.shape[-1]) + net = self.relu(net) + net = self.fc(net) + return net + +from .FAN_temporal_feature_extractor import TemporalTransformer3DModel +from einops import rearrange + + +class FAN_SA(nn.Module): + def __init__(self): + super(FAN_SA, self).__init__() + self.num_modules = 1 + + # Base part + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.conv2 = ConvBlock(64, 128) + self.conv3 = ConvBlock(128, 128) + self.conv4 = ConvBlock(128, 256) + + # Stacking part + hg_module = 0 + self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) + self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) + self.add_module( + 'conv_last' + str(hg_module), + nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0), + ) + self.add_module( + 'l' + str(hg_module), nn.Conv2d(256, 68, kernel_size=1, stride=1, padding=0) + ) + self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) + + if hg_module < self.num_modules - 1: + self.add_module( + 'bl' + str(hg_module), + nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0), + ) + self.add_module( + 'al' + str(hg_module), + nn.Conv2d(68, 256, kernel_size=1, stride=1, padding=0), + ) + + self.avgpool = nn.MaxPool2d((2, 2), 2) + self.conv6 = nn.Conv2d(68, 1, 3, 2, 1) + self.fc = nn.Linear(28 * 28, 512) + # self.conv6 = nn.Conv2d(68, 2, 3, 2, 1) + # self.fc = nn.Linear(28 * 28 * 2, 1024) + self.bn5 = nn.BatchNorm2d(68) + self.relu = nn.ReLU(True) + + # Add by zxc + self.att_1 = TemporalTransformer3DModel( + in_channels=128, + sample_size=112, + patch_size=4, + attention_block_types=("Spatial_Self",), + zero_initialize=True, + ) + self.att_2 = TemporalTransformer3DModel( + in_channels=256, + sample_size=56, + patch_size=2, + attention_block_types=("Spatial_Self",), + zero_initialize=True, + ) + self.att_3 = TemporalTransformer3DModel( + in_channels=256, + sample_size=56, + patch_size=2, + attention_block_types=("Spatial_Self",), + zero_initialize=True, + ) + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def forward(self, x): + x = F.relu(self.bn1(self.conv1(x)), True) # 112 + x = self.conv2(x) # [B, 128, 112, 112] + + # Temp Self-Att: [B*h*w, T, 128*p*p] [B*28*28, T, 1024] + x = rearrange(x, "(b f) c h w -> b c f h w", f=1) + x = self.att_1(x, skip=True)[:, :, 0] + + x = F.max_pool2d(x, 2) # 56 + x = self.conv3(x) + x = self.conv4(x) # [B, 256, 56, 56] + + # Temp Self-Att: [B*h*w, T, 256*p*p] [B*28*28, T, 1024] + x = rearrange(x, "(b f) c h w -> b c f h w", f=1) + x = self.att_2(x, skip=True)[:, :, 0] + + previous = x + + i = 0 + hg = self._modules['m' + str(i)](previous) + + ll = hg + ll = self._modules['top_m_' + str(i)](ll) + + ll = self._modules['bn_end' + str(i)]( + self._modules['conv_last' + str(i)](ll) + ) # [B, 256, 56, 56] + # Temp Cross-Att: [B*28*28, 1, 1024]*[B*28*28, T, 1024] + ll = rearrange(ll, "(b f) c h w -> b c f h w", f=1) + ll = self.att_3(ll, skip=True)[:, :, 0] # "b c 1 h w -> b c h w" + # print('att3', torch.abs(ll).mean().item()) + + tmp_out = self._modules['l' + str(i)](F.relu(ll)) + + net = self.relu(self.bn5(tmp_out)) # [B, 68, 56, 56] + net = self.conv6(net) # 28 # [B, 1, 28, 28] + net = net.view(-1, net.shape[-2] * net.shape[-1] * net.shape[1]) + net = self.relu(net) + net = self.fc(net) + return net diff --git a/src/scope/core/pipelines/personalive/modules/FAN_temporal_feature_extractor.py b/src/scope/core/pipelines/personalive/modules/FAN_temporal_feature_extractor.py new file mode 100644 index 00000000..d1968803 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/FAN_temporal_feature_extractor.py @@ -0,0 +1,231 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers.models.embeddings import PatchEmbed + +from einops import rearrange +from .motion_module import TemporalTransformerBlock, zero_module, random_module + +def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, + stride=strd, padding=padding, bias=bias) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, out_planes): + super(ConvBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = conv3x3(in_planes, int(out_planes / 2)) + self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) + self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) + self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) + self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) + + if in_planes != out_planes: + self.downsample = nn.Sequential( + nn.BatchNorm2d(in_planes), + nn.ReLU(True), + nn.Conv2d(in_planes, out_planes, + kernel_size=1, stride=1, bias=False), + ) + else: + self.downsample = None + + def forward(self, x): + residual = x + + out1 = self.bn1(x) + out1 = F.relu(out1, True) + out1 = self.conv1(out1) + + out2 = self.bn2(out1) + out2 = F.relu(out2, True) + out2 = self.conv2(out2) + + out3 = self.bn3(out2) + out3 = F.relu(out3, True) + out3 = self.conv3(out3) + + out3 = torch.cat((out1, out2, out3), 1) + + if self.downsample is not None: + residual = self.downsample(residual) + + out3 += residual + + return out3 + + +class HourGlass(nn.Module): + def __init__(self, num_modules, depth, num_features): + super(HourGlass, self).__init__() + self.num_modules = num_modules + self.depth = depth + self.features = num_features + self.dropout = nn.Dropout(0.5) + + self._generate_network(self.depth) + + def _generate_network(self, level): + self.add_module('b1_' + str(level), ConvBlock(256, 256)) + + self.add_module('b2_' + str(level), ConvBlock(256, 256)) + + if level > 1: + self._generate_network(level - 1) + else: + self.add_module('b2_plus_' + str(level), ConvBlock(256, 256)) + + self.add_module('b3_' + str(level), ConvBlock(256, 256)) + + def _forward(self, level, inp): + # Upper branch + up1 = inp + up1 = self._modules['b1_' + str(level)](up1) + up1 = self.dropout(up1) + # Lower branch + low1 = F.max_pool2d(inp, 2, stride=2) + low1 = self._modules['b2_' + str(level)](low1) + + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = low1 + low2 = self._modules['b2_plus_' + str(level)](low2) + + low3 = low2 + low3 = self._modules['b3_' + str(level)](low3) + up1size = up1.size() + rescale_size = (up1size[2], up1size[3]) + up2 = F.upsample(low3, size=rescale_size, mode='bilinear') + + return up1 + up2 + + def forward(self, x): + return self._forward(self.depth, x) + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads=8, + attention_head_dim=128, + num_layers=1, + attention_block_types=("Temporal_Self",), + sample_size=56, + patch_size=2, + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=0, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=True, + temporal_position_encoding_max_len=24, + zero_initialize=True + ): + super().__init__() + self.in_channels = in_channels + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + # self.proj_in = nn.Linear(in_channels, inner_dim) # patchEmbed中有 + if cross_attention_dim == 0 and any([block_name.endswith("_Cross") for block_name in attention_block_types]): + cross_attention_dim = inner_dim + self.cross_frame_attention_mode = cross_frame_attention_mode + if cross_frame_attention_mode is not None: + assert cross_frame_attention_mode in ['Spatial', 'Temporal'], cross_attention_dim + assert any([block_name.endswith("_Cross") for block_name in attention_block_types]) + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=None, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + for _ in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim // (patch_size**2), in_channels) + + self.patch_size = patch_size + self.sample_size = sample_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + interpolation_scale=1, + ) + # 在转onnx模型时，需要设置为False + self.pos_embed.pos_embed.requires_grad = False + self.proj_out = zero_module(self.proj_out) if zero_initialize else random_module(self.proj_out) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, skip=True): + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + batch, _, video_length = hidden_states.shape[:3] + residual = hidden_states + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + _, _, height, width = hidden_states.shape + + hidden_states = self.norm(hidden_states) # 需要先norm，再加posEmb，否则Emb偏执太大了 + grid_h, grid_w = height // self.patch_size, width // self.patch_size, + + hidden_states = self.pos_embed(hidden_states) # [(bf), l, c] # 偏执太大了 + # print( + # round(torch.abs(hidden_states).mean().item(), 6), + # round(torch.abs(norm_hidden_states).mean().item(), 6), + # ) + if self.cross_frame_attention_mode is not None: + assert encoder_hidden_states is None + encoder_hidden_states = rearrange(hidden_states, "(b f) d c -> b f d c", b=batch) + + hidden_states = encoder_hidden_states[:, -1] # [b, d, c] + residual = residual[:, :, -1:] + video_length = 1 + if self.cross_frame_attention_mode == 'Temporal': # 用所有帧来condition最后一帧（当前帧） + encoder_hidden_states = rearrange(encoder_hidden_states, "b f d c -> (b d) f c") + elif self.cross_frame_attention_mode == 'Spatial': + encoder_hidden_states = rearrange(encoder_hidden_states, "b f d c -> b (f d) c") + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + video_length=video_length, + att_flag=False + ) + + hidden_states = hidden_states.reshape( + shape=(batch * video_length, grid_h, grid_w, self.patch_size, self.patch_size, -1) + ) + hidden_states = self.proj_out(hidden_states) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + hidden_states = hidden_states.reshape(shape=(batch * video_length, self.in_channels, height, width)) + + hidden_states = rearrange(hidden_states, "(b f) c h w -> b c f h w", b=batch) + # print( + # 'out', + # round(torch.abs(hidden_states).mean().item(), 6), + # round(torch.abs(residual).mean().item(), 6), + # ) + output = (hidden_states + residual) if skip else hidden_states + + return output diff --git a/src/scope/core/pipelines/personalive/modules/__init__.py b/src/scope/core/pipelines/personalive/modules/__init__.py new file mode 100644 index 00000000..b00bbbeb --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/__init__.py @@ -0,0 +1,30 @@ +"""PersonaLive neural network modules. + +These are ported from the original PersonaLive implementation at +https://github.com/GVCLab/PersonaLive +""" + +from .motion_module import zero_module, get_motion_module +from .mutual_self_attention import ReferenceAttentionControl +from .unet_2d_condition import UNet2DConditionModel +from .unet_3d import UNet3DConditionModel +from .motion_encoder import MotEncoder +from .pose_guider import PoseGuider +from .motion_extractor import MotionExtractor +from .scheduler_ddim import DDIMScheduler +from .util import draw_keypoints, get_boxes, crop_face + +__all__ = [ + "zero_module", + "get_motion_module", + "ReferenceAttentionControl", + "UNet2DConditionModel", + "UNet3DConditionModel", + "MotEncoder", + "PoseGuider", + "MotionExtractor", + "DDIMScheduler", + "draw_keypoints", + "get_boxes", + "crop_face", +] diff --git a/src/scope/core/pipelines/personalive/modules/attention.py b/src/scope/core/pipelines/personalive/modules/attention.py new file mode 100644 index 00000000..fd8b4359 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/attention.py @@ -0,0 +1,463 @@ +from typing import Any, Dict, Optional + +import torch +from diffusers.models.attention import AdaLayerNorm, Attention, FeedForward +from diffusers.models.embeddings import SinusoidalPositionalEmbedding +from einops import rearrange +from torch import nn + +from diffusers.models.attention import * +from diffusers.models.attention_processor import * + +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the attention computation to float32. This is useful for mixed precision training. + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + attention_type (`str`, *optional*, defaults to `"default"`): + The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. + positional_embeddings (`str`, *optional*, defaults to `None`): + The type of positional embeddings to apply to. + num_positional_embeddings (`int`, *optional*, defaults to `None`): + The maximum number of positional embeddings to apply. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single' + norm_eps: float = 1e-5, + final_dropout: bool = False, + attention_type: str = "default", + positional_embeddings: Optional[str] = None, + num_positional_embeddings: Optional[int] = None, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = ( + num_embeds_ada_norm is not None + ) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = ( + num_embeds_ada_norm is not None + ) and norm_type == "ada_norm" + self.use_ada_layer_norm_single = norm_type == "ada_norm_single" + self.use_layer_norm = norm_type == "layer_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + if positional_embeddings and (num_positional_embeddings is None): + raise ValueError( + "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined." + ) + + if positional_embeddings == "sinusoidal": + self.pos_embed = SinusoidalPositionalEmbedding( + dim, max_seq_length=num_positional_embeddings + ) + else: + self.pos_embed = None + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm( + dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps + ) + + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm( + dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps + ) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim + if not double_self_attention + else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + if not self.use_ada_layer_norm_single: + self.norm3 = nn.LayerNorm( + dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps + ) + + self.ff = FeedForward( + dim, + dropout=dropout, + activation_fn=activation_fn, + final_dropout=final_dropout, + ) + + # 4. Fuser + if attention_type == "gated" or attention_type == "gated-text-image": + self.fuser = GatedSelfAttentionDense( + dim, cross_attention_dim, num_attention_heads, attention_head_dim + ) + + # 5. Scale-shift for PixArt-Alpha. + if self.use_ada_layer_norm_single: + self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ) -> torch.FloatTensor: + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + batch_size = hidden_states.shape[0] + + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + elif self.use_layer_norm: + norm_hidden_states = self.norm1(hidden_states) + elif self.use_ada_layer_norm_single: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) + ).chunk(6, dim=1) + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + norm_hidden_states = norm_hidden_states.squeeze(1) + else: + raise ValueError("Incorrect norm used") + + if self.pos_embed is not None: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + # 1. Retrieve lora scale. + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + # 2. Prepare GLIGEN inputs + cross_attention_kwargs = ( + cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + ) + gligen_kwargs = cross_attention_kwargs.pop("gligen", None) + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if self.only_cross_attention + else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + elif self.use_ada_layer_norm_single: + attn_output = gate_msa * attn_output + + hidden_states = attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 2.5 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + + # 3. Cross-Attention + if self.attn2 is not None: + if self.use_ada_layer_norm: + norm_hidden_states = self.norm2(hidden_states, timestep) + elif self.use_ada_layer_norm_zero or self.use_layer_norm: + norm_hidden_states = self.norm2(hidden_states) + elif self.use_ada_layer_norm_single: + # For PixArt norm2 isn't applied here: + # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 + norm_hidden_states = hidden_states + else: + raise ValueError("Incorrect norm") + + if self.pos_embed is not None and self.use_ada_layer_norm_single is False: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + if not self.use_ada_layer_norm_single: + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = ( + norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ) + + if self.use_ada_layer_norm_single: + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + ff_output = self.ff(norm_hidden_states, scale=lora_scale) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + elif self.use_ada_layer_norm_single: + ff_output = gate_mlp * ff_output + + hidden_states = ff_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + return hidden_states + + +class TemporalBasicTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + self.use_ada_layer_norm = num_embeds_ada_norm is not None + self.unet_use_cross_frame_attention = unet_use_cross_frame_attention + self.unet_use_temporal_attention = unet_use_temporal_attention + + # SC-Attn + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + self.norm1 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim) + ) + + # Cross-Attn + if cross_attention_dim is not None: + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + else: + self.attn2 = None + + if cross_attention_dim is not None: + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim) + ) + else: + self.norm2 = None + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + self.use_ada_layer_norm_zero = False + + # Temp-Attn + assert unet_use_temporal_attention is not None + if unet_use_temporal_attention: + self.attn_temp = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + nn.init.zeros_(self.attn_temp.to_out[0].weight.data) + self.norm_temp = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim) + ) + + def set_use_cross_frame_attention(self, value): + self.unet_use_cross_frame_attention = value + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + timestep=None, + attention_mask=None, + video_length=None, + reference=None, + ): + norm_hidden_states = ( + self.norm1(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm1(hidden_states) + ) + + if self.unet_use_cross_frame_attention: + hidden_states = ( + self.attn1( + norm_hidden_states, + attention_mask=attention_mask, + video_length=video_length, + ) + + hidden_states + ) + else: + if reference is not None: + bank_fea = [ + rearrange( + reference.unsqueeze(1).repeat(1, video_length, 1, 1), + "b t l c -> (b t) l c", + ) + ] + modify_norm_hidden_states = torch.cat( + [norm_hidden_states] + bank_fea, dim=1 + ) + + hidden_states = ( + self.attn1(norm_hidden_states, encoder_hidden_states=modify_norm_hidden_states, attention_mask=attention_mask) + + hidden_states + ) + else: + hidden_states = ( + self.attn1(norm_hidden_states, attention_mask=attention_mask) + + hidden_states + ) + + if self.attn2 is not None: + # Cross-Attention + norm_hidden_states = ( + self.norm2(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm2(hidden_states) + ) + hidden_states = ( + self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + + hidden_states + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + # Temporal-Attention + if self.unet_use_temporal_attention: + d = hidden_states.shape[1] + hidden_states = rearrange( + hidden_states, "(b f) d c -> (b d) f c", f=video_length + ) + norm_hidden_states = ( + self.norm_temp(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm_temp(hidden_states) + ) + hidden_states = self.attn_temp(norm_hidden_states) + hidden_states + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states diff --git a/src/scope/core/pipelines/personalive/modules/camera.py b/src/scope/core/pipelines/personalive/modules/camera.py new file mode 100644 index 00000000..a3dd9426 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/camera.py @@ -0,0 +1,73 @@ +# coding: utf-8 + +""" +functions for processing and transforming 3D facial keypoints +""" + +import numpy as np +import torch +import torch.nn.functional as F + +PI = np.pi + + +def headpose_pred_to_degree(pred): + """ + pred: (bs, 66) or (bs, 1) or others + """ + if pred.ndim > 1 and pred.shape[1] == 66: + # NOTE: note that the average is modified to 97.5 + device = pred.device + idx_tensor = [idx for idx in range(0, 66)] + idx_tensor = torch.FloatTensor(idx_tensor).to(device) + pred = F.softmax(pred, dim=1) + degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 97.5 + + return degree + + return pred + + +def get_rotation_matrix(pitch_, yaw_, roll_): + """ the input is in degree + """ + # transform to radian + pitch = pitch_ / 180 * PI + yaw = yaw_ / 180 * PI + roll = roll_ / 180 * PI + + device = pitch.device + + if pitch.ndim == 1: + pitch = pitch.unsqueeze(1) + if yaw.ndim == 1: + yaw = yaw.unsqueeze(1) + if roll.ndim == 1: + roll = roll.unsqueeze(1) + + # calculate the euler matrix + bs = pitch.shape[0] + ones = torch.ones([bs, 1]).to(device) + zeros = torch.zeros([bs, 1]).to(device) + x, y, z = pitch, yaw, roll + + rot_x = torch.cat([ + ones, zeros, zeros, + zeros, torch.cos(x), -torch.sin(x), + zeros, torch.sin(x), torch.cos(x) + ], dim=1).reshape([bs, 3, 3]) + + rot_y = torch.cat([ + torch.cos(y), zeros, torch.sin(y), + zeros, ones, zeros, + -torch.sin(y), zeros, torch.cos(y) + ], dim=1).reshape([bs, 3, 3]) + + rot_z = torch.cat([ + torch.cos(z), -torch.sin(z), zeros, + torch.sin(z), torch.cos(z), zeros, + zeros, zeros, ones + ], dim=1).reshape([bs, 3, 3]) + + rot = rot_z @ rot_y @ rot_x + return rot.permute(0, 2, 1) # transpose diff --git a/src/scope/core/pipelines/personalive/modules/convnextv2.py b/src/scope/core/pipelines/personalive/modules/convnextv2.py new file mode 100644 index 00000000..910c4313 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/convnextv2.py @@ -0,0 +1,215 @@ +# coding: utf-8 + +""" +This moudle is adapted to the ConvNeXtV2 version for the extraction of implicit keypoints, poses, and expression deformation. +""" + +import torch +import torch.nn as nn +from .liveportrait_util import LayerNorm, DropPath, trunc_normal_, GRN +from einops import rearrange + +__all__ = ['convnextv2_tiny'] + + +class Block(nn.Module): + """ ConvNeXtV2 Block. + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + """ + + def __init__(self, dim, drop_path=0.): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.grn = GRN(4 * dim) + self.pwconv2 = nn.Linear(4 * dim, dim) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x): + input = x + x = self.dwconv(x) + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.grn(x) + x = self.pwconv2(x) + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + x = input + self.drop_path(x) + return x + + +class ConvNeXtV2(nn.Module): + """ ConvNeXt V2 + + Args: + in_chans (int): Number of input image channels. Default: 3 + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + drop_path_rate (float): Stochastic depth rate. Default: 0. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__( + self, + in_chans=3, + depths=[3, 3, 9, 3], + dims=[96, 192, 384, 768], + drop_path_rate=0., + **kwargs + ): + super().__init__() + self.depths = depths + self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers + stem = nn.Sequential( + nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first") + ) + self.downsample_layers.append(stem) + for i in range(3): + downsample_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2), + ) + self.downsample_layers.append(downsample_layer) + + self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(4): + stage = nn.Sequential( + *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer + + # NOTE: the output semantic items + num_bins = kwargs.get('num_bins', 66) + num_kp = kwargs.get('num_kp', 24) # the number of implicit keypoints + self.fc_kp = nn.Linear(dims[-1], 3 * num_kp) # implicit keypoints + + # print('dims[-1]: ', dims[-1]) + self.fc_scale = nn.Linear(dims[-1], 1) # scale + self.fc_pitch = nn.Linear(dims[-1], num_bins) # pitch bins + self.fc_yaw = nn.Linear(dims[-1], num_bins) # yaw bins + self.fc_roll = nn.Linear(dims[-1], num_bins) # roll bins + self.fc_t = nn.Linear(dims[-1], 3) # translation + self.fc_exp = nn.Linear(dims[-1], 3 * num_kp) # expression / delta + + def _init_weights(self, m): + if isinstance(m, (nn.Conv2d, nn.Linear)): + trunc_normal_(m.weight, std=.02) + nn.init.constant_(m.bias, 0) + + def forward_features(self, x): + for i in range(4): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C) + + def forward(self, x): + x = self.forward_features(x) + + # implicit keypoints + kp = self.fc_kp(x) + + # pose and expression deformation + pitch = self.fc_pitch(x) + yaw = self.fc_yaw(x) + roll = self.fc_roll(x) + t = self.fc_t(x) + # exp = self.fc_exp(x) + scale = self.fc_scale(x) + + ret_dct = { + 'pitch': pitch, + 'yaw': yaw, + 'roll': roll, + 't': t, + # 'exp': exp, + 'scale': scale, + + 'kp': kp, # canonical keypoint + } + + return ret_dct + + +def convnextv2_tiny(**kwargs): + model = ConvNeXtV2(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs) + return model + +class ConvNeXt(nn.Module): + """ ConvNeXt V2 + + Args: + in_chans (int): Number of input image channels. Default: 3 + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + drop_path_rate (float): Stochastic depth rate. Default: 0. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__( + self, + in_chans=3, + depths=[3, 3, 9, 3], + dims=[96, 192, 384, 768], + drop_path_rate=0., + **kwargs + ): + super().__init__() + self.depths = depths + self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers + stem = nn.Sequential( + nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first") + ) + self.downsample_layers.append(stem) + for i in range(3): + downsample_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2), + ) + self.downsample_layers.append(downsample_layer) + + self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(4): + stage = nn.Sequential( + *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer + + def _init_weights(self, m): + if isinstance(m, (nn.Conv2d, nn.Linear)): + trunc_normal_(m.weight, std=.02) + nn.init.constant_(m.bias, 0) + + def forward_features(self, x): + for i in range(4): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C) + + def forward(self, x): + x = self.forward_features(x) + return x + + +def convnextv2(**kwargs): + model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs) + return model diff --git a/src/scope/core/pipelines/personalive/modules/liveportrait_util.py b/src/scope/core/pipelines/personalive/modules/liveportrait_util.py new file mode 100644 index 00000000..a58ce587 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/liveportrait_util.py @@ -0,0 +1,492 @@ +# coding: utf-8 + +""" +This file defines various neural network modules and utility functions, including convolutional and residual blocks, +normalizations, and functions for spatial transformation and tensor manipulation. +""" + +from torch import nn +import torch.nn.functional as F +import torch +import torch.nn.utils.spectral_norm as spectral_norm +import math +import warnings +import collections.abc +from itertools import repeat + +def kp2gaussian(kp, spatial_size, kp_variance): + """ + Transform a keypoint into gaussian like representation + """ + mean = kp + + coordinate_grid = make_coordinate_grid(spatial_size, mean) + number_of_leading_dimensions = len(mean.shape) - 1 + shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape + coordinate_grid = coordinate_grid.view(*shape) + repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1) + coordinate_grid = coordinate_grid.repeat(*repeats) + + # Preprocess kp shape + shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3) + mean = mean.view(*shape) + + mean_sub = (coordinate_grid - mean) + + out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) + + return out + + +def make_coordinate_grid(spatial_size, ref, **kwargs): + d, h, w = spatial_size + x = torch.arange(w).type(ref.dtype).to(ref.device) + y = torch.arange(h).type(ref.dtype).to(ref.device) + z = torch.arange(d).type(ref.dtype).to(ref.device) + + # NOTE: must be right-down-in + x = (2 * (x / (w - 1)) - 1) # the x axis faces to the right + y = (2 * (y / (h - 1)) - 1) # the y axis faces to the bottom + z = (2 * (z / (d - 1)) - 1) # the z axis faces to the inner + + yy = y.view(1, -1, 1).repeat(d, 1, w) + xx = x.view(1, 1, -1).repeat(d, h, 1) + zz = z.view(-1, 1, 1).repeat(1, h, w) + + meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3) + + return meshed + + +class ConvT2d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, stride=2, padding=1, output_padding=1): + super(ConvT2d, self).__init__() + + self.convT = nn.ConvTranspose2d(in_features, out_features, kernel_size=kernel_size, stride=stride, + padding=padding, output_padding=output_padding) + self.norm = nn.InstanceNorm2d(out_features) + + def forward(self, x): + out = self.convT(x) + out = self.norm(out) + out = F.leaky_relu(out) + return out + + +class ResBlock3d(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock3d, self).__init__() + self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) + self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) + self.norm1 = nn.BatchNorm3d(in_features, affine=True) + self.norm2 = nn.BatchNorm3d(in_features, affine=True) + + def forward(self, x): + out = self.norm1(x) + out = F.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = F.relu(out) + out = self.conv2(out) + out += x + return out + + +class UpBlock3d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(UpBlock3d, self).__init__() + + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = nn.BatchNorm3d(out_features, affine=True) + + def forward(self, x): + out = F.interpolate(x, scale_factor=(1, 2, 2)) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class DownBlock2d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = nn.BatchNorm2d(out_features, affine=True) + self.pool = nn.AvgPool2d(kernel_size=(2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class DownBlock3d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock3d, self).__init__() + ''' + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups, stride=(1, 2, 2)) + ''' + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = nn.BatchNorm3d(out_features, affine=True) + self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class SameBlock2d(nn.Module): + """ + Simple block, preserve spatial resolution. + """ + + def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = nn.BatchNorm2d(out_features, affine=True) + if lrelu: + self.ac = nn.LeakyReLU() + else: + self.ac = nn.ReLU() + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.ac(out) + return out + + +class Encoder(nn.Module): + """ + Hourglass Encoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Encoder, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), min(max_features, block_expansion * (2 ** (i + 1))), kernel_size=3, padding=1)) + self.down_blocks = nn.ModuleList(down_blocks) + + def forward(self, x): + outs = [x] + for down_block in self.down_blocks: + outs.append(down_block(outs[-1])) + return outs + + +class Decoder(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder, self).__init__() + + up_blocks = [] + + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + self.out_filters = block_expansion + in_features + + self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1) + self.norm = nn.BatchNorm3d(self.out_filters, affine=True) + + def forward(self, x): + out = x.pop() + for up_block in self.up_blocks: + out = up_block(out) + skip = x.pop() + out = torch.cat([out, skip], dim=1) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class Hourglass(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) + self.out_filters = self.decoder.out_filters + + def forward(self, x): + return self.decoder(self.encoder(x)) + + +class SPADE(nn.Module): + def __init__(self, norm_nc, label_nc): + super().__init__() + + self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) + nhidden = 128 + + self.mlp_shared = nn.Sequential( + nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1), + nn.ReLU()) + self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + + def forward(self, x, segmap): + normalized = self.param_free_norm(x) + segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') + actv = self.mlp_shared(segmap) + gamma = self.mlp_gamma(actv) + beta = self.mlp_beta(actv) + out = normalized * (1 + gamma) + beta + return out + + +class SPADEResnetBlock(nn.Module): + def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1): + super().__init__() + # Attributes + self.learned_shortcut = (fin != fout) + fmiddle = min(fin, fout) + self.use_se = use_se + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + # apply spectral norm if specified + if 'spectral' in norm_G: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + # define normalization layers + self.norm_0 = SPADE(fin, label_nc) + self.norm_1 = SPADE(fmiddle, label_nc) + if self.learned_shortcut: + self.norm_s = SPADE(fin, label_nc) + + def forward(self, x, seg1): + x_s = self.shortcut(x, seg1) + dx = self.conv_0(self.actvn(self.norm_0(x, seg1))) + dx = self.conv_1(self.actvn(self.norm_1(dx, seg1))) + out = x_s + dx + return out + + def shortcut(self, x, seg1): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, seg1)) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) + + +def filter_state_dict(state_dict, remove_name='fc'): + new_state_dict = {} + for key in state_dict: + if remove_name in key: + continue + new_state_dict[key] = state_dict[key] + return new_state_dict + + +class GRN(nn.Module): + """ GRN (Global Response Normalization) layer + """ + + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim)) + self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x + + +class LayerNorm(nn.Module): + r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape, dtype=torch.float32)) + self.bias = nn.Parameter(torch.zeros(normalized_shape, dtype=torch.float32)) + self.eps = float(eps) + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape, ) + + def _apply(self, fn): + """ + 重写 _apply，完全接管参数的转换逻辑。 + 拦截所有 .cuda(), .cpu(), .half(), .to() 操作。 + """ + + for name, param in self._parameters.items(): + if param is not None: + dummy_probe = param.data.view(-1)[:1] + + try: + target_tensor = fn(dummy_probe) + + target_device = target_tensor.device + target_dtype = target_tensor.dtype + except: + target_device = param.device + target_dtype = param.dtype + + if name in ['weight', 'bias']: + # 核心逻辑：如果是 weight/bias，且目标是半精度，则强制保持 FP32 + if target_dtype in [torch.float16, torch.bfloat16]: + new_data = param.data.to(device=target_device, dtype=torch.float32) + else: + new_data = fn(param.data) + else: + new_data = fn(param.data) + + param.data = new_data + + if param.grad is not None: + param.grad.data = param.grad.data.to(device=new_data.device, dtype=new_data.dtype) + + for name, buf in self._buffers.items(): + if buf is not None: + self._buffers[name] = fn(buf) + + return self + + def forward(self, x): + dtype = x.dtype + x = x.float() + if self.data_format == "channels_last": + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + elif self.data_format == "channels_first": + x = x.permute(0, 2, 3, 1) # BCHW → BHWC + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) # BHWC → BCHW + return x.to(dtype) + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def drop_path(x, drop_prob=0., training=False, scale_by_keep=True): + """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob=None, scale_by_keep=True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + return parse + +to_2tuple = _ntuple(2) diff --git a/src/scope/core/pipelines/personalive/modules/motion_encoder.py b/src/scope/core/pipelines/personalive/modules/motion_encoder.py new file mode 100644 index 00000000..740b1adb --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/motion_encoder.py @@ -0,0 +1,42 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .FAN_feature_extractor import FAN_SA +from einops import rearrange +from diffusers.models.embeddings import get_1d_sincos_pos_embed_from_grid +from diffusers.models.modeling_utils import ModelMixin + +def zero_module(module): + # Zero out the parameters of a module and return it. + assert isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear), type(module) + for p in module.parameters(): + p.detach().zero_() + return module + +class MotEncoder(ModelMixin): + def __init__(self, out_ch=16): + super().__init__() + self.model = FAN_SA() + self.out_drop = None #nn.Dropout(p=0.4) + self.out_ch = out_ch + expr_dim = 512 + # Use torch.arange and output_type='pt' for diffusers >= 0.34.0 compatibility + pos_grid = torch.arange(expr_dim // out_ch, dtype=torch.float32) + extra_pos_embed = get_1d_sincos_pos_embed_from_grid(out_ch, pos_grid, output_type='pt') + self.register_buffer("pe", extra_pos_embed.float().unsqueeze(0)) + self.final_proj = nn.Linear(expr_dim, expr_dim) + self.out_bn = None + + def change_out_dim(self, out_ch): + self.out_proj = nn.Linear(self.out_ch, out_ch) + + def set_attn_processor(self, processor): + self.model.set_attn_processor(processor) + + def forward(self, x): + x = x.to(self.dtype) + latent = self.model(rearrange(x, "b c f h w -> (b f) c h w")) + latent = self.final_proj(latent) + latent = rearrange(latent, "b (l c) -> b l c", c=self.out_ch) + self.pe + latent = rearrange(latent, "(b f) l c -> b f l c", f=x.shape[2]) + return latent diff --git a/src/scope/core/pipelines/personalive/modules/motion_extractor.py b/src/scope/core/pipelines/personalive/modules/motion_extractor.py new file mode 100644 index 00000000..bdd23080 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/motion_extractor.py @@ -0,0 +1,212 @@ +# coding: utf-8 + +""" +Motion extractor(M), which directly predicts the canonical keypoints, head pose and expression deformation of the input image +""" + +from torch import nn +import torch +from diffusers.models.modeling_utils import ModelMixin +from .convnextv2 import convnextv2_tiny +from .liveportrait_util import filter_state_dict +from .camera import headpose_pred_to_degree, get_rotation_matrix + +model_dict = { + 'convnextv2_tiny': convnextv2_tiny, +} + + +class MotionExtractor(ModelMixin): + def __init__(self, **kwargs): + super(MotionExtractor, self).__init__() + + # default is convnextv2_base + backbone = kwargs.get('backbone', 'convnextv2_tiny') + self.detector = model_dict.get(backbone)(**kwargs) + self.register_buffer('idx_tensor', torch.arange(66, dtype=torch.float32)) + + def headpose_pred_to_degree(self, pred): + """ + pred: (bs, 66) or (bs, 1) or others + """ + if pred.ndim > 1 and pred.shape[1] == 66: + # NOTE: note that the average is modified to 97.5 + prob = torch.nn.functional.softmax(pred, dim=1) + degree = torch.matmul(prob, self.idx_tensor) + degree = degree * 3 - 97.5 + + return degree + + return pred + + def load_pretrained(self, init_path: str): + if init_path not in (None, ''): + state_dict = torch.load(init_path, map_location=lambda storage, loc: storage)['model'] + state_dict = filter_state_dict(state_dict, remove_name='head') + ret = self.detector.load_state_dict(state_dict, strict=False) + print(f'Load pretrained model from {init_path}, ret: {ret}') + + def forward(self, x): + kp_info = self.detector(x) + return self.get_kp(kp_info) + + def get_kp(self, kp_info): + bs = kp_info['kp'].shape[0] + + angles_raw = torch.cat([kp_info['pitch'], kp_info['yaw'], kp_info['roll']], dim=0) # (3, 66) + angles_deg = self.headpose_pred_to_degree(angles_raw)[:, None] # (B, 3) + pitch, yaw, roll = torch.chunk(angles_deg, chunks=3, dim=0) + + + kp = kp_info['kp'].reshape(bs, -1, 3) # BxNx3 + t, scale = kp_info['t'], kp_info['scale'] + + rot_mat = get_rotation_matrix(pitch, yaw, roll).to(self.dtype) # (bs, 3, 3) + + if kp.ndim == 2: + num_kp = kp.shape[1] // 3 # Bx(num_kpx3) + else: + num_kp = kp.shape[1] # Bxnum_kpx3 + + # Eqn.2: s * (R * x_c,s) + t + kp_transformed = kp.view(bs, num_kp, 3) @ rot_mat# + exp.view(bs, num_kp, 3) + kp_transformed *= scale[..., None] # (bs, k, 3) * (bs, 1, 1) = (bs, k, 3) + kp_transformed[:, :, 0:2] += t[:, None, 0:2] # remove z, only apply tx ty + + return kp_transformed + + def interpolate_tensors(self, a: torch.Tensor, b: torch.Tensor, num: int = 10) -> torch.Tensor: + if a.shape != b.shape: + raise ValueError(f"Shape mismatch: a.shape={a.shape}, b.shape={b.shape}") + + B, *rest = a.shape + alphas = torch.linspace(0, 1, num, device=a.device, dtype=a.dtype) + view_shape = (num,) + (1,) * len(rest) + alphas = alphas.view(view_shape) # (1, num, 1, 1, ...) + + result = (1 - alphas) * a + alphas * b + return result[:-1] + + def interpolate_kps(self, ref, motion, num_interp, t_scale=0.5, s_scale=0): + kp1 = self.detector(ref.to(self.dtype)) + kp2_list = [] + for i in range(0, motion.shape[0], 256): + motion_chunk = motion[i:i+256] + kp2_chunk = self.detector(motion_chunk.to(self.dtype)) + kp2_list.append(kp2_chunk) + kp2 = {} + for key in kp2_list[0].keys(): + kp2[key] = torch.cat([kp2_chunk[key] for kp2_chunk in kp2_list], dim=0) + + angles_raw = torch.cat([kp1['pitch'], kp1['yaw'], kp1['roll']], dim=0) # (3, 66) + angles_deg = self.headpose_pred_to_degree(angles_raw) # (B, 3) + pitch_1, yaw_1, roll_1 = torch.chunk(angles_deg, chunks=3, dim=0) + + angles_raw = torch.cat([kp2['pitch'], kp2['yaw'], kp2['roll']], dim=0) # (3, 66) + angles_deg = self.headpose_pred_to_degree(angles_raw) # (B, 3) + pitch_2, yaw_2, roll_2 = torch.chunk(angles_deg, chunks=3, dim=0) + + pitch_interp = self.interpolate_tensors(pitch_1, pitch_2[:1], num_interp) # Bx(num_interp)x1 + yaw_interp = self.interpolate_tensors(yaw_1, yaw_2[:1], num_interp) # Bx(num_interp)x1 + roll_interp = self.interpolate_tensors(roll_1, roll_2[:1], num_interp) # Bx(num_interp)x1 + + t_1 = kp1['t'] + t_2 = kp2['t'] + t_2 = (t_2 - t_2[0]) * t_scale + t_1 + t_interp = self.interpolate_tensors(t_1, t_2[:1], num_interp) + + s_1 = kp1['scale'] + s_2 = kp2['scale'] + s_2 = s_2 * s_scale + s_1 + s_interp = self.interpolate_tensors(s_1, s_2[:1], num_interp) + + kp = kp1['kp'].repeat(num_interp+motion.shape[0]-1, 1) + + kps_interp = { + 'pitch': torch.cat([pitch_interp, pitch_2], dim=0), + 'yaw': torch.cat([yaw_interp, yaw_2], dim=0), + 'roll': torch.cat([roll_interp, roll_2], dim=0), + 't': torch.cat([t_interp, t_2], dim=0), + 'scale': torch.cat([s_interp, s_2], dim=0), + 'kp': kp + } + + kp_intrep = self.get_kp(kps_interp) + + return kp_intrep + + + def interpolate_kps_online(self, ref, motion, num_interp, t_scale=0.5, s_scale=0): + kp1 = self.detector(ref.to(self.dtype)) + kp_frame1 = self.detector(motion[:1].to(self.dtype)) + kp2 = self.detector(motion.to(self.dtype)) + + angles_raw = torch.cat([kp1['pitch'], kp1['yaw'], kp1['roll']], dim=0) # (3, 66) + angles_deg = self.headpose_pred_to_degree(angles_raw) # (B, 3) + pitch_1, yaw_1, roll_1 = torch.chunk(angles_deg, chunks=3, dim=0) + + angles_raw = torch.cat([kp2['pitch'], kp2['yaw'], kp2['roll']], dim=0) # (3, 66) + angles_deg = self.headpose_pred_to_degree(angles_raw) # (B, 3) + pitch_2, yaw_2, roll_2 = torch.chunk(angles_deg, chunks=3, dim=0) + + pitch_interp = self.interpolate_tensors(pitch_1, pitch_2[:1], num_interp) # Bx(num_interp)x1 + yaw_interp = self.interpolate_tensors(yaw_1, yaw_2[:1], num_interp) # Bx(num_interp)x1 + roll_interp = self.interpolate_tensors(roll_1, roll_2[:1], num_interp) # Bx(num_interp)x1 + + t_1 = kp1['t'] + t_2 = kp2['t'] + t_2 = (t_2 - t_2[0]) * t_scale + t_1 + t_interp = self.interpolate_tensors(t_1, t_2[:1], num_interp) + + s_1 = kp1['scale'] + s_2 = kp2['scale'] + s_2 = s_2 * s_scale + s_1 + s_interp = self.interpolate_tensors(s_1, s_2[:1], num_interp) + + kp = kp1['kp'].repeat(num_interp+motion.shape[0]-1, 1) + + kps_interp = { + 'pitch': torch.cat([pitch_interp, pitch_2], dim=0), + 'yaw': torch.cat([yaw_interp, yaw_2], dim=0), + 'roll': torch.cat([roll_interp, roll_2], dim=0), + 't': torch.cat([t_interp, t_2], dim=0), + 'scale': torch.cat([s_interp, s_2], dim=0), + 'kp': kp + } + + kp_intrep = self.get_kp(kps_interp) + + kp_dri = self.get_kp(kp2) + + return kp_intrep, kp1, kp_frame1, kp_dri + + def get_kps(self, kp_ref, kp_frame1, motion, t_scale=0.5, s_scale=0): + kps_motion = self.detector(motion.to(self.dtype)) + + kps_dri = self.get_kp(kps_motion) + + t_ref = kp_ref['t'] + t_frame1 = kp_frame1['t'] + t_motion = kps_motion['t'] + kps_motion['t'] = (t_motion - t_frame1) * t_scale + t_ref + + s_ref = kp_ref['scale'] + s_motion = kps_motion['scale'] + kps_motion['scale'] = s_motion * s_scale + s_ref + + + kps_motion['kp'] = kp_ref['kp'].repeat(motion.shape[0], 1) + + kps_motion = self.get_kp(kps_motion) + + return kps_motion, kps_dri + + def inference(self, ref, motion): + kps_ref = self.detector(ref.to(self.dtype)) + kps_motion = self.detector(motion.to(self.dtype)) + kps_motion['kp'] = kps_ref['kp'] + + kp_s = self.get_kp(kps_ref) + kp_d = self.get_kp(kps_motion) + + return kp_s, kp_d diff --git a/src/scope/core/pipelines/personalive/modules/motion_module.py b/src/scope/core/pipelines/personalive/modules/motion_module.py new file mode 100644 index 00000000..f3f0ba74 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/motion_module.py @@ -0,0 +1,472 @@ +import math +from dataclasses import dataclass +from typing import Callable, Optional + +import torch +from diffusers.models.attention import FeedForward +from diffusers.models.attention_processor import Attention, AttnProcessor +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from einops import rearrange, repeat +from torch import nn + + +def zero_module(module): + # Zero out the parameters of a module and return it. + assert isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear), type(module) + for p in module.parameters(): + p.detach().zero_() + return module + +def random_module(m): + assert isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear), type(m) + # Initialize weights with He initialization and zero out the biases + n = (m.kernel_size[0] * m.kernel_size[1] * m.in_channels) if isinstance(m, nn.Conv2d) else m.in_features + nn.init.normal_(m.weight, mean=0.0, std=math.sqrt(2. / n)) + if m.bias is not None: + nn.init.zeros_(m.bias) + return m + + +@dataclass +class TemporalTransformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict): + if motion_module_type == "Vanilla": + return VanillaTemporalModule( + in_channels=in_channels, + **motion_module_kwargs, + ) + elif motion_module_type == "RefImage_Vanilla": + return VanillaTemporalModule( + in_channels=in_channels, + skip_ref_image=True, + **motion_module_kwargs, + ) + elif motion_module_type == "RefImageCond_Vanilla": + return VanillaTemporalModule( + in_channels=in_channels, + cond_ref_image=True, + **motion_module_kwargs, + ) + else: + raise ValueError + + +class VanillaTemporalModule(nn.Module): + + def __init__( + self, + in_channels, + num_attention_heads=8, + num_transformer_block=2, + attention_block_types=("Temporal_Self", "Temporal_Self"), + cross_attention_dim=768, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + temporal_attention_dim_div=1, + zero_initialize=True, + skip_ref_image=False, + cond_ref_image=False, + ): + super().__init__() + self.skip_ref_image = skip_ref_image + self.cond_ref_image = cond_ref_image + + self.temporal_transformer = TemporalTransformer3DModel( + in_channels=in_channels, + num_attention_heads=num_attention_heads, + attention_head_dim=in_channels + // num_attention_heads + // temporal_attention_dim_div, + num_layers=num_transformer_block, + attention_block_types=attention_block_types, + cross_attention_dim=cross_attention_dim, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + + if zero_initialize: + self.temporal_transformer.proj_out = zero_module( + self.temporal_transformer.proj_out + ) + + def set_use_cross_frame_attention(self, value): + self.skip_ref_image = value + + def forward( + self, + input_tensor, + temb, + encoder_hidden_states, + attention_mask=None, + anchor_frame_idx=None, + debug=False + ): + hidden_states = input_tensor + if self.skip_ref_image: + # if input_tensor.shape[2] > 1: + hidden_states, ref_hidden_states = input_tensor[:, :, :-1], input_tensor[:, :, -1:] + + hidden_states = self.temporal_transformer( + hidden_states, encoder_hidden_states, attention_mask, debug=debug + ) + + output = hidden_states + if self.skip_ref_image: + # if input_tensor.shape[2] > 1: + output = torch.cat([output, ref_hidden_states], dim=2) + elif self.cond_ref_image: + output = torch.cat([output[:, :, :-1], input_tensor[:, :, -1:]], dim=2) + return output + + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads, + attention_head_dim, + num_layers, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + for d in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, debug=False): + assert ( + hidden_states.dim() == 5 + ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + if encoder_hidden_states is not None and encoder_hidden_states.ndim == 4: + assert encoder_hidden_states.shape[1] == video_length, (video_length, encoder_hidden_states.shape) + encoder_hidden_states = rearrange(encoder_hidden_states, "b d n c -> (b d) n c",) + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * weight, inner_dim + ) + hidden_states = self.proj_in(hidden_states) + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + video_length=video_length, + ) + + # output + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + if False: + print( + 'TemporalModule', + hidden_states.shape, + # round(torch.abs(residual).mean().item(), 6), + # round(torch.abs(residual).max().item(), 6), + # round(torch.abs(hidden_states).mean().item(), 6), + # round(torch.abs(hidden_states).max().item(), 6), + ) + # hidden_states *= 0 + output = hidden_states + residual + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + + return output + + +class TemporalTransformerBlock(nn.Module): + + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + proj_out_dim=None, + ): + super().__init__() + + attention_blocks = [] + norms = [] + + for block_name in attention_block_types: + attention_blocks.append( + VersatileAttention( + attention_mode=block_name.split("_")[0], + cross_attention_dim=cross_attention_dim + if block_name.endswith("_Cross") + else None, + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + ) + norms.append(nn.LayerNorm(dim)) + + self.attention_blocks = nn.ModuleList(attention_blocks) + self.norms = nn.ModuleList(norms) + + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.ff_norm = nn.LayerNorm(dim) + + self.proj_out = nn.Linear(dim, proj_out_dim) if proj_out_dim is not None else None + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + video_length=None, + att_flag=False + ): + for attention_block, norm in zip(self.attention_blocks, self.norms): + norm_hidden_states = norm(hidden_states) + if att_flag: + print( + 'block', + round(torch.abs(hidden_states).mean().item(), 6), + round(torch.abs(norm_hidden_states).mean().item(), 6), + ) + hidden_states = ( + attention_block( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if attention_block.is_cross_attention + else None, + video_length=video_length, + att_flag=att_flag + ) + + hidden_states + ) + + hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states + + output = hidden_states if self.proj_out is None else self.proj_out(hidden_states) + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model, dropout=0.0, max_len=24): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x): + x = x + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class VersatileAttention(Attention): + def __init__( + self, + attention_mode=None, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + assert attention_mode in ["Temporal", "Spatial"], attention_mode + + self.attention_mode = attention_mode + self.is_cross_attention = kwargs["cross_attention_dim"] is not None + + self.pos_encoder = ( + PositionalEncoding( + kwargs["query_dim"], + dropout=0.0, + max_len=temporal_position_encoding_max_len, + ) + if (temporal_position_encoding and attention_mode == "Temporal") + else None + ) + + def extra_repr(self): + return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}" + + def set_use_memory_efficient_attention_xformers( + self, + use_memory_efficient_attention_xformers: bool, + attention_op: Optional[Callable] = None, + ): + if use_memory_efficient_attention_xformers: + if not is_xformers_available(): + raise ModuleNotFoundError( + ( + "Refer to https://github.com/facebookresearch/xformers for more information on how to install" + " xformers" + ), + name="xformers", + ) + elif not torch.cuda.is_available(): + raise ValueError( + "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" + " only available for GPU " + ) + else: + try: + # Make sure we can run the memory efficient attention + _ = xformers.ops.memory_efficient_attention( + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + ) + except Exception as e: + raise e + + # XFormersAttnProcessor corrupts video generation and work with Pytorch 1.13. + # Pytorch 2.0.1 AttnProcessor works the same as XFormersAttnProcessor in Pytorch 1.13. + # You don't need XFormersAttnProcessor here. + # processor = XFormersAttnProcessor( + # attention_op=attention_op, + # ) + processor = AttnProcessor() + else: + processor = AttnProcessor() + + self.set_processor(processor) + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + video_length=None, + bank=None, + att_flag=False, + **cross_attention_kwargs, + ): + if self.attention_mode == "Temporal": + d = hidden_states.shape[1] # d means HxW + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) + + if encoder_hidden_states is not None: + if not encoder_hidden_states.shape[0] == hidden_states.shape[0]: + encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) + + if bank is not None and self.attention_mode == "Temporal" and not self.is_cross_attention: + # motion_frames作为之前的帧，引入motion module进行condition + modify_norm_hidden_states = torch.cat(bank + [hidden_states], dim=1) + + if self.pos_encoder is not None: + modify_norm_hidden_states = self.pos_encoder(modify_norm_hidden_states) + + hidden_states = self.processor( + self, + hidden_states, + encoder_hidden_states=modify_norm_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) # 改为cross-att + + else: + if self.pos_encoder is not None: + hidden_states = self.pos_encoder(hidden_states) + inp = hidden_states + hidden_states = self.processor( + self, + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if att_flag: + print( + 'ver_att', + round(torch.abs(inp).mean().item(), 6), + round(torch.abs(encoder_hidden_states).mean().item(), 6), + round(torch.abs(hidden_states).mean().item(), 6), + ) + + if self.attention_mode == "Temporal": + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states \ No newline at end of file diff --git a/src/scope/core/pipelines/personalive/modules/mutual_self_attention.py b/src/scope/core/pipelines/personalive/modules/mutual_self_attention.py new file mode 100644 index 00000000..a89a63cd --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/mutual_self_attention.py @@ -0,0 +1,463 @@ +from typing import Any, Dict, Optional + +import torch +from einops import rearrange + +from .attention import TemporalBasicTransformerBlock, BasicTransformerBlock + + +def torch_dfs(model: torch.nn.Module): + result = [model] + for child in model.children(): + result += torch_dfs(child) + return result + + +class ReferenceAttentionControl: + def __init__( + self, + unet, + mode="write", + do_classifier_free_guidance=False, + attention_auto_machine_weight=float("inf"), + gn_auto_machine_weight=1.0, + style_fidelity=1.0, + reference_attn=True, + reference_adain=False, + fusion_blocks="midup", + batch_size=1, + cache_kv=False, + ) -> None: + # 10. Modify self attention and group norm + self.unet = unet + assert mode in ["read", "write"] + assert fusion_blocks in ["midup", "full"] + self.reference_attn = reference_attn + self.reference_adain = reference_adain + self.fusion_blocks = fusion_blocks + self.cache_kv = cache_kv + self.register_reference_hooks( + mode, + do_classifier_free_guidance, + attention_auto_machine_weight, + gn_auto_machine_weight, + style_fidelity, + reference_attn, + reference_adain, + fusion_blocks, + batch_size=batch_size, + cache_kv=self.cache_kv, + ) + + def register_reference_hooks( + self, + mode, + do_classifier_free_guidance, + attention_auto_machine_weight, + gn_auto_machine_weight, + style_fidelity, + reference_attn, + reference_adain, + dtype=torch.float16, + batch_size=1, + num_images_per_prompt=1, + device=torch.device("cpu"), + fusion_blocks="midup", + cache_kv=False, + ): + MODE = mode + do_classifier_free_guidance = do_classifier_free_guidance + attention_auto_machine_weight = attention_auto_machine_weight + gn_auto_machine_weight = gn_auto_machine_weight + style_fidelity = style_fidelity + reference_attn = reference_attn + reference_adain = reference_adain + fusion_blocks = fusion_blocks + num_images_per_prompt = num_images_per_prompt + cache_kv = cache_kv + dtype = dtype + if do_classifier_free_guidance: + uc_mask = ( + torch.Tensor( + [1] * batch_size * num_images_per_prompt * 16 + + [0] * batch_size * num_images_per_prompt * 16 + ) + .to(device) + .bool() + ) + else: + uc_mask = ( + torch.Tensor([0] * batch_size * num_images_per_prompt * 2) + .to(device) + .bool() + ) + + def hacked_basic_transformer_inner_forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + video_length=None, + ): + if self.use_ada_layer_norm: # False + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + ( + norm_hidden_states, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) = self.norm1( + hidden_states, + timestep, + class_labels, + hidden_dtype=hidden_states.dtype, + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + # 1. Self-Attention + # self.only_cross_attention = False + cross_attention_kwargs = ( + cross_attention_kwargs if cross_attention_kwargs is not None else {} + ) + if self.only_cross_attention: + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if self.only_cross_attention + else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + else: + if MODE == "write": + self.bank.append(norm_hidden_states.clone()) + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if self.only_cross_attention + else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if MODE == "read": + kv_cache = norm_hidden_states.clone() + kv_cache = rearrange( + kv_cache, "(b t) l c -> b t l c", t=video_length + ) + self.kv_cache = kv_cache[:,:2,:,:] + + bank_fea = [ + rearrange( + d.unsqueeze(1).repeat(1, video_length, 1, 1), + "b t l c -> (b t) l c", + ) + for d in self.bank + ] + + if self.kv_bank is not None and cache_kv: + ahead_fea = self.kv_bank.unsqueeze(1).repeat(1, video_length, 1, 1, 1) + ahead_fea = rearrange(ahead_fea, "b t n l c -> (b t) (n l) c") + bank_fea.append(ahead_fea) + + modify_norm_hidden_states = torch.cat( + [norm_hidden_states] + bank_fea, dim=1 + ) + + hidden_states_uc = ( + self.attn1( + norm_hidden_states, + encoder_hidden_states=modify_norm_hidden_states, + attention_mask=attention_mask, + ) + # self.attn1( + # modify_norm_hidden_states, + # encoder_hidden_states=modify_norm_hidden_states, + # attention_mask=attention_mask, + # )[:, : hidden_states.shape[-2], :] + + hidden_states + ) + if do_classifier_free_guidance: + hidden_states_c = hidden_states_uc.clone() + _uc_mask = uc_mask.clone() + if hidden_states.shape[0] != _uc_mask.shape[0]: + _uc_mask = ( + torch.Tensor( + [1] * (hidden_states.shape[0] // 2) + + [0] * (hidden_states.shape[0] // 2) + ) + .to(device) + .bool() + ) + if self.kv_bank is not None: + # if False: + modify_norm_hidden_states = torch.cat( + [norm_hidden_states, ahead_fea], dim=1 + ) + else: + modify_norm_hidden_states = norm_hidden_states + hidden_states_c[_uc_mask] = ( + self.attn1( + norm_hidden_states[_uc_mask], + encoder_hidden_states=modify_norm_hidden_states[_uc_mask], + attention_mask=attention_mask, + ) + + hidden_states[_uc_mask] + ) + hidden_states = hidden_states_c.clone() + else: + hidden_states = hidden_states_uc + + # self.bank.clear() + if self.attn2 is not None: + # Cross-Attention + norm_hidden_states = ( + self.norm2(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm2(hidden_states) + ) + hidden_states = ( + self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + + hidden_states + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + # Temporal-Attention + if self.unet_use_temporal_attention: + d = hidden_states.shape[1] + hidden_states = rearrange( + hidden_states, "(b f) d c -> (b d) f c", f=video_length + ) + norm_hidden_states = ( + self.norm_temp(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm_temp(hidden_states) + ) + hidden_states = ( + self.attn_temp(norm_hidden_states) + hidden_states + ) + hidden_states = rearrange( + hidden_states, "(b d) f c -> (b f) d c", d=d + ) + + return hidden_states + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm2(hidden_states) + ) + + # 2. Cross-Attention + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = ( + norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ) + + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states + + if self.reference_attn: + if self.fusion_blocks == "midup": + attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + attn_modules = sorted( + attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + + for i, module in enumerate(attn_modules): + module._original_inner_forward = module.forward + if isinstance(module, BasicTransformerBlock): + module.forward = hacked_basic_transformer_inner_forward.__get__( + module, BasicTransformerBlock + ) + if isinstance(module, TemporalBasicTransformerBlock): + module.forward = hacked_basic_transformer_inner_forward.__get__( + module, TemporalBasicTransformerBlock + ) + + module.bank = [] + if(self.cache_kv): + module.kv_bank = None + module.kv_cache = None + module.attn_weight = float(i) / float(len(attn_modules)) + + def update(self, writer, dtype=torch.float16, drop_ratio=0.): + if self.reference_attn: + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, TemporalBasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in ( + torch_dfs(writer.unet.mid_block) + + torch_dfs(writer.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, TemporalBasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in torch_dfs(writer.unet) + if isinstance(module, BasicTransformerBlock) + ] + + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + writer_attn_modules = sorted( + writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r, w in zip(reader_attn_modules, writer_attn_modules): + if drop_ratio > 0: + r.bank = [] + for v in w.bank: + N, L, D = v.shape # batch, length, dim + len_keep = int(L * (1 - drop_ratio)) + + noise = torch.rand(N, L) # noise in [0, 1] + ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove + ids_keep = ids_shuffle[:, :len_keep].to(v.device) + visible_tokens = torch.gather(v.clone(), dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) + r.bank.append((visible_tokens).to(dtype)) + + else: + r.bank = [v.clone().to(dtype) for v in w.bank] + # w.bank.clear() + + def update_hkf(self, writer, dtype=torch.float16, drop_ratio=0.): + if self.reference_attn: + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, TemporalBasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in ( + torch_dfs(writer.unet.mid_block) + + torch_dfs(writer.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, TemporalBasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in torch_dfs(writer.unet) + if isinstance(module, BasicTransformerBlock) + ] + + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + writer_attn_modules = sorted( + writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r, w in zip(reader_attn_modules, writer_attn_modules): + if r.kv_bank is None: + r.kv_bank = torch.cat([v.clone().unsqueeze(1).to(dtype) for v in w.bank], dim=1) + else: + r.kv_bank = torch.cat([r.kv_bank] + [v.clone().unsqueeze(1).to(dtype) for v in w.bank], dim=1).to(dtype) + + def output(self, dtype=torch.float16): + res = {} + for i in range(3): + for j in range(2): + res[f"d{i}{j}"] = torch.cat([v.clone().to(dtype=dtype, device=self.unet.device) for v in self.unet.down_blocks[i].attentions[j].transformer_blocks[0].bank], dim=1) + res["m"] = torch.cat([v.clone().to(dtype=dtype, device=self.unet.device) for v in self.unet.mid_block.attentions[0].transformer_blocks[0].bank], dim=1) + for i in range(1, 4): + for j in range(3): + res[f"u{i}{j}"] = torch.cat([v.clone().to(dtype=dtype, device=self.unet.device) for v in self.unet.up_blocks[i].attentions[j].transformer_blocks[0].bank], dim=1) + return res + + def clear(self): + if self.reference_attn: + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r in reader_attn_modules: + r.bank.clear() + if self.cache_kv: + r.kv_bank=None + r.kv_cache=None diff --git a/src/scope/core/pipelines/personalive/modules/pose_guider.py b/src/scope/core/pipelines/personalive/modules/pose_guider.py new file mode 100644 index 00000000..445fc495 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/pose_guider.py @@ -0,0 +1,57 @@ +from typing import Tuple + +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from diffusers.models.modeling_utils import ModelMixin + +from .motion_module import zero_module +from .resnet import InflatedConv3d + + +class PoseGuider(ModelMixin): + def __init__( + self, + conditioning_embedding_channels: int = 320, + conditioning_channels: int = 3, + block_out_channels: Tuple[int] = (16, 32, 96, 256), + ): + super().__init__() + self.conv_in = InflatedConv3d( + conditioning_channels, block_out_channels[0], kernel_size=3, padding=1 + ) + + self.blocks = nn.ModuleList([]) + + for i in range(len(block_out_channels) - 1): + channel_in = block_out_channels[i] + channel_out = block_out_channels[i + 1] + self.blocks.append( + InflatedConv3d(channel_in, channel_in, kernel_size=3, padding=1) + ) + self.blocks.append( + InflatedConv3d( + channel_in, channel_out, kernel_size=3, padding=1, stride=2 + ) + ) + + self.conv_out_modify = zero_module( + InflatedConv3d( + block_out_channels[-1], + conditioning_embedding_channels, + kernel_size=3, + padding=1, + ) + ) + + def forward(self, conditioning): + embedding = self.conv_in(conditioning) + embedding = F.silu(embedding) + + for block in self.blocks: + embedding = block(embedding) + embedding = F.silu(embedding) + + embedding = self.conv_out_modify(embedding) + + return embedding diff --git a/src/scope/core/pipelines/personalive/modules/resnet.py b/src/scope/core/pipelines/personalive/modules/resnet.py new file mode 100644 index 00000000..ea731445 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/resnet.py @@ -0,0 +1,255 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from typing import Dict, Optional + + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class InflatedGroupNorm(nn.GroupNorm): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + def __init__( + self, + channels, + use_conv=False, + use_conv_transpose=False, + out_channels=None, + name="conv", + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + raise NotImplementedError + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate( + hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest" + ) + else: + hidden_states = F.interpolate( + hidden_states, size=output_size, mode="nearest" + ) + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + # if self.use_conv: + # if self.name == "conv": + # hidden_states = self.conv(hidden_states) + # else: + # hidden_states = self.Conv2d_0(hidden_states) + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + def __init__( + self, channels, use_conv=False, out_channels=None, padding=1, name="conv" + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + self.conv = InflatedConv3d( + self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + raise NotImplementedError + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + raise NotImplementedError + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlock3D(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + time_embedding_norm="default", + output_scale_factor=1.0, + use_in_shortcut=None, + use_inflated_groupnorm=None, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.time_embedding_norm = time_embedding_norm + self.output_scale_factor = output_scale_factor + + if groups_out is None: + groups_out = groups + + assert use_inflated_groupnorm != None + if use_inflated_groupnorm: + self.norm1 = InflatedGroupNorm( + num_groups=groups, num_channels=in_channels, eps=eps, affine=True + ) + else: + self.norm1 = torch.nn.GroupNorm( + num_groups=groups, num_channels=in_channels, eps=eps, affine=True + ) + + self.conv1 = InflatedConv3d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + time_emb_proj_out_channels = out_channels + elif self.time_embedding_norm == "scale_shift": + time_emb_proj_out_channels = out_channels * 2 + else: + raise ValueError( + f"unknown time_embedding_norm : {self.time_embedding_norm} " + ) + + self.time_emb_proj = torch.nn.Linear( + temb_channels, time_emb_proj_out_channels + ) + else: + self.time_emb_proj = None + + if use_inflated_groupnorm: + self.norm2 = InflatedGroupNorm( + num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True + ) + else: + self.norm2 = torch.nn.GroupNorm( + num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True + ) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = InflatedConv3d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + elif non_linearity == "mish": + self.nonlinearity = Mish() + elif non_linearity == "silu": + self.nonlinearity = nn.SiLU() + + self.use_in_shortcut = ( + self.in_channels != self.out_channels + if use_in_shortcut is None + else use_in_shortcut + ) + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = InflatedConv3d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + if temb is not None: + video_length = hidden_states.shape[2] + temb = self.time_emb_proj(self.nonlinearity(temb)) + temb = rearrange(temb, "(b f) c -> b c f", f=video_length)[:, :, :, None, None] + + # if temb is not None: + # temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + +class Mish(torch.nn.Module): + def forward(self, hidden_states): + return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) diff --git a/src/scope/core/pipelines/personalive/modules/scheduler_ddim.py b/src/scope/core/pipelines/personalive/modules/scheduler_ddim.py new file mode 100644 index 00000000..cc526d8f --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/scheduler_ddim.py @@ -0,0 +1,550 @@ +# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion +# and https://github.com/hojonathanho/diffusion + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +from einops import rearrange +import numpy as np +import torch + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.utils import BaseOutput +from diffusers.utils.torch_utils import randn_tensor +from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin + + +@dataclass +# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM +class DDIMSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + The predicted denoised sample `(x_{0})` based on the model output from the current timestep. + `pred_original_sample` can be used to preview progress or for guidance. + """ + + prev_sample: torch.FloatTensor + pred_original_sample: Optional[torch.FloatTensor] = None + + +# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar +def betas_for_alpha_bar( + num_diffusion_timesteps, + max_beta=0.999, + alpha_transform_type="cosine", +): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of + (1-beta) over time from t = [0,1]. + + Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up + to that part of the diffusion process. + + + Args: + num_diffusion_timesteps (`int`): the number of betas to produce. + max_beta (`float`): the maximum beta to use; use values lower than 1 to + prevent singularities. + alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. + Choose from `cosine` or `exp` + + Returns: + betas (`np.ndarray`): the betas used by the scheduler to step the model outputs + """ + if alpha_transform_type == "cosine": + + def alpha_bar_fn(t): + return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 + + elif alpha_transform_type == "exp": + + def alpha_bar_fn(t): + return math.exp(t * -12.0) + + else: + raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") + + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) + return torch.tensor(betas, dtype=torch.float32) + + +def rescale_zero_terminal_snr(betas): + """ + Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) + + + Args: + betas (`torch.FloatTensor`): + the betas that the scheduler is being initialized with. + + Returns: + `torch.FloatTensor`: rescaled betas with zero terminal SNR + """ + # Convert betas to alphas_bar_sqrt + alphas = 1.0 - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + alphas_bar_sqrt = alphas_cumprod.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 # Revert sqrt + alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod + alphas = torch.cat([alphas_bar[0:1], alphas]) + betas = 1 - alphas + + return betas + + +class DDIMScheduler(SchedulerMixin, ConfigMixin): + """ + `DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with + non-Markovian guidance. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + beta_start (`float`, defaults to 0.0001): + The starting `beta` value of inference. + beta_end (`float`, defaults to 0.02): + The final `beta` value. + beta_schedule (`str`, defaults to `"linear"`): + The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from + `linear`, `scaled_linear`, or `squaredcos_cap_v2`. + trained_betas (`np.ndarray`, *optional*): + Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. + clip_sample (`bool`, defaults to `True`): + Clip the predicted sample for numerical stability. + clip_sample_range (`float`, defaults to 1.0): + The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. + set_alpha_to_one (`bool`, defaults to `True`): + Each diffusion step uses the alphas product value at that step and at the previous one. For the final step + there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, + otherwise it uses the alpha value at step 0. + steps_offset (`int`, defaults to 0): + An offset added to the inference steps. You can use a combination of `offset=1` and + `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable + Diffusion. + prediction_type (`str`, defaults to `epsilon`, *optional*): + Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), + `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen + Video](https://imagen.research.google/video/paper.pdf) paper). + thresholding (`bool`, defaults to `False`): + Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such + as Stable Diffusion. + dynamic_thresholding_ratio (`float`, defaults to 0.995): + The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. + sample_max_value (`float`, defaults to 1.0): + The threshold value for dynamic thresholding. Valid only when `thresholding=True`. + timestep_spacing (`str`, defaults to `"leading"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + rescale_betas_zero_snr (`bool`, defaults to `False`): + Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and + dark samples instead of limiting it to samples with medium brightness. Loosely related to + [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). + """ + + _compatibles = [e.name for e in KarrasDiffusionSchedulers] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + beta_start: float = 0.0001, + beta_end: float = 0.02, + beta_schedule: str = "linear", + trained_betas: Optional[Union[np.ndarray, List[float]]] = None, + clip_sample: bool = True, + set_alpha_to_one: bool = True, + steps_offset: int = 0, + prediction_type: str = "epsilon", + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + clip_sample_range: float = 1.0, + sample_max_value: float = 1.0, + timestep_spacing: str = "leading", + rescale_betas_zero_snr: bool = False, + ): + if trained_betas is not None: + self.betas = torch.tensor(trained_betas, dtype=torch.float32) + elif beta_schedule == "linear": + self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) + elif beta_schedule == "scaled_linear": + # this schedule is very specific to the latent diffusion model. + self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 + elif beta_schedule == "squaredcos_cap_v2": + # Glide cosine schedule + self.betas = betas_for_alpha_bar(num_train_timesteps) + else: + raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") + + # Rescale for zero SNR + if rescale_betas_zero_snr: + self.betas = rescale_zero_terminal_snr(self.betas) + + self.alphas = 1.0 - self.betas + self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) + + # At every step in ddim, we are looking into the previous alphas_cumprod + # For the final step, there is no previous alphas_cumprod because we are already at 0 + # `set_alpha_to_one` decides whether we set this parameter simply to one or + # whether we use the final alpha of the "non-previous" one. + self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] + + # standard deviation of the initial noise distribution + self.init_noise_sigma = 1.0 + + # setable values + self.num_inference_steps = None + self.step_length = None + self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) + + def to(self, device): + self.betas = self.betas.to(device) + self.alphas = self.alphas.to(device) + self.alphas_cumprod = self.alphas_cumprod.to(device) + self.final_alpha_cumprod = self.final_alpha_cumprod.to(device) + + def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + return sample + + def _get_variance(self, alpha_prod_t, alpha_prod_t_prev): + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_prev = 1 - alpha_prod_t_prev + + variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) + + return variance + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample + def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, *remaining_dims = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.config.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, *remaining_dims) + sample = sample.to(dtype) + + return sample + + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, num_train_timesteps = None): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + """ + if num_inference_steps > self.config.num_train_timesteps: + raise ValueError( + f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" + f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" + f" maximal {self.config.num_train_timesteps} timesteps." + ) + + self.num_inference_steps = num_inference_steps + + # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891 + if self.config.timestep_spacing == "linspace": + timesteps = ( + np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps) + .round()[::-1] + .copy() + .astype(np.int64) + ) + elif self.config.timestep_spacing == "leading": + step_ratio = self.config.num_train_timesteps // self.num_inference_steps + # creates integer timesteps by multiplying by ratio + # casting to int to avoid issues when num_inference_step is power of 3 + timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64) + timesteps += self.config.steps_offset + elif self.config.timestep_spacing == "trailing": + step_ratio = self.config.num_train_timesteps / self.num_inference_steps + # creates integer timesteps by multiplying by ratio + # casting to int to avoid issues when num_inference_step is power of 3 + timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64) + timesteps -= 1 + else: + raise ValueError( + f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'." + ) + + self.timesteps = torch.from_numpy(timesteps).to(device) + + def set_step_length(self, step_length): + self.step_length = step_length + + def step( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + eta: float = 0.0, + use_clipped_model_output: bool = False, + generator=None, + variance_noise: Optional[torch.FloatTensor] = None, + return_dict: bool = True, + ) -> Union[DDIMSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + eta (`float`): + The weight of noise for added noise in diffusion step. + use_clipped_model_output (`bool`, defaults to `False`): + If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary + because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no + clipping has happened, "corrected" `model_output` would coincide with the one provided as input and + `use_clipped_model_output` has no effect. + generator (`torch.Generator`, *optional*): + A random number generator. + variance_noise (`torch.FloatTensor`): + Alternative to generating noise with `generator` by directly providing the noise for the variance + itself. Useful for methods such as [`CycleDiffusion`]. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`. + + Returns: + [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] is returned, otherwise a + tuple is returned where the first element is the sample tensor. + + """ + if self.num_inference_steps is None and self.step_length is None: + raise ValueError( + "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" + ) + + # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf + # Ideally, read DDIM paper in-detail understanding + + # Notation ( -> + # - pred_noise_t -> e_theta(x_t, t) + # - pred_original_sample -> f_theta(x_t, t) or x_0 + # - std_dev_t -> sigma_t + # - eta -> η + # - pred_sample_direction -> "direction pointing to x_t" + # - pred_prev_sample -> "x_t-1" + + # 1. get previous step value (=t-1) + if self.step_length is not None: + prev_timestep = timestep - self.step_length + else: + prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps + + # 2. compute alphas, betas + alpha_prod_t = self.alphas_cumprod[timestep] + if isinstance(prev_timestep, torch.Tensor): + alpha_prod_t = alpha_prod_t.view(-1, 1, 1, 1) + prev_timestep = prev_timestep.to(self.alphas_cumprod.device) + alpha_prod_t_prev = torch.where(prev_timestep >= 0, self.alphas_cumprod[prev_timestep], self.final_alpha_cumprod) + alpha_prod_t_prev = alpha_prod_t_prev.view(-1, 1, 1, 1) + else: + alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod + + # alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod + + beta_prod_t = 1 - alpha_prod_t + + # 3. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + if self.config.prediction_type == "epsilon": + pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) + pred_epsilon = model_output + elif self.config.prediction_type == "sample": + pred_original_sample = model_output + pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) + elif self.config.prediction_type == "v_prediction": + pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output + pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" + " `v_prediction`" + ) + + # 4. Clip or threshold "predicted x_0" + if self.config.thresholding: + pred_original_sample = self._threshold_sample(pred_original_sample) + elif self.config.clip_sample: + pred_original_sample = pred_original_sample.clamp( + -self.config.clip_sample_range, self.config.clip_sample_range + ) + + # 5. compute variance: "sigma_t(η)" -> see formula (16) + # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) + variance = self._get_variance(alpha_prod_t, alpha_prod_t_prev) + std_dev_t = eta * variance ** (0.5) + + if use_clipped_model_output: + # the pred_epsilon is always re-derived from the clipped x_0 in Glide + pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) + + # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon + + # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction + + if eta > 0: + if variance_noise is not None and generator is not None: + raise ValueError( + "Cannot pass both generator and variance_noise. Please make sure that either `generator` or" + " `variance_noise` stays `None`." + ) + + if variance_noise is None: + variance_noise = randn_tensor( + model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype + ) + variance = std_dev_t * variance_noise + + prev_sample = prev_sample + variance + + if not return_dict: + return (prev_sample, pred_original_sample) + + return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise + def add_noise( + self, + original_samples: torch.FloatTensor, + noise: torch.FloatTensor, + timesteps: torch.IntTensor, + ) -> torch.FloatTensor: + # Make sure alphas_cumprod and timestep have same device and dtype as original_samples + # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement + # for the subsequent add_noise calls + shape_len = len(original_samples.shape) + if shape_len == 5: + b, c, f, h, w = original_samples.shape + original_samples = rearrange(original_samples, "b c f h w -> (b f) c h w") + noise = rearrange(noise, "b c f h w -> (b f) c h w") + + self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device) + alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype) + timesteps = timesteps.to(original_samples.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + while len(sqrt_alpha_prod.shape) < len(original_samples.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise + + if shape_len == 5: + noisy_samples = rearrange(noisy_samples, "(b f) c h w -> b c f h w", b=b) + return noisy_samples + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity + def get_velocity( + self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor + ) -> torch.FloatTensor: + # Make sure alphas_cumprod and timestep have same device and dtype as sample + self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device) + alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype) + timesteps = timesteps.to(sample.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + while len(sqrt_alpha_prod.shape) < len(sample.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample + return velocity + + def __len__(self): + return self.config.num_train_timesteps diff --git a/src/scope/core/pipelines/personalive/modules/transformer_2d.py b/src/scope/core/pipelines/personalive/modules/transformer_2d.py new file mode 100644 index 00000000..4c0995bf --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/transformer_2d.py @@ -0,0 +1,395 @@ +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import PixArtAlphaTextProjection as CaptionProjection +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.normalization import AdaLayerNormSingle +from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version +from torch import nn + +from .attention import BasicTransformerBlock + + +@dataclass +class Transformer2DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + ref_feature: torch.FloatTensor + + +class Transformer2DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + norm_eps: float = 1e-5, + attention_type: str = "default", + caption_channels: int = None, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv + linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate( + "norm_type!=num_embeds_ada_norm", + "1.0.0", + deprecation_message, + standard_warn=False, + ) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif ( + not self.is_input_continuous + and not self.is_input_vectorized + and not self.is_input_patches + ): + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, + num_channels=in_channels, + eps=1e-6, + affine=True, + ) + if use_linear_projection: + self.proj_in = linear_cls(in_channels, inner_dim) + else: + self.proj_in = conv_cls( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + double_self_attention=double_self_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + norm_eps=norm_eps, + attention_type=attention_type, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = linear_cls(inner_dim, in_channels) + else: + self.proj_out = conv_cls( + inner_dim, in_channels, kernel_size=1, stride=1, padding=0 + ) + + # 5. PixArt-Alpha blocks. + self.adaln_single = None + self.use_additional_conditions = False + if norm_type == "ada_norm_single": + self.use_additional_conditions = self.config.sample_size == 128 + # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use + # additional conditions until we find better name + self.adaln_single = AdaLayerNormSingle( + inner_dim, use_additional_conditions=self.use_additional_conditions + ) + + self.caption_projection = None + if caption_channels is not None: + self.caption_projection = CaptionProjection( + in_features=caption_channels, hidden_size=inner_dim + ) + + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + cross_attention_kwargs ( `Dict[str, Any]`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + attention_mask ( `torch.Tensor`, *optional*): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = ( + 1 - encoder_attention_mask.to(hidden_states.dtype) + ) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # Retrieve lora scale. + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + # 1. Input + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = ( + self.proj_in(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_in(hidden_states) + ) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * width, inner_dim + ) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * width, inner_dim + ) + hidden_states = ( + self.proj_in(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_in(hidden_states) + ) + + # 2. Blocks + if self.caption_projection is not None: + batch_size = hidden_states.shape[0] + encoder_hidden_states = self.caption_projection(encoder_hidden_states) + encoder_hidden_states = encoder_hidden_states.view( + batch_size, -1, hidden_states.shape[-1] + ) + + ref_feature = hidden_states.reshape(batch, height, width, inner_dim) + for block in self.transformer_blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + timestep, + cross_attention_kwargs, + class_labels, + **ckpt_kwargs, + ) + else: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = ( + hidden_states.reshape(batch, height, width, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + hidden_states = ( + self.proj_out(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_out(hidden_states) + ) + else: + hidden_states = ( + self.proj_out(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_out(hidden_states) + ) + hidden_states = ( + hidden_states.reshape(batch, height, width, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + + output = hidden_states + residual + if not return_dict: + return (output, ref_feature) + + return Transformer2DModelOutput(sample=output, ref_feature=ref_feature) diff --git a/src/scope/core/pipelines/personalive/modules/transformer_3d.py b/src/scope/core/pipelines/personalive/modules/transformer_3d.py new file mode 100644 index 00000000..d10a3a48 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/transformer_3d.py @@ -0,0 +1,182 @@ +from dataclasses import dataclass +from typing import Optional, Dict + +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from einops import rearrange, repeat +from torch import nn + +from .attention import TemporalBasicTransformerBlock + + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +class Transformer3DModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + # Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + TemporalBasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d( + inner_dim, in_channels, kernel_size=1, stride=1, padding=0 + ) + + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + timestep=None, + return_dict: bool = True, + reference=None, + **block_kwargs, + ): + # Input + assert ( + hidden_states.dim() == 5 + ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + if encoder_hidden_states.shape[0] != hidden_states.shape[0]: + encoder_hidden_states = repeat( + encoder_hidden_states, "b n c -> (b f) n c", f=video_length + ) + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * weight, inner_dim + ) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * weight, inner_dim + ) + hidden_states = self.proj_in(hidden_states) + + # Blocks + for i, block in enumerate(self.transformer_blocks): + if reference is None: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + video_length=video_length, + **block_kwargs + ) + else: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + video_length=video_length, + reference=reference, + **block_kwargs + ) + + # Output + if not self.use_linear_projection: + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + + output = hidden_states + residual + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) diff --git a/src/scope/core/pipelines/personalive/modules/unet_2d_blocks.py b/src/scope/core/pipelines/personalive/modules/unet_2d_blocks.py new file mode 100644 index 00000000..99c7ed02 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/unet_2d_blocks.py @@ -0,0 +1,1073 @@ +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import Attention +from diffusers.models import DualTransformer2DModel +from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D +from diffusers.utils import is_torch_version, logging +from diffusers.utils.torch_utils import apply_freeu +from torch import nn + +from .transformer_2d import Transformer2DModel + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def get_down_block( + down_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + temb_channels: int, + add_downsample: bool, + resnet_eps: float, + resnet_act_fn: str, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + downsample_padding: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + downsample_type: Optional[str] = None, + dropout: float = 0.0, +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = ( + down_block_type[7:] + if down_block_type.startswith("UNetRes") + else down_block_type + ) + if down_block_type == "DownBlock2D": + return DownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "CrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnDownBlock2D" + ) + return CrossAttnDownBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + add_upsample: bool, + resnet_eps: float, + resnet_act_fn: str, + resolution_idx: Optional[int] = None, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + upsample_type: Optional[str] = None, + dropout: float = 0.0, +) -> nn.Module: + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = ( + up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + ) + if up_block_type == "UpBlock2D": + return UpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "CrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnUpBlock2D" + ) + return CrossAttnUpBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + ) + + raise ValueError(f"{up_block_type} does not exist.") + + +class AutoencoderTinyBlock(nn.Module): + """ + Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU + blocks. + + Args: + in_channels (`int`): The number of input channels. + out_channels (`int`): The number of output channels. + act_fn (`str`): + ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`. + + Returns: + `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to + `out_channels`. + """ + + def __init__(self, in_channels: int, out_channels: int, act_fn: str): + super().__init__() + act_fn = get_activation(act_fn) + self.conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + act_fn, + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + act_fn, + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + ) + self.skip = ( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + if in_channels != out_channels + else nn.Identity() + ) + self.fuse = nn.ReLU() + + def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: + return self.fuse(self.conv(x) + self.skip(x)) + + +class UNetMidBlock2D(nn.Module): + """ + A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. + + Args: + in_channels (`int`): The number of input channels. + temb_channels (`int`): The number of temporal embedding channels. + dropout (`float`, *optional*, defaults to 0.0): The dropout rate. + num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. + resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. + resnet_time_scale_shift (`str`, *optional*, defaults to `default`): + The type of normalization to apply to the time embeddings. This can help to improve the performance of the + model on tasks with long-range temporal dependencies. + resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks. + resnet_groups (`int`, *optional*, defaults to 32): + The number of groups to use in the group normalization layers of the resnet blocks. + attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks. + resnet_pre_norm (`bool`, *optional*, defaults to `True`): + Whether to use pre-normalization for the resnet blocks. + add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks. + attention_head_dim (`int`, *optional*, defaults to 1): + Dimension of a single attention head. The number of attention heads is determined based on this value and + the number of input channels. + output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor. + + Returns: + `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, + in_channels, height, width)`. + + """ + + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + attn_groups: Optional[int] = None, + resnet_pre_norm: bool = True, + add_attention: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + ): + super().__init__() + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + self.add_attention = add_attention + + if attn_groups is None: + attn_groups = ( + resnet_groups if resnet_time_scale_shift == "default" else None + ) + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + if attention_head_dim is None: + logger.warn( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}." + ) + attention_head_dim = in_channels + + for _ in range(num_layers): + if self.add_attention: + attentions.append( + Attention( + in_channels, + heads=in_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=attn_groups, + spatial_norm_dim=temb_channels + if resnet_time_scale_shift == "spatial" + else None, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + else: + attentions.append(None) + + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if attn is not None: + hidden_states = attn(hidden_states, temb=temb) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class UNetMidBlock2DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + output_scale_factor: float = 1.0, + cross_attention_dim: int = 1280, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + + # support for variable transformer layers per block + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for i in range(num_layers): + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + else: + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + + return hidden_states + + +class CrossAttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + downsample_padding: int = 1, + add_downsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + additional_residuals: Optional[torch.FloatTensor] = None, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + blocks = list(zip(self.resnets, self.attentions)) + + for i, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + hidden_states = hidden_states + additional_residuals + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale=lora_scale) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class DownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb, scale=scale) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale=scale) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler( + hidden_states, upsample_size, scale=lora_scale + ) + + return hidden_states + + +class UpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + scale: float = 1.0, + ) -> torch.FloatTensor: + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb, scale=scale) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size, scale=scale) + + return hidden_states diff --git a/src/scope/core/pipelines/personalive/modules/unet_2d_condition.py b/src/scope/core/pipelines/personalive/modules/unet_2d_condition.py new file mode 100644 index 00000000..38969ba3 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/unet_2d_condition.py @@ -0,0 +1,1311 @@ +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import ( + ADDED_KV_ATTENTION_PROCESSORS, + CROSS_ATTENTION_PROCESSORS, + AttentionProcessor, + AttnAddedKVProcessor, + AttnProcessor, +) +from diffusers.models.embeddings import ( + GaussianFourierProjection, + ImageHintTimeEmbedding, + ImageProjection, + ImageTimeEmbedding, + TextImageProjection, + TextImageTimeEmbedding, + TextTimeEmbedding, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.embeddings import GLIGENTextBoundingboxProjection as PositionNet +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils import ( + USE_PEFT_BACKEND, + BaseOutput, + deprecate, + logging, + scale_lora_layers, + unscale_lora_layers, +) + +from .unet_2d_blocks import ( + UNetMidBlock2D, + UNetMidBlock2DCrossAttn, + get_down_block, + get_up_block, +) + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet2DConditionOutput(BaseOutput): + """ + The output of [`UNet2DConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + ref_features: Tuple[torch.FloatTensor] = None + + +class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. + flip_sin_to_cos (`bool`, *optional*, defaults to `False`): + Whether to flip the sin to cos in the time embedding. + freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): + The tuple of downsample blocks to use. + mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): + Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or + `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): + The tuple of upsample blocks to use. + only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): + Whether to include self-attention in the basic transformer blocks, see + [`~models.attention.BasicTransformerBlock`]. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. + mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. + norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. + If `None`, normalization and activation layers is skipped in post-processing. + norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling + blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + encoder_hid_dim (`int`, *optional*, defaults to None): + If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` + dimension to `cross_attention_dim`. + encoder_hid_dim_type (`str`, *optional*, defaults to `None`): + If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text + embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. + attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. + num_attention_heads (`int`, *optional*): + The number of attention heads. If not defined, defaults to `attention_head_dim` + resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config + for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. + class_embed_type (`str`, *optional*, defaults to `None`): + The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, + `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. + addition_embed_type (`str`, *optional*, defaults to `None`): + Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or + "text". "text" will use the `TextTimeEmbedding` layer. + addition_time_embed_dim: (`int`, *optional*, defaults to `None`): + Dimension for the timestep embeddings. + num_class_embeds (`int`, *optional*, defaults to `None`): + Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing + class conditioning with `class_embed_type` equal to `None`. + time_embedding_type (`str`, *optional*, defaults to `positional`): + The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. + time_embedding_dim (`int`, *optional*, defaults to `None`): + An optional override for the dimension of the projected time embedding. + time_embedding_act_fn (`str`, *optional*, defaults to `None`): + Optional activation function to use only once on the time embeddings before they are passed to the rest of + the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. + timestep_post_act (`str`, *optional*, defaults to `None`): + The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. + time_cond_proj_dim (`int`, *optional*, defaults to `None`): + The dimension of `cond_proj` layer in the timestep embedding. + conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`, + *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`, + *optional*): The dimension of the `class_labels` input when + `class_embed_type="projection"`. Required when `class_embed_type="projection"`. + class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time + embeddings with the class embeddings. + mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): + Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If + `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the + `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` + otherwise. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: Union[int, Tuple[int]] = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + dropout: float = 0.0, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: Union[int, Tuple[int]] = 1280, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int]]] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: int = 1.0, + time_embedding_type: str = "positional", + time_embedding_dim: Optional[int] = None, + time_embedding_act_fn: Optional[str] = None, + timestep_post_act: Optional[str] = None, + time_cond_proj_dim: Optional[int] = None, + conv_in_kernel: int = 3, + conv_out_kernel: int = 3, + projection_class_embeddings_input_dim: Optional[int] = None, + attention_type: str = "default", + class_embeddings_concat: bool = False, + mid_block_only_cross_attention: Optional[bool] = None, + cross_attention_norm: Optional[str] = None, + addition_embed_type_num_heads=64, + ): + super().__init__() + + self.sample_size = sample_size + + if num_attention_heads is not None: + raise ValueError( + "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." + ) + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len( + only_cross_attention + ) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + if ( + isinstance(transformer_layers_per_block, list) + and reverse_transformer_layers_per_block is None + ): + for layer_number_per_block in transformer_layers_per_block: + if isinstance(layer_number_per_block, list): + raise ValueError( + "Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet." + ) + + # input + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in = nn.Conv2d( + in_channels, + block_out_channels[0], + kernel_size=conv_in_kernel, + padding=conv_in_padding, + ) + + # time + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 + if time_embed_dim % 2 != 0: + raise ValueError( + f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}." + ) + self.time_proj = GaussianFourierProjection( + time_embed_dim // 2, + set_W_to_weight=False, + log=False, + flip_sin_to_cos=flip_sin_to_cos, + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 + + self.time_proj = Timesteps( + block_out_channels[0], flip_sin_to_cos, freq_shift + ) + timestep_input_dim = block_out_channels[0] + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + post_act_fn=timestep_post_act, + cond_proj_dim=time_cond_proj_dim, + ) + + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + logger.info( + "encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined." + ) + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 + self.encoder_hid_proj = ImageProjection( + image_embed_dim=encoder_hid_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding( + timestep_input_dim, time_embed_dim, act_fn=act_fn + ) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding( + projection_class_embeddings_input_dim, time_embed_dim + ) + elif class_embed_type == "simple_projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" + ) + self.class_embedding = nn.Linear( + projection_class_embeddings_input_dim, time_embed_dim + ) + else: + self.class_embedding = None + + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, + time_embed_dim, + num_heads=addition_embed_type_num_heads, + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, + image_embed_dim=cross_attention_dim, + time_embed_dim=time_embed_dim, + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps( + addition_time_embed_dim, flip_sin_to_cos, freq_shift + ) + self.add_embedding = TimestepEmbedding( + projection_class_embeddings_input_dim, time_embed_dim + ) + elif addition_embed_type == "image": + # Kandinsky 2.2 + self.add_embedding = ImageTimeEmbedding( + image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "image_hint": + # Kandinsky 2.2 ControlNet + self.add_embedding = ImageHintTimeEmbedding( + image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type is not None: + raise ValueError( + f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'." + ) + + if time_embedding_act_fn is None: + self.time_embed_act = None + else: + self.time_embed_act = get_activation(time_embedding_act_fn) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = only_cross_attention + + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = False + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len( + down_block_types + ) + + if class_embeddings_concat: + # The time embeddings are concatenated with the class embeddings. The dimension of the + # time embeddings passed to the down, middle, and up blocks is twice the dimension of the + # regular time embeddings + blocks_time_embed_dim = time_embed_dim * 2 + else: + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] + if attention_head_dim[i] is not None + else output_channel, + dropout=dropout, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock2DCrossAttn": + self.mid_block = UNetMidBlock2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + dropout=dropout, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": + raise NotImplementedError(f"Unsupport mid_block_type: {mid_block_type}") + elif mid_block_type == "UNetMidBlock2D": + self.mid_block = UNetMidBlock2D( + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + dropout=dropout, + num_layers=0, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_groups=norm_num_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + add_attention=False, + ) + elif mid_block_type is None: + self.mid_block = None + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = ( + list(reversed(transformer_layers_per_block)) + if reverse_transformer_layers_per_block is None + else reverse_transformer_layers_per_block + ) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resolution_idx=i, + resnet_groups=norm_num_groups, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] + if attention_head_dim[i] is not None + else output_channel, + dropout=dropout, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_num_groups is not None: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + + self.conv_act = get_activation(act_fn) + + else: + self.conv_norm_out = None + self.conv_act = None + self.conv_norm_out = None + + conv_out_padding = (conv_out_kernel - 1) // 2 + # self.conv_out = nn.Conv2d( + # block_out_channels[0], + # out_channels, + # kernel_size=conv_out_kernel, + # padding=conv_out_padding, + # ) + + if attention_type in ["gated", "gated-text-image"]: + positive_len = 768 + if isinstance(cross_attention_dim, int): + positive_len = cross_attention_dim + elif isinstance(cross_attention_dim, tuple) or isinstance( + cross_attention_dim, list + ): + positive_len = cross_attention_dim[0] + + feature_type = "text-only" if attention_type == "gated" else "text-image" + self.position_net = PositionNet( + positive_len=positive_len, + out_dim=cross_attention_dim, + feature_type=feature_type, + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor( + return_deprecated_lora=True + ) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor( + self, + processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], + _remove_lora=False, + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor, _remove_lora=_remove_lora) + else: + module.set_processor( + processor.pop(f"{name}.processor"), _remove_lora=_remove_lora + ) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all( + proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS + for proc in self.attn_processors.values() + ): + processor = AttnAddedKVProcessor() + elif all( + proc.__class__ in CROSS_ATTENTION_PROCESSORS + for proc in self.attn_processors.values() + ): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor, _remove_lora=True) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = ( + num_sliceable_layers * [slice_size] + if not isinstance(slice_size, list) + else slice_size + ) + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice( + module: torch.nn.Module, slice_size: List[int] + ): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def enable_freeu(self, s1, s2, b1, b2): + r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. + + The suffixes after the scaling factors represent the stage blocks where they are being applied. + + Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that + are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. + + Args: + s1 (`float`): + Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to + mitigate the "oversmoothing effect" in the enhanced denoising process. + s2 (`float`): + Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to + mitigate the "oversmoothing effect" in the enhanced denoising process. + b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. + b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. + """ + for i, upsample_block in enumerate(self.up_blocks): + setattr(upsample_block, "s1", s1) + setattr(upsample_block, "s2", s2) + setattr(upsample_block, "b1", b1) + setattr(upsample_block, "b2", b2) + + def disable_freeu(self): + """Disables the FreeU mechanism.""" + freeu_keys = {"s1", "s2", "b1", "b2"} + for i, upsample_block in enumerate(self.up_blocks): + for k in freeu_keys: + if ( + hasattr(upsample_block, k) + or getattr(upsample_block, k, None) is not None + ): + setattr(upsample_block, k, None) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + only_return_middle_fea: bool = False, + ) -> Union[UNet2DConditionOutput, Tuple]: + r""" + The [`UNet2DConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. + class_labels (`torch.Tensor`, *optional*, defaults to `None`): + Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. + timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): + Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed + through the `self.time_embedding` layer to obtain the timestep embeddings. + attention_mask (`torch.Tensor`, *optional*, defaults to `None`): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): + A tuple of tensors that if specified are added to the residuals of down unet blocks. + mid_block_additional_residual: (`torch.Tensor`, *optional*): + A tensor that if specified is added to the residual of the middle unet block. + encoder_attention_mask (`torch.Tensor`): + A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If + `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, + which adds large negative values to the attention scores corresponding to "discard" tokens. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*): + additional residuals to be added to UNet long skip connections from down blocks to up blocks for + example from ControlNet side model(s) + mid_block_additional_residual (`torch.Tensor`, *optional*): + additional residual to be added to UNet mid block output, for example from ControlNet side model + down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): + additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + for dim in sample.shape[-2:]: + if dim % default_overall_up_factor != 0: + # Forward upsample size to force interpolation output size. + forward_upsample_size = True + break + + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None: + encoder_attention_mask = ( + 1 - encoder_attention_mask.to(sample.dtype) + ) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 0. center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError( + "class_labels should be provided when num_class_embeds > 0" + ) + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # there might be better ways to encapsulate this. + class_labels = class_labels.to(dtype=sample.dtype) + + class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) + + if self.config.class_embeddings_concat: + emb = torch.cat([emb, class_emb], dim=-1) + else: + emb = emb + class_emb + + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + elif self.config.addition_embed_type == "text_image": + # Kandinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + + image_embs = added_cond_kwargs.get("image_embeds") + text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) + aug_emb = self.add_embedding(text_embs, image_embs) + elif self.config.addition_embed_type == "text_time": + # SDXL - style + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + elif self.config.addition_embed_type == "image": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + aug_emb = self.add_embedding(image_embs) + elif self.config.addition_embed_type == "image_hint": + # Kandinsky 2.2 - style + if ( + "image_embeds" not in added_cond_kwargs + or "hint" not in added_cond_kwargs + ): + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + hint = added_cond_kwargs.get("hint") + aug_emb, hint = self.add_embedding(image_embs, hint) + sample = torch.cat([sample, hint], dim=1) + + emb = emb + aug_emb if aug_emb is not None else emb + + if self.time_embed_act is not None: + emb = self.time_embed_act(emb) + + if ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "text_proj" + ): + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) + elif ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "text_image_proj" + ): + # Kadinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj( + encoder_hidden_states, image_embeds + ) + elif ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "image_proj" + ): + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(image_embeds) + elif ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "ip_image_proj" + ): + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + image_embeds = self.encoder_hid_proj(image_embeds).to( + encoder_hidden_states.dtype + ) + encoder_hidden_states = torch.cat( + [encoder_hidden_states, image_embeds], dim=1 + ) + + # 2. pre-process + sample = self.conv_in(sample) + + # 2.5 GLIGEN position net + if ( + cross_attention_kwargs is not None + and cross_attention_kwargs.get("gligen", None) is not None + ): + cross_attention_kwargs = cross_attention_kwargs.copy() + gligen_args = cross_attention_kwargs.pop("gligen") + cross_attention_kwargs["gligen"] = { + "objs": self.position_net(**gligen_args) + } + + # 3. down + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + + is_controlnet = ( + mid_block_additional_residual is not None + and down_block_additional_residuals is not None + ) + # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets + is_adapter = down_intrablock_additional_residuals is not None + # maintain backward compatibility for legacy usage, where + # T2I-Adapter and ControlNet both use down_block_additional_residuals arg + # but can only use one or the other + if ( + not is_adapter + and mid_block_additional_residual is None + and down_block_additional_residuals is not None + ): + deprecate( + "T2I should not use down_block_additional_residuals", + "1.3.0", + "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ + and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ + for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", + standard_warn=False, + ) + down_intrablock_additional_residuals = down_block_additional_residuals + is_adapter = True + + down_block_res_samples = (sample,) + tot_referece_features = () + for downsample_block in self.down_blocks: + if ( + hasattr(downsample_block, "has_cross_attention") + and downsample_block.has_cross_attention + ): + # For t2i-adapter CrossAttnDownBlock2D + additional_residuals = {} + if is_adapter and len(down_intrablock_additional_residuals) > 0: + additional_residuals[ + "additional_residuals" + ] = down_intrablock_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + **additional_residuals, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, temb=emb, scale=lora_scale + ) + if is_adapter and len(down_intrablock_additional_residuals) > 0: + sample += down_intrablock_additional_residuals.pop(0) + + down_block_res_samples += res_samples + + if only_return_middle_fea: + return sample + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = ( + down_block_res_sample + down_block_additional_residual + ) + new_down_block_res_samples = new_down_block_res_samples + ( + down_block_res_sample, + ) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + if self.mid_block is not None: + if ( + hasattr(self.mid_block, "has_cross_attention") + and self.mid_block.has_cross_attention + ): + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = self.mid_block(sample, emb) + + # To support T2I-Adapter-XL + if ( + is_adapter + and len(down_intrablock_additional_residuals) > 0 + and sample.shape == down_intrablock_additional_residuals[0].shape + ): + sample += down_intrablock_additional_residuals.pop(0) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if ( + hasattr(upsample_block, "has_cross_attention") + and upsample_block.has_cross_attention + ): + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + scale=lora_scale, + ) + + # 6. post-process + # if self.conv_norm_out: + # sample = self.conv_norm_out(sample) + # sample = self.conv_act(sample) + # sample = self.conv_out(sample) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (sample,) + + return UNet2DConditionOutput(sample=sample) diff --git a/src/scope/core/pipelines/personalive/modules/unet_3d.py b/src/scope/core/pipelines/personalive/modules/unet_3d.py new file mode 100644 index 00000000..fa9df12f --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/unet_3d.py @@ -0,0 +1,717 @@ +from collections import OrderedDict +from dataclasses import dataclass +import pdb +from os import PathLike +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint +import torch.nn.functional as F +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.attention_processor import AttentionProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging +from safetensors.torch import load_file + +from .resnet import InflatedConv3d, InflatedGroupNorm +from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block +from .motion_module import get_motion_module + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + use_inflated_groupnorm=False, + # Additional + use_motion_module=False, + use_temporal_module=False, + motion_module_resolutions=(1, 2, 4, 8), + motion_module_mid_block=False, + motion_module_decoder_only=False, + motion_module_type=None, + temporal_module_type=None, + motion_module_kwargs={}, + temporal_module_kwargs={}, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + + # input + self.conv_in = InflatedConv3d( + in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1) + ) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2**i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + use_temporal_module=use_temporal_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + temporal_module_type=temporal_module_type, + motion_module_kwargs=motion_module_kwargs, + temporal_module_kwargs=temporal_module_kwargs + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and motion_module_mid_block, + use_temporal_module=use_temporal_module and motion_module_mid_block, + motion_module_type=motion_module_type, + temporal_module_type=temporal_module_type, + motion_module_kwargs=motion_module_kwargs, + temporal_module_kwargs=temporal_module_kwargs, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions), + use_temporal_module=use_temporal_module + and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + temporal_module_type=temporal_module_type, + motion_module_kwargs=motion_module_kwargs, + temporal_module_kwargs=temporal_module_kwargs, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + self.conv_act = nn.SiLU() + self.conv_out = InflatedConv3d( + block_out_channels[0], out_channels, kernel_size=3, padding=1 + ) + + @property + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + if "temporal_transformer" not in sub_name: + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + if "temporal_transformer" not in name: + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = ( + num_slicable_layers * [slice_size] + if not isinstance(slice_size, list) + else slice_size + ) + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice( + module: torch.nn.Module, slice_size: List[int] + ): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def set_use_cross_frame_attention(self, value): + + def fn_recursive_set_use_cf_att(module: torch.nn.Module, value): + if hasattr(module, "set_use_cross_frame_attention"): + module.set_use_cross_frame_attention(value) + + for child in module.children(): + fn_recursive_set_use_cf_att(child, value) + + for module in self.children(): + fn_recursive_set_use_cf_att(module, value) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + pose_cond_fea = None, + attention_mask: Optional[torch.Tensor] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + return_dict: bool = True, + skip_mm: bool = False, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + # try: + # timesteps = timesteps.expand(sample.shape[0]) + # except Exception: + # timesteps = timesteps.expand(sample.shape[0] * sample.shape[2]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + if self.class_embedding is not None: + if class_labels is None: + raise ValueError( + "class_labels should be provided when num_class_embeds > 0" + ) + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + if pose_cond_fea is not None: + sample = sample + pose_cond_fea + + # down + down_block_res_samples = (sample,) + block_count = 1 + for downsample_block in self.down_blocks: + if ( + hasattr(downsample_block, "has_cross_attention") + and downsample_block.has_cross_attention + ): + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + skip_mm=skip_mm, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + skip_mm=skip_mm, + ) + # if pose_cond_fea is not None: + # sample = sample + pose_cond_fea[block_count] + # block_count += 1 + down_block_res_samples += res_samples + + if down_block_additional_residuals is not None: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = ( + down_block_res_sample + down_block_additional_residual + ) + new_down_block_res_samples += (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # mid + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + skip_mm=skip_mm, + ) + + if mid_block_additional_residual is not None: + sample = sample + mid_block_additional_residual + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if ( + hasattr(upsample_block, "has_cross_attention") + and upsample_block.has_cross_attention + ): + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + skip_mm=skip_mm, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + encoder_hidden_states=encoder_hidden_states, + skip_mm=skip_mm, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + @classmethod + def from_pretrained_2d( + cls, + pretrained_model_path: PathLike, + motion_module_path: PathLike, + subfolder=None, + unet_additional_kwargs=None, + mm_zero_proj_out=False, + ): + pretrained_model_path = Path(pretrained_model_path) + motion_module_path = Path(motion_module_path) + if subfolder is not None: + pretrained_model_path = pretrained_model_path.joinpath(subfolder) + logger.info( + f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..." + ) + + config_file = pretrained_model_path / "config.json" + if not (config_file.exists() and config_file.is_file()): + raise RuntimeError(f"{config_file} does not exist or is not a file") + + unet_config = cls.load_config(config_file) + unet_config["_class_name"] = cls.__name__ + unet_config["down_block_types"] = [ + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ] + unet_config["up_block_types"] = [ + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ] + unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn" + + model = cls.from_config(unet_config, **unet_additional_kwargs) + # load the vanilla weights + if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists(): + logger.debug( + f"loading safeTensors weights from {pretrained_model_path} ..." + ) + state_dict = load_file( + pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu" + ) + + elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists(): + logger.debug(f"loading weights from {pretrained_model_path} ...") + state_dict = torch.load( + pretrained_model_path.joinpath(WEIGHTS_NAME), + map_location="cpu", + weights_only=True, + ) + else: + raise FileNotFoundError(f"no weights file found in {pretrained_model_path}") + + # load the motion module weights + if motion_module_path.exists() and motion_module_path.is_file(): + if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]: + logger.info(f"Load motion module params from {motion_module_path}") + motion_state_dict = torch.load( + motion_module_path, map_location="cpu", weights_only=True + ) + elif motion_module_path.suffix.lower() == ".safetensors": + motion_state_dict = load_file(motion_module_path, device="cpu") + else: + raise RuntimeError( + f"unknown file format for motion module weights: {motion_module_path.suffix}" + ) + + motion_state_dict = { + k.replace('motion_modules.', 'temporal_modules.'): v for k, v in motion_state_dict.items() if not "pos_encoder" in k + } + + if mm_zero_proj_out: + logger.info(f"Zero initialize proj_out layers in motion module...") + new_motion_state_dict = OrderedDict() + for k in motion_state_dict: + if "proj_out" in k: + continue + new_motion_state_dict[k] = motion_state_dict[k] + motion_state_dict = new_motion_state_dict + + # merge the state dicts + state_dict.update(motion_state_dict) + + # load the weights into the model + m, u = model.load_state_dict(state_dict, strict=False) + logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};") + + params = [ + p.numel() if "temporal_modules" in n else 0 + for n, p in model.named_parameters() + ] + mm_params = [ + p.numel() if "motion_modules" in n else 0 + for n, p in model.named_parameters() + ] + logger.info( + f"Loaded {sum(mm_params) / 1e6}M-parameter motion module, Loaded {sum(params) / 1e6}M-parameter temporal module" + ) + + return model diff --git a/src/scope/core/pipelines/personalive/modules/unet_3d_blocks.py b/src/scope/core/pipelines/personalive/modules/unet_3d_blocks.py new file mode 100644 index 00000000..26de3be2 --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/unet_3d_blocks.py @@ -0,0 +1,1119 @@ +import pdb +from typing import Dict, Optional +import torch +from torch import nn + +from .motion_module import get_motion_module +from .resnet import Downsample3D, ResnetBlock3D, Upsample3D +from .transformer_3d import Transformer3DModel + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, +): + down_block_type = ( + down_block_type[7:] + if down_block_type.startswith("UNetRes") + else down_block_type + ) + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + use_temporal_module=use_temporal_module, + temporal_module_type=temporal_module_type, + temporal_module_kwargs=temporal_module_kwargs, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnDownBlock3D" + ) + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + use_temporal_module=use_temporal_module, + temporal_module_type=temporal_module_type, + temporal_module_kwargs=temporal_module_kwargs, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, +): + up_block_type = ( + up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + ) + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + use_temporal_module=use_temporal_module, + temporal_module_type=temporal_module_type, + temporal_module_kwargs=temporal_module_kwargs, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnUpBlock3D" + ) + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + use_temporal_module=use_temporal_module, + temporal_module_type=temporal_module_type, + temporal_module_kwargs=temporal_module_kwargs, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, + **transformer_kwargs, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ] + attentions = [] + motion_modules = [] + + for _ in range(num_layers): + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + **transformer_kwargs + ) + ) + motion_modules.append( + get_motion_module( + in_channels=in_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + self.temporal_modules = nn.ModuleList( + [ + ( + get_motion_module( + in_channels=in_channels, + motion_module_type=temporal_module_type, + motion_module_kwargs=temporal_module_kwargs, + ) + if use_temporal_module + else None + ) + for _ in range(num_layers) + ] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + temb=None, + encoder_hidden_states=None, + attention_mask=None, + skip_mm=False, + mid_reference=None, + ): + if isinstance(encoder_hidden_states, list): + encoder_hidden_states, motion_hidden_states = encoder_hidden_states + else: + motion_hidden_states = encoder_hidden_states + + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet, motion_module, temporal_module in zip( + self.attentions, self.resnets[1:], self.motion_modules, self.temporal_modules + ): + if self.training and self.gradient_checkpointing: + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + if (motion_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + motion_hidden_states, + ) + if (temporal_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(temporal_module), + hidden_states.requires_grad_(), + temb, + None, + ) + # hidden_states = ( + # temporal_module(hidden_states, temb, encoder_hidden_states=None) + # if (temporal_module is not None) and not skip_mm + # else hidden_states + # ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + reference=mid_reference, + ).sample + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=motion_hidden_states + ) + if (motion_module is not None) and not skip_mm + else hidden_states + ) + hidden_states = ( + temporal_module(hidden_states, temb, encoder_hidden_states=None, debug=True) + if (temporal_module is not None) and not skip_mm + else hidden_states + ) + + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, + **transformer_kwargs, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + **transformer_kwargs, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + self.temporal_modules = nn.ModuleList( + [ + ( + get_motion_module( + in_channels=out_channels, + motion_module_type=temporal_module_type, + motion_module_kwargs=temporal_module_kwargs, + ) + if use_temporal_module + else None + ) + for _ in range(num_layers) + ] + ) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + temb=None, + encoder_hidden_states=None, + attention_mask=None, + skip_mm=False, + down_reference=None, + ): + if isinstance(encoder_hidden_states, list): + encoder_hidden_states, motion_hidden_states = encoder_hidden_states + else: + motion_hidden_states = encoder_hidden_states + + output_states = () + + for i, (resnet, attn, motion_module, temporal_module) in enumerate( + zip(self.resnets, self.attentions, self.motion_modules, self.temporal_modules) + ): + reference = None + if not(down_reference is None): + reference = down_reference[i] + # self.gradient_checkpointing = False + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + + # add motion module + if (motion_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + motion_hidden_states, + ) + if (temporal_module is not None) and not skip_mm: + # hidden_states = torch.utils.checkpoint.checkpoint( + # create_custom_forward(temporal_module), + # hidden_states.requires_grad_(), + # temb, + # None, + # ) + hidden_states = ( + temporal_module(hidden_states, temb, encoder_hidden_states=None) + if (temporal_module is not None) and not skip_mm + else hidden_states + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + reference=reference, + ).sample + + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=motion_hidden_states + ) + if (motion_module is not None) and not skip_mm + else hidden_states + ) + hidden_states = ( + temporal_module(hidden_states, temb, encoder_hidden_states=None, debug=True) + if (temporal_module is not None) and not skip_mm + else hidden_states + ) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + # use_motion_module = False + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + self.temporal_modules = nn.ModuleList( + [ + ( + get_motion_module( + in_channels=out_channels, + motion_module_type=temporal_module_type, + motion_module_kwargs=temporal_module_kwargs, + ) + if use_temporal_module + else None + ) + for _ in range(num_layers) + ] + ) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, skip_mm=False): + output_states = () + if isinstance(encoder_hidden_states, list): + encoder_hidden_states, motion_hidden_states = encoder_hidden_states + else: + motion_hidden_states = encoder_hidden_states + for resnet, motion_module, temporal_module in zip( + self.resnets, self.motion_modules, self.temporal_modules + ): + # print(f"DownBlock3D {self.gradient_checkpointing = }") + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + if (motion_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + motion_hidden_states, + ) + + if (temporal_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(temporal_module), + hidden_states.requires_grad_(), + temb, + None, + ) + else: + hidden_states = resnet(hidden_states, temb) + + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=motion_hidden_states + ) + if (motion_module is not None) and not skip_mm + else hidden_states + ) + hidden_states = ( + temporal_module( + hidden_states, temb, encoder_hidden_states=None, debug=True + ) + if (temporal_module is not None) and not skip_mm + else hidden_states + ) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_motion_module=None, + use_inflated_groupnorm=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, + **transformer_kwargs, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + **transformer_kwargs, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + self.temporal_modules = nn.ModuleList( + [ + ( + get_motion_module( + in_channels=out_channels, + motion_module_type=temporal_module_type, + motion_module_kwargs=temporal_module_kwargs, + ) + if use_temporal_module + else None + ) + for _ in range(num_layers) + ] + ) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + attention_mask=None, + skip_mm=False, + up_reference=None, + ): + if isinstance(encoder_hidden_states, list): + encoder_hidden_states, motion_hidden_states = encoder_hidden_states + else: + motion_hidden_states = encoder_hidden_states + for i, (resnet, attn, motion_module, temporal_module) in enumerate( + zip(self.resnets, self.attentions, self.motion_modules, self.temporal_modules) + ): + reference = None + if not(up_reference is None): + reference = up_reference[i] + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + # hidden_states = attn( + # hidden_states, + # encoder_hidden_states=encoder_hidden_states, + # ).sample + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + if (motion_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + motion_hidden_states, + ) + if (temporal_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(temporal_module), + hidden_states.requires_grad_(), + temb, + None, + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + reference=reference + ).sample + + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=motion_hidden_states + ) + if (motion_module is not None) and not skip_mm + else hidden_states + ) + + # add temporal_module + hidden_states = ( + temporal_module(hidden_states, temb, encoder_hidden_states=None, debug=True) + if (temporal_module is not None) and not skip_mm + else hidden_states + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + use_temporal_module=None, + temporal_module_type=None, + temporal_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + # use_motion_module = False + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + self.temporal_modules = nn.ModuleList( + [ + ( + get_motion_module( + in_channels=out_channels, + motion_module_type=temporal_module_type, + motion_module_kwargs=temporal_module_kwargs, + ) + if use_temporal_module + else None + ) + for _ in range(num_layers) + ] + ) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + upsample_size=None, + encoder_hidden_states=None, + skip_mm=False, + ): + if isinstance(encoder_hidden_states, list): + encoder_hidden_states, motion_hidden_states = encoder_hidden_states + else: + motion_hidden_states = encoder_hidden_states + for resnet, motion_module, temporal_module in zip(self.resnets, self.motion_modules, self.temporal_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + # print(f"UpBlock3D {self.gradient_checkpointing = }") + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + if (motion_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + motion_hidden_states, + ) + if (temporal_module is not None) and not skip_mm: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(temporal_module), + hidden_states.requires_grad_(), + temb, + None, + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=motion_hidden_states + ) + if (motion_module is not None) and not skip_mm + else hidden_states + ) + hidden_states = ( + temporal_module(hidden_states, temb, encoder_hidden_states=None, debug=True) + if (temporal_module is not None) and not skip_mm + else hidden_states + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/src/scope/core/pipelines/personalive/modules/util.py b/src/scope/core/pipelines/personalive/modules/util.py new file mode 100644 index 00000000..7f9ccbfe --- /dev/null +++ b/src/scope/core/pipelines/personalive/modules/util.py @@ -0,0 +1,313 @@ +import importlib +import os +import os.path as osp +import shutil +import sys +from pathlib import Path + +import av +import numpy as np +import torch +import torchvision +from einops import rearrange +from PIL import Image +import cv2 + + +def save_checkpoint(model, save_dir, prefix, ckpt_num, logger, total_limit=None): + save_path = osp.join(save_dir, f"{prefix}-{ckpt_num}.pth") + + if total_limit is not None: + checkpoints = os.listdir(save_dir) + checkpoints = [d for d in checkpoints if d.startswith(prefix)] + checkpoints = sorted( + checkpoints, key=lambda x: int(x.split("-")[1].split(".")[0]) + ) + + if len(checkpoints) >= total_limit: + num_to_remove = len(checkpoints) - total_limit + 1 + removing_checkpoints = checkpoints[0:num_to_remove] + logger.info( + f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" + ) + logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") + + for removing_checkpoint in removing_checkpoints: + removing_checkpoint = os.path.join(save_dir, removing_checkpoint) + os.remove(removing_checkpoint) + + state_dict = model.state_dict() + torch.save(state_dict, save_path) + + +def create_code_snapshot(root, dst_path, extensions=(".py", ".h", ".cpp", ".cu", ".cc", ".cuh", ".json", ".sh", ".bat", ".yaml"), exclude=()): + """Creates tarball with the source code""" + import tarfile + from pathlib import Path + with tarfile.open(str(dst_path), "w:gz") as tar: + for path in Path(root).rglob("*"): + if '.git' in path.parts: + continue + exclude_flag = False + if len(exclude) > 0: + for k in exclude: + if k in path.parts: + exclude_flag = True + if exclude_flag: + continue + if path.suffix.lower() in extensions: + try: + tar.add(path.as_posix(), arcname=path.relative_to( + root).as_posix(), recursive=True) + except: + print(path) + assert False, 'Error occur in create_code_snapshot' + +def seed_everything(seed): + import random + + import numpy as np + + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed % (2**32)) + random.seed(seed) + + +def import_filename(filename): + spec = importlib.util.spec_from_file_location("mymodule", filename) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return module + + +def delete_additional_ckpt(base_path, num_keep): + dirs = [] + for d in os.listdir(base_path): + if d.startswith("checkpoint-"): + dirs.append(d) + num_tot = len(dirs) + if num_tot <= num_keep: + return + # ensure ckpt is sorted and delete the ealier! + del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep] + for d in del_dirs: + path_to_dir = osp.join(base_path, d) + if osp.exists(path_to_dir): + shutil.rmtree(path_to_dir) + + +def save_videos_from_pil(pil_images, path, fps=8, crf=None): + import av + + save_fmt = Path(path).suffix + os.makedirs(os.path.dirname(path), exist_ok=True) + width, height = pil_images[0].size + + if save_fmt == ".mp4": + if True: + codec = "libx264" + container = av.open(path, "w") + stream = container.add_stream(codec, rate=fps) + + stream.width = width + stream.height = height + if crf is not None: + stream.options = {'crf': str(crf)} + + for pil_image in pil_images: + # pil_image = Image.fromarray(image_arr).convert("RGB") + av_frame = av.VideoFrame.from_image(pil_image) + container.mux(stream.encode(av_frame)) + container.mux(stream.encode()) + container.close() + else: + + video_writer = cv2.VideoWriter( + path.replace('.mp4', '_cv.mp4'), cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height) + ) + for pil_image in pil_images: + img_np = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) + video_writer.write(img_np) + video_writer.release() + + elif save_fmt == ".gif": + pil_images[0].save( + fp=path, + format="GIF", + append_images=pil_images[1:], + save_all=True, + duration=(1 / fps * 1000), + loop=0, + ) + else: + raise ValueError("Unsupported file type. Use .mp4 or .gif.") + + +def save_videos_grid(videos_, path: str, rescale=False, n_rows=6, fps=8, crf=None): + if not isinstance(videos_, list): videos_ = [videos_] + + outputs = [] + vid_len = videos_[0].shape[2] + for i in range(vid_len): + output = [] + for videos in videos_: + videos = rearrange(videos, "b c t h w -> t b c h w") + height, width = videos.shape[-2:] + + x = torchvision.utils.make_grid(videos[i], nrow=n_rows) # (c h w) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) # (h w c) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + output.append(x) + + output = Image.fromarray(np.concatenate(output, axis=0)) + outputs.append(output) + + os.makedirs(os.path.dirname(path), exist_ok=True) + save_videos_from_pil(outputs, path, fps, crf) + + +def save_videos_grid_ori(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8): + videos = rearrange(videos, "b c t h w -> t b c h w") + height, width = videos.shape[-2:] + outputs = [] + + for x in videos: + x = torchvision.utils.make_grid(x, nrow=n_rows) # (c h w) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) # (h w c) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + x = Image.fromarray(x) + + outputs.append(x) + + os.makedirs(os.path.dirname(path), exist_ok=True) + + save_videos_from_pil(outputs, path, fps) + + +def read_frames(video_path): + container = av.open(video_path) + + video_stream = next(s for s in container.streams if s.type == "video") + frames = [] + for packet in container.demux(video_stream): + for frame in packet.decode(): + image = Image.frombytes( + "RGB", + (frame.width, frame.height), + frame.to_rgb().to_ndarray(), + ) + frames.append(image) + + return frames + + +def get_fps(video_path): + container = av.open(video_path) + video_stream = next(s for s in container.streams if s.type == "video") + fps = video_stream.average_rate + container.close() + return fps + +def draw_keypoints(keypoints, height=512, width=512, device="cuda"): + colors = torch.tensor([ + [255, 0, 0], + [255, 255, 0], + [0, 255, 0], + [0, 255, 255], + [0, 0, 255], + [255, 0, 255], + [255, 0, 85], + ], device=device, dtype=torch.float32) + + selected = torch.tensor([1, 2, 3, 4, 12, 15, 20], device=device) + B = keypoints.shape[0] + + # [B, len(selected), 2] + pts = keypoints[:, selected] * 0.5 + 0.5 + pts[..., 0] *= width + pts[..., 1] *= height + pts = pts.long() + + canvas = torch.zeros((B, 3, height, width), device=device) + radius = 4 + + for i, color in enumerate(colors): + x = pts[:, i, 0] + y = pts[:, i, 1] + mask = ( + (x[:, None, None] - torch.arange(width, device=device)) ** 2 + + (y[:, None, None] - torch.arange(height, device=device)[:, None]) ** 2 + ) <= radius**2 + canvas[:, 0] += color[0] / 255.0 * mask + canvas[:, 1] += color[1] / 255.0 * mask + canvas[:, 2] += color[2] / 255.0 * mask + + return canvas.clamp(0, 1) + +def get_boxes(keypoints, height=512, width=512): + selected = torch.tensor([1, 2, 3, 4, 12, 15, 20]) + + # [B, len(selected), 2] + pts = keypoints[:, selected] * 0.5 + 0.5 + pts[..., 0] *= width + pts[..., 1] *= height + pts = pts.long() + + cx = pts[..., 0].float().mean(dim=1) # [B] + cy = pts[..., 1].float().mean(dim=1) # [B] + + min_y = pts[..., 1].float().min(dim=1)[0] # [B] + + side = (cy - min_y) * 2.0 + side = side * 1.7 + + x1 = (cx - side / 2 * 0.95).clamp(0, width - 1).long() + y1 = (cy - side / 2 * 0.95).clamp(0, height - 1).long() + x2 = (cx + side / 2 * 1.05).clamp(0, width - 1).long() + y2 = (cy + side / 2 * 1.05).clamp(0, height - 1).long() + + boxes = torch.stack([x1, y1, x2, y2], dim=1) # [B, 4] + + return boxes + +def crop_face(image_pil, face_mesh): + image = np.array(image_pil) + h, w = image.shape[:2] + results = face_mesh.process(image) + face_landmarks = results.multi_face_landmarks[0] + coords = [(int(l.x * w), int(l.y * h)) for l in face_landmarks.landmark] + xs, ys = zip(*coords) + x1, y1 = min(xs), min(ys) + x2, y2 = max(xs), max(ys) + face_box = (x1, y1, x2, y2) + + left, top, right, bot = scale_bb(face_box, scale=1.1, size=image.shape[:2]) + + face_patch = image[int(top) : int(bot), int(left) : int(right)] + + return face_patch + +def scale_bb(bbox, scale, size): + left, top, right, bot = bbox + width = right - left + height = bot - top + length = max(width, height) * scale + center_X = (left + right) * 0.5 + center_Y = (top + bot) * 0.5 + left, top, right, bot = [ + center_X - length / 2, + center_Y - length / 2, + center_X + length / 2, + center_Y + length / 2, + ] + left = max(0, left) + top = max(0, top) + right = min(size[1] - 1, right) + bot = min(size[0] - 1, bot) + return np.array([left, top, right, bot]) \ No newline at end of file diff --git a/src/scope/core/pipelines/personalive/pipeline.py b/src/scope/core/pipelines/personalive/pipeline.py new file mode 100644 index 00000000..1a3da79c --- /dev/null +++ b/src/scope/core/pipelines/personalive/pipeline.py @@ -0,0 +1,656 @@ +"""PersonaLive pipeline for real-time portrait animation. + +Animates a reference portrait image using driving video frames. +Based on: https://github.com/GVCLab/PersonaLive +""" + +import logging +import time +from collections import deque +from pathlib import Path +from typing import TYPE_CHECKING + +import numpy as np +import torch +import torch.nn.functional as F +from diffusers import AutoencoderKL +from diffusers.image_processor import VaeImageProcessor +from einops import rearrange +from omegaconf import OmegaConf +from PIL import Image +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection + +from ..interface import Pipeline, Requirements +from ..process import preprocess_chunk, postprocess_chunk +from ..schema import PersonaLiveConfig +from ..utils import load_model_config +from .components import FaceDetector +from .modules import ( + DDIMScheduler, + MotEncoder, + MotionExtractor, + PoseGuider, + ReferenceAttentionControl, + UNet2DConditionModel, + UNet3DConditionModel, + draw_keypoints, + get_boxes, +) + +if TYPE_CHECKING: + from ..schema import BasePipelineConfig + +logger = logging.getLogger(__name__) + + +class PersonaLivePipeline(Pipeline): + """PersonaLive portrait animation pipeline. + + This pipeline animates a reference portrait image using driving video frames. + It requires: + 1. A reference image to be set once via `fuse_reference()` + 2. Continuous driving video frames via `__call__()` + + The output is animated video frames of the reference portrait following + the motion from the driving video. + """ + + @classmethod + def get_config_class(cls) -> type["BasePipelineConfig"]: + return PersonaLiveConfig + + def __init__( + self, + config, + device: torch.device | None = None, + dtype: torch.dtype = torch.float16, + ): + """Initialize PersonaLive pipeline. + + Args: + config: Pipeline configuration (OmegaConf or dict-like). + device: Target device for models. + dtype: Data type for model weights. + """ + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + self.device = device + self.dtype = dtype + self.numpy_dtype = np.float16 if dtype == torch.float16 else np.float32 + + # Load model config + model_config = load_model_config(config, __file__) + + # Get paths from config + model_dir = Path(getattr(config, "model_dir", ".")) + personalive_dir = model_dir / "PersonaLive" / "pretrained_weights" + + # Model paths + pretrained_base_path = personalive_dir / "sd-image-variations-diffusers" + vae_path = personalive_dir / "sd-vae-ft-mse" + personalive_weights = personalive_dir / "personalive" + image_encoder_path = pretrained_base_path / "image_encoder" + + # Pipeline parameters + self.temporal_window_size = getattr(model_config, "temporal_window_size", 4) + self.temporal_adaptive_step = getattr(model_config, "temporal_adaptive_step", 4) + self.num_inference_steps = getattr(model_config, "num_inference_steps", 4) + self.batch_size = getattr(model_config, "batch_size", 1) + + # Resolution + self.height = getattr(config, "height", 512) + self.width = getattr(config, "width", 512) + self.ref_height = getattr(model_config, "reference_image_height", 512) + self.ref_width = getattr(model_config, "reference_image_width", 512) + + # Load inference config for UNet kwargs + unet_additional_kwargs = getattr(model_config, "unet_additional_kwargs", {}) + if isinstance(unet_additional_kwargs, OmegaConf): + unet_additional_kwargs = OmegaConf.to_container(unet_additional_kwargs) + + # Scheduler config + sched_kwargs = getattr(model_config, "noise_scheduler_kwargs", {}) + if isinstance(sched_kwargs, OmegaConf): + sched_kwargs = OmegaConf.to_container(sched_kwargs) + + logger.info("Loading PersonaLive models...") + start = time.time() + + # Load pose guider + self.pose_guider = PoseGuider().to(device=device, dtype=dtype) + pose_guider_path = personalive_weights / "pose_guider.pth" + if pose_guider_path.exists(): + state_dict = torch.load(pose_guider_path, map_location="cpu") + self.pose_guider.load_state_dict(state_dict) + del state_dict + logger.info(f"Loaded pose_guider in {time.time() - start:.2f}s") + + # Load motion encoder + start = time.time() + self.motion_encoder = MotEncoder().to(dtype=dtype, device=device).eval() + motion_encoder_path = personalive_weights / "motion_encoder.pth" + if motion_encoder_path.exists(): + state_dict = torch.load(motion_encoder_path, map_location="cpu") + self.motion_encoder.load_state_dict(state_dict) + del state_dict + logger.info(f"Loaded motion_encoder in {time.time() - start:.2f}s") + + # Load pose encoder (motion extractor) + start = time.time() + self.pose_encoder = MotionExtractor(num_kp=21).to(device=device, dtype=dtype).eval() + pose_encoder_path = personalive_weights / "motion_extractor.pth" + if pose_encoder_path.exists(): + state_dict = torch.load(pose_encoder_path, map_location="cpu") + self.pose_encoder.load_state_dict(state_dict, strict=False) + del state_dict + logger.info(f"Loaded pose_encoder in {time.time() - start:.2f}s") + + # Load denoising UNet (3D) + start = time.time() + self.denoising_unet = UNet3DConditionModel.from_pretrained_2d( + str(pretrained_base_path), + "", + subfolder="unet", + unet_additional_kwargs=unet_additional_kwargs, + ).to(dtype=dtype, device=device) + + denoising_unet_path = personalive_weights / "denoising_unet.pth" + if denoising_unet_path.exists(): + state_dict = torch.load(denoising_unet_path, map_location="cpu") + self.denoising_unet.load_state_dict(state_dict, strict=False) + del state_dict + + temporal_module_path = personalive_weights / "temporal_module.pth" + if temporal_module_path.exists(): + state_dict = torch.load(temporal_module_path, map_location="cpu") + self.denoising_unet.load_state_dict(state_dict, strict=False) + del state_dict + logger.info(f"Loaded denoising_unet in {time.time() - start:.2f}s") + + # Load reference UNet (2D) + start = time.time() + self.reference_unet = UNet2DConditionModel.from_pretrained( + str(pretrained_base_path), + subfolder="unet", + ).to(dtype=dtype, device=device) + + reference_unet_path = personalive_weights / "reference_unet.pth" + if reference_unet_path.exists(): + state_dict = torch.load(reference_unet_path, map_location="cpu") + self.reference_unet.load_state_dict(state_dict) + del state_dict + logger.info(f"Loaded reference_unet in {time.time() - start:.2f}s") + + # Setup reference attention control + self.reference_control_writer = ReferenceAttentionControl( + self.reference_unet, + do_classifier_free_guidance=False, + mode="write", + batch_size=self.batch_size, + fusion_blocks="full", + ) + self.reference_control_reader = ReferenceAttentionControl( + self.denoising_unet, + do_classifier_free_guidance=False, + mode="read", + batch_size=self.batch_size, + fusion_blocks="full", + cache_kv=True, + ) + + # Load VAE + start = time.time() + self.vae = AutoencoderKL.from_pretrained(str(vae_path)).to( + device=device, dtype=dtype + ) + logger.info(f"Loaded VAE in {time.time() - start:.2f}s") + + # Load image encoder + start = time.time() + self.image_encoder = CLIPVisionModelWithProjection.from_pretrained( + str(image_encoder_path), + ).to(device=device, dtype=dtype) + logger.info(f"Loaded image_encoder in {time.time() - start:.2f}s") + + # Setup scheduler + self.scheduler = DDIMScheduler(**sched_kwargs) + self.timesteps = torch.tensor([999, 666, 333, 0], device=device).long() + self.scheduler.set_step_length(333) + + # Setup generator + seed = getattr(config, "seed", 42) + self.generator = torch.Generator(device) + self.generator.manual_seed(seed) + + # Image processors + self.vae_scale_factor = 8 + self.ref_image_processor = VaeImageProcessor( + vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True + ) + self.clip_image_processor = CLIPImageProcessor() + self.cond_image_processor = VaeImageProcessor( + vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=True + ) + + # Face detector + self.face_detector = FaceDetector() + + # State variables + self.first_frame = True + self.motion_bank = None + self.count = 0 + self.num_khf = 0 # Number of history keyframes + + # Temporal buffers + self.latents_pile = deque([]) + self.pose_pile = deque([]) + self.motion_pile = deque([]) + + # Reference state (set via fuse_reference) + self.reference_fused = False + self.encoder_hidden_states = None + self.ref_image_tensor = None + self.ref_image_latents = None + self.ref_cond_tensor = None + self.kps_ref = None + self.kps_frame1 = None + + # Enable memory efficient attention + self._enable_xformers() + + torch.cuda.empty_cache() + logger.info("PersonaLive pipeline initialized") + + def _enable_xformers(self): + """Enable xformers memory efficient attention if available.""" + try: + self.reference_unet.enable_xformers_memory_efficient_attention() + self.denoising_unet.enable_xformers_memory_efficient_attention() + logger.info("Enabled xformers memory efficient attention") + except Exception as e: + logger.warning(f"Could not enable xformers: {e}") + + def prepare(self, **kwargs) -> Requirements | None: + """Return input requirements. + + PersonaLive requires: + - Reference image to be fused first via fuse_reference() + - Driving video frames (temporal_window_size frames per call) + + Returns Requirements only when video mode is signaled (video key in kwargs), + following the same pattern as other video pipelines. + """ + # Only return requirements when video mode is signaled + # This is indicated by FrameProcessor setting video=True in prepare_params + if kwargs.get("video") is not None: + return Requirements(input_size=self.temporal_window_size) + return None + + def _fast_resize(self, images: torch.Tensor, height: int, width: int) -> torch.Tensor: + """Fast bilinear resize of image tensor.""" + return F.interpolate( + images, + size=(height, width), + mode="bilinear", + align_corners=False, + ) + + def _interpolate_tensors(self, a: torch.Tensor, b: torch.Tensor, num: int) -> torch.Tensor: + """Linear interpolation between tensors a and b.""" + if a.shape != b.shape: + raise ValueError(f"Shape mismatch: a.shape={a.shape}, b.shape={b.shape}") + + B, _, *rest = a.shape + alphas = torch.linspace(0, 1, num, device=a.device, dtype=a.dtype) + view_shape = (1, num) + (1,) * len(rest) + alphas = alphas.view(view_shape) + + result = (1 - alphas) * a + alphas * b + return result + + def _calculate_dis(self, A: torch.Tensor, B: torch.Tensor, threshold: float = 10.0): + """Calculate distance between motion features for keyframe selection.""" + A_flat = A.view(A.size(1), -1).clone() + B_flat = B.view(B.size(1), -1).clone() + + dist = torch.cdist(B_flat.to(torch.float32), A_flat.to(torch.float32), p=2) + min_dist, min_idx = dist.min(dim=1) + + idx_to_add = torch.nonzero(min_dist[:1] > threshold, as_tuple=False).squeeze(1).tolist() + + if len(idx_to_add) > 0: + B_to_add = B[:, idx_to_add] + A_new = torch.cat([A, B_to_add], dim=1) + else: + A_new = A + + return idx_to_add, A_new, min_idx + + def _crop_face_tensor(self, image_tensor: torch.Tensor, boxes) -> torch.Tensor: + """Crop face from tensor using bounding box.""" + left, top, right, bot = boxes + left, top, right, bottom = map(int, (left, top, right, bot)) + + face_patch = image_tensor[:, top:bottom, left:right] + face_patch = F.interpolate( + face_patch.unsqueeze(0), + size=(224, 224), + mode="bilinear", + align_corners=False, + ) + return face_patch + + @torch.no_grad() + def fuse_reference(self, ref_image: Image.Image): + """Fuse reference image into the pipeline. + + This must be called before processing driving frames. + + Args: + ref_image: PIL Image of the reference portrait. + """ + logger.info("Fusing reference image...") + + # Process for CLIP - resize to 224x224 first (matching official implementation) + clip_image = self.clip_image_processor.preprocess( + ref_image.resize((224, 224)), return_tensors="pt" + ).pixel_values + + # Process for VAE + ref_image_tensor = self.ref_image_processor.preprocess( + ref_image, height=self.ref_height, width=self.ref_width + ) + + # Get CLIP embeddings + clip_image_embeds = self.image_encoder( + clip_image.to(self.image_encoder.device, dtype=self.image_encoder.dtype) + ).image_embeds + self.encoder_hidden_states = clip_image_embeds.unsqueeze(1) + + # Encode reference image + ref_image_tensor = ref_image_tensor.to(dtype=self.vae.dtype, device=self.vae.device) + self.ref_image_tensor = ref_image_tensor.squeeze(0) + ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean + ref_image_latents = ref_image_latents * 0.18215 + + # Run reference UNet to cache features + self.reference_unet( + ref_image_latents.to(self.reference_unet.device), + torch.zeros((self.batch_size,), dtype=self.dtype, device=self.reference_unet.device), + encoder_hidden_states=self.encoder_hidden_states, + return_dict=False, + ) + self.reference_control_reader.update(self.reference_control_writer) + self.encoder_hidden_states = self.encoder_hidden_states.to(self.device) + + # Prepare conditioning tensor for pose encoder + ref_cond_tensor = self.cond_image_processor.preprocess( + ref_image, height=256, width=256 + ).to(device=self.device, dtype=self.pose_encoder.dtype) + self.ref_cond_tensor = ref_cond_tensor / 2 + 0.5 + self.ref_image_latents = ref_image_latents + + # Reset state - clear ALL piles first to handle re-fusing + self.first_frame = True + self.motion_bank = None + self.count = 0 + self.num_khf = 0 + self.latents_pile.clear() + self.pose_pile.clear() + self.motion_pile.clear() + + # Initialize latent piles with padding + padding_num = (self.temporal_adaptive_step - 1) * self.temporal_window_size + init_latents = ref_image_latents.unsqueeze(2).repeat(1, 1, padding_num, 1, 1) + noise = torch.randn_like(init_latents) + init_timesteps = reversed(self.timesteps).repeat_interleave(self.temporal_window_size, dim=0) + noisy_latents_first = self.scheduler.add_noise(init_latents, noise, init_timesteps[:padding_num]) + + for i in range(self.temporal_adaptive_step - 1): + l = i * self.temporal_window_size + r = (i + 1) * self.temporal_window_size + self.latents_pile.append(noisy_latents_first[:, :, l:r]) + + self.reference_fused = True + logger.info("Reference image fused successfully") + + @torch.no_grad() + def __call__( + self, + video: torch.Tensor | list[torch.Tensor] | None = None, + **kwargs, + ) -> torch.Tensor: + """Process driving video frames. + + Args: + video: Driving video frames. Can be: + - Tensor of shape (B, C, T, H, W) in [0, 1] range + - List of frame tensors in THWC format and [0, 255] range + - None (not supported) + **kwargs: Additional parameters (ignored). + + Returns: + Animated output frames in THWC format and [0, 1] range. + """ + if not self.reference_fused: + raise RuntimeError( + "Reference image must be fused before processing. " + "Call fuse_reference() first." + ) + + # Convert input to expected format + if video is None: + # No video frames yet - return empty tensor + # This can happen during streaming startup + logger.debug("No video frames provided, returning empty output") + return torch.empty(0, self.height, self.width, 3, device="cpu") + + # Use standard preprocess_chunk for list input (from frame_processor) + if isinstance(video, list): + # preprocess_chunk expects list of (1, H, W, C) tensors + # Returns (B, C, T, H, W) tensor in [-1, 1] range + frames = preprocess_chunk( + video, + self.device, + self.dtype, + height=self.height, + width=self.width, + ) + else: + # Already a tensor - ensure correct device/dtype + frames = video.to(device=self.device, dtype=self.dtype) + + # Process input frames + output = self._process_frames(frames) + + return output + + @torch.no_grad() + def _process_frames(self, images: torch.Tensor) -> torch.Tensor: + """Process driving frames through the pipeline. + + Args: + images: Input tensor of shape (B, C, T, H, W) in [-1, 1] or [0, 1] range. + + Returns: + Output frames tensor of shape (T, H, W, C) in [0, 1] range. + """ + batch_size = self.batch_size + device = self.device + temporal_window_size = self.temporal_window_size + temporal_adaptive_step = self.temporal_adaptive_step + + # Reshape from (B, C, T, H, W) to (T, C, H, W) for processing + if images.dim() == 5: + images = images.squeeze(0).permute(1, 0, 2, 3) # (T, C, H, W) + + # PersonaLive expects exactly temporal_window_size frames per call + # If we receive more, process only the required number + num_frames = images.shape[0] + if num_frames != temporal_window_size: + logger.warning( + f"Expected {temporal_window_size} frames but received {num_frames}. " + f"Processing only the last {temporal_window_size} frames." + ) + images = images[-temporal_window_size:] + + # Store original images for face cropping (they should be in [0, 1] after preprocess_chunk) + # But ensure they're in [0, 1] range for consistency + if images.min() < 0: + images = images / 2 + 0.5 + images = images.clamp(0, 1) + + # Resize for pose encoder (matching official: resize first, then normalize) + # Official does: fast_resize(images, 256, 256) then / 2 + 0.5 + # This suggests images are in [-1, 1] before resize in official + # But since we have [0, 1], we'll resize directly and ensure [0, 1] range + tgt_cond_tensor = self._fast_resize(images, 256, 256) + # Ensure [0, 1] range (official normalizes after resize, but we already have [0, 1]) + tgt_cond_tensor = tgt_cond_tensor.clamp(0, 1) + + # Get keypoints + if self.first_frame: + mot_bbox_param, kps_ref, kps_frame1, kps_dri = self.pose_encoder.interpolate_kps_online( + self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + ) + self.kps_ref = kps_ref + self.kps_frame1 = kps_frame1 + else: + mot_bbox_param, kps_dri = self.pose_encoder.get_kps( + self.kps_ref, self.kps_frame1, tgt_cond_tensor + ) + + # Draw keypoints and get bounding boxes + keypoints = draw_keypoints(mot_bbox_param, device=device) + boxes = get_boxes(kps_dri) + keypoints = rearrange(keypoints.unsqueeze(2), 'f c b h w -> b c f h w') + keypoints = keypoints.to(device=device, dtype=self.pose_guider.dtype) + + # Process motion features + if self.first_frame: + ref_box = get_boxes(mot_bbox_param[:1]) + ref_face = self._crop_face_tensor(self.ref_image_tensor, ref_box[0]) + motion_face = [ref_face] + for i, frame in enumerate(images): + motion_face.append(self._crop_face_tensor(frame, boxes[i])) + pose_cond_tensor = torch.cat(motion_face, dim=0).transpose(0, 1) + pose_cond_tensor = pose_cond_tensor.unsqueeze(0) + motion_hidden_states = self.motion_encoder(pose_cond_tensor) + ref_motion = motion_hidden_states[:, :1] + dri_motion = motion_hidden_states[:, 1:] + + init_motion_hidden_states = self._interpolate_tensors( + ref_motion, dri_motion[:, :1], num=12 + 1 + )[:, :-1] + for i in range(temporal_adaptive_step - 1): + l = i * temporal_window_size + r = (i + 1) * temporal_window_size + self.motion_pile.append(init_motion_hidden_states[:, l:r]) + self.motion_pile.append(dri_motion) + + self.motion_bank = ref_motion + else: + motion_face = [] + for i, frame in enumerate(images): + motion_face.append(self._crop_face_tensor(frame, boxes[i])) + pose_cond_tensor = torch.cat(motion_face, dim=0).transpose(0, 1) + pose_cond_tensor = pose_cond_tensor.unsqueeze(0) + motion_hidden_states = self.motion_encoder(pose_cond_tensor) + self.motion_pile.append(motion_hidden_states) + + # Encode pose features + pose_fea = self.pose_guider(keypoints) + if self.first_frame: + for i in range(temporal_adaptive_step): + l = i * temporal_window_size + r = (i + 1) * temporal_window_size + self.pose_pile.append(pose_fea[:, :, l:r]) + self.first_frame = False + else: + self.pose_pile.append(pose_fea) + + # Prepare noisy latents for new frames + latents = self.ref_image_latents.unsqueeze(2).repeat(1, 1, temporal_window_size, 1, 1) + noise = torch.randn_like(latents) + latents = self.scheduler.add_noise(latents, noise, self.timesteps[:1]) + self.latents_pile.append(latents) + + # Combine piles + jump = 1 + motion_hidden_state = torch.cat(list(self.motion_pile), dim=1) + pose_cond_fea = torch.cat(list(self.pose_pile), dim=2) + + # Check for keyframe addition + idx_to_add = [] + if self.count > 8: + idx_to_add, self.motion_bank, idx_his = self._calculate_dis( + self.motion_bank, motion_hidden_state, threshold=17.0 + ) + + # Denoise + latents_model_input = torch.cat(list(self.latents_pile), dim=2) + for j in range(jump): + timesteps = reversed(self.timesteps[j::jump]).repeat_interleave(temporal_window_size, dim=0) + timesteps = torch.stack([timesteps] * batch_size) + timesteps = rearrange(timesteps, 'b f -> (b f)') + + noise_pred = self.denoising_unet( + latents_model_input, + timesteps, + encoder_hidden_states=[ + self.encoder_hidden_states, + motion_hidden_state, + ], + pose_cond_fea=pose_cond_fea, + return_dict=False, + )[0] + + clip_length = noise_pred.shape[2] + mid_noise_pred = rearrange(noise_pred, 'b c f h w -> (b f) c h w') + mid_latents = rearrange(latents_model_input, 'b c f h w -> (b f) c h w') + latents_model_input, pred_original_sample = self.scheduler.step( + mid_noise_pred, timesteps, mid_latents, + generator=self.generator, return_dict=False + ) + latents_model_input = rearrange(latents_model_input, '(b f) c h w -> b c f h w', f=clip_length) + pred_original_sample = rearrange(pred_original_sample, '(b f) c h w -> b c f h w', f=clip_length) + latents_model_input = torch.cat([ + pred_original_sample[:, :, :temporal_window_size], + latents_model_input[:, :, temporal_window_size:] + ], dim=2) + latents_model_input = latents_model_input.to(dtype=self.dtype) + + # History keyframe mechanism + if len(idx_to_add) > 0 and self.num_khf < 3: + self.reference_control_writer.clear() + self.reference_unet( + pred_original_sample[:, :, 0].to(self.reference_unet.dtype), + torch.zeros((batch_size,), dtype=self.dtype, device=self.reference_unet.device), + encoder_hidden_states=self.encoder_hidden_states, + return_dict=False, + ) + self.reference_control_reader.update_hkf(self.reference_control_writer) + logger.debug("Added history keyframe") + self.num_khf += 1 + + # Update latent piles + for i in range(len(self.latents_pile)): + self.latents_pile[i] = latents_model_input[ + :, :, i * temporal_adaptive_step:(i + 1) * temporal_adaptive_step, :, : + ] + + # Pop oldest latents and decode + self.pose_pile.popleft() + self.motion_pile.popleft() + latents = self.latents_pile.popleft() + latents = 1 / 0.18215 * latents + latents = rearrange(latents, "b c f h w -> (b f) c h w") + video = self.vae.decode(latents).sample + video = rearrange(video, "b c h w -> b h w c") + video = (video / 2 + 0.5).clamp(0, 1) + + self.count += 1 + + # Return in THWC format [0, 1] + return video.cpu() diff --git a/src/scope/core/pipelines/registry.py b/src/scope/core/pipelines/registry.py index 493804be..d1f45c3e 100644 --- a/src/scope/core/pipelines/registry.py +++ b/src/scope/core/pipelines/registry.py @@ -71,6 +71,7 @@ def _register_pipelines(): from .krea_realtime_video.pipeline import KreaRealtimeVideoPipeline from .longlive.pipeline import LongLivePipeline from .passthrough.pipeline import PassthroughPipeline + from .personalive.pipeline import PersonaLivePipeline from .reward_forcing.pipeline import RewardForcingPipeline from .streamdiffusionv2.pipeline import StreamDiffusionV2Pipeline @@ -81,6 +82,7 @@ def _register_pipelines(): StreamDiffusionV2Pipeline, PassthroughPipeline, RewardForcingPipeline, + PersonaLivePipeline, ]: config_class = pipeline_class.get_config_class() PipelineRegistry.register(config_class.pipeline_id, pipeline_class) diff --git a/src/scope/core/pipelines/schema.py b/src/scope/core/pipelines/schema.py index db11474e..d9aa0651 100644 --- a/src/scope/core/pipelines/schema.py +++ b/src/scope/core/pipelines/schema.py @@ -418,6 +418,49 @@ class PassthroughConfig(BasePipelineConfig): ) +class PersonaLiveConfig(BasePipelineConfig): + """Configuration for PersonaLive portrait animation pipeline. + + PersonaLive animates a reference portrait image using driving video frames. + It requires a reference image to be set once, then processes driving video frames. + """ + + pipeline_id: ClassVar[str] = "personalive" + pipeline_name: ClassVar[str] = "PersonaLive" + pipeline_description: ClassVar[str] = ( + "Real-time portrait animation from reference image and driving video" + ) + + # Mode support - video only (requires both reference image + driving video) + supported_modes: ClassVar[list[InputMode]] = ["video"] + default_mode: ClassVar[InputMode] = "video" + + # PersonaLive defaults + height: int = Field(default=512, ge=1, description="Output height in pixels") + width: int = Field(default=512, ge=1, description="Output width in pixels") + input_size: int | None = Field( + default=4, + description="Number of driving video frames per chunk", + ) + + # PersonaLive-specific parameters + temporal_window_size: int = Field( + default=4, + ge=1, + description="Temporal window size for processing", + ) + temporal_adaptive_step: int = Field( + default=4, + ge=1, + description="Temporal adaptive step for denoising", + ) + num_inference_steps: int = Field( + default=4, + ge=1, + description="Number of denoising steps (typically 4 for real-time)", + ) + + # Registry of pipeline config classes PIPELINE_CONFIGS: dict[str, type[BasePipelineConfig]] = { "longlive": LongLiveConfig, @@ -425,6 +468,7 @@ class PassthroughConfig(BasePipelineConfig): "krea-realtime-video": KreaRealtimeVideoConfig, "reward-forcing": RewardForcingConfig, "passthrough": PassthroughConfig, + "personalive": PersonaLiveConfig, } diff --git a/src/scope/server/app.py b/src/scope/server/app.py index 0f854727..35a24e76 100644 --- a/src/scope/server/app.py +++ b/src/scope/server/app.py @@ -37,6 +37,7 @@ IceCandidateRequest, IceServerConfig, IceServersResponse, + PersonaLiveReferenceResponse, PipelineLoadRequest, PipelineSchemasResponse, PipelineStatusResponse, @@ -311,6 +312,70 @@ async def get_pipeline_status( raise HTTPException(status_code=500, detail=str(e)) from e +@app.post("/api/v1/personalive/reference") +async def set_personalive_reference( + request: Request, + pipeline_manager: PipelineManager = Depends(get_pipeline_manager), +): + """Set the reference image for PersonaLive pipeline. + + This endpoint accepts a reference portrait image that will be animated + based on driving video frames. The image should be a face-centered portrait. + + The request body should be the raw image bytes with appropriate content-type + header (image/jpeg, image/png, etc.). + + This must be called after loading the PersonaLive pipeline and before + starting video streaming. + """ + from io import BytesIO + from PIL import Image + + try: + # Check if PersonaLive pipeline is loaded + status_info = await pipeline_manager.get_status_info_async() + if status_info.get("pipeline_id") != "personalive": + raise HTTPException( + status_code=400, + detail="PersonaLive pipeline must be loaded first" + ) + if status_info.get("status") != "loaded": + raise HTTPException( + status_code=400, + detail=f"Pipeline not ready. Current status: {status_info.get('status')}" + ) + + # Get image from request body + body = await request.body() + if not body: + raise HTTPException(status_code=400, detail="No image data provided") + + # Load image + try: + image = Image.open(BytesIO(body)).convert("RGB") + except Exception as e: + raise HTTPException(status_code=400, detail=f"Invalid image data: {e}") from e + + # Get pipeline and fuse reference + pipeline = pipeline_manager.get_pipeline() + if pipeline is None: + raise HTTPException(status_code=500, detail="Pipeline not available") + + # Fuse reference image + pipeline.fuse_reference(image) + + return PersonaLiveReferenceResponse( + success=True, + message="Reference image set successfully. Ready to process driving video." + ) + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error setting PersonaLive reference: {e}") + raise HTTPException(status_code=500, detail=str(e)) from e + + @app.get("/api/v1/pipelines/schemas", response_model=PipelineSchemasResponse) async def get_pipeline_schemas(): """Get configuration schemas and defaults for all available pipelines. diff --git a/src/scope/server/download_models.py b/src/scope/server/download_models.py index 354b8a1d..15df3dc8 100644 --- a/src/scope/server/download_models.py +++ b/src/scope/server/download_models.py @@ -219,6 +219,73 @@ def download_reward_forcing_pipeline() -> None: ) +def download_personalive_pipeline() -> None: + """ + Download models for the PersonaLive portrait animation pipeline. + + Downloads from three sources: + 1. lambdalabs/sd-image-variations-diffusers - Base UNet + CLIP Image Encoder + (SD fine-tuned to accept CLIP image embeddings instead of text) + 2. stabilityai/sd-vae-ft-mse - VAE (improved fine-tuned version) + 3. huaichang/PersonaLive - PersonaLive-specific weights: + - denoising_unet.pth, reference_unet.pth + - motion_encoder.pth, motion_extractor.pth + - pose_guider.pth, temporal_module.pth + """ + models_root = ensure_models_dir() + personalive_root = models_root / "PersonaLive" / "pretrained_weights" + personalive_root.mkdir(parents=True, exist_ok=True) + + # 1. Download sd-image-variations-diffusers (base model with CLIP image encoder) + # This provides: image_encoder/, unet/, model_index.json + sd_variations_dst = personalive_root / "sd-image-variations-diffusers" + if not sd_variations_dst.exists(): + print("Downloading sd-image-variations-diffusers from lambdalabs...") + snapshot_download( + repo_id="lambdalabs/sd-image-variations-diffusers", + local_dir=str(sd_variations_dst), + local_dir_use_symlinks=False, + revision="v2.0", # Use v2.0 as recommended + ignore_patterns=["*.md", "*.txt"], + ) + print(f"[OK] Downloaded sd-image-variations-diffusers to: {sd_variations_dst}") + else: + print(f"[SKIP] sd-image-variations-diffusers already exists at: {sd_variations_dst}") + + # 2. Download sd-vae-ft-mse (improved VAE) + # This provides: config.json, diffusion_pytorch_model.safetensors + vae_dst = personalive_root / "sd-vae-ft-mse" + if not vae_dst.exists(): + print("Downloading sd-vae-ft-mse from stabilityai...") + snapshot_download( + repo_id="stabilityai/sd-vae-ft-mse", + local_dir=str(vae_dst), + local_dir_use_symlinks=False, + ignore_patterns=["*.md", "*.txt"], + ) + print(f"[OK] Downloaded sd-vae-ft-mse to: {vae_dst}") + else: + print(f"[SKIP] sd-vae-ft-mse already exists at: {vae_dst}") + + # 3. Download PersonaLive-specific weights + # This provides: denoising_unet.pth, reference_unet.pth, motion_encoder.pth, etc. + personalive_weights_dst = personalive_root / "personalive" + if not personalive_weights_dst.exists(): + print("Downloading PersonaLive weights from huaichang/PersonaLive...") + snapshot_download( + repo_id="huaichang/PersonaLive", + local_dir=str(personalive_weights_dst), + local_dir_use_symlinks=False, + # Only download the .pth weight files, not ONNX/TRT + allow_patterns=["*.pth"], + ) + print(f"[OK] Downloaded PersonaLive weights to: {personalive_weights_dst}") + else: + print(f"[SKIP] PersonaLive weights already exist at: {personalive_weights_dst}") + + print(f"\n[OK] PersonaLive pipeline models ready at: {personalive_root}") + + def download_models(pipeline_id: str | None = None) -> None: """ Download models. If pipeline_id is None, downloads all pipelines. @@ -226,7 +293,7 @@ def download_models(pipeline_id: str | None = None) -> None: Args: pipeline_id: Optional pipeline ID. Supports "streamdiffusionv2", "longlive", - "krea-realtime-video", or "reward-forcing". + "krea-realtime-video", "reward-forcing", or "personalive". If None, downloads all pipelines. """ if pipeline_id is None: @@ -235,6 +302,7 @@ def download_models(pipeline_id: str | None = None) -> None: download_longlive_pipeline() download_krea_realtime_video_pipeline() download_reward_forcing_pipeline() + download_personalive_pipeline() elif pipeline_id == "streamdiffusionv2": download_streamdiffusionv2_pipeline() elif pipeline_id == "longlive": @@ -243,9 +311,11 @@ def download_models(pipeline_id: str | None = None) -> None: download_krea_realtime_video_pipeline() elif pipeline_id == "reward-forcing": download_reward_forcing_pipeline() + elif pipeline_id == "personalive": + download_personalive_pipeline() else: raise ValueError( - f"Unknown pipeline: {pipeline_id}. Supported pipelines: streamdiffusionv2, longlive, krea-realtime-video, reward-forcing" + f"Unknown pipeline: {pipeline_id}. Supported pipelines: streamdiffusionv2, longlive, krea-realtime-video, reward-forcing, personalive" ) print("\nAll downloads complete.") @@ -266,6 +336,7 @@ def main(): python download_models.py --pipeline longlive python download_models.py --pipeline krea-realtime-video python download_models.py --pipeline reward-forcing + python download_models.py --pipeline personalive python download_models.py -p streamdiffusionv2 """, ) @@ -274,7 +345,7 @@ def main(): "-p", type=str, default=None, - help="Pipeline ID to download (e.g., 'streamdiffusionv2', 'longlive', 'krea-realtime-video', 'reward-forcing'). If not specified, downloads all pipelines.", + help="Pipeline ID to download (e.g., 'streamdiffusionv2', 'longlive', 'krea-realtime-video', 'reward-forcing', 'personalive'). If not specified, downloads all pipelines.", ) args = parser.parse_args() diff --git a/src/scope/server/models_config.py b/src/scope/server/models_config.py index c6ce6ace..cfc2b133 100644 --- a/src/scope/server/models_config.py +++ b/src/scope/server/models_config.py @@ -123,6 +123,19 @@ def get_required_model_files(pipeline_id: str | None = None) -> list[Path]: models_dir / "Reward-Forcing-T2V-1.3B" / "rewardforcing.pt", ] + # personalive pipeline + if pipeline_id == "personalive": + return [ + models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "denoising_unet.pth", + models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "reference_unet.pth", + models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "motion_encoder.pth", + models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "motion_extractor.pth", + models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "pose_guider.pth", + models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "temporal_module.pth", + models_dir / "PersonaLive" / "pretrained_weights" / "sd-image-variations-diffusers" / "model_index.json", + models_dir / "PersonaLive" / "pretrained_weights" / "sd-vae-ft-mse" / "config.json", + ] + # Default: nothing is required return [] diff --git a/src/scope/server/patch_xformers.py b/src/scope/server/patch_xformers.py new file mode 100644 index 00000000..89281ca6 --- /dev/null +++ b/src/scope/server/patch_xformers.py @@ -0,0 +1,85 @@ +"""Patch xformers flash.py to set FLASH_VER_LAST to 2.8.3.""" + +import importlib.util +import sys +from pathlib import Path + + +def find_xformers_flash_file() -> Path | None: + """Find the xformers flash.py file in the installed package.""" + try: + import xformers + + # Get the package path + spec = importlib.util.find_spec("xformers") + if spec is None or spec.origin is None: + return None + + # xformers package location + package_path = Path(spec.origin).parent + flash_file = package_path / "ops" / "fmha" / "flash.py" + + if flash_file.exists(): + return flash_file + + return None + except ImportError: + print("Error: xformers is not installed", file=sys.stderr) + return None + + +def patch_xformers_flash() -> bool: + """Patch xformers flash.py to set FLASH_VER_LAST to 2.8.3.""" + flash_file = find_xformers_flash_file() + + if flash_file is None: + print("Error: Could not find xformers flash.py file", file=sys.stderr) + return False + + try: + # Read the file + content = flash_file.read_text(encoding="utf-8") + + # Check if already patched + if 'FLASH_VER_LAST = parse_version("2.8.3")' in content: + print(f"✓ xformers flash.py already patched: {flash_file}") + return True + + # Find and replace the FLASH_VER_LAST line + lines = content.splitlines() + patched = False + + for i, line in enumerate(lines): + # Look for FLASH_VER_LAST assignment + if "FLASH_VER_LAST" in line and "parse_version" in line: + # Preserve original indentation + indent = len(line) - len(line.lstrip()) + # Replace with our desired version, preserving indentation + lines[i] = ( + " " * indent + + 'FLASH_VER_LAST = parse_version("2.8.3") # last supported, inclusive' + ) + patched = True + break + + if not patched: + print( + "Warning: Could not find FLASH_VER_LAST line to patch", + file=sys.stderr, + ) + return False + + # Write the patched content back + flash_file.write_text("\n".join(lines) + "\n", encoding="utf-8") + print(f"✓ Successfully patched xformers flash.py: {flash_file}") + return True + + except Exception as e: + print(f"Error patching xformers flash.py: {e}", file=sys.stderr) + return False + + +def main() -> None: + """Main entry point for the patch command.""" + success = patch_xformers_flash() + sys.exit(0 if success else 1) diff --git a/src/scope/server/pipeline_manager.py b/src/scope/server/pipeline_manager.py index 884bd43b..fdb5b954 100644 --- a/src/scope/server/pipeline_manager.py +++ b/src/scope/server/pipeline_manager.py @@ -491,6 +491,38 @@ def _load_pipeline_implementation( logger.info("RewardForcing pipeline initialized") return pipeline + elif pipeline_id == "personalive": + from scope.core.pipelines import PersonaLivePipeline + + from .models_config import get_models_dir + + config = OmegaConf.create( + { + "model_dir": str(get_models_dir()), + } + ) + + # Apply load parameters (resolution, seed) to config + height = 512 + width = 512 + seed = 42 + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + seed = load_params.get("seed", 42) + + config["height"] = height + config["width"] = width + config["seed"] = seed + + pipeline = PersonaLivePipeline( + config, + device=torch.device("cuda"), + dtype=torch.float16, # PersonaLive uses fp16 + ) + logger.info("PersonaLive pipeline initialized") + return pipeline + else: raise ValueError(f"Invalid pipeline ID: {pipeline_id}") diff --git a/src/scope/server/schema.py b/src/scope/server/schema.py index 1916dab7..7052358d 100644 --- a/src/scope/server/schema.py +++ b/src/scope/server/schema.py @@ -8,6 +8,7 @@ from scope.core.pipelines.schema import ( KreaRealtimeVideoConfig, LongLiveConfig, + PersonaLiveConfig, StreamDiffusionV2Config, ) from scope.core.pipelines.utils import Quantization @@ -360,6 +361,40 @@ class KreaRealtimeVideoLoadParams(LoRAEnabledLoadParams): ) +class PersonaLiveLoadParams(PipelineLoadParams): + """Load parameters for PersonaLive portrait animation pipeline. + + Defaults are derived from PersonaLiveConfig to ensure consistency. + Note: A reference image must be uploaded via /api/v1/personalive/reference + before the pipeline can process driving video frames. + """ + + height: int = Field( + default=PersonaLiveConfig.model_fields["height"].default, + description="Target video height", + ge=256, + le=1024, + ) + width: int = Field( + default=PersonaLiveConfig.model_fields["width"].default, + description="Target video width", + ge=256, + le=1024, + ) + seed: int = Field( + default=42, # Default seed from BasePipelineConfig + description="Random seed for generation", + ge=0, + ) + + +class PersonaLiveReferenceResponse(BaseModel): + """Response after setting PersonaLive reference image.""" + + success: bool = Field(..., description="Whether the reference image was set successfully") + message: str = Field(..., description="Status message") + + class PipelineLoadRequest(BaseModel): """Pipeline load request schema.""" @@ -371,6 +406,7 @@ class PipelineLoadRequest(BaseModel): | PassthroughLoadParams | LongLiveLoadParams | KreaRealtimeVideoLoadParams + | PersonaLiveLoadParams | None ) = Field(default=None, description="Pipeline-specific load parameters") diff --git a/uv.lock b/uv.lock index 82d4a89b..24068139 100644 --- a/uv.lock +++ b/uv.lock @@ -2,15 +2,35 @@ version = 1 revision = 3 requires-python = ">=3.10.12" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and sys_platform == 'linux'", - 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"sha256:9538be803969c6e1ca16a3ece921e472c24f79970b10be1087a389dcb66e412a", size = 12105990, upload-time = "2025-08-15T11:48:56.584Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ce/16/4ed2fbf7c20ba20565d9a1fc544c2bef39d409c8eca1949654c6ff9f8452/xformers-0.0.32.post2-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3d1fefe0006eb00a730b3b197ff41aab085f1209b50419baab6152445f09e508", size = 117196673, upload-time = "2025-08-15T11:48:46.726Z" }, + { url = "https://files.pythonhosted.org/packages/50/19/ea64609a535f87a44cb177dc37c6d6a58f6d540adfff819d565fb0657cf7/xformers-0.0.32.post2-cp39-abi3-win_amd64.whl", hash = "sha256:87fa13f4b406ed640554cea6687d0020b496bd6b446ca17d24f9438a079e6f09", size = 100221079, upload-time = "2025-08-15T11:48:53.234Z" }, +] + [[package]] name = "yarl" version = "1.22.0" From 0584a9df0960594dff29a90f6f94c267d2d68fcc Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Tue, 16 Dec 2025 13:45:17 -0500 Subject: [PATCH 2/7] TRT support for personalive. Signed-off-by: BuffMcBigHuge --- pyproject.toml | 12 +- .../core/pipelines/personalive/model.yaml | 7 + .../core/pipelines/personalive/pipeline.py | 250 ++++++++++++- .../personalive/tensorrt/__init__.py | 58 ++++ .../pipelines/personalive/tensorrt/builder.py | 243 +++++++++++++ .../pipelines/personalive/tensorrt/convert.py | 328 ++++++++++++++++++ .../pipelines/personalive/tensorrt/export.py | 129 +++++++ .../personalive/tensorrt/framed_model.py | 305 ++++++++++++++++ .../pipelines/personalive/tensorrt/runner.py | 211 +++++++++++ uv.lock | 270 ++++++++------ 10 files changed, 1707 insertions(+), 106 deletions(-) create mode 100644 src/scope/core/pipelines/personalive/tensorrt/__init__.py create mode 100644 src/scope/core/pipelines/personalive/tensorrt/builder.py create mode 100644 src/scope/core/pipelines/personalive/tensorrt/convert.py create mode 100644 src/scope/core/pipelines/personalive/tensorrt/export.py create mode 100644 src/scope/core/pipelines/personalive/tensorrt/framed_model.py create mode 100644 src/scope/core/pipelines/personalive/tensorrt/runner.py diff --git a/pyproject.toml b/pyproject.toml index c3f53f57..329a881f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,9 +57,16 @@ dependencies = [ "PyOpenGL>=3.1.10; sys_platform == 'win32'", # PersonaLive dependencies "mediapipe>=0.10.11", - "decord>=0.6.0", "xformers>=0.0.32.post2", - "scikit-image>=0.22.0", +] + +[project.optional-dependencies] +tensorrt = [ + "tensorrt>=10.0.0; sys_platform == 'linux' or sys_platform == 'win32'", + "tensorrt-cu12>=10.0.0; sys_platform == 'linux' or sys_platform == 'win32'", + "polygraphy>=0.49.0", + "onnx>=1.17.0", + "onnx-graphsurgeon>=0.5.0", ] [project.scripts] @@ -67,6 +74,7 @@ daydream-scope = "scope.server.app:main" build = "scope.server.build:main" download_models = "scope.server.download_models:main" patch-xformers = "scope.server.patch_xformers:main" +convert-personalive-trt = "scope.core.pipelines.personalive.tensorrt.convert:main" [project.urls] Homepage = "https://github.com/daydreamlive/scope" diff --git a/src/scope/core/pipelines/personalive/model.yaml b/src/scope/core/pipelines/personalive/model.yaml index 520642fb..b26b4f36 100644 --- a/src/scope/core/pipelines/personalive/model.yaml +++ b/src/scope/core/pipelines/personalive/model.yaml @@ -9,6 +9,13 @@ vae_model_path: "sd-vae-ft-mse" # PersonaLive custom weights personalive_weights_path: "PersonaLive/pretrained_weights/personalive" +# TensorRT acceleration (optional) +# Run `convert-personalive-trt` to generate the engine file +# Engine path format: tensorrt/unet_work_{height}x{width}.engine +tensorrt: + onnx_path: "PersonaLive/pretrained_weights/onnx/unet_opt/unet_opt.onnx" + engine_dir: "PersonaLive/pretrained_weights/tensorrt" + # Inference configuration dtype: "fp16" batch_size: 1 diff --git a/src/scope/core/pipelines/personalive/pipeline.py b/src/scope/core/pipelines/personalive/pipeline.py index 1a3da79c..2062ff86 100644 --- a/src/scope/core/pipelines/personalive/pipeline.py +++ b/src/scope/core/pipelines/personalive/pipeline.py @@ -2,6 +2,10 @@ Animates a reference portrait image using driving video frames. Based on: https://github.com/GVCLab/PersonaLive + +Supports optional TensorRT acceleration when installed: + pip install daydream-scope[tensorrt] + convert-personalive-trt --model-dir ./models --height 512 --width 512 """ import logging @@ -37,6 +41,12 @@ get_boxes, ) +# TensorRT support (optional) +try: + from .tensorrt import TRT_AVAILABLE, TRTRunner, get_engine_path +except ImportError: + TRT_AVAILABLE = False + if TYPE_CHECKING: from ..schema import BasePipelineConfig @@ -259,8 +269,16 @@ def __init__( # Enable memory efficient attention self._enable_xformers() + # TensorRT support (optional) + self.use_tensorrt = False + self.trt_runner = None + self._model_dir = model_dir # Store for TensorRT path lookup + + if TRT_AVAILABLE: + self._init_tensorrt(model_dir) + torch.cuda.empty_cache() - logger.info("PersonaLive pipeline initialized") + logger.info(f"PersonaLive pipeline initialized (TensorRT: {self.use_tensorrt})") def _enable_xformers(self): """Enable xformers memory efficient attention if available.""" @@ -271,6 +289,41 @@ def _enable_xformers(self): except Exception as e: logger.warning(f"Could not enable xformers: {e}") + def _init_tensorrt(self, model_dir: Path): + """Initialize TensorRT engine if available. + + Args: + model_dir: Base model directory. + """ + engine_path = get_engine_path(model_dir, self.height, self.width) + + if not engine_path.exists(): + logger.info( + f"TensorRT engine not found at {engine_path}. " + f"Run 'convert-personalive-trt' to create it for faster inference." + ) + return + + try: + device_index = self.device.index if self.device.index is not None else 0 + self.trt_runner = TRTRunner(engine_path, device=self.device) + + # Setup output-to-input bindings for recurrent state + self.trt_runner.bind({ + "motion_hidden_states_out": "motion_hidden_states", + "pose_cond_fea_out": "pose_cond_fea", + "latents": "sample", + }) + + self.use_tensorrt = True + logger.info(f"TensorRT engine loaded from {engine_path}") + + except Exception as e: + logger.warning(f"Failed to load TensorRT engine: {e}") + logger.warning("Falling back to PyTorch inference") + self.trt_runner = None + self.use_tensorrt = False + def prepare(self, **kwargs) -> Requirements | None: """Return input requirements. @@ -381,7 +434,29 @@ def fuse_reference(self, ref_image: Image.Image): encoder_hidden_states=self.encoder_hidden_states, return_dict=False, ) - self.reference_control_reader.update(self.reference_control_writer) + + # Handle TensorRT vs PyTorch mode differently + if self.use_tensorrt: + # For TensorRT, extract reference hidden states and prefill + reference_hidden_states = self.reference_control_writer.output() + self._reference_hidden_states = reference_hidden_states + + # Prefill TensorRT runner with constant inputs + self.trt_runner.clear_prefill() + self.trt_runner.prefill(encoder_hidden_states=self.encoder_hidden_states) + + # Prefill reference hidden states + ref_hidden_names = [ + "d00", "d01", "d10", "d11", "d20", "d21", "m", + "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" + ] + for name in ref_hidden_names: + if name in reference_hidden_states: + self.trt_runner.prefill(**{name: reference_hidden_states[name]}) + else: + # For PyTorch, update the reader + self.reference_control_reader.update(self.reference_control_writer) + self.encoder_hidden_states = self.encoder_hidden_states.to(self.device) # Prepare conditioning tensor for pose encoder @@ -412,6 +487,18 @@ def fuse_reference(self, ref_image: Image.Image): r = (i + 1) * self.temporal_window_size self.latents_pile.append(noisy_latents_first[:, :, l:r]) + # For TensorRT, also prefill initial latents + if self.use_tensorrt: + sample = torch.cat(list(self.latents_pile), dim=2) + # Add placeholder for new latents + new_latents = self.ref_image_latents.unsqueeze(2).repeat( + 1, 1, self.temporal_window_size, 1, 1 + ) + noise = torch.randn_like(new_latents) + new_latents = self.scheduler.add_noise(new_latents, noise, self.timesteps[-1:]) + sample = torch.cat([sample, new_latents], dim=2) + self.trt_runner.prefill(latents=sample) + self.reference_fused = True logger.info("Reference image fused successfully") @@ -470,6 +557,165 @@ def __call__( def _process_frames(self, images: torch.Tensor) -> torch.Tensor: """Process driving frames through the pipeline. + Args: + images: Input tensor of shape (B, C, T, H, W) in [-1, 1] or [0, 1] range. + + Returns: + Output frames tensor of shape (T, H, W, C) in [0, 1] range. + """ + # Route to TensorRT or PyTorch implementation + if self.use_tensorrt: + return self._process_frames_trt(images) + else: + return self._process_frames_pytorch(images) + + @torch.no_grad() + def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: + """Process frames using TensorRT engine. + + Args: + images: Input tensor of shape (B, C, T, H, W) in [-1, 1] or [0, 1] range. + + Returns: + Output frames tensor of shape (T, H, W, C) in [0, 1] range. + """ + batch_size = self.batch_size + device = self.device + temporal_window_size = self.temporal_window_size + temporal_adaptive_step = self.temporal_adaptive_step + + # Reshape from (B, C, T, H, W) to (T, C, H, W) for processing + if images.dim() == 5: + images = images.squeeze(0).permute(1, 0, 2, 3) + + num_frames = images.shape[0] + if num_frames != temporal_window_size: + images = images[-temporal_window_size:] + + if images.min() < 0: + images = images / 2 + 0.5 + images = images.clamp(0, 1) + + # Get keypoints using pose encoder (still PyTorch) + tgt_cond_tensor = self._fast_resize(images, 256, 256).clamp(0, 1) + + if self.first_frame: + mot_bbox_param, kps_ref, kps_frame1, kps_dri = self.pose_encoder.interpolate_kps_online( + self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + ) + self.kps_ref = kps_ref + self.kps_frame1 = kps_frame1 + else: + mot_bbox_param, kps_dri = self.pose_encoder.get_kps( + self.kps_ref, self.kps_frame1, tgt_cond_tensor + ) + + keypoints = draw_keypoints(mot_bbox_param, device=device) + boxes = get_boxes(kps_dri) + keypoints = rearrange(keypoints.unsqueeze(2), 'f c b h w -> b c f h w') + keypoints = keypoints.to(device=device, dtype=self.dtype) + + # Prepare motion input (face crops) + if self.first_frame: + ref_box = get_boxes(mot_bbox_param[:1]) + ref_face = self._crop_face_tensor(self.ref_image_tensor, ref_box[0]) + motion_face = [ref_face] + for i, frame in enumerate(images): + motion_face.append(self._crop_face_tensor(frame, boxes[i])) + pose_cond_tensor = torch.cat(motion_face, dim=0).transpose(0, 1) + pose_cond_tensor = pose_cond_tensor.unsqueeze(0) + + # For first frame, compute initial states using PyTorch motion encoder + motion_hidden_states = self.motion_encoder(pose_cond_tensor) + ref_motion = motion_hidden_states[:, :1] + dri_motion = motion_hidden_states[:, 1:] + + init_motion_hidden_states = self._interpolate_tensors( + ref_motion, dri_motion[:, :1], num=12 + 1 + )[:, :-1] + + # Compute initial pose features using PyTorch pose guider + pose_fea = self.pose_guider(keypoints) + + # Prefill TensorRT with initial states + self.trt_runner.prefill( + motion_hidden_states_out=init_motion_hidden_states, + pose_cond_fea_out=pose_fea[:, :, :temporal_window_size * (temporal_adaptive_step - 1)], + ) + + self.motion_bank = ref_motion + self.first_frame = False + + # Prepare TensorRT inputs + pose = keypoints[:, :, -temporal_window_size:] + motion = dri_motion.transpose(1, 2).unsqueeze(0) # Reshape for TRT + else: + motion_face = [] + for i, frame in enumerate(images): + motion_face.append(self._crop_face_tensor(frame, boxes[i])) + motion = torch.cat(motion_face, dim=0).transpose(0, 1).unsqueeze(0) + pose = keypoints + + # Prepare new noise for next iteration + new_noise = torch.randn( + batch_size, 4, temporal_window_size, + self.height // 8, self.width // 8, + device=device, dtype=self.dtype + ) + + # Run TensorRT inference + outputs = self.trt_runner( + output_names=["pred_video", "motion_out", "latent_first"], + return_torch=True, + pose=pose, + motion=motion.to(self.dtype), + new_noise=new_noise, + ) + + video = outputs["pred_video"] + motion_out = outputs.get("motion_out") + + # Keyframe tracking + if motion_out is not None and self.count > 8: + idx_to_add, self.motion_bank, _ = self._calculate_dis( + self.motion_bank, motion_out.unsqueeze(0), threshold=17.0 + ) + + if len(idx_to_add) > 0 and self.num_khf < 3: + # Update reference hidden states for keyframe + latent_first = outputs.get("latent_first") + if latent_first is not None: + self.reference_control_writer.clear() + self.reference_unet( + latent_first.to(self.reference_unet.dtype), + torch.zeros((batch_size,), dtype=self.dtype, device=device), + encoder_hidden_states=self.encoder_hidden_states, + return_dict=False, + ) + new_ref_hidden = self.reference_control_writer.output() + + # Concatenate new keyframe features + ref_hidden_names = [ + "d00", "d01", "d10", "d11", "d20", "d21", "m", + "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" + ] + for name in ref_hidden_names: + if name in self._reference_hidden_states and name in new_ref_hidden: + self._reference_hidden_states[name] = torch.cat( + [self._reference_hidden_states[name], new_ref_hidden[name]], dim=1 + ) + self.trt_runner.prefill(**{name: self._reference_hidden_states[name]}) + + logger.debug("Added history keyframe (TensorRT)") + self.num_khf += 1 + + self.count += 1 + return video.cpu() + + @torch.no_grad() + def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: + """Process frames using PyTorch (original implementation). + Args: images: Input tensor of shape (B, C, T, H, W) in [-1, 1] or [0, 1] range. diff --git a/src/scope/core/pipelines/personalive/tensorrt/__init__.py b/src/scope/core/pipelines/personalive/tensorrt/__init__.py new file mode 100644 index 00000000..4ec3e734 --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/__init__.py @@ -0,0 +1,58 @@ +"""TensorRT acceleration for PersonaLive pipeline. + +This module provides TensorRT acceleration for the PersonaLive pipeline using +NVIDIA's official TensorRT and polygraphy packages. + +Installation: + pip install daydream-scope[tensorrt] + +Usage: + # Convert models to TensorRT (run once) + convert-personalive-trt --model-dir ./models --height 512 --width 512 + + # The pipeline will automatically use TensorRT when engine is available +""" + +# Check for TensorRT availability before importing +TRT_AVAILABLE = False +try: + import tensorrt # noqa: F401 + from polygraphy.backend.trt import TrtRunner as _TrtRunner # noqa: F401 + + TRT_AVAILABLE = True +except ImportError: + pass + +if TRT_AVAILABLE: + from .builder import build_engine, get_engine_path, is_engine_available + from .runner import TRTRunner + + __all__ = [ + "build_engine", + "get_engine_path", + "is_engine_available", + "TRTRunner", + "TRT_AVAILABLE", + ] +else: + # Provide stub functions when TRT is not available + def get_engine_path(*args, **kwargs): + raise RuntimeError("TensorRT not available. Install with: pip install daydream-scope[tensorrt]") + + def build_engine(*args, **kwargs): + raise RuntimeError("TensorRT not available. Install with: pip install daydream-scope[tensorrt]") + + def is_engine_available(*args, **kwargs): + return False + + class TRTRunner: + def __init__(self, *args, **kwargs): + raise RuntimeError("TensorRT not available. Install with: pip install daydream-scope[tensorrt]") + + __all__ = [ + "build_engine", + "get_engine_path", + "is_engine_available", + "TRTRunner", + "TRT_AVAILABLE", + ] diff --git a/src/scope/core/pipelines/personalive/tensorrt/builder.py b/src/scope/core/pipelines/personalive/tensorrt/builder.py new file mode 100644 index 00000000..12e63f16 --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/builder.py @@ -0,0 +1,243 @@ +"""TensorRT engine builder using polygraphy. + +This module provides utilities to build TensorRT engines from ONNX models +using NVIDIA's polygraphy library. +""" + +import logging +import os +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from polygraphy.backend.trt import Profile + +logger = logging.getLogger(__name__) + +# Check for TensorRT availability +TRT_AVAILABLE = False +try: + import tensorrt as trt + from polygraphy.backend.trt import ( + CreateConfig, + Profile, + engine_from_network, + network_from_onnx_path, + save_engine, + ) + from polygraphy.logger import G_LOGGER + + TRT_AVAILABLE = True + # Set polygraphy logger to WARNING to reduce noise + G_LOGGER.severity = G_LOGGER.WARNING +except ImportError: + pass + + +def get_engine_path(model_dir: Path, height: int = 512, width: int = 512) -> Path: + """Get the expected TensorRT engine path for given resolution. + + Args: + model_dir: Base model directory. + height: Output height. + width: Output width. + + Returns: + Path to the TensorRT engine file. + """ + tensorrt_dir = model_dir / "PersonaLive" / "pretrained_weights" / "tensorrt" + return tensorrt_dir / f"unet_work_{height}x{width}.engine" + + +def get_onnx_path(model_dir: Path) -> Path: + """Get the ONNX model path. + + Args: + model_dir: Base model directory. + + Returns: + Path to the optimized ONNX model. + """ + onnx_dir = model_dir / "PersonaLive" / "pretrained_weights" / "onnx" + return onnx_dir / "unet_opt" / "unet_opt.onnx" + + +def create_optimization_profile( + batch_size: int = 1, + height: int = 512, + width: int = 512, + temporal_window_size: int = 4, + temporal_adaptive_step: int = 4, +) -> "Profile": + """Create TensorRT optimization profile for PersonaLive. + + This defines the input shapes for the TensorRT engine, including + min/opt/max shapes for dynamic dimensions. + + Args: + batch_size: Batch size (usually 1). + height: Output height. + width: Output width. + temporal_window_size: Number of frames per temporal window. + temporal_adaptive_step: Number of adaptive steps. + + Returns: + Polygraphy Profile object. + """ + if not TRT_AVAILABLE: + raise RuntimeError("TensorRT is not available. Install with: pip install daydream-scope[tensorrt]") + + # Derived dimensions + tb = temporal_window_size * temporal_adaptive_step # temporal batch size (16) + lh, lw = height // 8, width // 8 # latent height/width + ml, mc = 32, 16 # motion latent size, motion channels + mh, mw = 224, 224 # motion input size + emb = 768 # CLIP embedding dim + lc, ic = 4, 3 # latent channels, image channels + + # UNet channel dimensions + cd0, cd1, cd2, cm = 320, 640, 1280, 1280 + cu1, cu2, cu3 = 1280, 640, 320 + + profile = Profile() + + # Fixed shape inputs + fixed_inputs = { + "sample": (batch_size, lc, tb, lh, lw), + "encoder_hidden_states": (batch_size, 1, emb), + "motion_hidden_states": (batch_size, temporal_window_size * (temporal_adaptive_step - 1), ml, mc), + "motion": (batch_size, ic, temporal_window_size, mh, mw), + "pose_cond_fea": (batch_size, cd0, temporal_window_size * (temporal_adaptive_step - 1), lh, lw), + "pose": (batch_size, ic, temporal_window_size, height, width), + "new_noise": (batch_size, lc, temporal_window_size, lh, lw), + } + + for name, shape in fixed_inputs.items(): + profile.add(name, min=shape, opt=shape, max=shape) + + # Dynamic shape inputs (for reference hidden states that can grow with keyframes) + # Base shapes at 1x, max at 4x for keyframe accumulation + dynamic_inputs = { + "d00": (batch_size, lh * lw, cd0), + "d01": (batch_size, lh * lw, cd0), + "d10": (batch_size, lh * lw // 4, cd1), + "d11": (batch_size, lh * lw // 4, cd1), + "d20": (batch_size, lh * lw // 16, cd2), + "d21": (batch_size, lh * lw // 16, cd2), + "m": (batch_size, lh * lw // 64, cm), + "u10": (batch_size, lh * lw // 16, cu1), + "u11": (batch_size, lh * lw // 16, cu1), + "u12": (batch_size, lh * lw // 16, cu1), + "u20": (batch_size, lh * lw // 4, cu2), + "u21": (batch_size, lh * lw // 4, cu2), + "u22": (batch_size, lh * lw // 4, cu2), + "u30": (batch_size, lh * lw, cu3), + "u31": (batch_size, lh * lw, cu3), + "u32": (batch_size, lh * lw, cu3), + } + + for name, base_shape in dynamic_inputs.items(): + dim0, dim1_base, dim2 = base_shape + # Allow 1x to 4x scaling for keyframe accumulation + min_shape = (dim0, dim1_base, dim2) + opt_shape = (dim0, dim1_base * 2, dim2) + max_shape = (dim0, dim1_base * 4, dim2) + profile.add(name, min=min_shape, opt=opt_shape, max=max_shape) + + return profile + + +def build_engine( + onnx_path: Path | str, + engine_path: Path | str, + height: int = 512, + width: int = 512, + fp16: bool = True, + batch_size: int = 1, + temporal_window_size: int = 4, + temporal_adaptive_step: int = 4, +) -> bool: + """Build TensorRT engine from ONNX model. + + Args: + onnx_path: Path to the ONNX model. + engine_path: Path to save the TensorRT engine. + height: Output height. + width: Output width. + fp16: Use FP16 precision. + batch_size: Batch size. + temporal_window_size: Temporal window size. + temporal_adaptive_step: Temporal adaptive steps. + + Returns: + True if engine was built successfully. + + Raises: + RuntimeError: If TensorRT is not available. + FileNotFoundError: If ONNX model doesn't exist. + """ + if not TRT_AVAILABLE: + raise RuntimeError("TensorRT is not available. Install with: pip install daydream-scope[tensorrt]") + + onnx_path = Path(onnx_path) + engine_path = Path(engine_path) + + if not onnx_path.exists(): + raise FileNotFoundError(f"ONNX model not found: {onnx_path}") + + # Create output directory + engine_path.parent.mkdir(parents=True, exist_ok=True) + + logger.info(f"Building TensorRT engine from {onnx_path}") + logger.info(f"Resolution: {height}x{width}, FP16: {fp16}") + + # Create optimization profile + profile = create_optimization_profile( + batch_size=batch_size, + height=height, + width=width, + temporal_window_size=temporal_window_size, + temporal_adaptive_step=temporal_adaptive_step, + ) + + # Build engine using polygraphy + logger.info("Loading ONNX model and building TensorRT engine...") + logger.info("This may take 10-30 minutes depending on your GPU.") + + try: + engine = engine_from_network( + network_from_onnx_path( + str(onnx_path), + flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM], + ), + config=CreateConfig( + fp16=fp16, + refittable=False, + profiles=[profile], + ), + ) + + logger.info(f"Saving engine to {engine_path}") + save_engine(engine, str(engine_path)) + + logger.info("TensorRT engine built successfully!") + return True + + except Exception as e: + logger.error(f"Failed to build TensorRT engine: {e}") + raise + + +def is_engine_available(model_dir: Path, height: int = 512, width: int = 512) -> bool: + """Check if a TensorRT engine is available for the given configuration. + + Args: + model_dir: Base model directory. + height: Output height. + width: Output width. + + Returns: + True if engine file exists. + """ + engine_path = get_engine_path(model_dir, height, width) + return engine_path.exists() diff --git a/src/scope/core/pipelines/personalive/tensorrt/convert.py b/src/scope/core/pipelines/personalive/tensorrt/convert.py new file mode 100644 index 00000000..99adf4b4 --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/convert.py @@ -0,0 +1,328 @@ +"""CLI script for converting PersonaLive models to TensorRT. + +This script handles the full conversion pipeline: +1. Load PyTorch models +2. Create bundled UNetWork model +3. Export to ONNX +4. Optimize ONNX +5. Build TensorRT engine + +Usage: + convert-personalive-trt --model-dir ./models --height 512 --width 512 +""" + +import argparse +import gc +import logging +import sys +import time +from pathlib import Path + +import torch +from diffusers import AutoencoderKL +from diffusers.models.attention_processor import AttnProcessor +from omegaconf import OmegaConf + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description="Convert PersonaLive models to TensorRT engine", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--model-dir", + type=Path, + required=True, + help="Path to the models directory containing PersonaLive weights", + ) + parser.add_argument( + "--height", + type=int, + default=512, + help="Output video height", + ) + parser.add_argument( + "--width", + type=int, + default=512, + help="Output video width", + ) + parser.add_argument( + "--batch-size", + type=int, + default=1, + help="Batch size", + ) + parser.add_argument( + "--fp32", + action="store_true", + help="Use FP32 instead of FP16 precision", + ) + parser.add_argument( + "--skip-onnx", + action="store_true", + help="Skip ONNX export (use existing ONNX files)", + ) + parser.add_argument( + "--device", + type=str, + default="cuda:0", + help="CUDA device to use", + ) + return parser.parse_args() + + +def load_models(model_dir: Path, device: torch.device, dtype: torch.dtype): + """Load all PersonaLive models. + + Args: + model_dir: Base model directory. + device: Target device. + dtype: Data type for models. + + Returns: + Dictionary containing all loaded models and config. + """ + from ..modules import ( + DDIMScheduler, + MotEncoder, + PoseGuider, + UNet3DConditionModel, + ) + + logger.info("Loading PersonaLive models...") + + # Paths + personalive_dir = model_dir / "PersonaLive" / "pretrained_weights" + pretrained_base_path = personalive_dir / "sd-image-variations-diffusers" + vae_path = personalive_dir / "sd-vae-ft-mse" + weights_path = personalive_dir / "personalive" + + # Load model config for UNet kwargs + config_path = Path(__file__).parent.parent / "model.yaml" + model_config = OmegaConf.load(config_path) + unet_kwargs = OmegaConf.to_container(model_config.unet_additional_kwargs) + sched_kwargs = OmegaConf.to_container(model_config.noise_scheduler_kwargs) + + # Load pose guider + logger.info("Loading PoseGuider...") + pose_guider = PoseGuider().to(device=device, dtype=dtype) + pose_guider_path = weights_path / "pose_guider.pth" + if pose_guider_path.exists(): + state_dict = torch.load(pose_guider_path, map_location="cpu") + pose_guider.load_state_dict(state_dict) + del state_dict + + # Load motion encoder + logger.info("Loading MotEncoder...") + motion_encoder = MotEncoder().to(dtype=dtype, device=device).eval() + motion_encoder_path = weights_path / "motion_encoder.pth" + if motion_encoder_path.exists(): + state_dict = torch.load(motion_encoder_path, map_location="cpu") + motion_encoder.load_state_dict(state_dict) + del state_dict + motion_encoder.set_attn_processor(AttnProcessor()) + + # Load denoising UNet + logger.info("Loading UNet3DConditionModel...") + denoising_unet = UNet3DConditionModel.from_pretrained_2d( + str(pretrained_base_path), + "", + subfolder="unet", + unet_additional_kwargs=unet_kwargs, + ).to(dtype=dtype, device=device) + + denoising_unet_path = weights_path / "denoising_unet.pth" + if denoising_unet_path.exists(): + state_dict = torch.load(denoising_unet_path, map_location="cpu") + denoising_unet.load_state_dict(state_dict, strict=False) + del state_dict + + temporal_module_path = weights_path / "temporal_module.pth" + if temporal_module_path.exists(): + state_dict = torch.load(temporal_module_path, map_location="cpu") + denoising_unet.load_state_dict(state_dict, strict=False) + del state_dict + + denoising_unet.set_attn_processor(AttnProcessor()) + + # Load VAE + logger.info("Loading VAE...") + vae = AutoencoderKL.from_pretrained(str(vae_path)).to(device=device, dtype=dtype) + vae.set_default_attn_processor() + + # Setup scheduler + scheduler = DDIMScheduler(**sched_kwargs) + scheduler.to(device) + + # Create timesteps tensor + # For TensorRT, we use a fixed timestep pattern + timesteps = torch.tensor( + [0, 0, 0, 0, 333, 333, 333, 333, 666, 666, 666, 666, 999, 999, 999, 999], + device=device, + ).long() + scheduler.set_step_length(333) + + return { + "pose_guider": pose_guider, + "motion_encoder": motion_encoder, + "denoising_unet": denoising_unet, + "vae": vae, + "scheduler": scheduler, + "timesteps": timesteps, + } + + +def export_to_onnx( + models: dict, + onnx_path: Path, + height: int, + width: int, + batch_size: int, + device: torch.device, + dtype: torch.dtype, +): + """Export bundled model to ONNX. + + Args: + models: Dictionary of loaded models. + onnx_path: Path to save ONNX model. + height: Output height. + width: Output width. + batch_size: Batch size. + device: Target device. + dtype: Data type. + """ + from .export import export_onnx, optimize_onnx + from .framed_model import UNetWork + + logger.info("Creating bundled UNetWork model...") + + # Create bundled model + unet_work = UNetWork( + pose_guider=models["pose_guider"], + motion_encoder=models["motion_encoder"], + denoising_unet=models["denoising_unet"], + vae=models["vae"], + scheduler=models["scheduler"], + timesteps=models["timesteps"], + ) + + # Generate sample inputs + logger.info("Generating sample inputs...") + sample_inputs = unet_work.get_sample_input(batch_size, height, width, dtype, device) + + # Export to ONNX + raw_onnx_path = onnx_path.parent / "unet" / "unet.onnx" + export_onnx( + model=unet_work, + onnx_path=raw_onnx_path, + sample_inputs=sample_inputs, + input_names=UNetWork.get_input_names(), + output_names=UNetWork.get_output_names(), + dynamic_axes=UNetWork.get_dynamic_axes(), + opset_version=17, + ) + + # Optimize ONNX + logger.info("Optimizing ONNX model...") + optimize_onnx(raw_onnx_path, onnx_path) + + # Clean up + del unet_work, sample_inputs + gc.collect() + torch.cuda.empty_cache() + + +def main(): + """Main entry point for TensorRT conversion.""" + args = parse_args() + + # Validate inputs + if not args.model_dir.exists(): + logger.error(f"Model directory not found: {args.model_dir}") + sys.exit(1) + + personalive_weights = args.model_dir / "PersonaLive" / "pretrained_weights" / "personalive" + if not personalive_weights.exists(): + logger.error(f"PersonaLive weights not found at: {personalive_weights}") + logger.error("Please download PersonaLive models first using: download_models") + sys.exit(1) + + # Check TensorRT availability + try: + from .builder import TRT_AVAILABLE, build_engine, get_engine_path, get_onnx_path + + if not TRT_AVAILABLE: + raise ImportError() + except ImportError: + logger.error("TensorRT is not available.") + logger.error("Install with: pip install daydream-scope[tensorrt]") + sys.exit(1) + + # Setup + device = torch.device(args.device) + dtype = torch.float32 if args.fp32 else torch.float16 + + logger.info(f"Converting PersonaLive models to TensorRT") + logger.info(f" Model directory: {args.model_dir}") + logger.info(f" Resolution: {args.height}x{args.width}") + logger.info(f" Precision: {'FP32' if args.fp32 else 'FP16'}") + logger.info(f" Device: {device}") + + start_time = time.time() + + # Get paths + onnx_path = get_onnx_path(args.model_dir) + engine_path = get_engine_path(args.model_dir, args.height, args.width) + + # Step 1: Export to ONNX (if needed) + if not args.skip_onnx or not onnx_path.exists(): + logger.info("Step 1: Exporting to ONNX...") + models = load_models(args.model_dir, device, dtype) + export_to_onnx( + models=models, + onnx_path=onnx_path, + height=args.height, + width=args.width, + batch_size=args.batch_size, + device=device, + dtype=dtype, + ) + # Clean up models + del models + gc.collect() + torch.cuda.empty_cache() + else: + logger.info("Step 1: Skipping ONNX export (using existing files)") + + # Step 2: Build TensorRT engine + logger.info("Step 2: Building TensorRT engine...") + logger.info("This may take 10-30 minutes depending on your GPU.") + + build_engine( + onnx_path=onnx_path, + engine_path=engine_path, + height=args.height, + width=args.width, + fp16=not args.fp32, + batch_size=args.batch_size, + ) + + elapsed = time.time() - start_time + logger.info(f"Conversion completed in {elapsed / 60:.1f} minutes") + logger.info(f"TensorRT engine saved to: {engine_path}") + logger.info("") + logger.info("The PersonaLive pipeline will automatically use TensorRT") + logger.info("when the engine file is present.") + + +if __name__ == "__main__": + main() diff --git a/src/scope/core/pipelines/personalive/tensorrt/export.py b/src/scope/core/pipelines/personalive/tensorrt/export.py new file mode 100644 index 00000000..3d7bebcc --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/export.py @@ -0,0 +1,129 @@ +"""ONNX export utilities for PersonaLive models. + +This module provides utilities to export PersonaLive models to ONNX format +for subsequent TensorRT conversion. +""" + +import gc +import logging +import os +from pathlib import Path + +import torch + +logger = logging.getLogger(__name__) + + +def export_onnx( + model: torch.nn.Module, + onnx_path: Path | str, + sample_inputs: dict[str, torch.Tensor], + input_names: list[str], + output_names: list[str], + dynamic_axes: dict[str, dict[int, str]] | None = None, + opset_version: int = 17, +) -> None: + """Export a PyTorch model to ONNX format. + + Args: + model: PyTorch model to export. + onnx_path: Path to save the ONNX model. + sample_inputs: Sample inputs for tracing. + input_names: Names of input tensors. + output_names: Names of output tensors. + dynamic_axes: Dynamic axis specifications. + opset_version: ONNX opset version. + """ + onnx_path = Path(onnx_path) + onnx_path.parent.mkdir(parents=True, exist_ok=True) + + logger.info(f"Exporting model to ONNX: {onnx_path}") + + # Prepare inputs as tuple in correct order + inputs = tuple(sample_inputs[name] for name in input_names) + + with torch.inference_mode(), torch.autocast("cuda"): + torch.onnx.export( + model, + inputs, + str(onnx_path), + export_params=True, + opset_version=opset_version, + do_constant_folding=True, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes or {}, + ) + + logger.info(f"ONNX model exported to {onnx_path}") + + # Clean up + gc.collect() + torch.cuda.empty_cache() + + +def optimize_onnx( + onnx_path: Path | str, + onnx_opt_path: Path | str, +) -> None: + """Optimize ONNX model and save with external data. + + For large models, this saves weights as external data to avoid + protobuf size limits. + + Args: + onnx_path: Path to input ONNX model. + onnx_opt_path: Path to save optimized ONNX model. + """ + try: + import onnx + except ImportError: + raise RuntimeError("onnx package required. Install with: pip install onnx") + + onnx_path = Path(onnx_path) + onnx_opt_path = Path(onnx_opt_path) + onnx_opt_path.parent.mkdir(parents=True, exist_ok=True) + + logger.info(f"Optimizing ONNX model: {onnx_path} -> {onnx_opt_path}") + + model = onnx.load(str(onnx_path)) + name = onnx_opt_path.stem + + # Save with external data for large models + onnx.save( + model, + str(onnx_opt_path), + save_as_external_data=True, + all_tensors_to_one_file=True, + location=f"{name}.onnx.data", + size_threshold=1024, + ) + + logger.info(f"Optimized ONNX model saved to {onnx_opt_path}") + + +def verify_onnx(onnx_path: Path | str) -> bool: + """Verify ONNX model is valid. + + Args: + onnx_path: Path to ONNX model. + + Returns: + True if model is valid. + """ + try: + import onnx + except ImportError: + logger.warning("onnx package not available, skipping verification") + return True + + onnx_path = Path(onnx_path) + + try: + model = onnx.load(str(onnx_path)) + onnx.checker.check_model(model) + logger.info(f"ONNX model {onnx_path} is valid") + return True + except Exception as e: + logger.error(f"ONNX model verification failed: {e}") + return False diff --git a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py new file mode 100644 index 00000000..0bec0373 --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py @@ -0,0 +1,305 @@ +"""Framed model for ONNX export. + +This module provides the UNetWork class that bundles multiple models +into a single module for ONNX export and TensorRT conversion. + +The bundled models include: +- PoseGuider: Extracts pose features from keypoint images +- MotEncoder: Encodes facial motion features +- UNet3DConditionModel: Main denoising UNet +- VAE decoder: Decodes latents to images +- DDIMScheduler: Denoising scheduler step +""" + +import torch +import torch.nn as nn +from einops import rearrange + + +class UNetWork(nn.Module): + """Bundled model for TensorRT export. + + This class wraps the pose_guider, motion_encoder, denoising_unet, + vae decoder, and scheduler into a single module that can be exported + to ONNX for TensorRT conversion. + + The forward pass handles a complete denoising step including: + 1. Encoding new pose features + 2. Encoding new motion features + 3. Running the UNet denoiser + 4. Scheduler step + 5. VAE decoding for the output frames + """ + + def __init__( + self, + pose_guider: nn.Module, + motion_encoder: nn.Module, + denoising_unet: nn.Module, + vae: nn.Module, + scheduler, + timesteps: torch.Tensor, + ): + """Initialize the bundled model. + + Args: + pose_guider: PoseGuider model. + motion_encoder: MotEncoder model. + denoising_unet: UNet3DConditionModel for denoising. + vae: AutoencoderKL for decoding. + scheduler: DDIMScheduler instance. + timesteps: Fixed timesteps tensor for denoising. + """ + super().__init__() + self.pose_guider = pose_guider + self.motion_encoder = motion_encoder + self.unet = denoising_unet + self.vae = vae + self.scheduler = scheduler + self.timesteps = timesteps + + def _decode_latents(self, latents: torch.Tensor) -> torch.Tensor: + """Decode latents to images using VAE. + + Args: + latents: Latent tensor of shape (B, C, H, W). + + Returns: + Image tensor of shape (B, H, W, C) in [0, 1] range. + """ + latents = latents / 0.18215 + images = self.vae.decode(latents).sample + images = rearrange(images, "b c h w -> b h w c") + images = (images / 2 + 0.5).clamp(0, 1) + return images + + def forward( + self, + sample: torch.Tensor, + encoder_hidden_states: torch.Tensor, + motion_hidden_states: torch.Tensor, + motion: torch.Tensor, + pose_cond_fea: torch.Tensor, + pose: torch.Tensor, + new_noise: torch.Tensor, + d00: torch.Tensor, + d01: torch.Tensor, + d10: torch.Tensor, + d11: torch.Tensor, + d20: torch.Tensor, + d21: torch.Tensor, + m: torch.Tensor, + u10: torch.Tensor, + u11: torch.Tensor, + u12: torch.Tensor, + u20: torch.Tensor, + u21: torch.Tensor, + u22: torch.Tensor, + u30: torch.Tensor, + u31: torch.Tensor, + u32: torch.Tensor, + ): + """Forward pass for the bundled model. + + Args: + sample: Noisy latent tensor (B, C, T, H, W). + encoder_hidden_states: CLIP embeddings (B, 1, D). + motion_hidden_states: Accumulated motion features (B, T, D1, D2). + motion: New face crop tensor (B, C, T, H, W). + pose_cond_fea: Accumulated pose features (B, C, T, H, W). + pose: New pose keypoints (B, C, T, H, W). + new_noise: Noise for next iteration (B, C, T, H, W). + d00-u32: Reference hidden states from reference UNet. + + Returns: + Tuple of: + - pred_video: Decoded video frames (T, H, W, C). + - latents: Updated latent tensor for next iteration. + - pose_cond_fea_out: Updated pose features. + - motion_hidden_states_out: Updated motion features. + - motion_out: Motion features for keyframe tracking. + - latent_first: First latent for potential keyframe update. + """ + # Encode new pose features + new_pose_cond_fea = self.pose_guider(pose) + pose_cond_fea = torch.cat([pose_cond_fea, new_pose_cond_fea], dim=2) + + # Encode new motion features + new_motion_hidden_states = self.motion_encoder(motion) + motion_hidden_states = torch.cat([motion_hidden_states, new_motion_hidden_states], dim=1) + + # Prepare encoder hidden states for UNet + encoder_hidden_states_combined = [encoder_hidden_states, motion_hidden_states] + + # Run UNet with explicit reference hidden states + score = self.unet( + sample, + self.timesteps, + encoder_hidden_states_combined, + pose_cond_fea, + d00, d01, d10, d11, d20, d21, m, + u10, u11, u12, u20, u21, u22, u30, u31, u32, + ) + + # Scheduler step + score = rearrange(score, 'b c f h w -> (b f) c h w') + sample_flat = rearrange(sample, 'b c f h w -> (b f) c h w') + + latents_model_input, pred_original_sample = self.scheduler.step( + score, self.timesteps, sample_flat, return_dict=False + ) + + latents_model_input = latents_model_input.to(sample.dtype) + pred_original_sample = pred_original_sample.to(sample.dtype) + + # Reshape back to 5D + latents_model_input = rearrange(latents_model_input, '(b f) c h w -> b c f h w', f=16) + + # Decode first 4 frames (temporal_window_size) + pred_video = self._decode_latents(pred_original_sample[:4]) + + # Prepare outputs for next iteration + # Shift latents: drop first temporal_window_size, add new_noise + latents = torch.cat([latents_model_input[:, :, 4:, :, :], new_noise], dim=2) + + # Shift pose and motion features + pose_cond_fea_out = pose_cond_fea[:, :, 4:, :, :] + motion_hidden_states_out = motion_hidden_states[:, 4:, :, :] + + # Motion for keyframe tracking (first frame's motion) + motion_out = motion_hidden_states[:, :1, :, :] + + # First predicted latent for potential keyframe reference update + latent_first = pred_original_sample[:1] + + return ( + pred_video, + latents, + pose_cond_fea_out, + motion_hidden_states_out, + motion_out, + latent_first, + ) + + def get_sample_input( + self, + batch_size: int, + height: int, + width: int, + dtype: torch.dtype, + device: torch.device, + ) -> dict[str, torch.Tensor]: + """Generate sample inputs for ONNX export. + + Args: + batch_size: Batch size (usually 1). + height: Output height. + width: Output width. + dtype: Data type (usually float16). + device: Target device. + + Returns: + Dictionary of sample input tensors. + """ + # Constants + tw = 4 # temporal_window_size + ts = 4 # temporal_adaptive_step + tb = tw * ts # temporal batch size (16) + ml, mc = 32, 16 # motion latent size, channels + mh, mw = 224, 224 # motion input size + + lh, lw = height // 8, width // 8 # latent height/width + + # UNet channels + cd0, cd1, cd2, cm = 320, 640, 1280, 1280 + cu1, cu2, cu3 = 1280, 640, 320 + + emb = 768 # CLIP embedding dim + lc, ic = 4, 3 # latent channels, image channels + + shapes = { + "sample": (batch_size, lc, tb, lh, lw), + "encoder_hidden_states": (batch_size, 1, emb), + "motion_hidden_states": (batch_size, tw * (ts - 1), ml, mc), + "motion": (batch_size, ic, tw, mh, mw), + "pose_cond_fea": (batch_size, cd0, tw * (ts - 1), lh, lw), + "pose": (batch_size, ic, tw, height, width), + "new_noise": (batch_size, lc, tw, lh, lw), + "d00": (batch_size, lh * lw, cd0), + "d01": (batch_size, lh * lw, cd0), + "d10": (batch_size, lh * lw // 4, cd1), + "d11": (batch_size, lh * lw // 4, cd1), + "d20": (batch_size, lh * lw // 16, cd2), + "d21": (batch_size, lh * lw // 16, cd2), + "m": (batch_size, lh * lw // 64, cm), + "u10": (batch_size, lh * lw // 16, cu1), + "u11": (batch_size, lh * lw // 16, cu1), + "u12": (batch_size, lh * lw // 16, cu1), + "u20": (batch_size, lh * lw // 4, cu2), + "u21": (batch_size, lh * lw // 4, cu2), + "u22": (batch_size, lh * lw // 4, cu2), + "u30": (batch_size, lh * lw, cu3), + "u31": (batch_size, lh * lw, cu3), + "u32": (batch_size, lh * lw, cu3), + } + + return {name: torch.randn(shape, dtype=dtype, device=device) for name, shape in shapes.items()} + + @staticmethod + def get_input_names() -> list[str]: + """Get list of input tensor names for ONNX export.""" + return [ + "sample", + "encoder_hidden_states", + "motion_hidden_states", + "motion", + "pose_cond_fea", + "pose", + "new_noise", + "d00", "d01", "d10", "d11", "d20", "d21", "m", + "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32", + ] + + @staticmethod + def get_output_names() -> list[str]: + """Get list of output tensor names for ONNX export.""" + return [ + "pred_video", + "latents", + "pose_cond_fea_out", + "motion_hidden_states_out", + "motion_out", + "latent_first", + ] + + @staticmethod + def get_dynamic_axes() -> dict[str, dict[int, str]]: + """Get dynamic axis specifications for ONNX export. + + Returns: + Dictionary mapping tensor names to their dynamic axes. + """ + return { + # Resolution-dependent axes + "sample": {3: "h_64", 4: "w_64"}, + "pose_cond_fea": {3: "h_64", 4: "w_64"}, + "pose": {3: "h_512", 4: "w_512"}, + "new_noise": {3: "h_64", 4: "w_64"}, + # Dynamic reference hidden states (for keyframe accumulation) + "d00": {1: "len_4096"}, + "d01": {1: "len_4096"}, + "u30": {1: "len_4096"}, + "u31": {1: "len_4096"}, + "u32": {1: "len_4096"}, + "d10": {1: "len_1024"}, + "d11": {1: "len_1024"}, + "u20": {1: "len_1024"}, + "u21": {1: "len_1024"}, + "u22": {1: "len_1024"}, + "d20": {1: "len_256"}, + "d21": {1: "len_256"}, + "u10": {1: "len_256"}, + "u11": {1: "len_256"}, + "u12": {1: "len_256"}, + "m": {1: "len_64"}, + } diff --git a/src/scope/core/pipelines/personalive/tensorrt/runner.py b/src/scope/core/pipelines/personalive/tensorrt/runner.py new file mode 100644 index 00000000..70f9df8c --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/runner.py @@ -0,0 +1,211 @@ +"""TensorRT engine runner using polygraphy. + +This module provides a TensorRT engine runner that wraps the engine +and handles inference with PyTorch tensor I/O. +""" + +import logging +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +logger = logging.getLogger(__name__) + +# Check for TensorRT availability +TRT_AVAILABLE = False +try: + import tensorrt as trt + from polygraphy.backend.common import BytesFromPath + from polygraphy.backend.trt import EngineFromBytes, TrtRunner + + TRT_AVAILABLE = True +except ImportError: + pass + + +class TRTRunner: + """TensorRT engine runner with PyTorch tensor support. + + This class wraps a TensorRT engine and provides methods for inference + with PyTorch tensors as input/output. + + Attributes: + engine_path: Path to the TensorRT engine file. + device: CUDA device for inference. + runner: Polygraphy TrtRunner instance. + """ + + def __init__( + self, + engine_path: Path | str, + device: torch.device | None = None, + ): + """Initialize TensorRT runner. + + Args: + engine_path: Path to the TensorRT engine file. + device: CUDA device for inference. + + Raises: + RuntimeError: If TensorRT is not available. + FileNotFoundError: If engine file doesn't exist. + """ + if not TRT_AVAILABLE: + raise RuntimeError("TensorRT is not available. Install with: pip install daydream-scope[tensorrt]") + + engine_path = Path(engine_path) + if not engine_path.exists(): + raise FileNotFoundError(f"TensorRT engine not found: {engine_path}") + + self.engine_path = engine_path + self.device = device or torch.device("cuda") + self._runner = None + self._engine = None + self._context = None + + # Pre-filled inputs (constants that don't change per frame) + self._prefilled: dict[str, np.ndarray] = {} + + # Output bindings (for reusing memory between outputs and inputs) + self._output_bindings: dict[str, str] = {} + + logger.info(f"Loading TensorRT engine from {engine_path}") + self._load_engine() + + def _load_engine(self): + """Load the TensorRT engine.""" + # Load engine bytes + engine_bytes = BytesFromPath(str(self.engine_path)) + + # Create engine from bytes + self._engine = EngineFromBytes(engine_bytes) + + # Create runner (context manager handles activation) + self._runner = TrtRunner(self._engine) + self._runner.activate() + + logger.info("TensorRT engine loaded successfully") + + def prefill(self, **inputs: torch.Tensor | np.ndarray): + """Pre-fill constant inputs that don't change per frame. + + These inputs will be automatically included in every inference call. + + Args: + **inputs: Named inputs as PyTorch tensors or numpy arrays. + """ + for name, tensor in inputs.items(): + if isinstance(tensor, torch.Tensor): + # Convert to numpy, ensuring contiguous memory + tensor = tensor.detach().cpu().numpy() + self._prefilled[name] = np.ascontiguousarray(tensor) + + def bind(self, output_to_input: dict[str, str]): + """Bind output tensors to input tensors for memory reuse. + + This allows outputs from one inference to be used as inputs + to the next inference without memory copies. + + Args: + output_to_input: Mapping from output names to input names. + """ + self._output_bindings = output_to_input.copy() + + def clear_prefill(self): + """Clear all pre-filled inputs.""" + self._prefilled.clear() + + def __call__( + self, + output_names: list[str] | None = None, + return_torch: bool = True, + **inputs: torch.Tensor | np.ndarray, + ) -> dict[str, torch.Tensor | np.ndarray]: + """Run inference on the TensorRT engine. + + Args: + output_names: List of output names to return. If None, returns all outputs. + return_torch: If True, return PyTorch tensors. Otherwise, return numpy arrays. + **inputs: Named inputs as PyTorch tensors or numpy arrays. + + Returns: + Dictionary mapping output names to tensors/arrays. + """ + if self._runner is None: + raise RuntimeError("TensorRT runner not initialized") + + # Prepare feed dict with prefilled values + feed_dict = dict(self._prefilled) + + # Add current inputs + for name, tensor in inputs.items(): + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + feed_dict[name] = np.ascontiguousarray(tensor) + + # Run inference + outputs = self._runner.infer(feed_dict=feed_dict) + + # Filter outputs if requested + if output_names is not None: + outputs = {k: v for k, v in outputs.items() if k in output_names} + + # Update prefilled values with bound outputs + for output_name, input_name in self._output_bindings.items(): + if output_name in outputs: + self._prefilled[input_name] = outputs[output_name] + + # Convert to PyTorch if requested + if return_torch: + outputs = { + name: torch.from_numpy(arr).to(self.device) + for name, arr in outputs.items() + } + + return outputs + + def get_input_names(self) -> list[str]: + """Get list of input tensor names.""" + if self._runner is None: + return [] + return list(self._runner.get_input_metadata().keys()) + + def get_output_names(self) -> list[str]: + """Get list of output tensor names.""" + if self._runner is None: + return [] + return list(self._runner.get_output_metadata().keys()) + + def get_input_shapes(self) -> dict[str, tuple]: + """Get input tensor shapes.""" + if self._runner is None: + return {} + return {name: tuple(meta.shape) for name, meta in self._runner.get_input_metadata().items()} + + def get_output_shapes(self) -> dict[str, tuple]: + """Get output tensor shapes.""" + if self._runner is None: + return {} + return {name: tuple(meta.shape) for name, meta in self._runner.get_output_metadata().items()} + + def __del__(self): + """Clean up resources.""" + if self._runner is not None: + try: + self._runner.deactivate() + except Exception: + pass + + def __repr__(self) -> str: + """String representation.""" + inputs = self.get_input_shapes() + outputs = self.get_output_shapes() + return ( + f"TRTRunner(\n" + f" engine={self.engine_path.name},\n" + f" inputs={list(inputs.keys())},\n" + f" outputs={list(outputs.keys())}\n" + f")" + ) diff --git a/uv.lock b/uv.lock index 50e54fd6..f10ec6f5 100644 --- a/uv.lock +++ b/uv.lock @@ -808,6 +808,19 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0d/c3/e90f4a4feae6410f914f8ebac129b9ae7a8c92eb60a638012dde42030a9d/cryptography-46.0.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:6b5063083824e5509fdba180721d55909ffacccc8adbec85268b48439423d78c", size = 3438528, upload-time = "2025-10-15T23:18:26.227Z" }, ] +[[package]] +name = "cuda-toolkit" +version = "13.0.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = 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c1ee453219860bcab964e3eb730192b67d8ee288 Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Wed, 17 Dec 2025 00:54:01 -0500 Subject: [PATCH 3/7] PersonaLive TensorRT testing, developement and profiling. Signed-off-by: BuffMcBigHuge --- .../core/pipelines/personalive/pipeline.py | 355 +++++++---- .../personalive/tensorrt/__init__.py | 32 + .../pipelines/personalive/tensorrt/convert.py | 124 ++-- .../personalive/tensorrt/engine_model.py | 394 ++++++++++++ .../pipelines/personalive/tensorrt/export.py | 106 +-- .../personalive/tensorrt/framed_model.py | 301 +++++---- .../pipelines/personalive/tensorrt/runner.py | 185 +++++- .../personalive/tensorrt/unet_explicit.py | 601 ++++++++++++++++++ 8 files changed, 1743 insertions(+), 355 deletions(-) create mode 100644 src/scope/core/pipelines/personalive/tensorrt/engine_model.py create mode 100644 src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py diff --git a/src/scope/core/pipelines/personalive/pipeline.py b/src/scope/core/pipelines/personalive/pipeline.py index 2062ff86..9481e6d9 100644 --- a/src/scope/core/pipelines/personalive/pipeline.py +++ b/src/scope/core/pipelines/personalive/pipeline.py @@ -10,7 +10,8 @@ import logging import time -from collections import deque +from collections import defaultdict, deque +from contextlib import contextmanager from pathlib import Path from typing import TYPE_CHECKING @@ -41,11 +42,61 @@ get_boxes, ) + +# Profiling utilities +_profiling_enabled = False +_profiling_timings: dict[str, list[float]] = defaultdict(list) + + +def enable_profiling(): + """Enable pipeline profiling.""" + global _profiling_enabled + _profiling_enabled = True + _profiling_timings.clear() + + +def disable_profiling(): + """Disable pipeline profiling.""" + global _profiling_enabled + _profiling_enabled = False + + +def reset_profiling(): + """Reset profiling timings.""" + _profiling_timings.clear() + + +def get_profiling_timings() -> dict[str, list[float]]: + """Get profiling timings dictionary.""" + return dict(_profiling_timings) + + +@contextmanager +def _profile_stage(name: str): + """Context manager to profile a pipeline stage.""" + if not _profiling_enabled: + yield + return + torch.cuda.synchronize() if torch.cuda.is_available() else None + start = time.perf_counter() + yield + torch.cuda.synchronize() if torch.cuda.is_available() else None + elapsed = time.perf_counter() - start + _profiling_timings[name].append(elapsed) + # TensorRT support (optional) try: - from .tensorrt import TRT_AVAILABLE, TRTRunner, get_engine_path + from .tensorrt import TRT_AVAILABLE, TRTRunner, get_engine_path, PYCUDA_AVAILABLE + + # Import EngineModel if pycuda is available (best performance) + if PYCUDA_AVAILABLE: + from .tensorrt import EngineModel + else: + EngineModel = None except ImportError: TRT_AVAILABLE = False + PYCUDA_AVAILABLE = False + EngineModel = None if TYPE_CHECKING: from ..schema import BasePipelineConfig @@ -272,6 +323,7 @@ def __init__( # TensorRT support (optional) self.use_tensorrt = False self.trt_runner = None + self._use_engine_model = False # True if using pycuda EngineModel self._model_dir = model_dir # Store for TensorRT path lookup if TRT_AVAILABLE: @@ -304,9 +356,33 @@ def _init_tensorrt(self, model_dir: Path): ) return + device_index = self.device.index if self.device.index is not None else 0 + + # Prefer EngineModel (pycuda-based) for best performance + if EngineModel is not None: + try: + self.trt_runner = EngineModel(engine_path, device_int=device_index) + self._use_engine_model = True + + # Setup output-to-input bindings for recurrent state (zero-copy) + self.trt_runner.bind({ + "motion_hidden_states_out": "motion_hidden_states", + "pose_cond_fea_out": "pose_cond_fea", + "latents": "sample", + }) + + self.use_tensorrt = True + logger.info(f"TensorRT engine loaded with pycuda (EngineModel) from {engine_path}") + return + + except Exception as e: + logger.warning(f"Failed to load EngineModel: {e}") + logger.info("Falling back to polygraphy TRTRunner") + + # Fall back to TRTRunner (polygraphy-based) try: - device_index = self.device.index if self.device.index is not None else 0 self.trt_runner = TRTRunner(engine_path, device=self.device) + self._use_engine_model = False # Setup output-to-input bindings for recurrent state self.trt_runner.bind({ @@ -316,13 +392,14 @@ def _init_tensorrt(self, model_dir: Path): }) self.use_tensorrt = True - logger.info(f"TensorRT engine loaded from {engine_path}") + logger.info(f"TensorRT engine loaded with polygraphy (TRTRunner) from {engine_path}") except Exception as e: logger.warning(f"Failed to load TensorRT engine: {e}") logger.warning("Falling back to PyTorch inference") self.trt_runner = None self.use_tensorrt = False + self._use_engine_model = False def prepare(self, **kwargs) -> Requirements | None: """Return input requirements. @@ -441,8 +518,9 @@ def fuse_reference(self, ref_image: Image.Image): reference_hidden_states = self.reference_control_writer.output() self._reference_hidden_states = reference_hidden_states - # Prefill TensorRT runner with constant inputs - self.trt_runner.clear_prefill() + # Clear previous prefill values (TRTRunner only - EngineModel persists buffers) + if hasattr(self.trt_runner, 'clear_prefill'): + self.trt_runner.clear_prefill() self.trt_runner.prefill(encoder_hidden_states=self.encoder_hidden_states) # Prefill reference hidden states @@ -487,7 +565,7 @@ def fuse_reference(self, ref_image: Image.Image): r = (i + 1) * self.temporal_window_size self.latents_pile.append(noisy_latents_first[:, :, l:r]) - # For TensorRT, also prefill initial latents + # For TensorRT, also prefill initial latents (using correct input name 'sample') if self.use_tensorrt: sample = torch.cat(list(self.latents_pile), dim=2) # Add placeholder for new latents @@ -497,7 +575,7 @@ def fuse_reference(self, ref_image: Image.Image): noise = torch.randn_like(new_latents) new_latents = self.scheduler.add_noise(new_latents, noise, self.timesteps[-1:]) sample = torch.cat([sample, new_latents], dim=2) - self.trt_runner.prefill(latents=sample) + self.trt_runner.prefill(sample=sample) # Use 'sample' (input name), not 'latents' (output name) self.reference_fused = True logger.info("Reference image fused successfully") @@ -584,133 +662,162 @@ def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: temporal_window_size = self.temporal_window_size temporal_adaptive_step = self.temporal_adaptive_step - # Reshape from (B, C, T, H, W) to (T, C, H, W) for processing - if images.dim() == 5: - images = images.squeeze(0).permute(1, 0, 2, 3) - - num_frames = images.shape[0] - if num_frames != temporal_window_size: - images = images[-temporal_window_size:] - - if images.min() < 0: - images = images / 2 + 0.5 - images = images.clamp(0, 1) - - # Get keypoints using pose encoder (still PyTorch) - tgt_cond_tensor = self._fast_resize(images, 256, 256).clamp(0, 1) - + # Input preprocessing + with _profile_stage("input_preprocessing"): + # Reshape from (B, C, T, H, W) to (T, C, H, W) for processing + if images.dim() == 5: + images = images.squeeze(0).permute(1, 0, 2, 3) + + num_frames = images.shape[0] + if num_frames != temporal_window_size: + images = images[-temporal_window_size:] + + if images.min() < 0: + images = images / 2 + 0.5 + images = images.clamp(0, 1) + + # Pose encoding (keypoint extraction) - this is the FAN/MediaPipe step + with _profile_stage("pose_encoding"): + tgt_cond_tensor = self._fast_resize(images, 256, 256).clamp(0, 1) + + if self.first_frame: + mot_bbox_param, kps_ref, kps_frame1, kps_dri = self.pose_encoder.interpolate_kps_online( + self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + ) + self.kps_ref = kps_ref + self.kps_frame1 = kps_frame1 + else: + mot_bbox_param, kps_dri = self.pose_encoder.get_kps( + self.kps_ref, self.kps_frame1, tgt_cond_tensor + ) + + # Keypoint drawing + with _profile_stage("keypoint_drawing"): + keypoints = draw_keypoints(mot_bbox_param, device=device) + boxes = get_boxes(kps_dri) + keypoints = rearrange(keypoints.unsqueeze(2), 'f c b h w -> b c f h w') + keypoints = keypoints.to(device=device, dtype=self.dtype) + + # Face cropping for motion input + with _profile_stage("face_cropping"): + if self.first_frame: + ref_box = get_boxes(mot_bbox_param[:1]) + ref_face = self._crop_face_tensor(self.ref_image_tensor, ref_box[0]) + motion_face = [ref_face] + for i, frame in enumerate(images): + motion_face.append(self._crop_face_tensor(frame, boxes[i])) + pose_cond_tensor = torch.cat(motion_face, dim=0).transpose(0, 1) + pose_cond_tensor = pose_cond_tensor.unsqueeze(0) + else: + motion_face = [] + for i, frame in enumerate(images): + motion_face.append(self._crop_face_tensor(frame, boxes[i])) + + # First frame initialization (motion encoder + pose guider + TRT prefill) if self.first_frame: - mot_bbox_param, kps_ref, kps_frame1, kps_dri = self.pose_encoder.interpolate_kps_online( - self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 - ) - self.kps_ref = kps_ref - self.kps_frame1 = kps_frame1 + with _profile_stage("first_frame_init"): + # For first frame, compute initial states using PyTorch motion encoder + motion_hidden_states = self.motion_encoder(pose_cond_tensor) + ref_motion = motion_hidden_states[:, :1] + dri_motion = motion_hidden_states[:, 1:] + + init_motion_hidden_states = self._interpolate_tensors( + ref_motion, dri_motion[:, :1], num=12 + 1 + )[:, :-1] + + # Compute initial pose features using PyTorch pose guider + pose_fea = self.pose_guider(keypoints) + + # Prefill TensorRT with initial motion and pose states + self.trt_runner.prefill( + motion_hidden_states=init_motion_hidden_states, + pose_cond_fea=pose_fea[:, :, :temporal_window_size * (temporal_adaptive_step - 1)], + ) + + self.motion_bank = ref_motion + self.first_frame = False + + # Prepare TensorRT inputs + pose = keypoints[:, :, -temporal_window_size:] + motion = torch.cat(motion_face[1:], dim=0).transpose(0, 1).unsqueeze(0) else: - mot_bbox_param, kps_dri = self.pose_encoder.get_kps( - self.kps_ref, self.kps_frame1, tgt_cond_tensor + with _profile_stage("motion_preparation"): + motion = torch.cat(motion_face, dim=0).transpose(0, 1).unsqueeze(0) + pose = keypoints + + # Prepare noise + with _profile_stage("noise_generation"): + new_noise = torch.randn( + batch_size, 4, temporal_window_size, + self.height // 8, self.width // 8, + device=device, dtype=self.dtype ) - keypoints = draw_keypoints(mot_bbox_param, device=device) - boxes = get_boxes(kps_dri) - keypoints = rearrange(keypoints.unsqueeze(2), 'f c b h w -> b c f h w') - keypoints = keypoints.to(device=device, dtype=self.dtype) - - # Prepare motion input (face crops) - if self.first_frame: - ref_box = get_boxes(mot_bbox_param[:1]) - ref_face = self._crop_face_tensor(self.ref_image_tensor, ref_box[0]) - motion_face = [ref_face] - for i, frame in enumerate(images): - motion_face.append(self._crop_face_tensor(frame, boxes[i])) - pose_cond_tensor = torch.cat(motion_face, dim=0).transpose(0, 1) - pose_cond_tensor = pose_cond_tensor.unsqueeze(0) - - # For first frame, compute initial states using PyTorch motion encoder - motion_hidden_states = self.motion_encoder(pose_cond_tensor) - ref_motion = motion_hidden_states[:, :1] - dri_motion = motion_hidden_states[:, 1:] - - init_motion_hidden_states = self._interpolate_tensors( - ref_motion, dri_motion[:, :1], num=12 + 1 - )[:, :-1] - - # Compute initial pose features using PyTorch pose guider - pose_fea = self.pose_guider(keypoints) - - # Prefill TensorRT with initial states - self.trt_runner.prefill( - motion_hidden_states_out=init_motion_hidden_states, - pose_cond_fea_out=pose_fea[:, :, :temporal_window_size * (temporal_adaptive_step - 1)], - ) - - self.motion_bank = ref_motion - self.first_frame = False - - # Prepare TensorRT inputs - pose = keypoints[:, :, -temporal_window_size:] - motion = dri_motion.transpose(1, 2).unsqueeze(0) # Reshape for TRT - else: - motion_face = [] - for i, frame in enumerate(images): - motion_face.append(self._crop_face_tensor(frame, boxes[i])) - motion = torch.cat(motion_face, dim=0).transpose(0, 1).unsqueeze(0) - pose = keypoints - - # Prepare new noise for next iteration - new_noise = torch.randn( - batch_size, 4, temporal_window_size, - self.height // 8, self.width // 8, - device=device, dtype=self.dtype - ) - - # Run TensorRT inference - outputs = self.trt_runner( - output_names=["pred_video", "motion_out", "latent_first"], - return_torch=True, - pose=pose, - motion=motion.to(self.dtype), - new_noise=new_noise, - ) + # TensorRT inference (includes motion_encoder, pose_guider, denoising_unet, vae_decode) + with _profile_stage("tensorrt_inference"): + # EngineModel and TRTRunner have slightly different APIs + if self._use_engine_model: + outputs = self.trt_runner( + output_names=["pred_video", "motion_out", "latent_first"], + return_torch=True, + pose=pose, + motion=motion.to(self.dtype), + new_noise=new_noise, + ) + else: + outputs = self.trt_runner( + output_list=["pred_video", "motion_out", "latent_first"], + return_tensor=True, + pose=pose, + motion=motion.to(self.dtype), + new_noise=new_noise, + ) video = outputs["pred_video"] motion_out = outputs.get("motion_out") # Keyframe tracking - if motion_out is not None and self.count > 8: - idx_to_add, self.motion_bank, _ = self._calculate_dis( - self.motion_bank, motion_out.unsqueeze(0), threshold=17.0 - ) - - if len(idx_to_add) > 0 and self.num_khf < 3: - # Update reference hidden states for keyframe - latent_first = outputs.get("latent_first") - if latent_first is not None: - self.reference_control_writer.clear() - self.reference_unet( - latent_first.to(self.reference_unet.dtype), - torch.zeros((batch_size,), dtype=self.dtype, device=device), - encoder_hidden_states=self.encoder_hidden_states, - return_dict=False, - ) - new_ref_hidden = self.reference_control_writer.output() - - # Concatenate new keyframe features - ref_hidden_names = [ - "d00", "d01", "d10", "d11", "d20", "d21", "m", - "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" - ] - for name in ref_hidden_names: - if name in self._reference_hidden_states and name in new_ref_hidden: - self._reference_hidden_states[name] = torch.cat( - [self._reference_hidden_states[name], new_ref_hidden[name]], dim=1 - ) - self.trt_runner.prefill(**{name: self._reference_hidden_states[name]}) - - logger.debug("Added history keyframe (TensorRT)") - self.num_khf += 1 + with _profile_stage("keyframe_tracking"): + if motion_out is not None and self.count > 8: + # motion_out from TensorRT is already 4D [B, 1, ml, mc], matching motion_bank + idx_to_add, self.motion_bank, _ = self._calculate_dis( + self.motion_bank, motion_out, threshold=17.0 + ) + + if len(idx_to_add) > 0 and self.num_khf < 3: + # Update reference hidden states for keyframe + latent_first = outputs.get("latent_first") + if latent_first is not None: + self.reference_control_writer.clear() + self.reference_unet( + latent_first.to(self.reference_unet.dtype), + torch.zeros((batch_size,), dtype=self.dtype, device=device), + encoder_hidden_states=self.encoder_hidden_states, + return_dict=False, + ) + new_ref_hidden = self.reference_control_writer.output() + + # Concatenate new keyframe features + ref_hidden_names = [ + "d00", "d01", "d10", "d11", "d20", "d21", "m", + "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" + ] + for name in ref_hidden_names: + if name in self._reference_hidden_states and name in new_ref_hidden: + self._reference_hidden_states[name] = torch.cat( + [self._reference_hidden_states[name], new_ref_hidden[name]], dim=1 + ) + self.trt_runner.prefill(**{name: self._reference_hidden_states[name]}) + + logger.debug("Added history keyframe (TensorRT)") + self.num_khf += 1 + + # Output to CPU + with _profile_stage("output_transfer"): + video = video.cpu() self.count += 1 - return video.cpu() + return video @torch.no_grad() def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: diff --git a/src/scope/core/pipelines/personalive/tensorrt/__init__.py b/src/scope/core/pipelines/personalive/tensorrt/__init__.py index 4ec3e734..d1bae587 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/__init__.py +++ b/src/scope/core/pipelines/personalive/tensorrt/__init__.py @@ -6,6 +6,9 @@ Installation: pip install daydream-scope[tensorrt] +For best performance, also install pycuda: + pip install pycuda + Usage: # Convert models to TensorRT (run once) convert-personalive-trt --model-dir ./models --height 512 --width 512 @@ -15,11 +18,30 @@ # Check for TensorRT availability before importing TRT_AVAILABLE = False +CUDA_BUFFERS_AVAILABLE = False +PYCUDA_AVAILABLE = False + try: import tensorrt # noqa: F401 from polygraphy.backend.trt import TrtRunner as _TrtRunner # noqa: F401 TRT_AVAILABLE = True + + # Check for zero-copy CUDA buffer support (polygraphy) + try: + from polygraphy.cuda import DeviceView # noqa: F401 + CUDA_BUFFERS_AVAILABLE = True + except ImportError: + pass + + # Check for pycuda (best performance) + try: + import pycuda.driver # noqa: F401 + import pycuda.autoinit # noqa: F401 + PYCUDA_AVAILABLE = True + except ImportError: + pass + except ImportError: pass @@ -27,12 +49,21 @@ from .builder import build_engine, get_engine_path, is_engine_available from .runner import TRTRunner + # Import EngineModel if pycuda is available + if PYCUDA_AVAILABLE: + from .engine_model import EngineModel + else: + EngineModel = None + __all__ = [ "build_engine", "get_engine_path", "is_engine_available", "TRTRunner", + "EngineModel", "TRT_AVAILABLE", + "CUDA_BUFFERS_AVAILABLE", + "PYCUDA_AVAILABLE", ] else: # Provide stub functions when TRT is not available @@ -55,4 +86,5 @@ def __init__(self, *args, **kwargs): "is_engine_available", "TRTRunner", "TRT_AVAILABLE", + "CUDA_BUFFERS_AVAILABLE", ] diff --git a/src/scope/core/pipelines/personalive/tensorrt/convert.py b/src/scope/core/pipelines/personalive/tensorrt/convert.py index 99adf4b4..390e75f5 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/convert.py +++ b/src/scope/core/pipelines/personalive/tensorrt/convert.py @@ -30,6 +30,14 @@ logger = logging.getLogger(__name__) +def log_gpu_memory(msg: str = ""): + """Log current GPU memory usage.""" + if torch.cuda.is_available(): + allocated = torch.cuda.memory_allocated() / 1024**3 + reserved = torch.cuda.memory_reserved() / 1024**3 + logger.info(f"GPU Memory {msg}: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved") + + def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( @@ -80,12 +88,12 @@ def parse_args(): def load_models(model_dir: Path, device: torch.device, dtype: torch.dtype): - """Load all PersonaLive models. + """Load all PersonaLive models with aggressive memory management. Args: model_dir: Base model directory. device: Target device. - dtype: Data type for models. + dtype: Data type for models (used for TensorRT, ONNX export uses FP32). Returns: Dictionary containing all loaded models and config. @@ -94,10 +102,19 @@ def load_models(model_dir: Path, device: torch.device, dtype: torch.dtype): DDIMScheduler, MotEncoder, PoseGuider, - UNet3DConditionModel, ) + # Use explicit reference UNet for TensorRT export + from .unet_explicit import UNet3DConditionModelExplicit + + def cleanup(): + """Aggressive memory cleanup.""" + gc.collect() + torch.cuda.empty_cache() + if torch.cuda.is_available(): + torch.cuda.synchronize() logger.info("Loading PersonaLive models...") + cleanup() # Paths personalive_dir = model_dir / "PersonaLive" / "pretrained_weights" @@ -111,54 +128,65 @@ def load_models(model_dir: Path, device: torch.device, dtype: torch.dtype): unet_kwargs = OmegaConf.to_container(model_config.unet_additional_kwargs) sched_kwargs = OmegaConf.to_container(model_config.noise_scheduler_kwargs) + # Use FP16 like official implementation - autocast handles dtype during export + export_dtype = dtype + # Load pose guider logger.info("Loading PoseGuider...") - pose_guider = PoseGuider().to(device=device, dtype=dtype) + pose_guider = PoseGuider().to(device=device, dtype=export_dtype) pose_guider_path = weights_path / "pose_guider.pth" if pose_guider_path.exists(): state_dict = torch.load(pose_guider_path, map_location="cpu") pose_guider.load_state_dict(state_dict) del state_dict + cleanup() - # Load motion encoder + # Load motion encoder (contains BatchNorm - must be FP32 for ONNX export) logger.info("Loading MotEncoder...") - motion_encoder = MotEncoder().to(dtype=dtype, device=device).eval() + motion_encoder = MotEncoder().to(dtype=export_dtype, device=device).eval() motion_encoder_path = weights_path / "motion_encoder.pth" if motion_encoder_path.exists(): state_dict = torch.load(motion_encoder_path, map_location="cpu") motion_encoder.load_state_dict(state_dict) del state_dict motion_encoder.set_attn_processor(AttnProcessor()) + cleanup() - # Load denoising UNet - logger.info("Loading UNet3DConditionModel...") - denoising_unet = UNet3DConditionModel.from_pretrained_2d( + # Load denoising UNet (explicit reference version for TensorRT) + logger.info("Loading UNet3DConditionModelExplicit for TensorRT...") + denoising_unet = UNet3DConditionModelExplicit.from_pretrained_2d( str(pretrained_base_path), "", subfolder="unet", unet_additional_kwargs=unet_kwargs, - ).to(dtype=dtype, device=device) + ).to(dtype=export_dtype, device=device) denoising_unet_path = weights_path / "denoising_unet.pth" if denoising_unet_path.exists(): state_dict = torch.load(denoising_unet_path, map_location="cpu") denoising_unet.load_state_dict(state_dict, strict=False) del state_dict + cleanup() temporal_module_path = weights_path / "temporal_module.pth" if temporal_module_path.exists(): state_dict = torch.load(temporal_module_path, map_location="cpu") denoising_unet.load_state_dict(state_dict, strict=False) del state_dict + cleanup() + # IMPORTANT: Use standard AttnProcessor for ONNX export + # xformers is NOT compatible with ONNX tracing! denoising_unet.set_attn_processor(AttnProcessor()) + cleanup() # Load VAE logger.info("Loading VAE...") - vae = AutoencoderKL.from_pretrained(str(vae_path)).to(device=device, dtype=dtype) + vae = AutoencoderKL.from_pretrained(str(vae_path)).to(device=device, dtype=export_dtype) vae.set_default_attn_processor() + cleanup() - # Setup scheduler + # Setup scheduler (CPU-only, minimal memory) scheduler = DDIMScheduler(**sched_kwargs) scheduler.to(device) @@ -170,6 +198,10 @@ def load_models(model_dir: Path, device: torch.device, dtype: torch.dtype): ).long() scheduler.set_step_length(333) + logger.info("All models loaded. Final memory cleanup...") + cleanup() + log_gpu_memory("after loading all models") + return { "pose_guider": pose_guider, "motion_encoder": motion_encoder, @@ -189,7 +221,7 @@ def export_to_onnx( device: torch.device, dtype: torch.dtype, ): - """Export bundled model to ONNX. + """Export bundled model to ONNX following official PersonaLive approach. Args: models: Dictionary of loaded models. @@ -198,12 +230,20 @@ def export_to_onnx( width: Output width. batch_size: Batch size. device: Target device. - dtype: Data type. + dtype: Data type for export. """ from .export import export_onnx, optimize_onnx from .framed_model import UNetWork + def cleanup(): + """Aggressive memory cleanup.""" + gc.collect() + torch.cuda.empty_cache() + if torch.cuda.is_available(): + torch.cuda.synchronize() + logger.info("Creating bundled UNetWork model...") + cleanup() # Create bundled model unet_work = UNetWork( @@ -215,30 +255,35 @@ def export_to_onnx( timesteps=models["timesteps"], ) - # Generate sample inputs - logger.info("Generating sample inputs...") - sample_inputs = unet_work.get_sample_input(batch_size, height, width, dtype, device) - # Export to ONNX + # Note: auto_cast=False because scope's MotEncoder has BatchNorm that + # doesn't work well with autocast (dtype mismatch issues) + log_gpu_memory("before ONNX export") raw_onnx_path = onnx_path.parent / "unet" / "unet.onnx" export_onnx( model=unet_work, onnx_path=raw_onnx_path, - sample_inputs=sample_inputs, - input_names=UNetWork.get_input_names(), - output_names=UNetWork.get_output_names(), - dynamic_axes=UNetWork.get_dynamic_axes(), - opset_version=17, + opt_image_height=height, + opt_image_width=width, + opt_batch_size=batch_size, + onnx_opset=17, + dtype=dtype, + device=device, + auto_cast=False, # Disabled due to BatchNorm dtype issues ) + # Note: export_onnx handles cleanup of unet_work internally + cleanup() - # Optimize ONNX + # Clear the models dict + for key in list(models.keys()): + del models[key] + cleanup() + log_gpu_memory("after ONNX export cleanup") + + # Optimize ONNX (CPU-only, doesn't need GPU) logger.info("Optimizing ONNX model...") optimize_onnx(raw_onnx_path, onnx_path) - - # Clean up - del unet_work, sample_inputs - gc.collect() - torch.cuda.empty_cache() + cleanup() def main(): @@ -271,9 +316,14 @@ def main(): device = torch.device(args.device) dtype = torch.float32 if args.fp32 else torch.float16 + # ONNX export uses 256x256 to save memory (official approach) + # TRT engine is built at target resolution with dynamic shapes + onnx_height, onnx_width = 256, 256 + logger.info(f"Converting PersonaLive models to TensorRT") logger.info(f" Model directory: {args.model_dir}") - logger.info(f" Resolution: {args.height}x{args.width}") + logger.info(f" Target resolution: {args.height}x{args.width}") + logger.info(f" ONNX export resolution: {onnx_height}x{onnx_width} (to save memory)") logger.info(f" Precision: {'FP32' if args.fp32 else 'FP16'}") logger.info(f" Device: {device}") @@ -283,15 +333,16 @@ def main(): onnx_path = get_onnx_path(args.model_dir) engine_path = get_engine_path(args.model_dir, args.height, args.width) - # Step 1: Export to ONNX (if needed) + # Step 1: Export to ONNX at 256x256 (if needed) + # This uses less memory - TRT handles target resolution via dynamic shapes if not args.skip_onnx or not onnx_path.exists(): - logger.info("Step 1: Exporting to ONNX...") + logger.info("Step 1: Exporting to ONNX at 256x256...") models = load_models(args.model_dir, device, dtype) export_to_onnx( models=models, onnx_path=onnx_path, - height=args.height, - width=args.width, + height=onnx_height, # 256 to save memory + width=onnx_width, # 256 to save memory batch_size=args.batch_size, device=device, dtype=dtype, @@ -303,9 +354,10 @@ def main(): else: logger.info("Step 1: Skipping ONNX export (using existing files)") - # Step 2: Build TensorRT engine + # Step 2: Build TensorRT engine at target resolution logger.info("Step 2: Building TensorRT engine...") - logger.info("This may take 10-30 minutes depending on your GPU.") + logger.info(f" Building for {args.height}x{args.width} resolution") + logger.info(" This may take 10-30 minutes depending on your GPU.") build_engine( onnx_path=onnx_path, diff --git a/src/scope/core/pipelines/personalive/tensorrt/engine_model.py b/src/scope/core/pipelines/personalive/tensorrt/engine_model.py new file mode 100644 index 00000000..6068e0d1 --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/engine_model.py @@ -0,0 +1,394 @@ +"""TensorRT engine runner using pycuda for direct CUDA memory management. + +Based on PersonaLive official implementation: +PersonaLive/src/modeling/engine_model.py + +This provides significantly better performance than polygraphy's TrtRunner +by using direct CUDA memory allocation and device-to-device copies. +""" + +import logging +import os +import traceback +from pathlib import Path + +import numpy as np +import torch + +logger = logging.getLogger(__name__) + +# Check for TensorRT and pycuda availability +PYCUDA_TRT_AVAILABLE = False +try: + import tensorrt as trt + import pycuda.driver as cuda + import pycuda.autoinit # noqa: F401 - Required for pycuda initialization + + PYCUDA_TRT_AVAILABLE = True + TRT_LOGGER = trt.Logger(trt.Logger.WARNING) +except ImportError as e: + logger.debug(f"pycuda/tensorrt not available: {e}") + + +def _get_engine(engine_file_path: str): + """Load TensorRT engine from file.""" + if os.path.exists(engine_file_path): + logger.info(f"Loading engine from file {engine_file_path}...") + with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + else: + raise FileNotFoundError(f"Engine file not found: {engine_file_path}") + + +def _numpy_to_torch_dtype(np_dtype): + """Convert numpy dtype to torch dtype.""" + mapping = { + np.float32: torch.float, + np.float64: torch.double, + np.float16: torch.half, + np.int32: torch.int32, + np.int64: torch.int64, + np.int16: torch.int16, + np.int8: torch.int8, + np.uint8: torch.uint8, + np.bool_: torch.bool, + } + return mapping.get(np_dtype, torch.float) + + +class EngineModel: + """TensorRT engine runner with pycuda for direct CUDA memory management. + + This provides significantly better performance than polygraphy by: + 1. Pre-allocating CUDA device memory for all inputs/outputs + 2. Using device-to-device copies (memcpy_dtod_async) when possible + 3. Using async TRT execution with CUDA streams + 4. True zero-copy binding for recurrent state + + Based on PersonaLive official implementation. + """ + + def __init__( + self, + engine_file_path: str | Path, + device_int: int = 0, + ): + """Initialize TensorRT engine with pycuda. + + Args: + engine_file_path: Path to the TensorRT engine file. + device_int: CUDA device index. + """ + if not PYCUDA_TRT_AVAILABLE: + raise RuntimeError( + "pycuda and tensorrt are required. " + "Install with: pip install pycuda tensorrt" + ) + + engine_file_path = str(engine_file_path) + if not os.path.exists(engine_file_path): + raise FileNotFoundError(f"Engine file not found: {engine_file_path}") + + self.device_int = device_int + self._torch_device = torch.device(f"cuda:{device_int}") + + # Create CUDA context for this device + self.ctx = cuda.Device(device_int).make_context() + + try: + # Load TensorRT engine + self.engine = _get_engine(engine_file_path) + + # Get input/output tensor names + self.input_names = [] + self.output_names = [] + + for binding in self.engine: + mode = self.engine.get_tensor_mode(binding) + if mode == trt.TensorIOMode.INPUT: + self.input_names.append(binding) + elif mode == trt.TensorIOMode.OUTPUT: + self.output_names.append(binding) + + # Helper to get safe shape (handle dynamic dimensions) + def get_safe_shape(engine, name): + shape = engine.get_tensor_shape(name) + if -1 in shape: + # Use max profile shape for dynamic dimensions + profile = engine.get_tensor_profile_shape(name, 0) + if profile: + logger.debug(f"Dynamic shape for {name}: {shape} -> Max: {profile[2]}") + return profile[2] + return shape + + # Get shapes and dtypes for all tensors + self.input_shapes = {name: get_safe_shape(self.engine, name) for name in self.input_names} + self.input_dtypes = {name: self.engine.get_tensor_dtype(name) for name in self.input_names} + self.input_nbytes = { + name: trt.volume(self.input_shapes[name]) * trt.nptype(self.input_dtypes[name])().itemsize + for name in self.input_names + } + + self.output_shapes = {name: get_safe_shape(self.engine, name) for name in self.output_names} + self.output_dtypes = {name: self.engine.get_tensor_dtype(name) for name in self.output_names} + self.output_nbytes = { + name: trt.volume(self.output_shapes[name]) * trt.nptype(self.output_dtypes[name])().itemsize + for name in self.output_names + } + + # Allocate CUDA device memory for inputs and outputs + self.dinputs = {name: cuda.mem_alloc(self.input_nbytes[name]) for name in self.input_names} + self.doutputs = {name: cuda.mem_alloc(self.output_nbytes[name]) for name in self.output_names} + + # Create execution context + self.context = self.engine.create_execution_context() + + # Create CUDA stream + self.stream = cuda.Stream() + + # Bind tensor addresses to context + for name in self.input_names: + self.context.set_tensor_address(name, int(self.dinputs[name])) + for name in self.output_names: + self.context.set_tensor_address(name, int(self.doutputs[name])) + + # Allocate page-locked host memory for outputs (for CPU transfer) + self.houtputs = { + name: cuda.pagelocked_empty( + trt.volume(self.output_shapes[name]), + dtype=trt.nptype(self.output_dtypes[name]) + ) + for name in self.output_names + } + + logger.info(f"TensorRT engine loaded successfully ({len(self.input_names)} inputs, {len(self.output_names)} outputs)") + + except Exception as e: + self.ctx.pop() + raise RuntimeError(f"Failed to initialize TensorRT engine: {e}") + + self.ctx.pop() + + def __call__( + self, + output_names: list[str] | None = None, + return_torch: bool = True, + **inputs: torch.Tensor | np.ndarray, + ) -> dict[str, torch.Tensor | np.ndarray]: + """Run inference on the TensorRT engine. + + Args: + output_names: List of output names to return. If None or empty, returns all. + return_torch: If True, return PyTorch tensors on CUDA. + **inputs: Named inputs as PyTorch tensors or numpy arrays. + + Returns: + Dictionary mapping output names to tensors/arrays. + """ + if output_names is None or len(output_names) == 0: + output_names = self.output_names + + self.ctx.push() + result = {} + + try: + # Copy inputs to device memory + for name, hinput in inputs.items(): + if name not in self.input_names: + continue + + if isinstance(hinput, torch.Tensor) and hinput.is_cuda and hinput.device.index == self.device_int: + # GPU tensor on same device - device-to-device copy (fast!) + hinput_con = hinput.contiguous() + cuda.memcpy_dtod_async( + self.dinputs[name], + hinput_con.data_ptr(), + self.input_nbytes[name], + self.stream + ) + else: + # CPU tensor or numpy array - host-to-device copy + if isinstance(hinput, torch.Tensor): + hinput = hinput.detach().cpu().numpy() + hinput_con = np.ascontiguousarray(hinput) + cuda.memcpy_htod_async(self.dinputs[name], hinput_con, self.stream) + + # Set input shapes for any inputs not provided (use prefilled) + for name in self.input_names: + if name not in inputs: + self.context.set_input_shape(name, self.input_shapes[name]) + + # Execute TensorRT inference asynchronously + self.context.execute_async_v3(self.stream.handle) + + # Copy outputs + if return_torch: + # Device-to-device copy to new torch tensor + for name in output_names: + t = torch.zeros( + trt.volume(self.output_shapes[name]), + device=self._torch_device, + dtype=_numpy_to_torch_dtype(trt.nptype(self.output_dtypes[name])) + ) + cuda.memcpy_dtod_async( + t.data_ptr(), + self.doutputs[name], + self.output_nbytes[name], + self.stream + ) + t = t.reshape(tuple(self.output_shapes[name])) + result[name] = t + else: + # Device-to-host copy to numpy + for name in output_names: + cuda.memcpy_dtoh_async(self.houtputs[name], self.doutputs[name], self.stream) + result[name] = self.houtputs[name].reshape(self.output_shapes[name]) + + # Synchronize stream + self.stream.synchronize() + + except Exception as e: + logger.error(f"TensorRT execution failed: {e}") + traceback.print_exc() + self.ctx.pop() + raise + + self.ctx.pop() + return result + + def prefill(self, **inputs: torch.Tensor | np.ndarray) -> bool: + """Pre-fill inputs/outputs with constant values. + + This copies data to the pre-allocated CUDA buffers. + + Args: + **inputs: Named inputs as PyTorch tensors or numpy arrays. + + Returns: + True if successful. + """ + self.ctx.push() + + try: + for name, hinput in inputs.items(): + # Check if it's an input or output buffer + if name in self.input_names: + dst_ptr = self.dinputs[name] + elif name in self.output_names: + dst_ptr = self.doutputs[name] + else: + logger.warning(f"Unknown tensor name for prefill: {name}") + continue + + # Calculate actual bytes to copy + if isinstance(hinput, torch.Tensor): + real_nbytes = hinput.numel() * hinput.element_size() + else: + real_nbytes = hinput.nbytes + + # Copy to device + if isinstance(hinput, torch.Tensor) and hinput.is_cuda and hinput.device.index == self.device_int: + # GPU tensor - device-to-device copy + hinput_con = hinput.contiguous() + cuda.memcpy_dtod_async(dst_ptr, hinput_con.data_ptr(), real_nbytes, self.stream) + else: + # CPU tensor/numpy - host-to-device copy + if isinstance(hinput, torch.Tensor): + hinput = hinput.detach().cpu().numpy() + hinput_con = np.ascontiguousarray(hinput) + cuda.memcpy_htod_async(dst_ptr, hinput_con, self.stream) + + self.stream.synchronize() + + except Exception as e: + logger.error(f"Prefill failed: {e}") + traceback.print_exc() + self.ctx.pop() + return False + + self.ctx.pop() + return True + + def bind(self, output_to_input: dict[str, str]) -> bool: + """Bind output buffers directly to input buffers for zero-copy recurrence. + + This sets the input tensor address to point to the output buffer, + eliminating any memory copies for recurrent state. + + Args: + output_to_input: Mapping from output names to input names. + + Returns: + True if successful. + """ + self.ctx.push() + + try: + for output_name, input_name in output_to_input.items(): + if output_name not in self.output_names: + logger.warning(f"Unknown output for bind: {output_name}") + continue + if input_name not in self.input_names: + logger.warning(f"Unknown input for bind: {input_name}") + continue + + # Point input address to output buffer + self.context.set_tensor_address(input_name, int(self.doutputs[output_name])) + + except Exception as e: + logger.error(f"Bind failed: {e}") + traceback.print_exc() + self.ctx.pop() + return False + + self.ctx.pop() + return True + + def clear_prefill(self): + """Clear is not needed for EngineModel - buffers persist.""" + pass + + def get_input_names(self) -> list[str]: + """Get list of input tensor names.""" + return list(self.input_names) + + def get_output_names(self) -> list[str]: + """Get list of output tensor names.""" + return list(self.output_names) + + def get_input_shapes(self) -> dict[str, tuple]: + """Get input tensor shapes.""" + return {name: tuple(shape) for name, shape in self.input_shapes.items()} + + def get_output_shapes(self) -> dict[str, tuple]: + """Get output tensor shapes.""" + return {name: tuple(shape) for name, shape in self.output_shapes.items()} + + def __del__(self): + """Clean up CUDA resources.""" + try: + if hasattr(self, 'ctx'): + self.ctx.push() + # Free CUDA memory + for ptr in getattr(self, 'dinputs', {}).values(): + ptr.free() + for ptr in getattr(self, 'doutputs', {}).values(): + ptr.free() + self.ctx.pop() + except Exception: + pass + + def __repr__(self) -> str: + """String representation.""" + r = "EngineModel(\n Inputs=[\n" + for name in self.input_names: + dtype = trt.nptype(self.input_dtypes[name]).__name__ + r += f" {name}: {dtype}{tuple(self.input_shapes[name])},\n" + r += " ],\n Outputs=[\n" + for name in self.output_names: + dtype = trt.nptype(self.output_dtypes[name]).__name__ + r += f" {name}: {dtype}{tuple(self.output_shapes[name])},\n" + r += " ]\n)" + return r + + diff --git a/src/scope/core/pipelines/personalive/tensorrt/export.py b/src/scope/core/pipelines/personalive/tensorrt/export.py index 3d7bebcc..8e5917d6 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/export.py +++ b/src/scope/core/pipelines/personalive/tensorrt/export.py @@ -1,12 +1,15 @@ """ONNX export utilities for PersonaLive models. -This module provides utilities to export PersonaLive models to ONNX format -for subsequent TensorRT conversion. +Adapted from PersonaLive official implementation: +PersonaLive/src/modeling/onnx_export.py + +Based on: https://github.com/NVIDIA/TensorRT/blob/main/demo/Diffusion/utilities.py """ import gc import logging import os +from contextlib import contextmanager from pathlib import Path import torch @@ -14,50 +17,78 @@ logger = logging.getLogger(__name__) +@contextmanager +def auto_cast_manager(enabled: bool): + """Context manager for autocast during ONNX export.""" + if enabled: + with torch.inference_mode(), torch.autocast("cuda"): + yield + else: + yield + + +@torch.no_grad() def export_onnx( model: torch.nn.Module, onnx_path: Path | str, - sample_inputs: dict[str, torch.Tensor], - input_names: list[str], - output_names: list[str], - dynamic_axes: dict[str, dict[int, str]] | None = None, - opset_version: int = 17, + opt_image_height: int, + opt_image_width: int, + opt_batch_size: int, + onnx_opset: int, + dtype: torch.dtype, + device: torch.device, + auto_cast: bool = True, ) -> None: """Export a PyTorch model to ONNX format. + This follows the official PersonaLive export approach. + Args: - model: PyTorch model to export. + model: PyTorch model with get_sample_input, get_input_names, + get_output_names, get_dynamic_axes methods. onnx_path: Path to save the ONNX model. - sample_inputs: Sample inputs for tracing. - input_names: Names of input tensors. - output_names: Names of output tensors. - dynamic_axes: Dynamic axis specifications. - opset_version: ONNX opset version. + opt_image_height: Optimization image height. + opt_image_width: Optimization image width. + opt_batch_size: Optimization batch size. + onnx_opset: ONNX opset version. + dtype: Data type for sample inputs. + device: Target device. + auto_cast: Whether to use autocast during export. """ onnx_path = Path(onnx_path) onnx_path.parent.mkdir(parents=True, exist_ok=True) logger.info(f"Exporting model to ONNX: {onnx_path}") + logger.info(f" Resolution: {opt_image_height}x{opt_image_width}") + logger.info(f" Batch size: {opt_batch_size}") + logger.info(f" Opset: {onnx_opset}") + logger.info(f" Auto cast: {auto_cast}") + + with auto_cast_manager(auto_cast): + # Generate sample inputs + inputs = model.get_sample_input( + opt_batch_size, opt_image_height, opt_image_width, dtype, device + ) - # Prepare inputs as tuple in correct order - inputs = tuple(sample_inputs[name] for name in input_names) + logger.info(f"Output names: {model.get_output_names()}") - with torch.inference_mode(), torch.autocast("cuda"): - torch.onnx.export( + # Export to ONNX - use torch.onnx.utils.export like official impl + torch.onnx.utils.export( model, - inputs, + inputs, # Pass dict directly, not as tuple str(onnx_path), export_params=True, - opset_version=opset_version, + opset_version=onnx_opset, do_constant_folding=True, - input_names=input_names, - output_names=output_names, - dynamic_axes=dynamic_axes or {}, + input_names=model.get_input_names(), + output_names=model.get_output_names(), + dynamic_axes=model.get_dynamic_axes(), ) logger.info(f"ONNX model exported to {onnx_path}") # Clean up + del model gc.collect() torch.cuda.empty_cache() @@ -84,7 +115,7 @@ def optimize_onnx( onnx_opt_path = Path(onnx_opt_path) onnx_opt_path.parent.mkdir(parents=True, exist_ok=True) - logger.info(f"Optimizing ONNX model: {onnx_path} -> {onnx_opt_path}") + logger.info(f"Saving ONNX model with external data: {onnx_opt_path}") model = onnx.load(str(onnx_path)) name = onnx_opt_path.stem @@ -99,31 +130,4 @@ def optimize_onnx( size_threshold=1024, ) - logger.info(f"Optimized ONNX model saved to {onnx_opt_path}") - - -def verify_onnx(onnx_path: Path | str) -> bool: - """Verify ONNX model is valid. - - Args: - onnx_path: Path to ONNX model. - - Returns: - True if model is valid. - """ - try: - import onnx - except ImportError: - logger.warning("onnx package not available, skipping verification") - return True - - onnx_path = Path(onnx_path) - - try: - model = onnx.load(str(onnx_path)) - onnx.checker.check_model(model) - logger.info(f"ONNX model {onnx_path} is valid") - return True - except Exception as e: - logger.error(f"ONNX model verification failed: {e}") - return False + logger.info("ONNX optimization done.") diff --git a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py index 0bec0373..85533c1d 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py +++ b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py @@ -1,32 +1,33 @@ -"""Framed model for ONNX export. +"""Bundled model for TensorRT export. -This module provides the UNetWork class that bundles multiple models +This module provides the UNetWork class that bundles multiple PersonaLive models into a single module for ONNX export and TensorRT conversion. -The bundled models include: -- PoseGuider: Extracts pose features from keypoint images -- MotEncoder: Encodes facial motion features -- UNet3DConditionModel: Main denoising UNet -- VAE decoder: Decodes latents to images -- DDIMScheduler: Denoising scheduler step +Based on PersonaLive official implementation: +PersonaLive/src/modeling/framed_models.py """ import torch -import torch.nn as nn +from torch import nn from einops import rearrange +try: + from polygraphy.backend.trt import Profile + POLYGRAPHY_AVAILABLE = True +except ImportError: + POLYGRAPHY_AVAILABLE = False + class UNetWork(nn.Module): """Bundled model for TensorRT export. - This class wraps the pose_guider, motion_encoder, denoising_unet, - vae decoder, and scheduler into a single module that can be exported - to ONNX for TensorRT conversion. + This class wraps the pose_guider, motion_encoder, denoising_unet (explicit reference), + vae decoder, and scheduler into a single module for ONNX/TensorRT export. The forward pass handles a complete denoising step including: 1. Encoding new pose features 2. Encoding new motion features - 3. Running the UNet denoiser + 3. Running the UNet denoiser with explicit reference injection 4. Scheduler step 5. VAE decoding for the output frames """ @@ -45,7 +46,7 @@ def __init__( Args: pose_guider: PoseGuider model. motion_encoder: MotEncoder model. - denoising_unet: UNet3DConditionModel for denoising. + denoising_unet: UNet3DConditionModelExplicit for denoising. vae: AutoencoderKL for decoding. scheduler: DDIMScheduler instance. timesteps: Fixed timesteps tensor for denoising. @@ -58,20 +59,13 @@ def __init__( self.scheduler = scheduler self.timesteps = timesteps - def _decode_latents(self, latents: torch.Tensor) -> torch.Tensor: - """Decode latents to images using VAE. - - Args: - latents: Latent tensor of shape (B, C, H, W). - - Returns: - Image tensor of shape (B, H, W, C) in [0, 1] range. - """ - latents = latents / 0.18215 - images = self.vae.decode(latents).sample - images = rearrange(images, "b c h w -> b h w c") - images = (images / 2 + 0.5).clamp(0, 1) - return images + def decode_slice(self, vae, x): + """Decode latents to images using VAE.""" + x = x / 0.18215 + x = vae.decode(x).sample + x = rearrange(x, "b c h w -> b h w c") + x = (x / 2 + 0.5).clamp(0, 1) + return x def forward( self, @@ -82,6 +76,7 @@ def forward( pose_cond_fea: torch.Tensor, pose: torch.Tensor, new_noise: torch.Tensor, + # Explicit reference hidden states d00: torch.Tensor, d01: torch.Tensor, d10: torch.Tensor, @@ -99,36 +94,36 @@ def forward( u31: torch.Tensor, u32: torch.Tensor, ): - """Forward pass for the bundled model. + """Forward pass for TensorRT inference. Args: - sample: Noisy latent tensor (B, C, T, H, W). - encoder_hidden_states: CLIP embeddings (B, 1, D). - motion_hidden_states: Accumulated motion features (B, T, D1, D2). - motion: New face crop tensor (B, C, T, H, W). - pose_cond_fea: Accumulated pose features (B, C, T, H, W). - pose: New pose keypoints (B, C, T, H, W). - new_noise: Noise for next iteration (B, C, T, H, W). + sample: Noisy latent (B, 4, 16, H/8, W/8). + encoder_hidden_states: CLIP embeddings (B, 1, 768). + motion_hidden_states: Accumulated motion features (B, 12, 32, 16). + motion: New motion face crops (B, 3, 4, 224, 224). + pose_cond_fea: Accumulated pose features (B, 320, 12, H/8, W/8). + pose: New pose keypoints (B, 3, 4, H, W). + new_noise: Noise for next iteration (B, 4, 4, H/8, W/8). d00-u32: Reference hidden states from reference UNet. Returns: Tuple of: - - pred_video: Decoded video frames (T, H, W, C). - - latents: Updated latent tensor for next iteration. + - pred_video: Decoded video frames (4, H, W, 3). + - latents: Updated latents for next iteration. - pose_cond_fea_out: Updated pose features. - motion_hidden_states_out: Updated motion features. - - motion_out: Motion features for keyframe tracking. - - latent_first: First latent for potential keyframe update. + - motion_out: First motion frame for keyframe detection. + - latent_first: First frame latent for potential keyframe update. """ - # Encode new pose features + # Encode new pose features and concatenate new_pose_cond_fea = self.pose_guider(pose) pose_cond_fea = torch.cat([pose_cond_fea, new_pose_cond_fea], dim=2) - # Encode new motion features + # Encode new motion features and concatenate new_motion_hidden_states = self.motion_encoder(motion) motion_hidden_states = torch.cat([motion_hidden_states, new_motion_hidden_states], dim=1) - # Prepare encoder hidden states for UNet + # Prepare encoder hidden states for UNet [clip_embeds, motion_features] encoder_hidden_states_combined = [encoder_hidden_states, motion_hidden_states] # Run UNet with explicit reference hidden states @@ -156,7 +151,7 @@ def forward( latents_model_input = rearrange(latents_model_input, '(b f) c h w -> b c f h w', f=16) # Decode first 4 frames (temporal_window_size) - pred_video = self._decode_latents(pred_original_sample[:4]) + pred_video = self.decode_slice(self.vae, pred_original_sample[:4]) # Prepare outputs for next iteration # Shift latents: drop first temporal_window_size, add new_noise @@ -166,126 +161,88 @@ def forward( pose_cond_fea_out = pose_cond_fea[:, :, 4:, :, :] motion_hidden_states_out = motion_hidden_states[:, 4:, :, :] - # Motion for keyframe tracking (first frame's motion) + # First motion frame for keyframe tracking motion_out = motion_hidden_states[:, :1, :, :] - # First predicted latent for potential keyframe reference update + # First frame latent for potential keyframe update latent_first = pred_original_sample[:1] - return ( - pred_video, - latents, - pose_cond_fea_out, - motion_hidden_states_out, - motion_out, - latent_first, - ) + return pred_video, latents, pose_cond_fea_out, motion_hidden_states_out, motion_out, latent_first - def get_sample_input( - self, - batch_size: int, - height: int, - width: int, - dtype: torch.dtype, - device: torch.device, - ) -> dict[str, torch.Tensor]: + def get_sample_input(self, batchsize: int, height: int, width: int, dtype, device): """Generate sample inputs for ONNX export. Args: - batch_size: Batch size (usually 1). - height: Output height. - width: Output width. - dtype: Data type (usually float16). + batchsize: Batch size (typically 1). + height: Output image height. + width: Output image width. + dtype: Tensor dtype. device: Target device. Returns: - Dictionary of sample input tensors. + Dictionary of sample tensors for ONNX export. """ - # Constants - tw = 4 # temporal_window_size - ts = 4 # temporal_adaptive_step - tb = tw * ts # temporal batch size (16) - ml, mc = 32, 16 # motion latent size, channels - mh, mw = 224, 224 # motion input size - + tw, ts, tb = 4, 4, 16 # temporal_window_size, temporal_adaptive_steps, temporal_batch + ml, mc, mh, mw = 32, 16, 224, 224 # motion latent dims + b, h, w = batchsize, height, width lh, lw = height // 8, width // 8 # latent height/width - - # UNet channels - cd0, cd1, cd2, cm = 320, 640, 1280, 1280 - cu1, cu2, cu3 = 1280, 640, 320 - - emb = 768 # CLIP embedding dim - lc, ic = 4, 3 # latent channels, image channels - - shapes = { - "sample": (batch_size, lc, tb, lh, lw), - "encoder_hidden_states": (batch_size, 1, emb), - "motion_hidden_states": (batch_size, tw * (ts - 1), ml, mc), - "motion": (batch_size, ic, tw, mh, mw), - "pose_cond_fea": (batch_size, cd0, tw * (ts - 1), lh, lw), - "pose": (batch_size, ic, tw, height, width), - "new_noise": (batch_size, lc, tw, lh, lw), - "d00": (batch_size, lh * lw, cd0), - "d01": (batch_size, lh * lw, cd0), - "d10": (batch_size, lh * lw // 4, cd1), - "d11": (batch_size, lh * lw // 4, cd1), - "d20": (batch_size, lh * lw // 16, cd2), - "d21": (batch_size, lh * lw // 16, cd2), - "m": (batch_size, lh * lw // 64, cm), - "u10": (batch_size, lh * lw // 16, cu1), - "u11": (batch_size, lh * lw // 16, cu1), - "u12": (batch_size, lh * lw // 16, cu1), - "u20": (batch_size, lh * lw // 4, cu2), - "u21": (batch_size, lh * lw // 4, cu2), - "u22": (batch_size, lh * lw // 4, cu2), - "u30": (batch_size, lh * lw, cu3), - "u31": (batch_size, lh * lw, cu3), - "u32": (batch_size, lh * lw, cu3), + cd0, cd1, cd2, cm, cu1, cu2, cu3 = 320, 640, 1280, 1280, 1280, 640, 320 # unet channels + emb = 768 # CLIP embedding dims + lc, ic = 4, 3 # latent/image channels + + profile = { + "sample": [b, lc, tb, lh, lw], + "encoder_hidden_states": [b, 1, emb], + "motion_hidden_states": [b, tw * (ts - 1), ml, mc], + "motion": [b, ic, tw, mh, mw], + "pose_cond_fea": [b, cd0, tw * (ts - 1), lh, lw], + "pose": [b, ic, tw, h, w], + "new_noise": [b, lc, tw, lh, lw], + "d00": [b, lh * lw, cd0], + "d01": [b, lh * lw, cd0], + "d10": [b, lh * lw // 4, cd1], + "d11": [b, lh * lw // 4, cd1], + "d20": [b, lh * lw // 16, cd2], + "d21": [b, lh * lw // 16, cd2], + "m": [b, lh * lw // 64, cm], + "u10": [b, lh * lw // 16, cu1], + "u11": [b, lh * lw // 16, cu1], + "u12": [b, lh * lw // 16, cu1], + "u20": [b, lh * lw // 4, cu2], + "u21": [b, lh * lw // 4, cu2], + "u22": [b, lh * lw // 4, cu2], + "u30": [b, lh * lw, cu3], + "u31": [b, lh * lw, cu3], + "u32": [b, lh * lw, cu3], } - - return {name: torch.randn(shape, dtype=dtype, device=device) for name, shape in shapes.items()} + return {k: torch.randn(profile[k], dtype=dtype, device=device) for k in profile} @staticmethod - def get_input_names() -> list[str]: - """Get list of input tensor names for ONNX export.""" + def get_input_names(): + """Get ordered input names for ONNX export.""" return [ - "sample", - "encoder_hidden_states", - "motion_hidden_states", - "motion", - "pose_cond_fea", - "pose", - "new_noise", + "sample", "encoder_hidden_states", "motion_hidden_states", + "motion", "pose_cond_fea", "pose", "new_noise", "d00", "d01", "d10", "d11", "d20", "d21", "m", - "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32", + "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" ] @staticmethod - def get_output_names() -> list[str]: - """Get list of output tensor names for ONNX export.""" + def get_output_names(): + """Get ordered output names for ONNX export.""" return [ - "pred_video", - "latents", - "pose_cond_fea_out", - "motion_hidden_states_out", - "motion_out", - "latent_first", + "pred_video", "latents", "pose_cond_fea_out", + "motion_hidden_states_out", "motion_out", "latent_first" ] @staticmethod - def get_dynamic_axes() -> dict[str, dict[int, str]]: - """Get dynamic axis specifications for ONNX export. - - Returns: - Dictionary mapping tensor names to their dynamic axes. - """ + def get_dynamic_axes(): + """Get dynamic axes for ONNX export.""" return { - # Resolution-dependent axes "sample": {3: "h_64", 4: "w_64"}, "pose_cond_fea": {3: "h_64", 4: "w_64"}, "pose": {3: "h_512", 4: "w_512"}, "new_noise": {3: "h_64", 4: "w_64"}, - # Dynamic reference hidden states (for keyframe accumulation) "d00": {1: "len_4096"}, "d01": {1: "len_4096"}, "u30": {1: "len_4096"}, @@ -303,3 +260,79 @@ def get_dynamic_axes() -> dict[str, dict[int, str]]: "u12": {1: "len_256"}, "m": {1: "len_64"}, } + + def get_dynamic_profile(self, batchsize: int, height: int, width: int): + """Get TensorRT optimization profile with dynamic shapes. + + Args: + batchsize: Batch size. + height: Target image height. + width: Target image width. + + Returns: + Polygraphy Profile object with min/opt/max shapes. + """ + if not POLYGRAPHY_AVAILABLE: + raise ImportError("polygraphy is required for TensorRT profiles") + + tw, ts, tb = 4, 4, 16 + ml, mc, mh, mw = 32, 16, 224, 224 + b, h, w = batchsize, height, width + lh, lw = height // 8, width // 8 + cd0, cd1, cd2, cm, cu1, cu2, cu3 = 320, 640, 1280, 1280, 1280, 640, 320 + emb = 768 + lc, ic = 4, 3 + + # Fixed inputs (don't change with keyframes) + fixed_inputs_map = { + "sample": (b, lc, tb, lh, lw), + "encoder_hidden_states": (b, 1, emb), + "motion_hidden_states": (b, tw * (ts - 1), ml, mc), + "motion": (b, ic, tw, mh, mw), + "pose_cond_fea": (b, cd0, tw * (ts - 1), lh, lw), + "pose": (b, ic, tw, h, w), + "new_noise": (b, lc, tw, lh, lw), + } + + # Dynamic inputs (grow with keyframe accumulation: 1x, 2x, 4x) + dynamic_inputs_map = { + "d00": (b, lh * lw, cd0), + "d01": (b, lh * lw, cd0), + "d10": (b, lh * lw // 4, cd1), + "d11": (b, lh * lw // 4, cd1), + "d20": (b, lh * lw // 16, cd2), + "d21": (b, lh * lw // 16, cd2), + "m": (b, lh * lw // 64, cm), + "u10": (b, lh * lw // 16, cu1), + "u11": (b, lh * lw // 16, cu1), + "u12": (b, lh * lw // 16, cu1), + "u20": (b, lh * lw // 4, cu2), + "u21": (b, lh * lw // 4, cu2), + "u22": (b, lh * lw // 4, cu2), + "u30": (b, lh * lw, cu3), + "u31": (b, lh * lw, cu3), + "u32": (b, lh * lw, cu3), + } + + profile = Profile() + + # Fixed inputs have same min/opt/max + for name, shape in fixed_inputs_map.items(): + shape_tuple = tuple(shape) + profile.add(name, min=shape_tuple, opt=shape_tuple, max=shape_tuple) + + # Dynamic inputs can grow 1x-4x in the sequence dimension + for name, base_shape in dynamic_inputs_map.items(): + dim0, dim1_base, dim2 = base_shape + + val_1x = dim1_base * 1 # 1 keyframe + val_2x = dim1_base * 2 # 2 keyframes + val_4x = dim1_base * 4 # 4 keyframes (max) + + min_shape = (dim0, val_1x, dim2) + opt_shape = (dim0, val_2x, dim2) + max_shape = (dim0, val_4x, dim2) + + profile.add(name, min=min_shape, opt=opt_shape, max=max_shape) + + return profile diff --git a/src/scope/core/pipelines/personalive/tensorrt/runner.py b/src/scope/core/pipelines/personalive/tensorrt/runner.py index 70f9df8c..974523fb 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/runner.py +++ b/src/scope/core/pipelines/personalive/tensorrt/runner.py @@ -2,6 +2,8 @@ This module provides a TensorRT engine runner that wraps the engine and handles inference with PyTorch tensor I/O. + +Uses CUDA device buffers (DeviceView) to avoid CPU-GPU memory transfers. """ import logging @@ -15,12 +17,20 @@ # Check for TensorRT availability TRT_AVAILABLE = False +CUDA_BUFFERS_AVAILABLE = False try: import tensorrt as trt from polygraphy.backend.common import BytesFromPath from polygraphy.backend.trt import EngineFromBytes, TrtRunner TRT_AVAILABLE = True + + # Check for CUDA buffer support (polygraphy >= 0.47) + try: + from polygraphy.cuda import DeviceView + CUDA_BUFFERS_AVAILABLE = True + except ImportError: + logger.warning("polygraphy.cuda.DeviceView not available - using numpy path (slower)") except ImportError: pass @@ -74,6 +84,11 @@ def __init__( logger.info(f"Loading TensorRT engine from {engine_path}") self._load_engine() + if CUDA_BUFFERS_AVAILABLE: + logger.info("TensorRT runner using CUDA device buffers (zero-copy)") + else: + logger.warning("TensorRT runner using numpy path (slower - install polygraphy>=0.47 for zero-copy)") + def _load_engine(self): """Load the TensorRT engine.""" # Load engine bytes @@ -92,15 +107,26 @@ def prefill(self, **inputs: torch.Tensor | np.ndarray): """Pre-fill constant inputs that don't change per frame. These inputs will be automatically included in every inference call. + When CUDA buffers are available, keeps tensors on GPU for zero-copy inference. Args: **inputs: Named inputs as PyTorch tensors or numpy arrays. """ for name, tensor in inputs.items(): - if isinstance(tensor, torch.Tensor): - # Convert to numpy, ensuring contiguous memory - tensor = tensor.detach().cpu().numpy() - self._prefilled[name] = np.ascontiguousarray(tensor) + if CUDA_BUFFERS_AVAILABLE: + # Keep as torch tensor on GPU for zero-copy inference + if isinstance(tensor, np.ndarray): + tensor = torch.from_numpy(tensor).to(self.device).contiguous() + elif isinstance(tensor, torch.Tensor): + if not tensor.is_cuda: + tensor = tensor.to(self.device) + tensor = tensor.contiguous() + self._prefilled[name] = tensor + else: + # Fallback to numpy for compatibility + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + self._prefilled[name] = np.ascontiguousarray(tensor) def bind(self, output_to_input: dict[str, str]): """Bind output tensors to input tensors for memory reuse. @@ -117,6 +143,46 @@ def clear_prefill(self): """Clear all pre-filled inputs.""" self._prefilled.clear() + def _tensor_to_device_view(self, tensor: torch.Tensor): + """Convert PyTorch tensor to polygraphy DeviceView (zero-copy). + + Args: + tensor: PyTorch CUDA tensor (must be contiguous). + + Returns: + DeviceView pointing to the tensor's GPU memory. + """ + if not CUDA_BUFFERS_AVAILABLE: + raise RuntimeError("DeviceView not available") + + # Ensure tensor is contiguous and on CUDA + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + + if not tensor.is_cuda: + tensor = tensor.cuda() + + # Map PyTorch dtype to numpy dtype properly + dtype_map = { + torch.float16: np.float16, + torch.float32: np.float32, + torch.float64: np.float64, + torch.int32: np.int32, + torch.int64: np.int64, + torch.int16: np.int16, + torch.int8: np.int8, + torch.uint8: np.uint8, + torch.bool: np.bool_, + } + np_dtype = dtype_map.get(tensor.dtype, np.float32) + + # Create DeviceView from tensor's data pointer + return DeviceView( + ptr=tensor.data_ptr(), + shape=tuple(tensor.shape), + dtype=np_dtype, + ) + def __call__( self, output_names: list[str] | None = None, @@ -125,6 +191,8 @@ def __call__( ) -> dict[str, torch.Tensor | np.ndarray]: """Run inference on the TensorRT engine. + Uses CUDA device buffers when available to avoid CPU-GPU memory transfers. + Args: output_names: List of output names to return. If None, returns all outputs. return_torch: If True, return PyTorch tensors. Otherwise, return numpy arrays. @@ -136,27 +204,124 @@ def __call__( if self._runner is None: raise RuntimeError("TensorRT runner not initialized") - # Prepare feed dict with prefilled values - feed_dict = dict(self._prefilled) + # Use CUDA device buffers if available (zero-copy path) + if CUDA_BUFFERS_AVAILABLE and return_torch: + return self._call_cuda_buffers(output_names, **inputs) + + # Fallback to numpy path (has CPU-GPU copy overhead) + return self._call_numpy(output_names, return_torch, **inputs) + + def _call_cuda_buffers( + self, + output_names: list[str] | None = None, + **inputs: torch.Tensor | np.ndarray, + ) -> dict[str, torch.Tensor]: + """Run inference using CUDA device buffers (zero-copy). + + Args: + output_names: List of output names to return. + **inputs: Named inputs as PyTorch tensors. + + Returns: + Dictionary mapping output names to PyTorch tensors. + """ + # Prepare feed dict with DeviceView wrappers + feed_dict = {} + + # Add prefilled values (convert from numpy to DeviceView if needed) + for name, arr in self._prefilled.items(): + if isinstance(arr, np.ndarray): + # Convert numpy prefill to torch tensor (one-time cost) + tensor = torch.from_numpy(arr).to(self.device).contiguous() + self._prefilled[name] = tensor + feed_dict[name] = self._tensor_to_device_view(tensor) + elif isinstance(arr, torch.Tensor): + feed_dict[name] = self._tensor_to_device_view(arr) + else: + # Already a DeviceView or compatible + feed_dict[name] = arr # Add current inputs for name, tensor in inputs.items(): + if isinstance(tensor, np.ndarray): + tensor = torch.from_numpy(tensor).to(self.device) if isinstance(tensor, torch.Tensor): - tensor = tensor.detach().cpu().numpy() - feed_dict[name] = np.ascontiguousarray(tensor) + if not tensor.is_cuda: + tensor = tensor.to(self.device) + feed_dict[name] = self._tensor_to_device_view(tensor.contiguous()) # Run inference outputs = self._runner.infer(feed_dict=feed_dict) + # Convert outputs to PyTorch tensors (they come back as DeviceView or numpy) + result = {} + for name, output in outputs.items(): + if hasattr(output, 'numpy'): + # DeviceView - create tensor from same memory + arr = output.numpy() + result[name] = torch.from_numpy(arr).to(self.device) + elif isinstance(output, np.ndarray): + result[name] = torch.from_numpy(output).to(self.device) + elif isinstance(output, torch.Tensor): + result[name] = output + else: + result[name] = output + + # Update prefilled values with bound outputs + for output_name, input_name in self._output_bindings.items(): + if output_name in result: + self._prefilled[input_name] = result[output_name] + # Filter outputs if requested if output_names is not None: - outputs = {k: v for k, v in outputs.items() if k in output_names} + result = {k: v for k, v in result.items() if k in output_names} - # Update prefilled values with bound outputs + return result + + def _call_numpy( + self, + output_names: list[str] | None = None, + return_torch: bool = True, + **inputs: torch.Tensor | np.ndarray, + ) -> dict[str, torch.Tensor | np.ndarray]: + """Run inference using numpy arrays (has CPU-GPU copy overhead). + + Args: + output_names: List of output names to return. + return_torch: If True, return PyTorch tensors. + **inputs: Named inputs as PyTorch tensors or numpy arrays. + + Returns: + Dictionary mapping output names to tensors/arrays. + """ + # Prepare feed dict with prefilled values + feed_dict = {} + + # Add prefilled numpy values + for name, arr in self._prefilled.items(): + if isinstance(arr, torch.Tensor): + arr = arr.detach().cpu().numpy() + feed_dict[name] = np.ascontiguousarray(arr) + + # Add current inputs + for name, tensor in inputs.items(): + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + feed_dict[name] = np.ascontiguousarray(tensor) + + # Run inference + outputs = self._runner.infer(feed_dict=feed_dict) + + # Update prefilled values with bound outputs BEFORE filtering + # This ensures recurrent states (latents->sample) are properly updated for output_name, input_name in self._output_bindings.items(): if output_name in outputs: self._prefilled[input_name] = outputs[output_name] + # Filter outputs if requested (after bindings are applied) + if output_names is not None: + outputs = {k: v for k, v in outputs.items() if k in output_names} + # Convert to PyTorch if requested if return_torch: outputs = { diff --git a/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py b/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py new file mode 100644 index 00000000..d3d0dd15 --- /dev/null +++ b/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py @@ -0,0 +1,601 @@ +"""UNet3DConditionModel with explicit reference inputs for TensorRT export. + +This module provides a modified UNet3D that accepts reference hidden states +as explicit forward parameters instead of using the attention processor +writer/reader pattern. This is required for ONNX/TensorRT export. + +Ported from PersonaLive official implementation: +PersonaLive/src/models/unet_3d_explicit_reference.py +""" + +from collections import OrderedDict +from dataclasses import dataclass +from os import PathLike +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.attention_processor import AttentionProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging +from safetensors.torch import load_file + +from ..modules.resnet import InflatedConv3d, InflatedGroupNorm +from ..modules.unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block + +logger = logging.get_logger(__name__) + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModelExplicit(ModelMixin, ConfigMixin): + """UNet3D with explicit reference hidden state inputs for TensorRT export. + + This model accepts reference hidden states (d00, d01, ..., u32) as explicit + forward parameters instead of using attention processor hooks. + """ + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + use_inflated_groupnorm=False, + # Additional + use_motion_module=False, + use_temporal_module=False, + motion_module_resolutions=(1, 2, 4, 8), + motion_module_mid_block=False, + motion_module_decoder_only=False, + motion_module_type=None, + temporal_module_type=None, + motion_module_kwargs={}, + temporal_module_kwargs={}, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + + # input + self.conv_in = InflatedConv3d( + in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1) + ) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2**i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + use_temporal_module=use_temporal_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + temporal_module_type=temporal_module_type, + motion_module_kwargs=motion_module_kwargs, + temporal_module_kwargs=temporal_module_kwargs + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and motion_module_mid_block, + use_temporal_module=use_temporal_module and motion_module_mid_block, + motion_module_type=motion_module_type, + temporal_module_type=temporal_module_type, + motion_module_kwargs=motion_module_kwargs, + temporal_module_kwargs=temporal_module_kwargs, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions), + use_temporal_module=use_temporal_module + and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + temporal_module_type=temporal_module_type, + motion_module_kwargs=motion_module_kwargs, + temporal_module_kwargs=temporal_module_kwargs, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + self.conv_act = nn.SiLU() + self.conv_out = InflatedConv3d( + block_out_channels[0], out_channels, kernel_size=3, padding=1 + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + """Returns attention processors.""" + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + if "temporal_transformer" not in sub_name: + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + if "temporal_transformer" not in name: + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + """Sets the attention processor.""" + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + pose_cond_fea: torch.Tensor, + # Explicit reference hidden states for TensorRT + d00: torch.Tensor, + d01: torch.Tensor, + d10: torch.Tensor, + d11: torch.Tensor, + d20: torch.Tensor, + d21: torch.Tensor, + m: torch.Tensor, + u10: torch.Tensor, + u11: torch.Tensor, + u12: torch.Tensor, + u20: torch.Tensor, + u21: torch.Tensor, + u22: torch.Tensor, + u30: torch.Tensor, + u31: torch.Tensor, + u32: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + return_dict: bool = True, + skip_mm: bool = False, + ) -> Union[UNet3DConditionOutput, Tuple]: + """Forward pass with explicit reference hidden states. + + Args: + sample: Noisy latent input (batch, channel, frames, height, width). + timestep: Timesteps tensor. + encoder_hidden_states: [clip_embeds, motion_hidden_states] list. + pose_cond_fea: Pose conditioning features. + d00-d21: Down block reference hidden states. + m: Mid block reference hidden states. + u10-u32: Up block reference hidden states. + + Returns: + UNet output sample tensor. + """ + default_overall_up_factor = 2**self.num_upsamplers + + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + forward_upsample_size = True + + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time embedding + timesteps = timestep + if not torch.is_tensor(timesteps): + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + t_emb = self.time_proj(timesteps) + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + if pose_cond_fea is not None: + sample = sample + pose_cond_fea + + # down blocks with explicit reference injection + down_block_res_samples = (sample,) + for i, downsample_block in enumerate(self.down_blocks): + # Set reference hidden states for this block + down_reference = None + if i == 0: + down_reference = [d00, d01] + elif i == 1: + down_reference = [d10, d11] + elif i == 2: + down_reference = [d20, d21] + + if ( + hasattr(downsample_block, "has_cross_attention") + and downsample_block.has_cross_attention + ): + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + skip_mm=skip_mm, + down_reference=down_reference, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + skip_mm=skip_mm, + ) + down_block_res_samples += res_samples + + if down_block_additional_residuals is not None: + new_down_block_res_samples = () + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples += (down_block_res_sample,) + down_block_res_samples = new_down_block_res_samples + + # mid block with explicit reference + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + skip_mm=skip_mm, + mid_reference=m, + ) + + if mid_block_additional_residual is not None: + sample = sample + mid_block_additional_residual + + # up blocks with explicit reference injection + for i, upsample_block in enumerate(self.up_blocks): + up_reference = None + if i == 1: + up_reference = [u10, u11, u12] + elif i == 2: + up_reference = [u20, u21, u22] + elif i == 3: + up_reference = [u30, u31, u32] + + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets):] + down_block_res_samples = down_block_res_samples[:-len(upsample_block.resnets)] + + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if ( + hasattr(upsample_block, "has_cross_attention") + and upsample_block.has_cross_attention + ): + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + skip_mm=skip_mm, + up_reference=up_reference, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + encoder_hidden_states=encoder_hidden_states, + skip_mm=skip_mm, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + return sample + + @classmethod + def from_pretrained_2d( + cls, + pretrained_model_path: PathLike, + motion_module_path: PathLike, + subfolder=None, + unet_additional_kwargs=None, + mm_zero_proj_out=False, + ): + """Load from pretrained 2D UNet weights.""" + pretrained_model_path = Path(pretrained_model_path) + motion_module_path = Path(motion_module_path) + if subfolder is not None: + pretrained_model_path = pretrained_model_path.joinpath(subfolder) + logger.info(f"Loading temporal unet from {pretrained_model_path}...") + + config_file = pretrained_model_path / "config.json" + if not (config_file.exists() and config_file.is_file()): + raise RuntimeError(f"{config_file} does not exist or is not a file") + + unet_config = cls.load_config(config_file) + unet_config["_class_name"] = cls.__name__ + unet_config["down_block_types"] = [ + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ] + unet_config["up_block_types"] = [ + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ] + unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn" + + model = cls.from_config(unet_config, **unet_additional_kwargs) + + # Load weights + if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists(): + state_dict = load_file( + pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu" + ) + elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists(): + state_dict = torch.load( + pretrained_model_path.joinpath(WEIGHTS_NAME), + map_location="cpu", + weights_only=True, + ) + else: + raise FileNotFoundError(f"No weights file found in {pretrained_model_path}") + + # Load motion module + if motion_module_path.exists() and motion_module_path.is_file(): + if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]: + motion_state_dict = torch.load( + motion_module_path, map_location="cpu", weights_only=True + ) + elif motion_module_path.suffix.lower() == ".safetensors": + motion_state_dict = load_file(motion_module_path, device="cpu") + else: + raise RuntimeError(f"Unknown format: {motion_module_path.suffix}") + + motion_state_dict = { + k.replace('motion_modules.', 'temporal_modules.'): v + for k, v in motion_state_dict.items() if "pos_encoder" not in k + } + + if mm_zero_proj_out: + motion_state_dict = { + k: v for k, v in motion_state_dict.items() if "proj_out" not in k + } + + state_dict.update(motion_state_dict) + + m, u = model.load_state_dict(state_dict, strict=False) + logger.debug(f"Missing keys: {len(m)}; Unexpected keys: {len(u)}") + + return model + From 5a455fae669f86fdbf577cceaf092852195f766a Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Wed, 17 Dec 2025 10:38:43 -0500 Subject: [PATCH 4/7] Hide warnings from polygraphy in console, fix to accumulation leak. Signed-off-by: BuffMcBigHuge --- src/scope/core/pipelines/personalive/pipeline.py | 2 ++ src/scope/core/pipelines/personalive/tensorrt/runner.py | 5 +++++ 2 files changed, 7 insertions(+) diff --git a/src/scope/core/pipelines/personalive/pipeline.py b/src/scope/core/pipelines/personalive/pipeline.py index 9481e6d9..85bfae4e 100644 --- a/src/scope/core/pipelines/personalive/pipeline.py +++ b/src/scope/core/pipelines/personalive/pipeline.py @@ -505,6 +505,8 @@ def fuse_reference(self, ref_image: Image.Image): ref_image_latents = ref_image_latents * 0.18215 # Run reference UNet to cache features + # Clear writer first to prevent accumulation from previous keyframes when re-fusing + self.reference_control_writer.clear() self.reference_unet( ref_image_latents.to(self.reference_unet.device), torch.zeros((self.batch_size,), dtype=self.dtype, device=self.reference_unet.device), diff --git a/src/scope/core/pipelines/personalive/tensorrt/runner.py b/src/scope/core/pipelines/personalive/tensorrt/runner.py index 974523fb..2362478c 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/runner.py +++ b/src/scope/core/pipelines/personalive/tensorrt/runner.py @@ -23,6 +23,11 @@ from polygraphy.backend.common import BytesFromPath from polygraphy.backend.trt import EngineFromBytes, TrtRunner + # Suppress polygraphy deprecation warnings about DeviceView.dtype + # (warns about future API change: DataType.from_dtype(device_view.dtype).numpy()) + from polygraphy.logger import G_LOGGER + G_LOGGER.severity = G_LOGGER.ERROR # Only show errors, not warnings + TRT_AVAILABLE = True # Check for CUDA buffer support (polygraphy >= 0.47) From cdb9f8adbf8ab154a297843ba5cebfec42e3ff69 Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Wed, 17 Dec 2025 17:35:04 -0500 Subject: [PATCH 5/7] Better pipeline conditioning logic for ui. Signed-off-by: BuffMcBigHuge --- .../src/components/InputAndControlsPanel.tsx | 70 ++- frontend/src/components/SettingsPanel.tsx | 559 +++++++++--------- frontend/src/data/pipelines.ts | 139 ++++- frontend/src/pages/StreamPage.tsx | 265 +++++---- frontend/src/types/index.ts | 18 + .../core/pipelines/personalive/__init__.py | 1 + .../pipelines/personalive/blocks/__init__.py | 1 + .../personalive/components/face_detector.py | 1 + .../core/pipelines/personalive/docs/README.md | 1 + .../pipelines/personalive/tensorrt/builder.py | 1 + 10 files changed, 623 insertions(+), 433 deletions(-) diff --git a/frontend/src/components/InputAndControlsPanel.tsx b/frontend/src/components/InputAndControlsPanel.tsx index 176bdef2..7222fa65 100644 --- a/frontend/src/components/InputAndControlsPanel.tsx +++ b/frontend/src/components/InputAndControlsPanel.tsx @@ -12,10 +12,22 @@ import { Badge } from "./ui/badge"; import { Input } from "./ui/input"; import { Upload } from "lucide-react"; import { LabelWithTooltip } from "./ui/label-with-tooltip"; +import { + Tooltip, + TooltipContent, + TooltipProvider, + TooltipTrigger, +} from "./ui/tooltip"; import type { VideoSourceMode } from "../hooks/useVideoSource"; import type { PromptItem, PromptTransition } from "../lib/api"; import type { InputMode } from "../types"; -import { pipelineIsMultiMode, pipelineRequiresReferenceImage } from "../data/pipelines"; +import { + pipelineIsMultiMode, + pipelineRequiresReferenceImage, + pipelineShowsPromptInput, + pipelineCanChangeReferenceWhileStreaming, + getPipelineReferenceImageDescription, +} from "../data/pipelines"; import { PromptInput } from "./PromptInput"; import { TimelinePromptEditor } from "./TimelinePromptEditor"; import type { TimelinePrompt } from "./PromptTimeline"; @@ -192,7 +204,7 @@ export function InputAndControlsPanel({ )} - {/* Reference Image upload - only show for PersonaLive */} + {/* Reference Image upload - only show for pipelines that require it */} {needsReferenceImage && (

Reference Portrait

@@ -215,18 +227,42 @@ export function InputAndControlsPanel({ onChange={handleReferenceImageUpload} className="hidden" id="reference-image-upload" - disabled={isStreaming || isConnecting || isUploadingReference} + disabled={ + isUploadingReference || + ((isStreaming || isConnecting) && + !pipelineCanChangeReferenceWhileStreaming(pipelineId)) + } /> - + {/* Upload button with tooltip when disabled during streaming */} + {(isStreaming || isConnecting) && + !pipelineCanChangeReferenceWhileStreaming(pipelineId) ? ( + + + +
+ +
+ + +

+ Reference image is processed when the pipeline loads. + Stop the stream to change it. +

+ + + + ) : ( + + )}
{isUploadingReference && (

@@ -235,8 +271,8 @@ export function InputAndControlsPanel({ )} {!referenceImageUrl && (

- This pipeline animates this portrait using your webcam/video as the - driving source. + {getPipelineReferenceImageDescription(pipelineId) || + "This pipeline requires a reference image."}

)} @@ -352,8 +388,8 @@ export function InputAndControlsPanel({ )} - {/* Prompts section - hide for PersonaLive (it uses image conditioning, not text) */} - {!needsReferenceImage && ( + {/* Prompts section - only show for pipelines that support text prompts */} + {pipelineShowsPromptInput(pipelineId) && (
{(() => { // The Input can have two states: Append (default) and Edit (when a prompt is selected and the video is paused) diff --git a/frontend/src/components/SettingsPanel.tsx b/frontend/src/components/SettingsPanel.tsx index d21ca03a..2d7213c5 100644 --- a/frontend/src/components/SettingsPanel.tsx +++ b/frontend/src/components/SettingsPanel.tsx @@ -20,7 +20,17 @@ import { Button } from "./ui/button"; import { Toggle } from "./ui/toggle"; import { SliderWithInput } from "./ui/slider-with-input"; import { Hammer, Info, Minus, Plus, RotateCcw } from "lucide-react"; -import { PIPELINES, pipelineSupportsLoRA } from "../data/pipelines"; +import { + PIPELINES, + pipelineSupportsLoRA, + pipelineShowsResolutionControl, + pipelineShowsSeedControl, + pipelineShowsDenoisingSteps, + pipelineShowsCacheManagement, + pipelineShowsQuantization, + pipelineShowsKvCacheAttentionBias, + pipelineShowsNoiseControls, +} from "../data/pipelines"; import { PARAMETER_METADATA } from "../data/parameterMetadata"; import { DenoisingStepsSlider } from "./DenoisingStepsSlider"; import { useLocalSliderValue } from "../hooks/useLocalSliderValue"; @@ -69,8 +79,6 @@ interface SettingsPanelProps { loraMergeStrategy?: LoraMergeStrategy; // Input mode for conditional rendering of noise controls inputMode?: InputMode; - // Whether this pipeline supports noise controls in video mode (schema-derived) - supportsNoiseControls?: boolean; // Spout settings spoutSender?: SettingsState["spoutSender"]; onSpoutSenderChange?: (spoutSender: SettingsState["spoutSender"]) => void; @@ -106,7 +114,6 @@ export function SettingsPanel({ onLorasChange, loraMergeStrategy = "permanent_merge", inputMode, - supportsNoiseControls = false, spoutSender, onSpoutSenderChange, spoutAvailable = false, @@ -331,231 +338,221 @@ export function SettingsPanel({
)} - {(pipelineId === "longlive" || - pipelineId === "streamdiffusionv2" || - pipelineId === "krea-realtime-video" || - pipelineId === "reward-forcing") && ( -
-
-
-
-
- -
- - { - const value = parseInt(e.target.value); - if (!isNaN(value)) { - handleResolutionChange("height", value); - } - }} - disabled={isStreaming} - className="text-center border-0 focus-visible:ring-0 focus-visible:ring-offset-0 h-8 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none" - min={MIN_DIMENSION} - max={2048} - /> - -
-
- {heightError && ( -

{heightError}

- )} -
- -
-
- -
- - { - const value = parseInt(e.target.value); - if (!isNaN(value)) { - handleResolutionChange("width", value); - } - }} - disabled={isStreaming} - className="text-center border-0 focus-visible:ring-0 focus-visible:ring-offset-0 h-8 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none" - min={MIN_DIMENSION} - max={2048} - /> - -
-
- {widthError && ( -

{widthError}

- )} -
- -
-
- -
- - { - const value = parseInt(e.target.value); - if (!isNaN(value)) { - handleSeedChange(value); - } - }} - disabled={isStreaming} - className="text-center border-0 focus-visible:ring-0 focus-visible:ring-offset-0 h-8 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none" - min={0} - max={2147483647} - /> - -
-
- {seedError && ( -

{seedError}

- )} + {/* Resolution controls */} + {pipelineShowsResolutionControl(pipelineId) && ( +
+
+
+ +
+ + { + const value = parseInt(e.target.value); + if (!isNaN(value)) { + handleResolutionChange("height", value); + } + }} + disabled={isStreaming} + className="text-center border-0 focus-visible:ring-0 focus-visible:ring-offset-0 h-8 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none" + min={MIN_DIMENSION} + max={2048} + /> +
+ {heightError && ( +

{heightError}

+ )}
-
- )} - - {(pipelineId === "longlive" || - pipelineId === "streamdiffusionv2" || - pipelineId === "krea-realtime-video" || - pipelineId === "reward-forcing") && ( -
-
-
- {pipelineId === "krea-realtime-video" && ( - parseFloat(v) || 1.0} - /> - )} -
- - {})} - variant="outline" - size="sm" - className="h-7" +
+
+ +
+
- -
- + + { + const value = parseInt(e.target.value); + if (!isNaN(value)) { + handleResolutionChange("width", value); + } + }} + disabled={isStreaming} + className="text-center border-0 focus-visible:ring-0 focus-visible:ring-offset-0 h-8 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none" + min={MIN_DIMENSION} + max={2048} />
+ {widthError && ( +

{widthError}

+ )}
)} - {(pipelineId === "longlive" || - pipelineId === "streamdiffusionv2" || - pipelineId === "krea-realtime-video" || - pipelineId === "reward-forcing") && ( + {/* Seed control */} + {pipelineShowsSeedControl(pipelineId) && ( +
+
+ +
+ + { + const value = parseInt(e.target.value); + if (!isNaN(value)) { + handleSeedChange(value); + } + }} + disabled={isStreaming} + className="text-center border-0 focus-visible:ring-0 focus-visible:ring-offset-0 h-8 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none" + min={0} + max={2147483647} + /> + +
+
+ {seedError && ( +

{seedError}

+ )} +
+ )} + + {/* KV Cache Attention Bias (krea-realtime-video specific) */} + {pipelineShowsKvCacheAttentionBias(pipelineId) && ( + parseFloat(v) || 1.0} + /> + )} + + {/* Cache management controls */} + {pipelineShowsCacheManagement(pipelineId) && ( +
+
+ + {})} + variant="outline" + size="sm" + className="h-7" + > + {manageCache ? "ON" : "OFF"} + +
+ +
+ + +
+
+ )} + + {/* Denoising steps slider */} + {pipelineShowsDenoisingSteps(pipelineId) && ( {})} @@ -564,84 +561,72 @@ export function SettingsPanel({ /> )} - {/* Noise controls - show for video mode on supported pipelines (schema-derived) */} - {inputMode === "video" && supportsNoiseControls && ( -
-
-
-
- - {})} - disabled={isStreaming} - variant="outline" - size="sm" - className="h-7" - > - {noiseController ? "ON" : "OFF"} - -
-
- - parseFloat(v) || 0.0} + {/* Noise controls - show for video mode on pipelines that support it */} + {inputMode === "video" && pipelineShowsNoiseControls(pipelineId) && ( +
+
+ + {})} + disabled={isStreaming} + variant="outline" + size="sm" + className="h-7" + > + {noiseController ? "ON" : "OFF"} +
+ + parseFloat(v) || 0.0} + />
)} - {(pipelineId === "longlive" || - pipelineId === "streamdiffusionv2" || - pipelineId === "krea-realtime-video" || - pipelineId === "reward-forcing") && ( -
-
-
-
- - -
-
-
+ {/* Quantization selector */} + {pipelineShowsQuantization(pipelineId) && ( +
+ +
)} diff --git a/frontend/src/data/pipelines.ts b/frontend/src/data/pipelines.ts index e380950e..52dbd320 100644 --- a/frontend/src/data/pipelines.ts +++ b/frontend/src/data/pipelines.ts @@ -8,6 +8,20 @@ export const DEFAULT_PROMPTS: Record = { "A 3D animated scene. A **panda** sitting in the grass, looking around.", }; +// UI capability flags - controls which UI elements are shown for each pipeline +export interface PipelineUICapabilities { + showTimeline?: boolean; // Show prompt timeline (default: true) + showPromptInput?: boolean; // Show prompt input controls (default: true) + showResolutionControl?: boolean; // Show resolution width/height controls + showSeedControl?: boolean; // Show seed control + showDenoisingSteps?: boolean; // Show denoising steps slider + showCacheManagement?: boolean; // Show cache management toggle and reset + showQuantization?: boolean; // Show quantization selector + showKvCacheAttentionBias?: boolean; // Show KV cache attention bias slider (krea-realtime-video) + showNoiseControls?: boolean; // Show noise scale and controller (video mode) + canChangeReferenceWhileStreaming?: boolean; // Allow changing reference image during stream +} + export interface PipelineInfo { name: string; about: string; @@ -19,10 +33,14 @@ export interface PipelineInfo { defaultTemporalInterpolationSteps?: number; // Default number of steps for temporal interpolation supportsLoRA?: boolean; // Whether this pipeline supports LoRA adapters requiresReferenceImage?: boolean; // Whether this pipeline requires a reference image (e.g. PersonaLive) + referenceImageDescription?: string; // Description of what the reference image is for // Multi-mode support supportedModes: InputMode[]; defaultMode: InputMode; + + // UI capabilities - controls which settings/controls are shown + ui?: PipelineUICapabilities; } export const PIPELINES: Record = { @@ -38,9 +56,18 @@ export const PIPELINES: Record = { defaultTemporalInterpolationMethod: "slerp", defaultTemporalInterpolationSteps: 0, supportsLoRA: true, - // Multi-mode support supportedModes: ["text", "video"], defaultMode: "video", + ui: { + showTimeline: true, + showPromptInput: true, + showResolutionControl: true, + showSeedControl: true, + showDenoisingSteps: true, + showCacheManagement: true, + showQuantization: true, + showNoiseControls: true, + }, }, longlive: { name: "LongLive", @@ -54,9 +81,18 @@ export const PIPELINES: Record = { defaultTemporalInterpolationMethod: "slerp", defaultTemporalInterpolationSteps: 0, supportsLoRA: true, - // Multi-mode support supportedModes: ["text", "video"], defaultMode: "text", + ui: { + showTimeline: true, + showPromptInput: true, + showResolutionControl: true, + showSeedControl: true, + showDenoisingSteps: true, + showCacheManagement: true, + showQuantization: true, + showNoiseControls: true, + }, }, "krea-realtime-video": { name: "Krea Realtime Video", @@ -70,9 +106,19 @@ export const PIPELINES: Record = { defaultTemporalInterpolationMethod: "linear", defaultTemporalInterpolationSteps: 4, supportsLoRA: true, - // Multi-mode support supportedModes: ["text", "video"], defaultMode: "text", + ui: { + showTimeline: true, + showPromptInput: true, + showResolutionControl: true, + showSeedControl: true, + showDenoisingSteps: true, + showCacheManagement: true, + showQuantization: true, + showKvCacheAttentionBias: true, + showNoiseControls: true, + }, }, "reward-forcing": { name: "RewardForcing", @@ -86,18 +132,36 @@ export const PIPELINES: Record = { defaultTemporalInterpolationMethod: "slerp", defaultTemporalInterpolationSteps: 0, supportsLoRA: true, - // Multi-mode support supportedModes: ["text", "video"], defaultMode: "text", + ui: { + showTimeline: true, + showPromptInput: true, + showResolutionControl: true, + showSeedControl: true, + showDenoisingSteps: true, + showCacheManagement: true, + showQuantization: true, + showNoiseControls: true, + }, }, passthrough: { name: "Passthrough", about: "A pipeline that returns the input video without any processing that is useful for testing and debugging.", requiresModels: false, - // Video-only pipeline supportedModes: ["video"], defaultMode: "video", + ui: { + showTimeline: false, + showPromptInput: false, + showResolutionControl: false, + showSeedControl: false, + showDenoisingSteps: false, + showCacheManagement: false, + showQuantization: false, + showNoiseControls: false, + }, }, personalive: { name: "PersonaLive", @@ -106,11 +170,22 @@ export const PIPELINES: Record = { "Real-time portrait animation pipeline from GVCLab. Animates a reference portrait image using driving video frames to transfer expressions and head movements.", estimatedVram: 12, requiresModels: true, - // Video-only pipeline - requires reference image + driving video supportedModes: ["video"], defaultMode: "video", - // PersonaLive requires a reference image to be uploaded first requiresReferenceImage: true, + referenceImageDescription: + "Portrait image to animate. Expressions and head movements from the driving video will be transferred to this image.", + ui: { + showTimeline: false, + showPromptInput: false, + showResolutionControl: true, + showSeedControl: true, + showDenoisingSteps: false, + showCacheManagement: false, + showQuantization: false, + showNoiseControls: false, + canChangeReferenceWhileStreaming: false, + }, }, }; @@ -141,3 +216,53 @@ export function getDefaultPromptForMode(mode: InputMode): string { export function pipelineRequiresReferenceImage(pipelineId: string): boolean { return PIPELINES[pipelineId]?.requiresReferenceImage === true; } + +export function getPipelineReferenceImageDescription( + pipelineId: string +): string | undefined { + return PIPELINES[pipelineId]?.referenceImageDescription; +} + +// UI capability helper functions + +export function pipelineShowsTimeline(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showTimeline !== false; +} + +export function pipelineShowsPromptInput(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showPromptInput !== false; +} + +export function pipelineShowsResolutionControl(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showResolutionControl === true; +} + +export function pipelineShowsSeedControl(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showSeedControl === true; +} + +export function pipelineShowsDenoisingSteps(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showDenoisingSteps === true; +} + +export function pipelineShowsCacheManagement(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showCacheManagement === true; +} + +export function pipelineShowsQuantization(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showQuantization === true; +} + +export function pipelineShowsKvCacheAttentionBias(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showKvCacheAttentionBias === true; +} + +export function pipelineShowsNoiseControls(pipelineId: string): boolean { + return PIPELINES[pipelineId]?.ui?.showNoiseControls === true; +} + +export function pipelineCanChangeReferenceWhileStreaming( + pipelineId: string +): boolean { + return PIPELINES[pipelineId]?.ui?.canChangeReferenceWhileStreaming !== false; +} diff --git a/frontend/src/pages/StreamPage.tsx b/frontend/src/pages/StreamPage.tsx index 0996119b..56698b7b 100644 --- a/frontend/src/pages/StreamPage.tsx +++ b/frontend/src/pages/StreamPage.tsx @@ -17,6 +17,7 @@ import { getPipelineDefaultMode, getDefaultPromptForMode, pipelineRequiresReferenceImage, + pipelineShowsTimeline, } from "../data/pipelines"; import type { InputMode, @@ -31,6 +32,7 @@ import { uploadPersonaLiveReference, } from "../lib/api"; import { sendLoRAScaleUpdates } from "../utils/loraHelpers"; +import { toast } from "sonner"; // Delay before resetting video reinitialization flag (ms) // This allows useVideoSource to detect the flag change and trigger reinitialization @@ -55,13 +57,8 @@ function buildLoRAParams( export function StreamPage() { // Use the stream state hook for settings management - const { - settings, - updateSettings, - getDefaults, - supportsNoiseControls, - spoutAvailable, - } = useStreamState(); + const { settings, updateSettings, getDefaults, spoutAvailable } = + useStreamState(); // Prompt state - use unified default prompts based on mode const initialMode = @@ -238,6 +235,10 @@ export function StreamPage() { }; const handlePipelineIdChange = (pipelineId: PipelineId) => { + console.log( + `[handlePipelineIdChange] Switching to pipeline: ${pipelineId}` + ); + // Stop the stream if it's currently running if (isStreaming) { stopStream(); @@ -634,6 +635,11 @@ export function StreamPage() { // Use override pipeline ID if provided, otherwise use current settings const pipelineIdToUse = overridePipelineId || settings.pipelineId; + // Debug: Log which pipeline is being started + console.log( + `[handleStartStream] Starting pipeline: ${pipelineIdToUse} (settings.pipelineId: ${settings.pipelineId}, override: ${overridePipelineId})` + ); + try { // Check if models are needed but not downloaded const pipelineInfo = PIPELINES[pipelineIdToUse]; @@ -642,6 +648,9 @@ export function StreamPage() { const status = await checkModelStatus(pipelineIdToUse); if (!status.downloaded) { // Show download dialog + console.log( + `[handleStartStream] Models not downloaded for: ${pipelineIdToUse}, showing download dialog` + ); setPipelineNeedsModels(pipelineIdToUse); setShowDownloadDialog(true); return false; // Stream did not start @@ -652,9 +661,6 @@ export function StreamPage() { } } - // Always load pipeline with current parameters - backend will handle the rest - console.log(`Loading ${pipelineIdToUse} pipeline...`); - // Determine current input mode const currentMode = settings.inputMode || getPipelineDefaultMode(pipelineIdToUse) || "text"; @@ -723,9 +729,11 @@ export function StreamPage() { } else if (pipelineIdToUse === "personalive" && resolution) { // PersonaLive requires a reference image if (!referenceImage) { - console.error( - "PersonaLive requires a reference image. Please upload one first." - ); + toast.error("Reference Image Required", { + description: + "Please upload a reference portrait image before starting PersonaLive.", + duration: 5000, + }); return false; } loadParams = { @@ -929,15 +937,27 @@ export function StreamPage() { isPlaying={!settings.paused} isDownloading={isDownloading} onPlayPauseToggle={() => { - // Use timeline's play/pause handler instead of direct video toggle - if (timelinePlayPauseRef.current) { + // For pipelines with timeline, use timeline's play/pause handler + // For pipelines without timeline (PersonaLive, Passthrough), toggle directly + if ( + pipelineShowsTimeline(settings.pipelineId) && + timelinePlayPauseRef.current + ) { timelinePlayPauseRef.current(); + } else { + handlePlayPauseToggle(); } }} onStartStream={() => { - // Use timeline's play/pause handler to start stream - if (timelinePlayPauseRef.current) { + // For pipelines with timeline, use timeline's play/pause handler + // For pipelines without timeline (PersonaLive, Passthrough), start directly + if ( + pipelineShowsTimeline(settings.pipelineId) && + timelinePlayPauseRef.current + ) { timelinePlayPauseRef.current(); + } else { + handleStartStream(); } }} onVideoPlaying={() => { @@ -949,111 +969,113 @@ export function StreamPage() { }} />
- {/* Timeline area - compact, always visible */} -
- { - // Update the left panel's prompt state to reflect current timeline prompt - const prompts = [{ text, weight: 100 }]; - setPromptItems(prompts); - - // Send to backend - use transition if streaming and transition steps > 0 - if (isStreaming && transitionSteps > 0) { - sendParameterUpdate({ - transition: { - target_prompts: prompts, - num_steps: transitionSteps, - temporal_interpolation_method: - temporalInterpolationMethod, - }, - }); - } else { - // Send direct prompts without transition - sendParameterUpdate({ - prompts, - prompt_interpolation_method: interpolationMethod, - denoising_step_list: settings.denoisingSteps || [700, 500], - }); - } - }} - onPromptItemsSubmit={( - prompts, - blockTransitionSteps, - blockTemporalInterpolationMethod - ) => { - // Update the left panel's prompt state to reflect current timeline prompt blend - setPromptItems(prompts); - - // Use transition params from block if provided, otherwise use global settings - const effectiveTransitionSteps = - blockTransitionSteps ?? transitionSteps; - const effectiveTemporalInterpolationMethod = - blockTemporalInterpolationMethod ?? - temporalInterpolationMethod; - - // Update the left panel's transition settings to reflect current block's values - if (blockTransitionSteps !== undefined) { - setTransitionSteps(blockTransitionSteps); + {/* Timeline area - only show for pipelines that support text prompts */} + {pipelineShowsTimeline(settings.pipelineId) && ( +
+ { + // Update the left panel's prompt state to reflect current timeline prompt + const prompts = [{ text, weight: 100 }]; + setPromptItems(prompts); + + // Send to backend - use transition if streaming and transition steps > 0 + if (isStreaming && transitionSteps > 0) { + sendParameterUpdate({ + transition: { + target_prompts: prompts, + num_steps: transitionSteps, + temporal_interpolation_method: + temporalInterpolationMethod, + }, + }); + } else { + // Send direct prompts without transition + sendParameterUpdate({ + prompts, + prompt_interpolation_method: interpolationMethod, + denoising_step_list: settings.denoisingSteps || [ + 700, 500, + ], + }); + } + }} + onPromptItemsSubmit={( + prompts, + blockTransitionSteps, + blockTemporalInterpolationMethod + ) => { + // Update the left panel's prompt state to reflect current timeline prompt blend + setPromptItems(prompts); + + // Use transition params from block if provided, otherwise use global settings + const effectiveTransitionSteps = + blockTransitionSteps ?? transitionSteps; + const effectiveTemporalInterpolationMethod = + blockTemporalInterpolationMethod ?? + temporalInterpolationMethod; + + // Update the left panel's transition settings to reflect current block's values + if (blockTransitionSteps !== undefined) { + setTransitionSteps(blockTransitionSteps); + } + if (blockTemporalInterpolationMethod !== undefined) { + setTemporalInterpolationMethod( + blockTemporalInterpolationMethod + ); + } + + // Send to backend - use transition if streaming and transition steps > 0 + if (isStreaming && effectiveTransitionSteps > 0) { + sendParameterUpdate({ + transition: { + target_prompts: prompts, + num_steps: effectiveTransitionSteps, + temporal_interpolation_method: + effectiveTemporalInterpolationMethod, + }, + }); + } else { + // Send direct prompts without transition + sendParameterUpdate({ + prompts, + prompt_interpolation_method: interpolationMethod, + denoising_step_list: settings.denoisingSteps || [ + 700, 500, + ], + }); + } + }} + disabled={ + isPipelineLoading || isConnecting || showDownloadDialog } - if (blockTemporalInterpolationMethod !== undefined) { - setTemporalInterpolationMethod( - blockTemporalInterpolationMethod - ); - } - - // Send to backend - use transition if streaming and transition steps > 0 - if (isStreaming && effectiveTransitionSteps > 0) { - sendParameterUpdate({ - transition: { - target_prompts: prompts, - num_steps: effectiveTransitionSteps, - temporal_interpolation_method: - effectiveTemporalInterpolationMethod, - }, - }); - } else { - // Send direct prompts without transition - sendParameterUpdate({ - prompts, - prompt_interpolation_method: interpolationMethod, - denoising_step_list: settings.denoisingSteps || [700, 500], - }); - } - }} - disabled={ - settings.pipelineId === "passthrough" || - settings.pipelineId === "personalive" || - isPipelineLoading || - isConnecting || - showDownloadDialog - } - isStreaming={isStreaming} - isVideoPaused={settings.paused} - timelineRef={timelineRef} - onLiveStateChange={setIsLive} - onLivePromptSubmit={handleLivePromptSubmit} - onDisconnect={stopStream} - onStartStream={handleStartStream} - onVideoPlayPauseToggle={handlePlayPauseToggle} - onPromptEdit={handleTimelinePromptEdit} - isCollapsed={isTimelineCollapsed} - onCollapseToggle={setIsTimelineCollapsed} - externalSelectedPromptId={externalSelectedPromptId} - settings={settings} - onSettingsImport={updateSettings} - onPlayPauseRef={timelinePlayPauseRef} - onVideoPlayingCallbackRef={onVideoPlayingCallbackRef} - onResetCache={handleResetCache} - onTimelinePromptsChange={handleTimelinePromptsChange} - onTimelineCurrentTimeChange={handleTimelineCurrentTimeChange} - onTimelinePlayingChange={handleTimelinePlayingChange} - isDownloading={isDownloading} - /> -
+ isStreaming={isStreaming} + isVideoPaused={settings.paused} + timelineRef={timelineRef} + onLiveStateChange={setIsLive} + onLivePromptSubmit={handleLivePromptSubmit} + onDisconnect={stopStream} + onStartStream={handleStartStream} + onVideoPlayPauseToggle={handlePlayPauseToggle} + onPromptEdit={handleTimelinePromptEdit} + isCollapsed={isTimelineCollapsed} + onCollapseToggle={setIsTimelineCollapsed} + externalSelectedPromptId={externalSelectedPromptId} + settings={settings} + onSettingsImport={updateSettings} + onPlayPauseRef={timelinePlayPauseRef} + onVideoPlayingCallbackRef={onVideoPlayingCallbackRef} + onResetCache={handleResetCache} + onTimelinePromptsChange={handleTimelinePromptsChange} + onTimelineCurrentTimeChange={handleTimelineCurrentTimeChange} + onTimelinePlayingChange={handleTimelinePlayingChange} + isDownloading={isDownloading} + /> +
+ )}
{/* Right Panel - Settings */} @@ -1104,7 +1126,6 @@ export function StreamPage() { onLorasChange={handleLorasChange} loraMergeStrategy={settings.loraMergeStrategy ?? "permanent_merge"} inputMode={settings.inputMode} - supportsNoiseControls={supportsNoiseControls(settings.pipelineId)} spoutSender={settings.spoutSender} onSpoutSenderChange={handleSpoutSenderChange} spoutAvailable={spoutAvailable} diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index ac4c0c74..88d661c3 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -76,6 +76,20 @@ export interface SettingsState { }; } +// UI capability flags - controls which UI elements are shown for each pipeline +export interface PipelineUICapabilities { + showTimeline?: boolean; // Show prompt timeline (default: true) + showPromptInput?: boolean; // Show prompt input controls (default: true) + showResolutionControl?: boolean; // Show resolution width/height controls + showSeedControl?: boolean; // Show seed control + showDenoisingSteps?: boolean; // Show denoising steps slider + showCacheManagement?: boolean; // Show cache management toggle and reset + showQuantization?: boolean; // Show quantization selector + showKvCacheAttentionBias?: boolean; // Show KV cache attention bias slider (krea-realtime-video) + showNoiseControls?: boolean; // Show noise scale and controller (video mode) + canChangeReferenceWhileStreaming?: boolean; // Allow changing reference image during stream +} + export interface PipelineInfo { name: string; about: string; @@ -89,8 +103,12 @@ export interface PipelineInfo { defaultTemporalInterpolationSteps?: number; supportsLoRA?: boolean; requiresReferenceImage?: boolean; // Whether this pipeline requires a reference image (e.g. PersonaLive) + referenceImageDescription?: string; // Description of what the reference image is for // Multi-mode support supportedModes: InputMode[]; defaultMode: InputMode; + + // UI capabilities - controls which settings/controls are shown + ui?: PipelineUICapabilities; } diff --git a/src/scope/core/pipelines/personalive/__init__.py b/src/scope/core/pipelines/personalive/__init__.py index f3995ef9..266517a5 100644 --- a/src/scope/core/pipelines/personalive/__init__.py +++ b/src/scope/core/pipelines/personalive/__init__.py @@ -3,3 +3,4 @@ from .pipeline import PersonaLivePipeline __all__ = ["PersonaLivePipeline"] + diff --git a/src/scope/core/pipelines/personalive/blocks/__init__.py b/src/scope/core/pipelines/personalive/blocks/__init__.py index fdb7b942..ebc248ee 100644 --- a/src/scope/core/pipelines/personalive/blocks/__init__.py +++ b/src/scope/core/pipelines/personalive/blocks/__init__.py @@ -1 +1,2 @@ """PersonaLive modular blocks for pipeline composition.""" + diff --git a/src/scope/core/pipelines/personalive/components/face_detector.py b/src/scope/core/pipelines/personalive/components/face_detector.py index 9724c95c..e011aac3 100644 --- a/src/scope/core/pipelines/personalive/components/face_detector.py +++ b/src/scope/core/pipelines/personalive/components/face_detector.py @@ -197,3 +197,4 @@ def close(self): def __del__(self): self.close() + diff --git a/src/scope/core/pipelines/personalive/docs/README.md b/src/scope/core/pipelines/personalive/docs/README.md index da470be1..affdfad0 100644 --- a/src/scope/core/pipelines/personalive/docs/README.md +++ b/src/scope/core/pipelines/personalive/docs/README.md @@ -122,3 +122,4 @@ PersonaLive uses a multi-component architecture: ## Credits Based on the PersonaLive implementation by GVCLab. + diff --git a/src/scope/core/pipelines/personalive/tensorrt/builder.py b/src/scope/core/pipelines/personalive/tensorrt/builder.py index 12e63f16..a97204be 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/builder.py +++ b/src/scope/core/pipelines/personalive/tensorrt/builder.py @@ -241,3 +241,4 @@ def is_engine_available(model_dir: Path, height: int = 512, width: int = 512) -> """ engine_path = get_engine_path(model_dir, height, width) return engine_path.exists() + From edf2b786909ff10e2c0ed472695e1e76799ea426 Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Wed, 17 Dec 2025 17:38:56 -0500 Subject: [PATCH 6/7] Linting and formatting. Signed-off-by: BuffMcBigHuge --- frontend/src/components/SettingsPanel.tsx | 4 +- .../core/pipelines/personalive/__init__.py | 1 - .../pipelines/personalive/blocks/__init__.py | 1 - .../personalive/components/face_detector.py | 8 +- .../core/pipelines/personalive/pipeline.py | 210 +++++++++++++----- .../personalive/tensorrt/__init__.py | 14 +- .../pipelines/personalive/tensorrt/builder.py | 24 +- .../pipelines/personalive/tensorrt/convert.py | 19 +- .../personalive/tensorrt/engine_model.py | 82 +++++-- .../personalive/tensorrt/framed_model.py | 91 ++++++-- .../pipelines/personalive/tensorrt/runner.py | 26 ++- .../personalive/tensorrt/unet_explicit.py | 29 ++- src/scope/server/app.py | 11 +- src/scope/server/download_models.py | 4 +- src/scope/server/models_config.py | 48 +++- src/scope/server/schema.py | 4 +- 16 files changed, 428 insertions(+), 148 deletions(-) diff --git a/frontend/src/components/SettingsPanel.tsx b/frontend/src/components/SettingsPanel.tsx index 2d7213c5..613118c7 100644 --- a/frontend/src/components/SettingsPanel.tsx +++ b/frontend/src/components/SettingsPanel.tsx @@ -622,9 +622,7 @@ export function SettingsPanel({ None - - fp8_e4m3fn (Dynamic) - + fp8_e4m3fn (Dynamic)
diff --git a/src/scope/core/pipelines/personalive/__init__.py b/src/scope/core/pipelines/personalive/__init__.py index 266517a5..f3995ef9 100644 --- a/src/scope/core/pipelines/personalive/__init__.py +++ b/src/scope/core/pipelines/personalive/__init__.py @@ -3,4 +3,3 @@ from .pipeline import PersonaLivePipeline __all__ = ["PersonaLivePipeline"] - diff --git a/src/scope/core/pipelines/personalive/blocks/__init__.py b/src/scope/core/pipelines/personalive/blocks/__init__.py index ebc248ee..fdb7b942 100644 --- a/src/scope/core/pipelines/personalive/blocks/__init__.py +++ b/src/scope/core/pipelines/personalive/blocks/__init__.py @@ -1,2 +1 @@ """PersonaLive modular blocks for pipeline composition.""" - diff --git a/src/scope/core/pipelines/personalive/components/face_detector.py b/src/scope/core/pipelines/personalive/components/face_detector.py index e011aac3..bea0e23c 100644 --- a/src/scope/core/pipelines/personalive/components/face_detector.py +++ b/src/scope/core/pipelines/personalive/components/face_detector.py @@ -10,6 +10,7 @@ try: import mediapipe as mp + MEDIAPIPE_AVAILABLE = True except ImportError: MEDIAPIPE_AVAILABLE = False @@ -58,9 +59,7 @@ def detect_face_landmarks(self, image: np.ndarray) -> list | None: return None def get_face_bounding_box( - self, - image: np.ndarray, - padding_ratio: float = 0.2 + self, image: np.ndarray, padding_ratio: float = 0.2 ) -> Tuple[int, int, int, int] | None: """Get bounding box for the detected face. @@ -192,9 +191,8 @@ def crop_face_tensor( def close(self): """Release resources.""" - if hasattr(self, 'face_mesh') and self.face_mesh: + if hasattr(self, "face_mesh") and self.face_mesh: self.face_mesh.close() def __del__(self): self.close() - diff --git a/src/scope/core/pipelines/personalive/pipeline.py b/src/scope/core/pipelines/personalive/pipeline.py index 85bfae4e..c8904e8f 100644 --- a/src/scope/core/pipelines/personalive/pipeline.py +++ b/src/scope/core/pipelines/personalive/pipeline.py @@ -84,6 +84,7 @@ def _profile_stage(name: str): elapsed = time.perf_counter() - start _profiling_timings[name].append(elapsed) + # TensorRT support (optional) try: from .tensorrt import TRT_AVAILABLE, TRTRunner, get_engine_path, PYCUDA_AVAILABLE @@ -199,7 +200,9 @@ def __init__( # Load pose encoder (motion extractor) start = time.time() - self.pose_encoder = MotionExtractor(num_kp=21).to(device=device, dtype=dtype).eval() + self.pose_encoder = ( + MotionExtractor(num_kp=21).to(device=device, dtype=dtype).eval() + ) pose_encoder_path = personalive_weights / "motion_extractor.pth" if pose_encoder_path.exists(): state_dict = torch.load(pose_encoder_path, map_location="cpu") @@ -291,7 +294,9 @@ def __init__( ) self.clip_image_processor = CLIPImageProcessor() self.cond_image_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=True + vae_scale_factor=self.vae_scale_factor, + do_convert_rgb=True, + do_normalize=True, ) # Face detector @@ -365,14 +370,18 @@ def _init_tensorrt(self, model_dir: Path): self._use_engine_model = True # Setup output-to-input bindings for recurrent state (zero-copy) - self.trt_runner.bind({ - "motion_hidden_states_out": "motion_hidden_states", - "pose_cond_fea_out": "pose_cond_fea", - "latents": "sample", - }) + self.trt_runner.bind( + { + "motion_hidden_states_out": "motion_hidden_states", + "pose_cond_fea_out": "pose_cond_fea", + "latents": "sample", + } + ) self.use_tensorrt = True - logger.info(f"TensorRT engine loaded with pycuda (EngineModel) from {engine_path}") + logger.info( + f"TensorRT engine loaded with pycuda (EngineModel) from {engine_path}" + ) return except Exception as e: @@ -385,14 +394,18 @@ def _init_tensorrt(self, model_dir: Path): self._use_engine_model = False # Setup output-to-input bindings for recurrent state - self.trt_runner.bind({ - "motion_hidden_states_out": "motion_hidden_states", - "pose_cond_fea_out": "pose_cond_fea", - "latents": "sample", - }) + self.trt_runner.bind( + { + "motion_hidden_states_out": "motion_hidden_states", + "pose_cond_fea_out": "pose_cond_fea", + "latents": "sample", + } + ) self.use_tensorrt = True - logger.info(f"TensorRT engine loaded with polygraphy (TRTRunner) from {engine_path}") + logger.info( + f"TensorRT engine loaded with polygraphy (TRTRunner) from {engine_path}" + ) except Exception as e: logger.warning(f"Failed to load TensorRT engine: {e}") @@ -417,7 +430,9 @@ def prepare(self, **kwargs) -> Requirements | None: return Requirements(input_size=self.temporal_window_size) return None - def _fast_resize(self, images: torch.Tensor, height: int, width: int) -> torch.Tensor: + def _fast_resize( + self, images: torch.Tensor, height: int, width: int + ) -> torch.Tensor: """Fast bilinear resize of image tensor.""" return F.interpolate( images, @@ -426,7 +441,9 @@ def _fast_resize(self, images: torch.Tensor, height: int, width: int) -> torch.T align_corners=False, ) - def _interpolate_tensors(self, a: torch.Tensor, b: torch.Tensor, num: int) -> torch.Tensor: + def _interpolate_tensors( + self, a: torch.Tensor, b: torch.Tensor, num: int + ) -> torch.Tensor: """Linear interpolation between tensors a and b.""" if a.shape != b.shape: raise ValueError(f"Shape mismatch: a.shape={a.shape}, b.shape={b.shape}") @@ -447,7 +464,9 @@ def _calculate_dis(self, A: torch.Tensor, B: torch.Tensor, threshold: float = 10 dist = torch.cdist(B_flat.to(torch.float32), A_flat.to(torch.float32), p=2) min_dist, min_idx = dist.min(dim=1) - idx_to_add = torch.nonzero(min_dist[:1] > threshold, as_tuple=False).squeeze(1).tolist() + idx_to_add = ( + torch.nonzero(min_dist[:1] > threshold, as_tuple=False).squeeze(1).tolist() + ) if len(idx_to_add) > 0: B_to_add = B[:, idx_to_add] @@ -499,7 +518,9 @@ def fuse_reference(self, ref_image: Image.Image): self.encoder_hidden_states = clip_image_embeds.unsqueeze(1) # Encode reference image - ref_image_tensor = ref_image_tensor.to(dtype=self.vae.dtype, device=self.vae.device) + ref_image_tensor = ref_image_tensor.to( + dtype=self.vae.dtype, device=self.vae.device + ) self.ref_image_tensor = ref_image_tensor.squeeze(0) ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean ref_image_latents = ref_image_latents * 0.18215 @@ -509,7 +530,9 @@ def fuse_reference(self, ref_image: Image.Image): self.reference_control_writer.clear() self.reference_unet( ref_image_latents.to(self.reference_unet.device), - torch.zeros((self.batch_size,), dtype=self.dtype, device=self.reference_unet.device), + torch.zeros( + (self.batch_size,), dtype=self.dtype, device=self.reference_unet.device + ), encoder_hidden_states=self.encoder_hidden_states, return_dict=False, ) @@ -521,14 +544,28 @@ def fuse_reference(self, ref_image: Image.Image): self._reference_hidden_states = reference_hidden_states # Clear previous prefill values (TRTRunner only - EngineModel persists buffers) - if hasattr(self.trt_runner, 'clear_prefill'): + if hasattr(self.trt_runner, "clear_prefill"): self.trt_runner.clear_prefill() self.trt_runner.prefill(encoder_hidden_states=self.encoder_hidden_states) # Prefill reference hidden states ref_hidden_names = [ - "d00", "d01", "d10", "d11", "d20", "d21", "m", - "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" + "d00", + "d01", + "d10", + "d11", + "d20", + "d21", + "m", + "u10", + "u11", + "u12", + "u20", + "u21", + "u22", + "u30", + "u31", + "u32", ] for name in ref_hidden_names: if name in reference_hidden_states: @@ -559,8 +596,12 @@ def fuse_reference(self, ref_image: Image.Image): padding_num = (self.temporal_adaptive_step - 1) * self.temporal_window_size init_latents = ref_image_latents.unsqueeze(2).repeat(1, 1, padding_num, 1, 1) noise = torch.randn_like(init_latents) - init_timesteps = reversed(self.timesteps).repeat_interleave(self.temporal_window_size, dim=0) - noisy_latents_first = self.scheduler.add_noise(init_latents, noise, init_timesteps[:padding_num]) + init_timesteps = reversed(self.timesteps).repeat_interleave( + self.temporal_window_size, dim=0 + ) + noisy_latents_first = self.scheduler.add_noise( + init_latents, noise, init_timesteps[:padding_num] + ) for i in range(self.temporal_adaptive_step - 1): l = i * self.temporal_window_size @@ -575,9 +616,13 @@ def fuse_reference(self, ref_image: Image.Image): 1, 1, self.temporal_window_size, 1, 1 ) noise = torch.randn_like(new_latents) - new_latents = self.scheduler.add_noise(new_latents, noise, self.timesteps[-1:]) + new_latents = self.scheduler.add_noise( + new_latents, noise, self.timesteps[-1:] + ) sample = torch.cat([sample, new_latents], dim=2) - self.trt_runner.prefill(sample=sample) # Use 'sample' (input name), not 'latents' (output name) + self.trt_runner.prefill( + sample=sample + ) # Use 'sample' (input name), not 'latents' (output name) self.reference_fused = True logger.info("Reference image fused successfully") @@ -683,8 +728,10 @@ def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: tgt_cond_tensor = self._fast_resize(images, 256, 256).clamp(0, 1) if self.first_frame: - mot_bbox_param, kps_ref, kps_frame1, kps_dri = self.pose_encoder.interpolate_kps_online( - self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + mot_bbox_param, kps_ref, kps_frame1, kps_dri = ( + self.pose_encoder.interpolate_kps_online( + self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + ) ) self.kps_ref = kps_ref self.kps_frame1 = kps_frame1 @@ -697,7 +744,7 @@ def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: with _profile_stage("keypoint_drawing"): keypoints = draw_keypoints(mot_bbox_param, device=device) boxes = get_boxes(kps_dri) - keypoints = rearrange(keypoints.unsqueeze(2), 'f c b h w -> b c f h w') + keypoints = rearrange(keypoints.unsqueeze(2), "f c b h w -> b c f h w") keypoints = keypoints.to(device=device, dtype=self.dtype) # Face cropping for motion input @@ -733,7 +780,9 @@ def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: # Prefill TensorRT with initial motion and pose states self.trt_runner.prefill( motion_hidden_states=init_motion_hidden_states, - pose_cond_fea=pose_fea[:, :, :temporal_window_size * (temporal_adaptive_step - 1)], + pose_cond_fea=pose_fea[ + :, :, : temporal_window_size * (temporal_adaptive_step - 1) + ], ) self.motion_bank = ref_motion @@ -750,9 +799,13 @@ def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: # Prepare noise with _profile_stage("noise_generation"): new_noise = torch.randn( - batch_size, 4, temporal_window_size, - self.height // 8, self.width // 8, - device=device, dtype=self.dtype + batch_size, + 4, + temporal_window_size, + self.height // 8, + self.width // 8, + device=device, + dtype=self.dtype, ) # TensorRT inference (includes motion_encoder, pose_guider, denoising_unet, vae_decode) @@ -801,15 +854,38 @@ def _process_frames_trt(self, images: torch.Tensor) -> torch.Tensor: # Concatenate new keyframe features ref_hidden_names = [ - "d00", "d01", "d10", "d11", "d20", "d21", "m", - "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" + "d00", + "d01", + "d10", + "d11", + "d20", + "d21", + "m", + "u10", + "u11", + "u12", + "u20", + "u21", + "u22", + "u30", + "u31", + "u32", ] for name in ref_hidden_names: - if name in self._reference_hidden_states and name in new_ref_hidden: + if ( + name in self._reference_hidden_states + and name in new_ref_hidden + ): self._reference_hidden_states[name] = torch.cat( - [self._reference_hidden_states[name], new_ref_hidden[name]], dim=1 + [ + self._reference_hidden_states[name], + new_ref_hidden[name], + ], + dim=1, + ) + self.trt_runner.prefill( + **{name: self._reference_hidden_states[name]} ) - self.trt_runner.prefill(**{name: self._reference_hidden_states[name]}) logger.debug("Added history keyframe (TensorRT)") self.num_khf += 1 @@ -866,8 +942,10 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: # Get keypoints if self.first_frame: - mot_bbox_param, kps_ref, kps_frame1, kps_dri = self.pose_encoder.interpolate_kps_online( - self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + mot_bbox_param, kps_ref, kps_frame1, kps_dri = ( + self.pose_encoder.interpolate_kps_online( + self.ref_cond_tensor, tgt_cond_tensor, num_interp=12 + 1 + ) ) self.kps_ref = kps_ref self.kps_frame1 = kps_frame1 @@ -879,7 +957,7 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: # Draw keypoints and get bounding boxes keypoints = draw_keypoints(mot_bbox_param, device=device) boxes = get_boxes(kps_dri) - keypoints = rearrange(keypoints.unsqueeze(2), 'f c b h w -> b c f h w') + keypoints = rearrange(keypoints.unsqueeze(2), "f c b h w -> b c f h w") keypoints = keypoints.to(device=device, dtype=self.pose_guider.dtype) # Process motion features @@ -926,7 +1004,9 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: self.pose_pile.append(pose_fea) # Prepare noisy latents for new frames - latents = self.ref_image_latents.unsqueeze(2).repeat(1, 1, temporal_window_size, 1, 1) + latents = self.ref_image_latents.unsqueeze(2).repeat( + 1, 1, temporal_window_size, 1, 1 + ) noise = torch.randn_like(latents) latents = self.scheduler.add_noise(latents, noise, self.timesteps[:1]) self.latents_pile.append(latents) @@ -946,9 +1026,11 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: # Denoise latents_model_input = torch.cat(list(self.latents_pile), dim=2) for j in range(jump): - timesteps = reversed(self.timesteps[j::jump]).repeat_interleave(temporal_window_size, dim=0) + timesteps = reversed(self.timesteps[j::jump]).repeat_interleave( + temporal_window_size, dim=0 + ) timesteps = torch.stack([timesteps] * batch_size) - timesteps = rearrange(timesteps, 'b f -> (b f)') + timesteps = rearrange(timesteps, "b f -> (b f)") noise_pred = self.denoising_unet( latents_model_input, @@ -962,18 +1044,28 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: )[0] clip_length = noise_pred.shape[2] - mid_noise_pred = rearrange(noise_pred, 'b c f h w -> (b f) c h w') - mid_latents = rearrange(latents_model_input, 'b c f h w -> (b f) c h w') + mid_noise_pred = rearrange(noise_pred, "b c f h w -> (b f) c h w") + mid_latents = rearrange(latents_model_input, "b c f h w -> (b f) c h w") latents_model_input, pred_original_sample = self.scheduler.step( - mid_noise_pred, timesteps, mid_latents, - generator=self.generator, return_dict=False + mid_noise_pred, + timesteps, + mid_latents, + generator=self.generator, + return_dict=False, + ) + latents_model_input = rearrange( + latents_model_input, "(b f) c h w -> b c f h w", f=clip_length + ) + pred_original_sample = rearrange( + pred_original_sample, "(b f) c h w -> b c f h w", f=clip_length + ) + latents_model_input = torch.cat( + [ + pred_original_sample[:, :, :temporal_window_size], + latents_model_input[:, :, temporal_window_size:], + ], + dim=2, ) - latents_model_input = rearrange(latents_model_input, '(b f) c h w -> b c f h w', f=clip_length) - pred_original_sample = rearrange(pred_original_sample, '(b f) c h w -> b c f h w', f=clip_length) - latents_model_input = torch.cat([ - pred_original_sample[:, :, :temporal_window_size], - latents_model_input[:, :, temporal_window_size:] - ], dim=2) latents_model_input = latents_model_input.to(dtype=self.dtype) # History keyframe mechanism @@ -981,7 +1073,9 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: self.reference_control_writer.clear() self.reference_unet( pred_original_sample[:, :, 0].to(self.reference_unet.dtype), - torch.zeros((batch_size,), dtype=self.dtype, device=self.reference_unet.device), + torch.zeros( + (batch_size,), dtype=self.dtype, device=self.reference_unet.device + ), encoder_hidden_states=self.encoder_hidden_states, return_dict=False, ) @@ -992,7 +1086,11 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: # Update latent piles for i in range(len(self.latents_pile)): self.latents_pile[i] = latents_model_input[ - :, :, i * temporal_adaptive_step:(i + 1) * temporal_adaptive_step, :, : + :, + :, + i * temporal_adaptive_step : (i + 1) * temporal_adaptive_step, + :, + :, ] # Pop oldest latents and decode diff --git a/src/scope/core/pipelines/personalive/tensorrt/__init__.py b/src/scope/core/pipelines/personalive/tensorrt/__init__.py index d1bae587..acb1b235 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/__init__.py +++ b/src/scope/core/pipelines/personalive/tensorrt/__init__.py @@ -30,6 +30,7 @@ # Check for zero-copy CUDA buffer support (polygraphy) try: from polygraphy.cuda import DeviceView # noqa: F401 + CUDA_BUFFERS_AVAILABLE = True except ImportError: pass @@ -38,6 +39,7 @@ try: import pycuda.driver # noqa: F401 import pycuda.autoinit # noqa: F401 + PYCUDA_AVAILABLE = True except ImportError: pass @@ -68,17 +70,23 @@ else: # Provide stub functions when TRT is not available def get_engine_path(*args, **kwargs): - raise RuntimeError("TensorRT not available. Install with: pip install daydream-scope[tensorrt]") + raise RuntimeError( + "TensorRT not available. Install with: pip install daydream-scope[tensorrt]" + ) def build_engine(*args, **kwargs): - raise RuntimeError("TensorRT not available. Install with: pip install daydream-scope[tensorrt]") + raise RuntimeError( + "TensorRT not available. Install with: pip install daydream-scope[tensorrt]" + ) def is_engine_available(*args, **kwargs): return False class TRTRunner: def __init__(self, *args, **kwargs): - raise RuntimeError("TensorRT not available. Install with: pip install daydream-scope[tensorrt]") + raise RuntimeError( + "TensorRT not available. Install with: pip install daydream-scope[tensorrt]" + ) __all__ = [ "build_engine", diff --git a/src/scope/core/pipelines/personalive/tensorrt/builder.py b/src/scope/core/pipelines/personalive/tensorrt/builder.py index a97204be..67dfbcbd 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/builder.py +++ b/src/scope/core/pipelines/personalive/tensorrt/builder.py @@ -85,7 +85,9 @@ def create_optimization_profile( Polygraphy Profile object. """ if not TRT_AVAILABLE: - raise RuntimeError("TensorRT is not available. Install with: pip install daydream-scope[tensorrt]") + raise RuntimeError( + "TensorRT is not available. Install with: pip install daydream-scope[tensorrt]" + ) # Derived dimensions tb = temporal_window_size * temporal_adaptive_step # temporal batch size (16) @@ -105,9 +107,20 @@ def create_optimization_profile( fixed_inputs = { "sample": (batch_size, lc, tb, lh, lw), "encoder_hidden_states": (batch_size, 1, emb), - "motion_hidden_states": (batch_size, temporal_window_size * (temporal_adaptive_step - 1), ml, mc), + "motion_hidden_states": ( + batch_size, + temporal_window_size * (temporal_adaptive_step - 1), + ml, + mc, + ), "motion": (batch_size, ic, temporal_window_size, mh, mw), - "pose_cond_fea": (batch_size, cd0, temporal_window_size * (temporal_adaptive_step - 1), lh, lw), + "pose_cond_fea": ( + batch_size, + cd0, + temporal_window_size * (temporal_adaptive_step - 1), + lh, + lw, + ), "pose": (batch_size, ic, temporal_window_size, height, width), "new_noise": (batch_size, lc, temporal_window_size, lh, lw), } @@ -177,7 +190,9 @@ def build_engine( FileNotFoundError: If ONNX model doesn't exist. """ if not TRT_AVAILABLE: - raise RuntimeError("TensorRT is not available. Install with: pip install daydream-scope[tensorrt]") + raise RuntimeError( + "TensorRT is not available. Install with: pip install daydream-scope[tensorrt]" + ) onnx_path = Path(onnx_path) engine_path = Path(engine_path) @@ -241,4 +256,3 @@ def is_engine_available(model_dir: Path, height: int = 512, width: int = 512) -> """ engine_path = get_engine_path(model_dir, height, width) return engine_path.exists() - diff --git a/src/scope/core/pipelines/personalive/tensorrt/convert.py b/src/scope/core/pipelines/personalive/tensorrt/convert.py index 390e75f5..a0e260a5 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/convert.py +++ b/src/scope/core/pipelines/personalive/tensorrt/convert.py @@ -35,7 +35,9 @@ def log_gpu_memory(msg: str = ""): if torch.cuda.is_available(): allocated = torch.cuda.memory_allocated() / 1024**3 reserved = torch.cuda.memory_reserved() / 1024**3 - logger.info(f"GPU Memory {msg}: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved") + logger.info( + f"GPU Memory {msg}: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved" + ) def parse_args(): @@ -103,6 +105,7 @@ def load_models(model_dir: Path, device: torch.device, dtype: torch.dtype): MotEncoder, PoseGuider, ) + # Use explicit reference UNet for TensorRT export from .unet_explicit import UNet3DConditionModelExplicit @@ -182,7 +185,9 @@ def cleanup(): # Load VAE logger.info("Loading VAE...") - vae = AutoencoderKL.from_pretrained(str(vae_path)).to(device=device, dtype=export_dtype) + vae = AutoencoderKL.from_pretrained(str(vae_path)).to( + device=device, dtype=export_dtype + ) vae.set_default_attn_processor() cleanup() @@ -295,7 +300,9 @@ def main(): logger.error(f"Model directory not found: {args.model_dir}") sys.exit(1) - personalive_weights = args.model_dir / "PersonaLive" / "pretrained_weights" / "personalive" + personalive_weights = ( + args.model_dir / "PersonaLive" / "pretrained_weights" / "personalive" + ) if not personalive_weights.exists(): logger.error(f"PersonaLive weights not found at: {personalive_weights}") logger.error("Please download PersonaLive models first using: download_models") @@ -323,7 +330,9 @@ def main(): logger.info(f"Converting PersonaLive models to TensorRT") logger.info(f" Model directory: {args.model_dir}") logger.info(f" Target resolution: {args.height}x{args.width}") - logger.info(f" ONNX export resolution: {onnx_height}x{onnx_width} (to save memory)") + logger.info( + f" ONNX export resolution: {onnx_height}x{onnx_width} (to save memory)" + ) logger.info(f" Precision: {'FP32' if args.fp32 else 'FP16'}") logger.info(f" Device: {device}") @@ -342,7 +351,7 @@ def main(): models=models, onnx_path=onnx_path, height=onnx_height, # 256 to save memory - width=onnx_width, # 256 to save memory + width=onnx_width, # 256 to save memory batch_size=args.batch_size, device=device, dtype=dtype, diff --git a/src/scope/core/pipelines/personalive/tensorrt/engine_model.py b/src/scope/core/pipelines/personalive/tensorrt/engine_model.py index 6068e0d1..7c87a467 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/engine_model.py +++ b/src/scope/core/pipelines/personalive/tensorrt/engine_model.py @@ -117,28 +117,46 @@ def get_safe_shape(engine, name): # Use max profile shape for dynamic dimensions profile = engine.get_tensor_profile_shape(name, 0) if profile: - logger.debug(f"Dynamic shape for {name}: {shape} -> Max: {profile[2]}") + logger.debug( + f"Dynamic shape for {name}: {shape} -> Max: {profile[2]}" + ) return profile[2] return shape # Get shapes and dtypes for all tensors - self.input_shapes = {name: get_safe_shape(self.engine, name) for name in self.input_names} - self.input_dtypes = {name: self.engine.get_tensor_dtype(name) for name in self.input_names} + self.input_shapes = { + name: get_safe_shape(self.engine, name) for name in self.input_names + } + self.input_dtypes = { + name: self.engine.get_tensor_dtype(name) for name in self.input_names + } self.input_nbytes = { - name: trt.volume(self.input_shapes[name]) * trt.nptype(self.input_dtypes[name])().itemsize + name: trt.volume(self.input_shapes[name]) + * trt.nptype(self.input_dtypes[name])().itemsize for name in self.input_names } - self.output_shapes = {name: get_safe_shape(self.engine, name) for name in self.output_names} - self.output_dtypes = {name: self.engine.get_tensor_dtype(name) for name in self.output_names} + self.output_shapes = { + name: get_safe_shape(self.engine, name) for name in self.output_names + } + self.output_dtypes = { + name: self.engine.get_tensor_dtype(name) for name in self.output_names + } self.output_nbytes = { - name: trt.volume(self.output_shapes[name]) * trt.nptype(self.output_dtypes[name])().itemsize + name: trt.volume(self.output_shapes[name]) + * trt.nptype(self.output_dtypes[name])().itemsize for name in self.output_names } # Allocate CUDA device memory for inputs and outputs - self.dinputs = {name: cuda.mem_alloc(self.input_nbytes[name]) for name in self.input_names} - self.doutputs = {name: cuda.mem_alloc(self.output_nbytes[name]) for name in self.output_names} + self.dinputs = { + name: cuda.mem_alloc(self.input_nbytes[name]) + for name in self.input_names + } + self.doutputs = { + name: cuda.mem_alloc(self.output_nbytes[name]) + for name in self.output_names + } # Create execution context self.context = self.engine.create_execution_context() @@ -156,12 +174,14 @@ def get_safe_shape(engine, name): self.houtputs = { name: cuda.pagelocked_empty( trt.volume(self.output_shapes[name]), - dtype=trt.nptype(self.output_dtypes[name]) + dtype=trt.nptype(self.output_dtypes[name]), ) for name in self.output_names } - logger.info(f"TensorRT engine loaded successfully ({len(self.input_names)} inputs, {len(self.output_names)} outputs)") + logger.info( + f"TensorRT engine loaded successfully ({len(self.input_names)} inputs, {len(self.output_names)} outputs)" + ) except Exception as e: self.ctx.pop() @@ -197,14 +217,18 @@ def __call__( if name not in self.input_names: continue - if isinstance(hinput, torch.Tensor) and hinput.is_cuda and hinput.device.index == self.device_int: + if ( + isinstance(hinput, torch.Tensor) + and hinput.is_cuda + and hinput.device.index == self.device_int + ): # GPU tensor on same device - device-to-device copy (fast!) hinput_con = hinput.contiguous() cuda.memcpy_dtod_async( self.dinputs[name], hinput_con.data_ptr(), self.input_nbytes[name], - self.stream + self.stream, ) else: # CPU tensor or numpy array - host-to-device copy @@ -228,20 +252,24 @@ def __call__( t = torch.zeros( trt.volume(self.output_shapes[name]), device=self._torch_device, - dtype=_numpy_to_torch_dtype(trt.nptype(self.output_dtypes[name])) + dtype=_numpy_to_torch_dtype( + trt.nptype(self.output_dtypes[name]) + ), ) cuda.memcpy_dtod_async( t.data_ptr(), self.doutputs[name], self.output_nbytes[name], - self.stream + self.stream, ) t = t.reshape(tuple(self.output_shapes[name])) result[name] = t else: # Device-to-host copy to numpy for name in output_names: - cuda.memcpy_dtoh_async(self.houtputs[name], self.doutputs[name], self.stream) + cuda.memcpy_dtoh_async( + self.houtputs[name], self.doutputs[name], self.stream + ) result[name] = self.houtputs[name].reshape(self.output_shapes[name]) # Synchronize stream @@ -287,10 +315,16 @@ def prefill(self, **inputs: torch.Tensor | np.ndarray) -> bool: real_nbytes = hinput.nbytes # Copy to device - if isinstance(hinput, torch.Tensor) and hinput.is_cuda and hinput.device.index == self.device_int: + if ( + isinstance(hinput, torch.Tensor) + and hinput.is_cuda + and hinput.device.index == self.device_int + ): # GPU tensor - device-to-device copy hinput_con = hinput.contiguous() - cuda.memcpy_dtod_async(dst_ptr, hinput_con.data_ptr(), real_nbytes, self.stream) + cuda.memcpy_dtod_async( + dst_ptr, hinput_con.data_ptr(), real_nbytes, self.stream + ) else: # CPU tensor/numpy - host-to-device copy if isinstance(hinput, torch.Tensor): @@ -333,7 +367,9 @@ def bind(self, output_to_input: dict[str, str]) -> bool: continue # Point input address to output buffer - self.context.set_tensor_address(input_name, int(self.doutputs[output_name])) + self.context.set_tensor_address( + input_name, int(self.doutputs[output_name]) + ) except Exception as e: logger.error(f"Bind failed: {e}") @@ -367,12 +403,12 @@ def get_output_shapes(self) -> dict[str, tuple]: def __del__(self): """Clean up CUDA resources.""" try: - if hasattr(self, 'ctx'): + if hasattr(self, "ctx"): self.ctx.push() # Free CUDA memory - for ptr in getattr(self, 'dinputs', {}).values(): + for ptr in getattr(self, "dinputs", {}).values(): ptr.free() - for ptr in getattr(self, 'doutputs', {}).values(): + for ptr in getattr(self, "doutputs", {}).values(): ptr.free() self.ctx.pop() except Exception: @@ -390,5 +426,3 @@ def __repr__(self) -> str: r += f" {name}: {dtype}{tuple(self.output_shapes[name])},\n" r += " ]\n)" return r - - diff --git a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py index 85533c1d..2d5bb0cf 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py +++ b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py @@ -13,6 +13,7 @@ try: from polygraphy.backend.trt import Profile + POLYGRAPHY_AVAILABLE = True except ImportError: POLYGRAPHY_AVAILABLE = False @@ -121,7 +122,9 @@ def forward( # Encode new motion features and concatenate new_motion_hidden_states = self.motion_encoder(motion) - motion_hidden_states = torch.cat([motion_hidden_states, new_motion_hidden_states], dim=1) + motion_hidden_states = torch.cat( + [motion_hidden_states, new_motion_hidden_states], dim=1 + ) # Prepare encoder hidden states for UNet [clip_embeds, motion_features] encoder_hidden_states_combined = [encoder_hidden_states, motion_hidden_states] @@ -132,13 +135,27 @@ def forward( self.timesteps, encoder_hidden_states_combined, pose_cond_fea, - d00, d01, d10, d11, d20, d21, m, - u10, u11, u12, u20, u21, u22, u30, u31, u32, + d00, + d01, + d10, + d11, + d20, + d21, + m, + u10, + u11, + u12, + u20, + u21, + u22, + u30, + u31, + u32, ) # Scheduler step - score = rearrange(score, 'b c f h w -> (b f) c h w') - sample_flat = rearrange(sample, 'b c f h w -> (b f) c h w') + score = rearrange(score, "b c f h w -> (b f) c h w") + sample_flat = rearrange(sample, "b c f h w -> (b f) c h w") latents_model_input, pred_original_sample = self.scheduler.step( score, self.timesteps, sample_flat, return_dict=False @@ -148,7 +165,9 @@ def forward( pred_original_sample = pred_original_sample.to(sample.dtype) # Reshape back to 5D - latents_model_input = rearrange(latents_model_input, '(b f) c h w -> b c f h w', f=16) + latents_model_input = rearrange( + latents_model_input, "(b f) c h w -> b c f h w", f=16 + ) # Decode first 4 frames (temporal_window_size) pred_video = self.decode_slice(self.vae, pred_original_sample[:4]) @@ -167,7 +186,14 @@ def forward( # First frame latent for potential keyframe update latent_first = pred_original_sample[:1] - return pred_video, latents, pose_cond_fea_out, motion_hidden_states_out, motion_out, latent_first + return ( + pred_video, + latents, + pose_cond_fea_out, + motion_hidden_states_out, + motion_out, + latent_first, + ) def get_sample_input(self, batchsize: int, height: int, width: int, dtype, device): """Generate sample inputs for ONNX export. @@ -182,11 +208,23 @@ def get_sample_input(self, batchsize: int, height: int, width: int, dtype, devic Returns: Dictionary of sample tensors for ONNX export. """ - tw, ts, tb = 4, 4, 16 # temporal_window_size, temporal_adaptive_steps, temporal_batch + tw, ts, tb = ( + 4, + 4, + 16, + ) # temporal_window_size, temporal_adaptive_steps, temporal_batch ml, mc, mh, mw = 32, 16, 224, 224 # motion latent dims b, h, w = batchsize, height, width lh, lw = height // 8, width // 8 # latent height/width - cd0, cd1, cd2, cm, cu1, cu2, cu3 = 320, 640, 1280, 1280, 1280, 640, 320 # unet channels + cd0, cd1, cd2, cm, cu1, cu2, cu3 = ( + 320, + 640, + 1280, + 1280, + 1280, + 640, + 320, + ) # unet channels emb = 768 # CLIP embedding dims lc, ic = 4, 3 # latent/image channels @@ -221,18 +259,41 @@ def get_sample_input(self, batchsize: int, height: int, width: int, dtype, devic def get_input_names(): """Get ordered input names for ONNX export.""" return [ - "sample", "encoder_hidden_states", "motion_hidden_states", - "motion", "pose_cond_fea", "pose", "new_noise", - "d00", "d01", "d10", "d11", "d20", "d21", "m", - "u10", "u11", "u12", "u20", "u21", "u22", "u30", "u31", "u32" + "sample", + "encoder_hidden_states", + "motion_hidden_states", + "motion", + "pose_cond_fea", + "pose", + "new_noise", + "d00", + "d01", + "d10", + "d11", + "d20", + "d21", + "m", + "u10", + "u11", + "u12", + "u20", + "u21", + "u22", + "u30", + "u31", + "u32", ] @staticmethod def get_output_names(): """Get ordered output names for ONNX export.""" return [ - "pred_video", "latents", "pose_cond_fea_out", - "motion_hidden_states_out", "motion_out", "latent_first" + "pred_video", + "latents", + "pose_cond_fea_out", + "motion_hidden_states_out", + "motion_out", + "latent_first", ] @staticmethod diff --git a/src/scope/core/pipelines/personalive/tensorrt/runner.py b/src/scope/core/pipelines/personalive/tensorrt/runner.py index 2362478c..1416de2b 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/runner.py +++ b/src/scope/core/pipelines/personalive/tensorrt/runner.py @@ -26,6 +26,7 @@ # Suppress polygraphy deprecation warnings about DeviceView.dtype # (warns about future API change: DataType.from_dtype(device_view.dtype).numpy()) from polygraphy.logger import G_LOGGER + G_LOGGER.severity = G_LOGGER.ERROR # Only show errors, not warnings TRT_AVAILABLE = True @@ -33,9 +34,12 @@ # Check for CUDA buffer support (polygraphy >= 0.47) try: from polygraphy.cuda import DeviceView + CUDA_BUFFERS_AVAILABLE = True except ImportError: - logger.warning("polygraphy.cuda.DeviceView not available - using numpy path (slower)") + logger.warning( + "polygraphy.cuda.DeviceView not available - using numpy path (slower)" + ) except ImportError: pass @@ -68,7 +72,9 @@ def __init__( FileNotFoundError: If engine file doesn't exist. """ if not TRT_AVAILABLE: - raise RuntimeError("TensorRT is not available. Install with: pip install daydream-scope[tensorrt]") + raise RuntimeError( + "TensorRT is not available. Install with: pip install daydream-scope[tensorrt]" + ) engine_path = Path(engine_path) if not engine_path.exists(): @@ -92,7 +98,9 @@ def __init__( if CUDA_BUFFERS_AVAILABLE: logger.info("TensorRT runner using CUDA device buffers (zero-copy)") else: - logger.warning("TensorRT runner using numpy path (slower - install polygraphy>=0.47 for zero-copy)") + logger.warning( + "TensorRT runner using numpy path (slower - install polygraphy>=0.47 for zero-copy)" + ) def _load_engine(self): """Load the TensorRT engine.""" @@ -261,7 +269,7 @@ def _call_cuda_buffers( # Convert outputs to PyTorch tensors (they come back as DeviceView or numpy) result = {} for name, output in outputs.items(): - if hasattr(output, 'numpy'): + if hasattr(output, "numpy"): # DeviceView - create tensor from same memory arr = output.numpy() result[name] = torch.from_numpy(arr).to(self.device) @@ -352,13 +360,19 @@ def get_input_shapes(self) -> dict[str, tuple]: """Get input tensor shapes.""" if self._runner is None: return {} - return {name: tuple(meta.shape) for name, meta in self._runner.get_input_metadata().items()} + return { + name: tuple(meta.shape) + for name, meta in self._runner.get_input_metadata().items() + } def get_output_shapes(self) -> dict[str, tuple]: """Get output tensor shapes.""" if self._runner is None: return {} - return {name: tuple(meta.shape) for name, meta in self._runner.get_output_metadata().items()} + return { + name: tuple(meta.shape) + for name, meta in self._runner.get_output_metadata().items() + } def __del__(self): """Clean up resources.""" diff --git a/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py b/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py index d3d0dd15..7cc80db2 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py +++ b/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py @@ -25,7 +25,11 @@ from safetensors.torch import load_file from ..modules.resnet import InflatedConv3d, InflatedGroupNorm -from ..modules.unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block +from ..modules.unet_3d_blocks import ( + UNetMidBlock3DCrossAttn, + get_down_block, + get_up_block, +) logger = logging.get_logger(__name__) @@ -41,6 +45,7 @@ class UNet3DConditionModelExplicit(ModelMixin, ConfigMixin): This model accepts reference hidden states (d00, d01, ..., u32) as explicit forward parameters instead of using attention processor hooks. """ + _supports_gradient_checkpointing = True @register_to_config @@ -169,7 +174,7 @@ def __init__( motion_module_type=motion_module_type, temporal_module_type=temporal_module_type, motion_module_kwargs=motion_module_kwargs, - temporal_module_kwargs=temporal_module_kwargs + temporal_module_kwargs=temporal_module_kwargs, ) self.down_blocks.append(down_block) @@ -404,7 +409,9 @@ def forward( if self.class_embedding is not None: if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") + raise ValueError( + "class_labels should be provided when num_class_embeds > 0" + ) if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) @@ -453,7 +460,9 @@ def forward( for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): - down_block_res_sample = down_block_res_sample + down_block_additional_residual + down_block_res_sample = ( + down_block_res_sample + down_block_additional_residual + ) new_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples @@ -482,8 +491,10 @@ def forward( is_final_block = i == len(self.up_blocks) - 1 - res_samples = down_block_res_samples[-len(upsample_block.resnets):] - down_block_res_samples = down_block_res_samples[:-len(upsample_block.resnets)] + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] @@ -583,8 +594,9 @@ def from_pretrained_2d( raise RuntimeError(f"Unknown format: {motion_module_path.suffix}") motion_state_dict = { - k.replace('motion_modules.', 'temporal_modules.'): v - for k, v in motion_state_dict.items() if "pos_encoder" not in k + k.replace("motion_modules.", "temporal_modules."): v + for k, v in motion_state_dict.items() + if "pos_encoder" not in k } if mm_zero_proj_out: @@ -598,4 +610,3 @@ def from_pretrained_2d( logger.debug(f"Missing keys: {len(m)}; Unexpected keys: {len(u)}") return model - diff --git a/src/scope/server/app.py b/src/scope/server/app.py index 35a24e76..603f796a 100644 --- a/src/scope/server/app.py +++ b/src/scope/server/app.py @@ -336,13 +336,12 @@ async def set_personalive_reference( status_info = await pipeline_manager.get_status_info_async() if status_info.get("pipeline_id") != "personalive": raise HTTPException( - status_code=400, - detail="PersonaLive pipeline must be loaded first" + status_code=400, detail="PersonaLive pipeline must be loaded first" ) if status_info.get("status") != "loaded": raise HTTPException( status_code=400, - detail=f"Pipeline not ready. Current status: {status_info.get('status')}" + detail=f"Pipeline not ready. Current status: {status_info.get('status')}", ) # Get image from request body @@ -354,7 +353,9 @@ async def set_personalive_reference( try: image = Image.open(BytesIO(body)).convert("RGB") except Exception as e: - raise HTTPException(status_code=400, detail=f"Invalid image data: {e}") from e + raise HTTPException( + status_code=400, detail=f"Invalid image data: {e}" + ) from e # Get pipeline and fuse reference pipeline = pipeline_manager.get_pipeline() @@ -366,7 +367,7 @@ async def set_personalive_reference( return PersonaLiveReferenceResponse( success=True, - message="Reference image set successfully. Ready to process driving video." + message="Reference image set successfully. Ready to process driving video.", ) except HTTPException: diff --git a/src/scope/server/download_models.py b/src/scope/server/download_models.py index 15df3dc8..d1ecc9b6 100644 --- a/src/scope/server/download_models.py +++ b/src/scope/server/download_models.py @@ -250,7 +250,9 @@ def download_personalive_pipeline() -> None: ) print(f"[OK] Downloaded sd-image-variations-diffusers to: {sd_variations_dst}") else: - print(f"[SKIP] sd-image-variations-diffusers already exists at: {sd_variations_dst}") + print( + f"[SKIP] sd-image-variations-diffusers already exists at: {sd_variations_dst}" + ) # 2. Download sd-vae-ft-mse (improved VAE) # This provides: config.json, diffusion_pytorch_model.safetensors diff --git a/src/scope/server/models_config.py b/src/scope/server/models_config.py index cfc2b133..7dc4acc3 100644 --- a/src/scope/server/models_config.py +++ b/src/scope/server/models_config.py @@ -126,14 +126,46 @@ def get_required_model_files(pipeline_id: str | None = None) -> list[Path]: # personalive pipeline if pipeline_id == "personalive": return [ - models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "denoising_unet.pth", - models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "reference_unet.pth", - models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "motion_encoder.pth", - models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "motion_extractor.pth", - models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "pose_guider.pth", - models_dir / "PersonaLive" / "pretrained_weights" / "personalive" / "temporal_module.pth", - models_dir / "PersonaLive" / "pretrained_weights" / "sd-image-variations-diffusers" / "model_index.json", - models_dir / "PersonaLive" / "pretrained_weights" / "sd-vae-ft-mse" / "config.json", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "personalive" + / "denoising_unet.pth", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "personalive" + / "reference_unet.pth", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "personalive" + / "motion_encoder.pth", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "personalive" + / "motion_extractor.pth", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "personalive" + / "pose_guider.pth", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "personalive" + / "temporal_module.pth", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "sd-image-variations-diffusers" + / "model_index.json", + models_dir + / "PersonaLive" + / "pretrained_weights" + / "sd-vae-ft-mse" + / "config.json", ] # Default: nothing is required diff --git a/src/scope/server/schema.py b/src/scope/server/schema.py index 7052358d..f17a41b4 100644 --- a/src/scope/server/schema.py +++ b/src/scope/server/schema.py @@ -391,7 +391,9 @@ class PersonaLiveLoadParams(PipelineLoadParams): class PersonaLiveReferenceResponse(BaseModel): """Response after setting PersonaLive reference image.""" - success: bool = Field(..., description="Whether the reference image was set successfully") + success: bool = Field( + ..., description="Whether the reference image was set successfully" + ) message: str = Field(..., description="Status message") From 85ce8a393d78184e1a6ef4784304d4dac8506bce Mon Sep 17 00:00:00 2001 From: BuffMcBigHuge Date: Mon, 22 Dec 2025 11:45:44 -0500 Subject: [PATCH 7/7] Partial lint. Signed-off-by: BuffMcBigHuge --- .../core/pipelines/personalive/__init__.py | 2 + .../pipelines/personalive/blocks/__init__.py | 2 + .../personalive/components/face_detector.py | 13 +++--- .../core/pipelines/personalive/docs/README.md | 2 + .../core/pipelines/personalive/pipeline.py | 17 ++++--- .../personalive/tensorrt/__init__.py | 2 +- .../pipelines/personalive/tensorrt/builder.py | 3 +- .../pipelines/personalive/tensorrt/convert.py | 2 +- .../personalive/tensorrt/engine_model.py | 6 +-- .../pipelines/personalive/tensorrt/export.py | 5 +-- .../personalive/tensorrt/framed_model.py | 2 +- .../pipelines/personalive/tensorrt/runner.py | 2 - .../personalive/tensorrt/unet_explicit.py | 44 ++++++++++--------- src/scope/server/app.py | 1 + src/scope/server/patch_xformers.py | 2 - 15 files changed, 55 insertions(+), 50 deletions(-) diff --git a/src/scope/core/pipelines/personalive/__init__.py b/src/scope/core/pipelines/personalive/__init__.py index f3995ef9..48ad939b 100644 --- a/src/scope/core/pipelines/personalive/__init__.py +++ b/src/scope/core/pipelines/personalive/__init__.py @@ -3,3 +3,5 @@ from .pipeline import PersonaLivePipeline __all__ = ["PersonaLivePipeline"] + + diff --git a/src/scope/core/pipelines/personalive/blocks/__init__.py b/src/scope/core/pipelines/personalive/blocks/__init__.py index fdb7b942..5409311e 100644 --- a/src/scope/core/pipelines/personalive/blocks/__init__.py +++ b/src/scope/core/pipelines/personalive/blocks/__init__.py @@ -1 +1,3 @@ """PersonaLive modular blocks for pipeline composition.""" + + diff --git a/src/scope/core/pipelines/personalive/components/face_detector.py b/src/scope/core/pipelines/personalive/components/face_detector.py index bea0e23c..c88cb11f 100644 --- a/src/scope/core/pipelines/personalive/components/face_detector.py +++ b/src/scope/core/pipelines/personalive/components/face_detector.py @@ -1,7 +1,6 @@ """Face detection component using MediaPipe for PersonaLive pipeline.""" import logging -from typing import Tuple import numpy as np import torch @@ -60,7 +59,7 @@ def detect_face_landmarks(self, image: np.ndarray) -> list | None: def get_face_bounding_box( self, image: np.ndarray, padding_ratio: float = 0.2 - ) -> Tuple[int, int, int, int] | None: + ) -> tuple[int, int, int, int] | None: """Get bounding box for the detected face. Args: @@ -103,7 +102,7 @@ def get_face_bounding_box( def crop_face( self, image: np.ndarray, - target_size: Tuple[int, int] = (512, 512), + target_size: tuple[int, int] = (512, 512), padding_ratio: float = 0.2, ) -> np.ndarray | None: """Crop and resize face from image. @@ -131,7 +130,7 @@ def crop_face( return np.array(face_pil) - def crop_face_from_pil(self, pil_image, target_size: Tuple[int, int] = (512, 512)): + def crop_face_from_pil(self, pil_image, target_size: tuple[int, int] = (512, 512)): """Crop face from a PIL image. Args: @@ -154,8 +153,8 @@ def crop_face_from_pil(self, pil_image, target_size: Tuple[int, int] = (512, 512 def crop_face_tensor( self, image_tensor: torch.Tensor, - boxes: Tuple[int, int, int, int], - target_size: Tuple[int, int] = (224, 224), + boxes: tuple[int, int, int, int], + target_size: tuple[int, int] = (224, 224), ) -> torch.Tensor: """Crop face region from a tensor using precomputed box. @@ -196,3 +195,5 @@ def close(self): def __del__(self): self.close() + + diff --git a/src/scope/core/pipelines/personalive/docs/README.md b/src/scope/core/pipelines/personalive/docs/README.md index affdfad0..9ce63dbf 100644 --- a/src/scope/core/pipelines/personalive/docs/README.md +++ b/src/scope/core/pipelines/personalive/docs/README.md @@ -123,3 +123,5 @@ PersonaLive uses a multi-component architecture: Based on the PersonaLive implementation by GVCLab. + + diff --git a/src/scope/core/pipelines/personalive/pipeline.py b/src/scope/core/pipelines/personalive/pipeline.py index c8904e8f..590e6c63 100644 --- a/src/scope/core/pipelines/personalive/pipeline.py +++ b/src/scope/core/pipelines/personalive/pipeline.py @@ -26,7 +26,7 @@ from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from ..interface import Pipeline, Requirements -from ..process import preprocess_chunk, postprocess_chunk +from ..process import preprocess_chunk from ..schema import PersonaLiveConfig from ..utils import load_model_config from .components import FaceDetector @@ -42,7 +42,6 @@ get_boxes, ) - # Profiling utilities _profiling_enabled = False _profiling_timings: dict[str, list[float]] = defaultdict(list) @@ -87,7 +86,7 @@ def _profile_stage(name: str): # TensorRT support (optional) try: - from .tensorrt import TRT_AVAILABLE, TRTRunner, get_engine_path, PYCUDA_AVAILABLE + from .tensorrt import PYCUDA_AVAILABLE, TRT_AVAILABLE, TRTRunner, get_engine_path # Import EngineModel if pycuda is available (best performance) if PYCUDA_AVAILABLE: @@ -604,9 +603,9 @@ def fuse_reference(self, ref_image: Image.Image): ) for i in range(self.temporal_adaptive_step - 1): - l = i * self.temporal_window_size + left_idx = i * self.temporal_window_size r = (i + 1) * self.temporal_window_size - self.latents_pile.append(noisy_latents_first[:, :, l:r]) + self.latents_pile.append(noisy_latents_first[:, :, left_idx:r]) # For TensorRT, also prefill initial latents (using correct input name 'sample') if self.use_tensorrt: @@ -977,9 +976,9 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: ref_motion, dri_motion[:, :1], num=12 + 1 )[:, :-1] for i in range(temporal_adaptive_step - 1): - l = i * temporal_window_size + left_idx = i * temporal_window_size r = (i + 1) * temporal_window_size - self.motion_pile.append(init_motion_hidden_states[:, l:r]) + self.motion_pile.append(init_motion_hidden_states[:, left_idx:r]) self.motion_pile.append(dri_motion) self.motion_bank = ref_motion @@ -996,9 +995,9 @@ def _process_frames_pytorch(self, images: torch.Tensor) -> torch.Tensor: pose_fea = self.pose_guider(keypoints) if self.first_frame: for i in range(temporal_adaptive_step): - l = i * temporal_window_size + left_idx = i * temporal_window_size r = (i + 1) * temporal_window_size - self.pose_pile.append(pose_fea[:, :, l:r]) + self.pose_pile.append(pose_fea[:, :, left_idx:r]) self.first_frame = False else: self.pose_pile.append(pose_fea) diff --git a/src/scope/core/pipelines/personalive/tensorrt/__init__.py b/src/scope/core/pipelines/personalive/tensorrt/__init__.py index acb1b235..2a76ebf2 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/__init__.py +++ b/src/scope/core/pipelines/personalive/tensorrt/__init__.py @@ -37,8 +37,8 @@ # Check for pycuda (best performance) try: - import pycuda.driver # noqa: F401 import pycuda.autoinit # noqa: F401 + import pycuda.driver # noqa: F401 PYCUDA_AVAILABLE = True except ImportError: diff --git a/src/scope/core/pipelines/personalive/tensorrt/builder.py b/src/scope/core/pipelines/personalive/tensorrt/builder.py index 67dfbcbd..1274805f 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/builder.py +++ b/src/scope/core/pipelines/personalive/tensorrt/builder.py @@ -5,7 +5,6 @@ """ import logging -import os from pathlib import Path from typing import TYPE_CHECKING @@ -256,3 +255,5 @@ def is_engine_available(model_dir: Path, height: int = 512, width: int = 512) -> """ engine_path = get_engine_path(model_dir, height, width) return engine_path.exists() + + diff --git a/src/scope/core/pipelines/personalive/tensorrt/convert.py b/src/scope/core/pipelines/personalive/tensorrt/convert.py index a0e260a5..7de1a140 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/convert.py +++ b/src/scope/core/pipelines/personalive/tensorrt/convert.py @@ -327,7 +327,7 @@ def main(): # TRT engine is built at target resolution with dynamic shapes onnx_height, onnx_width = 256, 256 - logger.info(f"Converting PersonaLive models to TensorRT") + logger.info("Converting PersonaLive models to TensorRT") logger.info(f" Model directory: {args.model_dir}") logger.info(f" Target resolution: {args.height}x{args.width}") logger.info( diff --git a/src/scope/core/pipelines/personalive/tensorrt/engine_model.py b/src/scope/core/pipelines/personalive/tensorrt/engine_model.py index 7c87a467..d2693eea 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/engine_model.py +++ b/src/scope/core/pipelines/personalive/tensorrt/engine_model.py @@ -20,9 +20,9 @@ # Check for TensorRT and pycuda availability PYCUDA_TRT_AVAILABLE = False try: - import tensorrt as trt - import pycuda.driver as cuda import pycuda.autoinit # noqa: F401 - Required for pycuda initialization + import pycuda.driver as cuda + import tensorrt as trt PYCUDA_TRT_AVAILABLE = True TRT_LOGGER = trt.Logger(trt.Logger.WARNING) @@ -185,7 +185,7 @@ def get_safe_shape(engine, name): except Exception as e: self.ctx.pop() - raise RuntimeError(f"Failed to initialize TensorRT engine: {e}") + raise RuntimeError(f"Failed to initialize TensorRT engine: {e}") from e self.ctx.pop() diff --git a/src/scope/core/pipelines/personalive/tensorrt/export.py b/src/scope/core/pipelines/personalive/tensorrt/export.py index 8e5917d6..8e7dfa03 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/export.py +++ b/src/scope/core/pipelines/personalive/tensorrt/export.py @@ -8,7 +8,6 @@ import gc import logging -import os from contextlib import contextmanager from pathlib import Path @@ -108,8 +107,8 @@ def optimize_onnx( """ try: import onnx - except ImportError: - raise RuntimeError("onnx package required. Install with: pip install onnx") + except ImportError as e: + raise RuntimeError("onnx package required. Install with: pip install onnx") from e onnx_path = Path(onnx_path) onnx_opt_path = Path(onnx_opt_path) diff --git a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py index 2d5bb0cf..a9ef436f 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/framed_model.py +++ b/src/scope/core/pipelines/personalive/tensorrt/framed_model.py @@ -8,8 +8,8 @@ """ import torch -from torch import nn from einops import rearrange +from torch import nn try: from polygraphy.backend.trt import Profile diff --git a/src/scope/core/pipelines/personalive/tensorrt/runner.py b/src/scope/core/pipelines/personalive/tensorrt/runner.py index 1416de2b..1efee6d9 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/runner.py +++ b/src/scope/core/pipelines/personalive/tensorrt/runner.py @@ -8,7 +8,6 @@ import logging from pathlib import Path -from typing import Any import numpy as np import torch @@ -19,7 +18,6 @@ TRT_AVAILABLE = False CUDA_BUFFERS_AVAILABLE = False try: - import tensorrt as trt from polygraphy.backend.common import BytesFromPath from polygraphy.backend.trt import EngineFromBytes, TrtRunner diff --git a/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py b/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py index 7cc80db2..d9aa521b 100644 --- a/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py +++ b/src/scope/core/pipelines/personalive/tensorrt/unet_explicit.py @@ -8,11 +8,9 @@ PersonaLive/src/models/unet_3d_explicit_reference.py """ -from collections import OrderedDict from dataclasses import dataclass from os import PathLike from pathlib import Path -from typing import Dict, List, Optional, Tuple, Union import torch import torch.nn as nn @@ -51,27 +49,27 @@ class UNet3DConditionModelExplicit(ModelMixin, ConfigMixin): @register_to_config def __init__( self, - sample_size: Optional[int] = None, + sample_size: int | None = None, in_channels: int = 4, out_channels: int = 4, center_input_sample: bool = False, flip_sin_to_cos: bool = True, freq_shift: int = 0, - down_block_types: Tuple[str] = ( + down_block_types: tuple[str] = ( "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D", ), mid_block_type: str = "UNetMidBlock3DCrossAttn", - up_block_types: Tuple[str] = ( + up_block_types: tuple[str] = ( "UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", ), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + only_cross_attention: bool | tuple[bool] = False, + block_out_channels: tuple[int] = (320, 640, 1280, 1280), layers_per_block: int = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, @@ -79,11 +77,11 @@ def __init__( norm_num_groups: int = 32, norm_eps: float = 1e-5, cross_attention_dim: int = 1280, - attention_head_dim: Union[int, Tuple[int]] = 8, + attention_head_dim: int | tuple[int] = 8, dual_cross_attention: bool = False, use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - num_class_embeds: Optional[int] = None, + class_embed_type: str | None = None, + num_class_embeds: int | None = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", use_inflated_groupnorm=False, @@ -95,12 +93,16 @@ def __init__( motion_module_decoder_only=False, motion_module_type=None, temporal_module_type=None, - motion_module_kwargs={}, - temporal_module_kwargs={}, + motion_module_kwargs=None, + temporal_module_kwargs=None, unet_use_cross_frame_attention=None, unet_use_temporal_attention=None, ): super().__init__() + if motion_module_kwargs is None: + motion_module_kwargs = {} + if temporal_module_kwargs is None: + temporal_module_kwargs = {} self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 @@ -283,14 +285,14 @@ def __init__( ) @property - def attn_processors(self) -> Dict[str, AttentionProcessor]: + def attn_processors(self) -> dict[str, AttentionProcessor]: """Returns attention processors.""" processors = {} def fn_recursive_add_processors( name: str, module: torch.nn.Module, - processors: Dict[str, AttentionProcessor], + processors: dict[str, AttentionProcessor], ): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.processor @@ -308,7 +310,7 @@ def fn_recursive_add_processors( return processors def set_attn_processor( - self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + self, processor: AttentionProcessor | dict[str, AttentionProcessor] ): """Sets the attention processor.""" count = len(self.attn_processors.keys()) @@ -335,7 +337,7 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): def forward( self, sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], + timestep: torch.Tensor | float | int, encoder_hidden_states: torch.Tensor, pose_cond_fea: torch.Tensor, # Explicit reference hidden states for TensorRT @@ -355,13 +357,13 @@ def forward( u30: torch.Tensor, u31: torch.Tensor, u32: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - mid_block_additional_residual: Optional[torch.Tensor] = None, + class_labels: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + down_block_additional_residuals: tuple[torch.Tensor] | None = None, + mid_block_additional_residual: torch.Tensor | None = None, return_dict: bool = True, skip_mm: bool = False, - ) -> Union[UNet3DConditionOutput, Tuple]: + ) -> UNet3DConditionOutput | tuple: """Forward pass with explicit reference hidden states. Args: diff --git a/src/scope/server/app.py b/src/scope/server/app.py index 603f796a..21448372 100644 --- a/src/scope/server/app.py +++ b/src/scope/server/app.py @@ -329,6 +329,7 @@ async def set_personalive_reference( starting video streaming. """ from io import BytesIO + from PIL import Image try: diff --git a/src/scope/server/patch_xformers.py b/src/scope/server/patch_xformers.py index 89281ca6..983cde33 100644 --- a/src/scope/server/patch_xformers.py +++ b/src/scope/server/patch_xformers.py @@ -8,8 +8,6 @@ def find_xformers_flash_file() -> Path | None: """Find the xformers flash.py file in the installed package.""" try: - import xformers - # Get the package path spec = importlib.util.find_spec("xformers") if spec is None or spec.origin is None: