diff --git a/frontend/src/components/DownloadDialog.tsx b/frontend/src/components/DownloadDialog.tsx
index d94362e8..7a5ffd16 100644
--- a/frontend/src/components/DownloadDialog.tsx
+++ b/frontend/src/components/DownloadDialog.tsx
@@ -9,11 +9,11 @@ import {
   DialogTitle,
 } from "./ui/dialog";
 import { Progress } from "./ui/progress";
-import { PIPELINES } from "../data/pipelines";
-import type { PipelineId, DownloadProgress } from "../types";
+import type { PipelineId, DownloadProgress, PipelineInfo } from "../types";
 
 interface DownloadDialogProps {
   open: boolean;
+  pipelines: Record<string, PipelineInfo> | null;
   pipelineId: PipelineId;
   onClose: () => void;
   onDownload: () => void;
@@ -23,13 +23,14 @@ interface DownloadDialogProps {
 
 export function DownloadDialog({
   open,
+  pipelines,
   pipelineId,
   onClose,
   onDownload,
   isDownloading = false,
   progress = null,
 }: DownloadDialogProps) {
-  const pipelineInfo = PIPELINES[pipelineId];
+  const pipelineInfo = pipelines?.[pipelineId];
   if (!pipelineInfo) return null;
 
   return (
diff --git a/frontend/src/components/InputAndControlsPanel.tsx b/frontend/src/components/InputAndControlsPanel.tsx
index ca3a0e06..67ef39ac 100644
--- a/frontend/src/components/InputAndControlsPanel.tsx
+++ b/frontend/src/components/InputAndControlsPanel.tsx
@@ -14,8 +14,7 @@ import { Upload, ArrowUp } from "lucide-react";
 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 type { InputMode, PipelineInfo } from "../types";
 import { PromptInput } from "./PromptInput";
 import { TimelinePromptEditor } from "./TimelinePromptEditor";
 import type { TimelinePrompt } from "./PromptTimeline";
@@ -24,6 +23,7 @@ import { Button } from "./ui/button";
 
 interface InputAndControlsPanelProps {
   className?: string;
+  pipelines: Record<string, PipelineInfo> | null;
   localStream: MediaStream | null;
   isInitializing: boolean;
   error: string | null;
@@ -73,6 +73,7 @@ interface InputAndControlsPanelProps {
 
 export function InputAndControlsPanel({
   className = "",
+  pipelines,
   localStream,
   isInitializing,
   error,
@@ -128,7 +129,8 @@ export function InputAndControlsPanel({
   const videoRef = useRef<HTMLVideoElement>(null);
 
   // Check if this pipeline supports multiple input modes
-  const isMultiMode = pipelineIsMultiMode(pipelineId);
+  const pipeline = pipelines?.[pipelineId];
+  const isMultiMode = (pipeline?.supportedModes?.length ?? 0) > 1;
 
   useEffect(() => {
     if (videoRef.current && localStream) {
@@ -329,6 +331,11 @@ 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;
 
+            // Hide prompts section if pipeline doesn't support prompts
+            if (pipeline?.supportsPrompts === false) {
+              return null;
+            }
+
             return (
               <div>
                 <div className="flex items-center justify-between mb-2">
@@ -358,7 +365,6 @@ export function InputAndControlsPanel({
                     onPromptsSubmit={onPromptsSubmit}
                     onTransitionSubmit={onTransitionSubmit}
                     disabled={
-                      pipelineId === "passthrough" ||
                       (_isTimelinePlaying &&
                         !isVideoPaused &&
                         !isAtEndOfTimeline()) ||
diff --git a/frontend/src/components/SettingsPanel.tsx b/frontend/src/components/SettingsPanel.tsx
index 83c31e40..b21d592b 100644
--- a/frontend/src/components/SettingsPanel.tsx
+++ b/frontend/src/components/SettingsPanel.tsx
@@ -20,11 +20,6 @@ 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,
-  pipelineSupportsVACE,
-} from "../data/pipelines";
 import { PARAMETER_METADATA } from "../data/parameterMetadata";
 import { DenoisingStepsSlider } from "./DenoisingStepsSlider";
 import {
@@ -38,13 +33,16 @@ import type {
   LoraMergeStrategy,
   SettingsState,
   InputMode,
+  PipelineInfo,
 } from "../types";
 import { LoRAManager } from "./LoRAManager";
 
-const MIN_DIMENSION = 16;
+// Minimum dimension for most pipelines (will be overridden by pipeline-specific minDimension from schema)
+const DEFAULT_MIN_DIMENSION = 1;
 
 interface SettingsPanelProps {
   className?: string;
+  pipelines: Record<string, PipelineInfo> | null;
   pipelineId: PipelineId;
   onPipelineIdChange?: (pipelineId: PipelineId) => void;
   isStreaming?: boolean;
@@ -93,6 +91,7 @@ interface SettingsPanelProps {
 
 export function SettingsPanel({
   className = "",
+  pipelines,
   pipelineId,
   onPipelineIdChange,
   isStreaming = false,
@@ -154,7 +153,7 @@ export function SettingsPanel({
       : null;
 
   const handlePipelineIdChange = (value: string) => {
-    if (value in PIPELINES) {
+    if (pipelines && value in pipelines) {
       onPipelineIdChange?.(value as PipelineId);
     }
   };
@@ -163,13 +162,9 @@ export function SettingsPanel({
     dimension: "height" | "width",
     value: number
   ) => {
-    const minValue =
-      pipelineId === "longlive" ||
-      pipelineId === "streamdiffusionv2" ||
-      pipelineId === "krea-realtime-video" ||
-      pipelineId === "reward-forcing"
-        ? MIN_DIMENSION
-        : 1;
+    // Get min dimension from pipeline schema, fallback to default
+    const currentPipeline = pipelines?.[pipelineId];
+    const minValue = currentPipeline?.minDimension ?? DEFAULT_MIN_DIMENSION;
     const maxValue = 2048;
 
     // Validate and set error state
@@ -208,13 +203,9 @@ export function SettingsPanel({
   };
 
   const decrementResolution = (dimension: "height" | "width") => {
-    const minValue =
-      pipelineId === "longlive" ||
-      pipelineId === "streamdiffusionv2" ||
-      pipelineId === "krea-realtime-video" ||
-      pipelineId === "reward-forcing"
-        ? MIN_DIMENSION
-        : 1;
+    // Get min dimension from pipeline schema, fallback to default
+    const currentPipeline = pipelines?.[pipelineId];
+    const minValue = currentPipeline?.minDimension ?? DEFAULT_MIN_DIMENSION;
     const newValue = Math.max(minValue, resolution[dimension] - 1);
     handleResolutionChange(dimension, newValue);
   };
@@ -248,7 +239,7 @@ export function SettingsPanel({
     handleSeedChange(newValue);
   };
 
-  const currentPipeline = PIPELINES[pipelineId];
+  const currentPipeline = pipelines?.[pipelineId];
 
   return (
     <Card className={`h-full flex flex-col ${className}`}>
@@ -267,11 +258,12 @@ export function SettingsPanel({
               <SelectValue placeholder="Select a pipeline" />
             </SelectTrigger>
             <SelectContent>
-              {Object.keys(PIPELINES).map(id => (
-                <SelectItem key={id} value={id}>
-                  {id}
-                </SelectItem>
-              ))}
+              {pipelines &&
+                Object.keys(pipelines).map(id => (
+                  <SelectItem key={id} value={id}>
+                    {id}
+                  </SelectItem>
+                ))}
             </SelectContent>
           </Select>
         </div>
@@ -350,7 +342,7 @@ export function SettingsPanel({
         )}
 
         {/* VACE Toggle */}
-        {pipelineSupportsVACE(pipelineId) && (
+        {currentPipeline?.supportsVACE && (
           <div className="space-y-2">
             <div className="flex items-center justify-between gap-2">
               <LabelWithTooltip
@@ -397,7 +389,7 @@ export function SettingsPanel({
           </div>
         )}
 
-        {pipelineSupportsLoRA(pipelineId) && (
+        {currentPipeline?.supportsLoRA && (
           <div className="space-y-4">
             <LoRAManager
               loras={loras}
@@ -409,10 +401,8 @@ export function SettingsPanel({
           </div>
         )}
 
-        {(pipelineId === "longlive" ||
-          pipelineId === "streamdiffusionv2" ||
-          pipelineId === "krea-realtime-video" ||
-          pipelineId === "reward-forcing") && (
+        {/* Resolution controls - shown for pipelines that support quantization (implies they need resolution config) */}
+        {pipelines?.[pipelineId]?.supportsQuantization && (
           <div className="space-y-4">
             <div className="space-y-2">
               <div className="space-y-2">
@@ -446,7 +436,10 @@ export function SettingsPanel({
                         }}
                         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}
+                        min={
+                          pipelines?.[pipelineId]?.minDimension ??
+                          DEFAULT_MIN_DIMENSION
+                        }
                         max={2048}
                       />
                       <Button
@@ -495,7 +488,10 @@ export function SettingsPanel({
                         }}
                         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}
+                        min={
+                          pipelines?.[pipelineId]?.minDimension ??
+                          DEFAULT_MIN_DIMENSION
+                        }
                         max={2048}
                       />
                       <Button
@@ -575,14 +571,13 @@ export function SettingsPanel({
           </div>
         )}
 
-        {(pipelineId === "longlive" ||
-          pipelineId === "streamdiffusionv2" ||
-          pipelineId === "krea-realtime-video" ||
-          pipelineId === "reward-forcing") && (
+        {/* Cache management controls - shown for pipelines that support it */}
+        {pipelines?.[pipelineId]?.supportsCacheManagement && (
           <div className="space-y-4">
             <div className="space-y-2">
               <div className="space-y-2 pt-2">
-                {pipelineId === "krea-realtime-video" && (
+                {/* KV Cache bias control - shown for pipelines that support it */}
+                {pipelines?.[pipelineId]?.supportsKvCacheBias && (
                   <SliderWithInput
                     label={PARAMETER_METADATA.kvCacheAttentionBias.label}
                     tooltip={PARAMETER_METADATA.kvCacheAttentionBias.tooltip}
@@ -638,10 +633,8 @@ export function SettingsPanel({
           </div>
         )}
 
-        {(pipelineId === "longlive" ||
-          pipelineId === "streamdiffusionv2" ||
-          pipelineId === "krea-realtime-video" ||
-          pipelineId === "reward-forcing") && (
+        {/* Denoising steps - shown for pipelines that support quantization (implies advanced diffusion features) */}
+        {pipelines?.[pipelineId]?.supportsQuantization && (
           <DenoisingStepsSlider
             value={denoisingSteps}
             onChange={onDenoisingStepsChange || (() => {})}
@@ -693,10 +686,8 @@ export function SettingsPanel({
           </div>
         )}
 
-        {(pipelineId === "longlive" ||
-          pipelineId === "streamdiffusionv2" ||
-          pipelineId === "krea-realtime-video" ||
-          pipelineId === "reward-forcing") && (
+        {/* Quantization controls - shown for pipelines that support it */}
+        {pipelines?.[pipelineId]?.supportsQuantization && (
           <div className="space-y-4">
             <div className="space-y-2">
               <div className="space-y-2 pt-2">
diff --git a/frontend/src/data/pipelines.ts b/frontend/src/data/pipelines.ts
index fae64ad7..152e9eb2 100644
--- a/frontend/src/data/pipelines.ts
+++ b/frontend/src/data/pipelines.ts
@@ -1,133 +1,12 @@
 import type { InputMode } from "../types";
 
-// Unified default prompts by mode (not per-pipeline)
-// These are used across all pipelines for consistency
+// Default prompts by mode - used across all pipelines for consistency
 export const DEFAULT_PROMPTS: Record<InputMode, string> = {
   text: "A 3D animated scene. A **panda** walks along a path towards the camera in a park on a spring day.",
   video:
     "A 3D animated scene. A **panda** sitting in the grass, looking around.",
 };
 
-export interface PipelineInfo {
-  name: string;
-  about: string;
-  docsUrl?: string;
-  modified?: boolean;
-  estimatedVram?: number; // GB
-  requiresModels?: boolean; // Whether this pipeline requires models to be downloaded
-  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
-  supportsVACE?: boolean; // Whether this pipeline supports VACE (Video All-In-One Creation and Editing)
-
-  // Multi-mode support
-  supportedModes: InputMode[];
-  defaultMode: InputMode;
-}
-
-export const PIPELINES: Record<string, PipelineInfo> = {
-  streamdiffusionv2: {
-    name: "StreamDiffusionV2",
-    docsUrl:
-      "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/streamdiffusionv2/docs/usage.md",
-    about:
-      "A streaming pipeline and autoregressive video diffusion model from the creators of the original StreamDiffusion project. The model is trained using Self-Forcing on Wan2.1 1.3b with modifications to support streaming. Includes VACE (All-In-One Video Creation and Editing) for reference image conditioning and structural guidance (depth, flow, pose).",
-    modified: true,
-    estimatedVram: 20,
-    requiresModels: true,
-    defaultTemporalInterpolationMethod: "slerp",
-    defaultTemporalInterpolationSteps: 0,
-    supportsLoRA: true,
-    supportsVACE: true,
-    // Multi-mode support
-    supportedModes: ["text", "video"],
-    defaultMode: "video",
-  },
-  longlive: {
-    name: "LongLive",
-    docsUrl:
-      "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/longlive/docs/usage.md",
-    about:
-      "A streaming pipeline and autoregressive video diffusion model from Nvidia, MIT, HKUST, HKU and THU. The model is trained using Self-Forcing on Wan2.1 1.3b with modifications to support smoother prompt switching and improved quality over longer time periods while maintaining fast generation. Includes VACE (All-In-One Video Creation and Editing) for reference image conditioning and structural guidance (depth, flow, pose).",
-    modified: true,
-    estimatedVram: 20,
-    requiresModels: true,
-    defaultTemporalInterpolationMethod: "slerp",
-    defaultTemporalInterpolationSteps: 0,
-    supportsLoRA: true,
-    supportsVACE: true,
-    // Multi-mode support
-    supportedModes: ["text", "video"],
-    defaultMode: "text",
-  },
-  "krea-realtime-video": {
-    name: "Krea Realtime Video",
-    docsUrl:
-      "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/krea_realtime_video/docs/usage.md",
-    about:
-      "A streaming pipeline and autoregressive video diffusion model from Krea. The model is trained using Self-Forcing on Wan2.1 14b.",
-    modified: true,
-    estimatedVram: 32,
-    requiresModels: true,
-    defaultTemporalInterpolationMethod: "linear",
-    defaultTemporalInterpolationSteps: 4,
-    supportsLoRA: true,
-    // Multi-mode support
-    supportedModes: ["text", "video"],
-    defaultMode: "text",
-  },
-  "reward-forcing": {
-    name: "RewardForcing",
-    docsUrl:
-      "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/reward_forcing/docs/usage.md",
-    about:
-      "A streaming pipeline and autoregressive video diffusion model from ZJU, Ant Group, SIAS-ZJU, HUST and SJTU. The model is trained with Rewarded Distribution Matching Distillation using Wan2.1 1.3b as the base model. Includes VACE (All-In-One Video Creation and Editing) for reference image conditioning and structural guidance (depth, flow, pose).",
-    modified: true,
-    estimatedVram: 20,
-    requiresModels: true,
-    defaultTemporalInterpolationMethod: "slerp",
-    defaultTemporalInterpolationSteps: 0,
-    supportsLoRA: true,
-    supportsVACE: true,
-    // Multi-mode support
-    supportedModes: ["text", "video"],
-    defaultMode: "text",
-  },
-  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",
-  },
-};
-
-export function pipelineSupportsLoRA(pipelineId: string): boolean {
-  return PIPELINES[pipelineId]?.supportsLoRA === true;
-}
-
-export function pipelineSupportsVACE(pipelineId: string): boolean {
-  return PIPELINES[pipelineId]?.supportsVACE === true;
-}
-
-export function pipelineSupportsMode(
-  pipelineId: string,
-  mode: InputMode
-): boolean {
-  return PIPELINES[pipelineId]?.supportedModes?.includes(mode) ?? false;
-}
-
-export function pipelineIsMultiMode(pipelineId: string): boolean {
-  const modes = PIPELINES[pipelineId]?.supportedModes ?? [];
-  return modes.length > 1;
-}
-
-export function getPipelineDefaultMode(pipelineId: string): InputMode {
-  return PIPELINES[pipelineId]?.defaultMode ?? "text";
-}
-
 export function getDefaultPromptForMode(mode: InputMode): string {
   return DEFAULT_PROMPTS[mode];
 }
diff --git a/frontend/src/hooks/usePipelines.ts b/frontend/src/hooks/usePipelines.ts
new file mode 100644
index 00000000..e173a6d4
--- /dev/null
+++ b/frontend/src/hooks/usePipelines.ts
@@ -0,0 +1,74 @@
+import { useState, useEffect } from "react";
+import { getPipelineSchemas } from "../lib/api";
+import type { InputMode, PipelineInfo } from "../types";
+
+export function usePipelines() {
+  const [pipelines, setPipelines] = useState<Record<
+    string,
+    PipelineInfo
+  > | null>(null);
+  const [isLoading, setIsLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
+
+  useEffect(() => {
+    let mounted = true;
+
+    async function fetchPipelines() {
+      try {
+        setIsLoading(true);
+        const schemas = await getPipelineSchemas();
+
+        if (!mounted) return;
+
+        // Transform to camelCase for TypeScript conventions
+        const transformed: Record<string, PipelineInfo> = {};
+        for (const [id, schema] of Object.entries(schemas.pipelines)) {
+          transformed[id] = {
+            name: schema.name,
+            about: schema.description,
+            supportedModes: schema.supported_modes as InputMode[],
+            defaultMode: schema.default_mode as InputMode,
+            supportsPrompts: schema.supports_prompts,
+            defaultTemporalInterpolationMethod:
+              schema.default_temporal_interpolation_method,
+            defaultTemporalInterpolationSteps:
+              schema.default_temporal_interpolation_steps,
+            docsUrl: schema.docs_url ?? undefined,
+            estimatedVram: schema.estimated_vram_gb ?? undefined,
+            requiresModels: schema.requires_models,
+            supportsLoRA: schema.supports_lora,
+            supportsVACE: schema.supports_vace,
+            supportsCacheManagement: schema.supports_cache_management,
+            supportsKvCacheBias: schema.supports_kv_cache_bias,
+            supportsQuantization: schema.supports_quantization,
+            minDimension: schema.min_dimension,
+            recommendedQuantizationVramThreshold:
+              schema.recommended_quantization_vram_threshold ?? undefined,
+            modified: schema.modified,
+          };
+        }
+
+        setPipelines(transformed);
+        setError(null);
+      } catch (err) {
+        if (!mounted) return;
+        const errorMessage =
+          err instanceof Error ? err.message : "Failed to fetch pipelines";
+        setError(errorMessage);
+        console.error("Failed to fetch pipelines:", err);
+      } finally {
+        if (mounted) {
+          setIsLoading(false);
+        }
+      }
+    }
+
+    fetchPipelines();
+
+    return () => {
+      mounted = false;
+    };
+  }, []);
+
+  return { pipelines, isLoading, error };
+}
diff --git a/frontend/src/hooks/useStreamState.ts b/frontend/src/hooks/useStreamState.ts
index 5d0cca87..68e7f3e3 100644
--- a/frontend/src/hooks/useStreamState.ts
+++ b/frontend/src/hooks/useStreamState.ts
@@ -13,11 +13,9 @@ import {
   type HardwareInfoResponse,
   type PipelineSchemasResponse,
 } from "../lib/api";
-import { getPipelineDefaultMode } from "../data/pipelines";
 
 // Generic fallback defaults used before schemas are loaded.
-// Resolution and denoising steps use conservative values; mode-specific
-// values are derived from pipelines.ts when possible.
+// Resolution and denoising steps use conservative values.
 const BASE_FALLBACK = {
   height: 320,
   width: 576,
@@ -26,9 +24,9 @@ const BASE_FALLBACK = {
 };
 
 // Get fallback defaults for a pipeline before schemas are loaded
-// Derives mode from pipelines.ts to stay in sync with frontend definitions
-function getFallbackDefaults(pipelineId: PipelineId, mode?: InputMode) {
-  const effectiveMode = mode ?? getPipelineDefaultMode(pipelineId);
+function getFallbackDefaults(mode?: InputMode) {
+  // Default to text mode if no mode specified (will be corrected when schemas load)
+  const effectiveMode = mode ?? "text";
   const isVideoMode = effectiveMode === "video";
 
   // Video mode gets noise controls, text mode doesn't
@@ -107,8 +105,7 @@ export function useStreamState() {
         };
       }
       // Fallback to derived defaults if schemas not loaded
-      // Mode is derived from pipelines.ts to stay in sync
-      return getFallbackDefaults(pipelineId, mode);
+      return getFallbackDefaults(mode);
     },
     [pipelineSchemas]
   );
@@ -126,18 +123,17 @@ export function useStreamState() {
       if (schema?.config_schema?.properties?.noise_scale) {
         return true;
       }
-      // Before schemas load, use fallback knowledge
-      return (
-        pipelineId === "streamdiffusionv2" ||
-        pipelineId === "longlive" ||
-        pipelineId === "krea-realtime-video"
-      );
+      // If schemas haven't loaded yet, return false (controls will appear once schemas load)
+      return false;
     },
     [pipelineSchemas]
   );
 
+  // Default pipeline ID to use before schemas load
+  const defaultPipelineId = "longlive";
+
   // Get initial defaults (use fallback since schemas haven't loaded yet)
-  const initialDefaults = getFallbackDefaults("longlive");
+  const initialDefaults = getFallbackDefaults("text");
 
   const [settings, setSettings] = useState<SettingsState>({
     pipelineId: "longlive",
@@ -177,7 +173,26 @@ export function useStreamState() {
         ]);
 
         if (schemasResult.status === "fulfilled") {
-          setPipelineSchemas(schemasResult.value);
+          const schemas = schemasResult.value;
+          setPipelineSchemas(schemas);
+
+          // Check if the default pipeline is available
+          // If not, switch to the first available pipeline
+          const availablePipelines = Object.keys(schemas.pipelines);
+
+          if (
+            !availablePipelines.includes(defaultPipelineId) &&
+            availablePipelines.length > 0
+          ) {
+            const firstPipelineId = availablePipelines[0] as PipelineId;
+            const firstPipelineSchema = schemas.pipelines[firstPipelineId];
+
+            setSettings(prev => ({
+              ...prev,
+              pipelineId: firstPipelineId,
+              inputMode: firstPipelineSchema.default_mode,
+            }));
+          }
         } else {
           console.error(
             "useStreamState: Failed to fetch pipeline schemas:",
@@ -201,31 +216,49 @@ export function useStreamState() {
     fetchInitialData();
   }, []);
 
-  // Set recommended quantization when krea-realtime-video is selected
-  // Reset to null when switching to other pipelines
+  // Update inputMode when schemas load or pipeline changes
+  // This sets the correct default mode for the pipeline
   useEffect(() => {
-    if (settings.pipelineId === "krea-realtime-video") {
-      // Krea pipeline: set quantization based on VRAM if hardware info is available
-      if (
-        hardwareInfo?.vram_gb !== null &&
-        hardwareInfo?.vram_gb !== undefined
-      ) {
-        // > 40GB = no quantization (null), <= 40GB = fp8_e4m3fn
-        const recommendedQuantization =
-          hardwareInfo.vram_gb > 40 ? null : "fp8_e4m3fn";
+    if (pipelineSchemas) {
+      const schema = pipelineSchemas.pipelines[settings.pipelineId];
+      if (schema?.default_mode) {
         setSettings(prev => ({
           ...prev,
-          quantization: recommendedQuantization,
+          inputMode: schema.default_mode,
         }));
       }
+    }
+    // Only run when schemas load or pipeline changes, NOT when inputMode changes
+  }, [pipelineSchemas, settings.pipelineId]);
+
+  // Set recommended quantization based on pipeline schema and available VRAM
+  useEffect(() => {
+    const schema = pipelineSchemas?.pipelines[settings.pipelineId];
+    const vramThreshold = schema?.recommended_quantization_vram_threshold;
+
+    // Only set quantization if pipeline has a recommendation and hardware info is available
+    if (
+      vramThreshold !== null &&
+      vramThreshold !== undefined &&
+      hardwareInfo?.vram_gb !== null &&
+      hardwareInfo?.vram_gb !== undefined
+    ) {
+      // If user's VRAM > threshold, no quantization needed (null)
+      // Otherwise, recommend fp8_e4m3fn quantization
+      const recommendedQuantization =
+        hardwareInfo.vram_gb > vramThreshold ? null : "fp8_e4m3fn";
+      setSettings(prev => ({
+        ...prev,
+        quantization: recommendedQuantization,
+      }));
     } else {
-      // Non-Krea pipeline: reset quantization to null (default for other pipelines)
+      // No recommendation from pipeline: reset quantization to null (default)
       setSettings(prev => ({
         ...prev,
         quantization: null,
       }));
     }
-  }, [settings.pipelineId, hardwareInfo]);
+  }, [settings.pipelineId, hardwareInfo, pipelineSchemas]);
 
   const updateMetrics = useCallback((newMetrics: Partial<SystemMetrics>) => {
     setSystemMetrics(prev => ({ ...prev, ...newMetrics }));
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index bdd19f7f..50fa5b81 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -1,8 +1,4 @@
-import type {
-  LoRAConfig,
-  IceServersResponse,
-  ModelStatusResponse,
-} from "../types";
+import type { IceServersResponse, ModelStatusResponse } from "../types";
 
 export interface PromptItem {
   text: string;
@@ -37,52 +33,12 @@ export interface PipelineLoadParams {
   [key: string]: unknown;
 }
 
-export interface PassthroughLoadParams extends PipelineLoadParams {
-  height?: number;
-  width?: number;
-}
-
-export interface StreamDiffusionV2LoadParams extends PipelineLoadParams {
-  height?: number;
-  width?: number;
-  seed?: number;
-  quantization?: "fp8_e4m3fn" | null;
-  loras?: LoRAConfig[];
-  lora_merge_mode?: "permanent_merge" | "runtime_peft";
-  // VACE (optional reference image conditioning for text mode)
-  vace_ref_images?: string[];
-  vace_context_scale?: number;
-}
-
-export interface LongLiveLoadParams extends PipelineLoadParams {
-  height?: number;
-  width?: number;
-  seed?: number;
-  quantization?: "fp8_e4m3fn" | null;
-  loras?: LoRAConfig[];
-  lora_merge_mode?: "permanent_merge" | "runtime_peft";
-  // VACE (optional reference image conditioning)
-  vace_ref_images?: string[];
-  vace_context_scale?: number;
-}
-
-export interface KreaRealtimeVideoLoadParams extends PipelineLoadParams {
-  height?: number;
-  width?: number;
-  seed?: number;
-  quantization?: "fp8_e4m3fn" | null;
-  loras?: LoRAConfig[];
-  lora_merge_mode?: "permanent_merge" | "runtime_peft";
-}
+// Generic load params - accepts any key-value pairs based on pipeline config
+export type PipelineLoadParamsGeneric = Record<string, unknown>;
 
 export interface PipelineLoadRequest {
   pipeline_id?: string;
-  load_params?:
-    | PassthroughLoadParams
-    | StreamDiffusionV2LoadParams
-    | LongLiveLoadParams
-    | KreaRealtimeVideoLoadParams
-    | null;
+  load_params?: PipelineLoadParamsGeneric | null;
 }
 
 export interface PipelineStatusResponse {
@@ -423,12 +379,29 @@ export interface PipelineSchemaInfo {
   name: string;
   description: string;
   version: string;
+  docs_url: string | null;
+  estimated_vram_gb: number | null;
+  requires_models: boolean;
+  supports_lora: boolean;
+  supports_vace: boolean;
+  // Pipeline config schema
   config_schema: PipelineConfigSchema;
   // Mode support - comes from config class
   supported_modes: ("text" | "video")[];
   default_mode: "text" | "video";
+  // Prompt and temporal interpolation support
+  supports_prompts: boolean;
+  default_temporal_interpolation_method: "linear" | "slerp";
+  default_temporal_interpolation_steps: number;
   // Mode-specific default overrides (optional)
   mode_defaults?: Record<"text" | "video", ModeDefaults>;
+  // UI capabilities
+  supports_cache_management: boolean;
+  supports_kv_cache_bias: boolean;
+  supports_quantization: boolean;
+  min_dimension: number;
+  recommended_quantization_vram_threshold: number | null;
+  modified: boolean;
 }
 
 export interface PipelineSchemasResponse {
diff --git a/frontend/src/pages/StreamPage.tsx b/frontend/src/pages/StreamPage.tsx
index 316100d7..0de1ef2e 100644
--- a/frontend/src/pages/StreamPage.tsx
+++ b/frontend/src/pages/StreamPage.tsx
@@ -12,12 +12,8 @@ import { useVideoSource } from "../hooks/useVideoSource";
 import { useWebRTCStats } from "../hooks/useWebRTCStats";
 import { usePipeline } from "../hooks/usePipeline";
 import { useStreamState } from "../hooks/useStreamState";
-import {
-  PIPELINES,
-  getPipelineDefaultMode,
-  getDefaultPromptForMode,
-  pipelineSupportsVACE,
-} from "../data/pipelines";
+import { usePipelines } from "../hooks/usePipelines";
+import { getDefaultPromptForMode } from "../data/pipelines";
 import { adjustResolutionForPipeline } from "../lib/utils";
 import type {
   InputMode,
@@ -67,6 +63,14 @@ function getVaceParams(
 }
 
 export function StreamPage() {
+  // Fetch available pipelines dynamically
+  const { pipelines } = usePipelines();
+
+  // Helper to get default mode for a pipeline
+  const getPipelineDefaultMode = (pipelineId: string): InputMode => {
+    return pipelines?.[pipelineId]?.defaultMode ?? "text";
+  };
+
   // Use the stream state hook for settings management
   const {
     settings,
@@ -254,7 +258,7 @@ export function StreamPage() {
       stopStream();
     }
 
-    const newPipeline = PIPELINES[pipelineId];
+    const newPipeline = pipelines?.[pipelineId];
     const modeToUse = newPipeline?.defaultMode || "text";
     const currentMode = settings.inputMode || "text";
 
@@ -340,7 +344,7 @@ export function StreamPage() {
 
             // Preserve the current input mode that the user selected before download
             // Only fall back to pipeline's default mode if no mode is currently set
-            const newPipeline = PIPELINES[pipelineId];
+            const newPipeline = pipelines?.[pipelineId];
             const currentMode =
               settings.inputMode || newPipeline?.defaultMode || "text";
             const defaults = getDefaults(pipelineId, currentMode);
@@ -611,9 +615,9 @@ export function StreamPage() {
     return () => document.removeEventListener("keydown", handleKeyDown);
   }, [selectedTimelinePrompt]);
 
-  // Update temporal interpolation defaults when pipeline changes
+  // Update temporal interpolation defaults and clear prompts when pipeline changes
   useEffect(() => {
-    const pipeline = PIPELINES[settings.pipelineId];
+    const pipeline = pipelines?.[settings.pipelineId];
     if (pipeline) {
       const defaultMethod =
         pipeline.defaultTemporalInterpolationMethod || "slerp";
@@ -621,8 +625,13 @@ export function StreamPage() {
 
       setTemporalInterpolationMethod(defaultMethod);
       setTransitionSteps(defaultSteps);
+
+      // Clear prompts if pipeline doesn't support them
+      if (pipeline.supportsPrompts === false) {
+        setPromptItems([{ text: "", weight: 1.0 }]);
+      }
     }
-  }, [settings.pipelineId]);
+  }, [settings.pipelineId, pipelines]);
 
   const handlePlayPauseToggle = () => {
     const newPausedState = !settings.paused;
@@ -662,7 +671,7 @@ export function StreamPage() {
 
     try {
       // Check if models are needed but not downloaded
-      const pipelineInfo = PIPELINES[pipelineIdToUse];
+      const pipelineInfo = pipelines?.[pipelineIdToUse];
       if (pipelineInfo?.requiresModels) {
         try {
           const status = await checkModelStatus(pipelineIdToUse);
@@ -685,9 +694,6 @@ export function StreamPage() {
       const currentMode =
         settings.inputMode || getPipelineDefaultMode(pipelineIdToUse) || "text";
 
-      // Prepare load parameters based on pipeline type
-      let loadParams = null;
-
       // Use settings.resolution if available, otherwise fall back to videoResolution
       let resolution = settings.resolution || videoResolution;
 
@@ -703,71 +709,49 @@ export function StreamPage() {
         }
       }
 
-      // Compute VACE enabled state once - enabled by default for text mode on VACE-supporting pipelines
-      const vaceEnabled =
-        settings.vaceEnabled ??
-        (pipelineSupportsVACE(pipelineIdToUse) && currentMode !== "video");
+      // Build load parameters dynamically based on pipeline capabilities and settings
+      // The backend will use only the parameters it needs based on the pipeline schema
+      const currentPipeline = pipelines?.[pipelineIdToUse];
+      let loadParams: Record<string, unknown> | null = null;
 
-      if (pipelineIdToUse === "streamdiffusionv2" && resolution) {
-        loadParams = {
-          height: resolution.height,
-          width: resolution.width,
-          seed: settings.seed ?? 42,
-          quantization: settings.quantization ?? null,
-          vace_enabled: vaceEnabled,
-          ...buildLoRAParams(settings.loras, settings.loraMergeStrategy),
-          ...getVaceParams(settings.refImages, settings.vaceContextScale),
-        };
-        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 === "passthrough" && resolution) {
-        loadParams = {
-          height: resolution.height,
-          width: resolution.width,
-        };
-        console.log(
-          `Loading with resolution: ${resolution.width}x${resolution.height}`
-        );
-      } else if (pipelineIdToUse === "longlive" && resolution) {
-        loadParams = {
-          height: resolution.height,
-          width: resolution.width,
-          seed: settings.seed ?? 42,
-          quantization: settings.quantization ?? null,
-          vace_enabled: vaceEnabled,
-          ...buildLoRAParams(settings.loras, settings.loraMergeStrategy),
-          ...getVaceParams(settings.refImages, settings.vaceContextScale),
-        };
-        console.log(
-          `Loading with resolution: ${resolution.width}x${resolution.height}, seed: ${loadParams.seed}, quantization: ${loadParams.quantization}`
-        );
-      } else if (pipelineIdToUse === "krea-realtime-video" && resolution) {
-        loadParams = {
-          height: resolution.height,
-          width: resolution.width,
-          seed: settings.seed ?? 42,
-          quantization:
-            settings.quantization !== undefined
-              ? settings.quantization
-              : "fp8_e4m3fn",
-          ...buildLoRAParams(settings.loras, settings.loraMergeStrategy),
-        };
-        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 === "reward-forcing" && resolution) {
+      if (resolution) {
+        // Start with common parameters
         loadParams = {
           height: resolution.height,
           width: resolution.width,
-          seed: settings.seed ?? 42,
-          quantization: settings.quantization ?? null,
-          vace_enabled: vaceEnabled,
-          ...buildLoRAParams(settings.loras, settings.loraMergeStrategy),
-          ...getVaceParams(settings.refImages, settings.vaceContextScale),
         };
+
+        // Add seed if pipeline supports quantization (implies it needs seed)
+        if (currentPipeline?.supportsQuantization) {
+          loadParams.seed = settings.seed ?? 42;
+          loadParams.quantization = settings.quantization ?? null;
+        }
+
+        // Add LoRA parameters if pipeline supports LoRA
+        if (currentPipeline?.supportsLoRA && settings.loras) {
+          const loraParams = buildLoRAParams(
+            settings.loras,
+            settings.loraMergeStrategy
+          );
+          loadParams = { ...loadParams, ...loraParams };
+        }
+
+        // Add VACE parameters if pipeline supports VACE
+        if (currentPipeline?.supportsVACE) {
+          const vaceEnabled = settings.vaceEnabled ?? currentMode !== "video";
+          loadParams.vace_enabled = vaceEnabled;
+
+          // Add VACE reference images if provided
+          const vaceParams = getVaceParams(
+            settings.refImages,
+            settings.vaceContextScale
+          );
+          loadParams = { ...loadParams, ...vaceParams };
+        }
+
         console.log(
-          `Loading with resolution: ${resolution.width}x${resolution.height}, seed: ${loadParams.seed}, quantization: ${loadParams.quantization}, lora_merge_mode: ${loadParams.lora_merge_mode}`
+          `Loading ${pipelineIdToUse} with resolution ${resolution.width}x${resolution.height}`,
+          loadParams
         );
       }
 
@@ -814,7 +798,7 @@ export function StreamPage() {
       };
 
       // Common parameters for pipelines that support prompts
-      if (pipelineIdToUse !== "passthrough") {
+      if (pipelineInfo?.supportsPrompts !== false) {
         initialParameters.prompts = promptItems;
         initialParameters.prompt_interpolation_method = interpolationMethod;
         initialParameters.denoising_step_list = settings.denoisingSteps || [
@@ -822,17 +806,13 @@ export function StreamPage() {
         ];
       }
 
-      // Cache management for krea_realtime_video, longlive, and reward-forcing
-      if (
-        pipelineIdToUse === "krea-realtime-video" ||
-        pipelineIdToUse === "longlive" ||
-        pipelineIdToUse === "reward-forcing"
-      ) {
+      // Cache management for pipelines that support it
+      if (pipelineInfo?.supportsCacheManagement) {
         initialParameters.manage_cache = settings.manageCache ?? true;
       }
 
-      // Krea-realtime-video-specific parameters
-      if (pipelineIdToUse === "krea-realtime-video") {
+      // KV cache bias for pipelines that support it
+      if (pipelineInfo?.supportsKvCacheBias) {
         initialParameters.kv_cache_attention_bias =
           settings.kvCacheAttentionBias ?? 1.0;
       }
@@ -885,6 +865,7 @@ export function StreamPage() {
         <div className="w-1/5">
           <InputAndControlsPanel
             className="h-full"
+            pipelines={pipelines}
             localStream={localStream}
             isInitializing={isInitializing}
             error={videoSourceError}
@@ -931,7 +912,7 @@ export function StreamPage() {
             spoutAvailable={spoutAvailable}
             vaceEnabled={
               settings.vaceEnabled ??
-              (pipelineSupportsVACE(settings.pipelineId) &&
+              (pipelines?.[settings.pipelineId]?.supportsVACE &&
                 settings.inputMode !== "video")
             }
             refImages={settings.refImages || []}
@@ -1049,12 +1030,7 @@ export function StreamPage() {
                   });
                 }
               }}
-              disabled={
-                settings.pipelineId === "passthrough" ||
-                isPipelineLoading ||
-                isConnecting ||
-                showDownloadDialog
-              }
+              disabled={isPipelineLoading || isConnecting || showDownloadDialog}
               isStreaming={isStreaming}
               isVideoPaused={settings.paused}
               timelineRef={timelineRef}
@@ -1084,6 +1060,7 @@ export function StreamPage() {
         <div className="w-1/5">
           <SettingsPanel
             className="h-full"
+            pipelines={pipelines}
             pipelineId={settings.pipelineId}
             onPipelineIdChange={handlePipelineIdChange}
             isStreaming={isStreaming}
@@ -1134,7 +1111,7 @@ export function StreamPage() {
             spoutAvailable={spoutAvailable}
             vaceEnabled={
               settings.vaceEnabled ??
-              (pipelineSupportsVACE(settings.pipelineId) &&
+              (pipelines?.[settings.pipelineId]?.supportsVACE &&
                 settings.inputMode !== "video")
             }
             onVaceEnabledChange={handleVaceEnabledChange}
@@ -1151,6 +1128,7 @@ export function StreamPage() {
       {pipelineNeedsModels && (
         <DownloadDialog
           open={showDownloadDialog}
+          pipelines={pipelines}
           pipelineId={pipelineNeedsModels as PipelineId}
           onClose={handleDialogClose}
           onDownload={handleDownloadModels}
diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts
index c16a0635..ba373389 100644
--- a/frontend/src/types/index.ts
+++ b/frontend/src/types/index.ts
@@ -1,9 +1,5 @@
-export type PipelineId =
-  | "streamdiffusionv2"
-  | "passthrough"
-  | "longlive"
-  | "krea-realtime-video"
-  | "reward-forcing";
+// Pipeline IDs are dynamic - any string returned from backend is valid
+export type PipelineId = string;
 
 // Input mode for pipeline operation
 export type InputMode = "text" | "video";
@@ -88,13 +84,22 @@ export interface PipelineInfo {
   defaultPrompt?: string;
   estimatedVram?: number;
   requiresModels?: boolean;
+  supportsPrompts?: boolean;
   defaultTemporalInterpolationMethod?: "linear" | "slerp";
   defaultTemporalInterpolationSteps?: number;
   supportsLoRA?: boolean;
+  supportsVACE?: boolean;
 
   // Multi-mode support
   supportedModes: InputMode[];
   defaultMode: InputMode;
+
+  // UI capabilities - tells frontend what controls to show
+  supportsCacheManagement?: boolean;
+  supportsKvCacheBias?: boolean;
+  supportsQuantization?: boolean;
+  minDimension?: number;
+  recommendedQuantizationVramThreshold?: number | null;
 }
 
 export interface DownloadProgress {
diff --git a/pyproject.toml b/pyproject.toml
index 2304b825..c0655e5c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,6 +47,8 @@ dependencies = [
     "sageattention==2.2.0; sys_platform == 'linux' or sys_platform == 'win32'",
     "safetensors>=0.6.2",
     "huggingface_hub>=0.25.0",
+    "pluggy>=1.5.0",
+    "click>=8.3.1",
     "peft>=0.17.1",
     "torchao==0.13.0",
     "kernels>=0.10.4",
@@ -66,6 +68,9 @@ Homepage = "https://github.com/daydreamlive/scope"
 Repository = "https://github.com/daydreamlive/scope"
 Issues = "https://github.com/daydreamlive/scope/issues"
 
+[tool.uv]
+preview = true
+
 [tool.uv.extra-build-dependencies]
 flash-attn = [{ requirement = "torch", match-runtime = true, marker = "sys_platform == 'linux' or sys_platform == 'win32'" }]
 
diff --git a/src/scope/core/__init__.py b/src/scope/core/__init__.py
index e69de29b..38baa921 100644
--- a/src/scope/core/__init__.py
+++ b/src/scope/core/__init__.py
@@ -0,0 +1,5 @@
+"""Core functionality for Scope."""
+
+from scope.core.plugins import hookimpl
+
+__all__ = ["hookimpl"]
diff --git a/src/scope/core/pipelines/krea_realtime_video/pipeline.py b/src/scope/core/pipelines/krea_realtime_video/pipeline.py
index f657edcc..082bf9a9 100644
--- a/src/scope/core/pipelines/krea_realtime_video/pipeline.py
+++ b/src/scope/core/pipelines/krea_realtime_video/pipeline.py
@@ -21,7 +21,6 @@
 from ..wan2_1.lora.mixin import LoRAEnabledPipeline
 from ..wan2_1.vae import WanVAEWrapper
 from .modular_blocks import KreaRealtimeVideoBlocks
-from .modules.causal_model import CausalWanModel
 
 if TYPE_CHECKING:
     from ..schema import BasePipelineConfig
@@ -49,6 +48,8 @@ def __init__(
         device: torch.device | None = None,
         dtype: torch.dtype = torch.bfloat16,
     ):
+        from .modules.causal_model import CausalWanModel
+
         # Validate resolution requirements
         # VAE downsample (8) * patch embedding downsample (2) = 16
         validate_resolution(
diff --git a/src/scope/core/pipelines/longlive/pipeline.py b/src/scope/core/pipelines/longlive/pipeline.py
index ea472085..9135d7b2 100644
--- a/src/scope/core/pipelines/longlive/pipeline.py
+++ b/src/scope/core/pipelines/longlive/pipeline.py
@@ -23,7 +23,6 @@
 from ..wan2_1.vace import VACEEnabledPipeline
 from ..wan2_1.vae import WanVAEWrapper
 from .modular_blocks import LongLiveBlocks
-from .modules.causal_model import CausalWanModel
 
 if TYPE_CHECKING:
     from ..schema import BasePipelineConfig
@@ -45,6 +44,8 @@ def __init__(
         device: torch.device | None = None,
         dtype: torch.dtype = torch.bfloat16,
     ):
+        from .modules.causal_model import CausalWanModel
+
         # Validate resolution requirements
         # VAE downsample (8) * patch embedding downsample (2) = 16
         validate_resolution(
diff --git a/src/scope/core/pipelines/passthrough/pipeline.py b/src/scope/core/pipelines/passthrough/pipeline.py
index 05ab6dfa..eaea5aab 100644
--- a/src/scope/core/pipelines/passthrough/pipeline.py
+++ b/src/scope/core/pipelines/passthrough/pipeline.py
@@ -48,9 +48,8 @@ def __call__(
             raise ValueError("Input cannot be None for PassthroughPipeline")
 
         if isinstance(input, list):
-            input = preprocess_chunk(
-                input, self.device, self.dtype, height=self.height, width=self.width
-            )
+            # Don't resize for passthrough - preserve original input resolution
+            input = preprocess_chunk(input, self.device, self.dtype)
 
         input = rearrange(input, "B C T H W -> B T C H W")
 
diff --git a/src/scope/core/pipelines/registry.py b/src/scope/core/pipelines/registry.py
index 493804be..b7c18ccf 100644
--- a/src/scope/core/pipelines/registry.py
+++ b/src/scope/core/pipelines/registry.py
@@ -5,12 +5,18 @@
 metadata retrieval.
 """
 
+import importlib
+import logging
 from typing import TYPE_CHECKING
 
+import torch
+
 if TYPE_CHECKING:
     from .interface import Pipeline
     from .schema import BasePipelineConfig
 
+logger = logging.getLogger(__name__)
+
 
 class PipelineRegistry:
     """Registry for managing available pipelines."""
@@ -64,27 +70,122 @@ def list_pipelines(cls) -> list[str]:
         return list(cls._pipelines.keys())
 
 
+def _get_gpu_vram_gb() -> float | None:
+    """Get total GPU VRAM in GB if available.
+
+    Returns:
+        Total VRAM in GB if GPU is available, None otherwise
+    """
+    try:
+        if torch.cuda.is_available():
+            _, total_mem = torch.cuda.mem_get_info(0)
+            return total_mem / (1024**3)
+    except Exception as e:
+        logger.warning(f"Failed to get GPU VRAM info: {e}")
+    return None
+
+
+def _should_register_pipeline(
+    estimated_vram_gb: float | None, vram_gb: float | None
+) -> bool:
+    """Determine if a pipeline should be registered based on GPU requirements.
+
+    Args:
+        estimated_vram_gb: Estimated/required VRAM in GB from pipeline config,
+            or None if no requirement
+        vram_gb: Total GPU VRAM in GB, or None if no GPU
+
+    Returns:
+        True if the pipeline should be registered, False otherwise
+    """
+    return estimated_vram_gb is None or vram_gb is not None
+
+
 # Register all available pipelines
 def _register_pipelines():
-    """Register all built-in pipelines."""
-    # Import lazily to avoid circular imports and heavy dependencies
-    from .krea_realtime_video.pipeline import KreaRealtimeVideoPipeline
-    from .longlive.pipeline import LongLivePipeline
-    from .passthrough.pipeline import PassthroughPipeline
-    from .reward_forcing.pipeline import RewardForcingPipeline
-    from .streamdiffusionv2.pipeline import StreamDiffusionV2Pipeline
-
-    # Register each pipeline with its ID from its config class
-    for pipeline_class in [
-        LongLivePipeline,
-        KreaRealtimeVideoPipeline,
-        StreamDiffusionV2Pipeline,
-        PassthroughPipeline,
-        RewardForcingPipeline,
-    ]:
-        config_class = pipeline_class.get_config_class()
-        PipelineRegistry.register(config_class.pipeline_id, pipeline_class)
+    """Register pipelines based on GPU availability and requirements."""
+    # Check GPU VRAM
+    vram_gb = _get_gpu_vram_gb()
+
+    if vram_gb is not None:
+        logger.info(f"GPU detected with {vram_gb:.1f} GB VRAM")
+    else:
+        logger.info("No GPU detected")
+
+    # Define pipeline imports with their module paths and class names
+    pipeline_configs = [
+        (
+            "streamdiffusionv2",
+            ".streamdiffusionv2.pipeline",
+            "StreamDiffusionV2Pipeline",
+        ),
+        ("longlive", ".longlive.pipeline", "LongLivePipeline"),
+        (
+            "krea_realtime_video",
+            ".krea_realtime_video.pipeline",
+            "KreaRealtimeVideoPipeline",
+        ),
+        (
+            "reward_forcing",
+            ".reward_forcing.pipeline",
+            "RewardForcingPipeline",
+        ),
+        ("passthrough", ".passthrough.pipeline", "PassthroughPipeline"),
+    ]
+
+    # Try to import and register each pipeline
+    for pipeline_name, module_path, class_name in pipeline_configs:
+        # Try to import the pipeline first to get its config
+        try:
+            module = importlib.import_module(module_path, package=__package__)
+            pipeline_class = getattr(module, class_name)
+
+            # Get the config class to check VRAM requirements
+            config_class = pipeline_class.get_config_class()
+            estimated_vram_gb = config_class.estimated_vram_gb
+
+            # Check if pipeline meets GPU requirements
+            should_register = _should_register_pipeline(estimated_vram_gb, vram_gb)
+            if not should_register:
+                logger.debug(
+                    f"Skipping {pipeline_name} pipeline - "
+                    f"does not meet GPU requirements "
+                    f"(required: {estimated_vram_gb} GB, "
+                    f"available: {vram_gb} GB)"
+                )
+                continue
+
+            # Register the pipeline
+            PipelineRegistry.register(config_class.pipeline_id, pipeline_class)
+            logger.debug(
+                f"Registered {pipeline_name} pipeline (ID: {config_class.pipeline_id})"
+            )
+        except ImportError as e:
+            logger.warning(
+                f"Could not import {pipeline_name} pipeline: {e}. "
+                f"This pipeline will not be available."
+            )
+        except Exception as e:
+            logger.warning(
+                f"Error loading {pipeline_name} pipeline: {e}. "
+                f"This pipeline will not be available."
+            )
+
+
+def _initialize_registry():
+    """Initialize registry with built-in pipelines and plugins."""
+    # Register built-in pipelines first
+    _register_pipelines()
+
+    # Load and register plugin pipelines
+    from scope.core.plugins import load_plugins, register_plugin_pipelines
+
+    load_plugins()
+    register_plugin_pipelines(PipelineRegistry)
+
+    pipeline_count = len(PipelineRegistry.list_pipelines())
+    logger.info(f"Registry initialized with {pipeline_count} pipeline(s)")
 
 
 # Auto-register pipelines on module import
-_register_pipelines()
+_initialize_registry()
diff --git a/src/scope/core/pipelines/reward_forcing/pipeline.py b/src/scope/core/pipelines/reward_forcing/pipeline.py
index 089ce5b6..439cda7d 100644
--- a/src/scope/core/pipelines/reward_forcing/pipeline.py
+++ b/src/scope/core/pipelines/reward_forcing/pipeline.py
@@ -22,7 +22,6 @@
 from ..wan2_1.vace.mixin import VACEEnabledPipeline
 from ..wan2_1.vae import WanVAEWrapper
 from .modular_blocks import RewardForcingBlocks
-from .modules.causal_model import CausalWanModel
 
 if TYPE_CHECKING:
     from ..schema import BasePipelineConfig
@@ -44,6 +43,8 @@ def __init__(
         device: torch.device | None = None,
         dtype: torch.dtype = torch.bfloat16,
     ):
+        from .modules.causal_model import CausalWanModel
+
         model_dir = getattr(config, "model_dir", None)
         generator_path = getattr(config, "generator_path", None)
         text_encoder_path = getattr(config, "text_encoder_path", None)
diff --git a/src/scope/core/pipelines/schema.py b/src/scope/core/pipelines/schema.py
index 36a0a69f..354916f8 100644
--- a/src/scope/core/pipelines/schema.py
+++ b/src/scope/core/pipelines/schema.py
@@ -59,11 +59,35 @@ class BasePipelineConfig(BaseModel):
     pipeline_name: ClassVar[str] = "Base Pipeline"
     pipeline_description: ClassVar[str] = "Base pipeline configuration"
     pipeline_version: ClassVar[str] = "1.0.0"
+    docs_url: ClassVar[str | None] = None
+    estimated_vram_gb: ClassVar[float | None] = None
+    requires_models: ClassVar[bool] = False
+    supports_lora: ClassVar[bool] = False
+    supports_vace: ClassVar[bool] = False
+
+    # UI capability metadata - tells frontend what controls to show
+    supports_cache_management: ClassVar[bool] = False
+    supports_kv_cache_bias: ClassVar[bool] = False
+    supports_quantization: ClassVar[bool] = False
+    min_dimension: ClassVar[int] = 1
+    # Whether this pipeline contains modifications based on the original project
+    modified: ClassVar[bool] = False
+    # Recommended quantization based on VRAM: if user's VRAM > this threshold (GB),
+    # quantization=null is recommended, otherwise fp8_e4m3fn is recommended.
+    # None means no specific recommendation (pipeline doesn't benefit from quantization).
+    recommended_quantization_vram_threshold: ClassVar[float | None] = None
 
     # Mode support - override in subclasses
     supported_modes: ClassVar[list[InputMode]] = ["text"]
     default_mode: ClassVar[InputMode] = "text"
 
+    # Prompt and temporal interpolation support
+    supports_prompts: ClassVar[bool] = True
+    default_temporal_interpolation_method: ClassVar[Literal["linear", "slerp"]] = (
+        "slerp"
+    )
+    default_temporal_interpolation_steps: ClassVar[int] = 0
+
     # Resolution settings
     height: int = Field(default=512, ge=1, description="Output height in pixels")
     width: int = Field(default=512, ge=1, description="Output width in pixels")
@@ -154,16 +178,31 @@ def get_schema_with_metadata(cls) -> dict[str, Any]:
         This is the primary method for API/UI schema generation.
 
         Returns:
-            Dict containing:
-            - Pipeline metadata (id, name, description, version)
-            - supported_modes: List of supported input modes
-            - default_mode: Default input mode
-            - mode_defaults: Dict of mode-specific default overrides
-            - config_schema: Full JSON schema for the config model
+            Dict containing pipeline metadata
         """
         metadata = cls.get_pipeline_metadata()
         metadata["supported_modes"] = cls.supported_modes
         metadata["default_mode"] = cls.default_mode
+        metadata["supports_prompts"] = cls.supports_prompts
+        metadata["default_temporal_interpolation_method"] = (
+            cls.default_temporal_interpolation_method
+        )
+        metadata["default_temporal_interpolation_steps"] = (
+            cls.default_temporal_interpolation_steps
+        )
+        metadata["docs_url"] = cls.docs_url
+        metadata["estimated_vram_gb"] = cls.estimated_vram_gb
+        metadata["requires_models"] = cls.requires_models
+        metadata["supports_lora"] = cls.supports_lora
+        metadata["supports_vace"] = cls.supports_vace
+        metadata["supports_cache_management"] = cls.supports_cache_management
+        metadata["supports_kv_cache_bias"] = cls.supports_kv_cache_bias
+        metadata["supports_quantization"] = cls.supports_quantization
+        metadata["min_dimension"] = cls.min_dimension
+        metadata["recommended_quantization_vram_threshold"] = (
+            cls.recommended_quantization_vram_threshold
+        )
+        metadata["modified"] = cls.modified
         metadata["config_schema"] = cls.model_json_schema()
 
         # Include mode-specific defaults if defined
@@ -188,40 +227,62 @@ def is_video_mode(self) -> bool:
 # Concrete pipeline configurations
 
 
-class LongLiveConfig(BasePipelineConfig):
-    """Configuration for LongLive pipeline.
+class StreamDiffusionV2Config(BasePipelineConfig):
+    """Configuration for StreamDiffusion V2 pipeline.
 
-    LongLive supports both text-to-video and video-to-video modes.
-    Default mode is text (T2V was the original training focus).
+    StreamDiffusionV2 supports both text-to-video and video-to-video modes.
+    Default mode is video (V2V was the original training focus).
     """
 
-    pipeline_id: ClassVar[str] = "longlive"
-    pipeline_name: ClassVar[str] = "LongLive"
+    pipeline_id: ClassVar[str] = "streamdiffusionv2"
+    pipeline_name: ClassVar[str] = "StreamDiffusionV2"
     pipeline_description: ClassVar[str] = (
-        "Long-form video generation with temporal consistency"
+        "A streaming pipeline and autoregressive video diffusion model from the creators of the original "
+        "StreamDiffusion project. The model is trained using Self-Forcing on Wan2.1 1.3b with modifications "
+        "to support streaming."
+    )
+    docs_url: ClassVar[str | None] = (
+        "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/streamdiffusionv2/docs/usage.md"
     )
+    estimated_vram_gb: ClassVar[float | None] = 20.0
+    requires_models: ClassVar[bool] = True
+    supports_lora: ClassVar[bool] = True
+    supports_vace: ClassVar[bool] = True
+
+    # UI capabilities
+    supports_cache_management: ClassVar[bool] = True
+    supports_quantization: ClassVar[bool] = True
+    min_dimension: ClassVar[int] = 16
+    modified: ClassVar[bool] = True
 
     # Mode support
     supported_modes: ClassVar[list[InputMode]] = ["text", "video"]
-    default_mode: ClassVar[InputMode] = "text"
+    default_mode: ClassVar[InputMode] = "video"
 
-    # LongLive defaults (text mode baseline)
-    height: int = Field(default=320, ge=1, description="Output height in pixels")
-    width: int = Field(default=576, ge=1, description="Output width in pixels")
+    # StreamDiffusion V2 defaults (video mode baseline since it's the default)
+    height: int = Field(default=512, ge=1, description="Output height in pixels")
+    width: int = Field(default=512, ge=1, description="Output width in pixels")
     denoising_steps: list[int] | None = Field(
-        default=[1000, 750, 500, 250],
+        default=[750, 250],
         description="Denoising step schedule for progressive generation",
     )
-    # noise_scale is None by default (text mode), overridden in video mode
     noise_scale: Annotated[float, Field(ge=0.0, le=1.0)] | None = Field(
-        default=None,
-        description="Amount of noise to add during video generation (video mode only)",
+        default=0.7,
+        description="Amount of noise to add during video generation",
+    )
+    noise_controller: bool | None = Field(
+        default=True,
+        description="Enable dynamic noise control during generation",
+    )
+    input_size: int | None = Field(
+        default=4,
+        description="Expected input video frame count",
     )
 
-    # VACE (optional reference image conditioning)
+    # VACE (optional reference image conditioning for text mode)
     ref_images: list[str] | None = Field(
         default=None,
-        description="List of reference image paths for VACE conditioning",
+        description="List of reference image paths for VACE conditioning in text mode",
     )
     vace_context_scale: float = Field(
         default=1.0,
@@ -232,65 +293,73 @@ class LongLiveConfig(BasePipelineConfig):
 
     @classmethod
     def get_mode_defaults(cls) -> dict[InputMode, ModeDefaults]:
-        """LongLive mode-specific defaults."""
+        """StreamDiffusionV2 mode-specific defaults."""
         return {
             "text": ModeDefaults(
-                # Text mode: no video input, no noise controls
+                # Text mode: distinct resolution, no video input, no noise controls
+                height=512,
+                width=512,
                 noise_scale=None,
                 noise_controller=None,
+                denoising_steps=[1000, 750],
             ),
             "video": ModeDefaults(
                 # Video mode: requires input frames, noise controls active
-                height=512,
-                width=512,
                 noise_scale=0.7,
                 noise_controller=True,
-                denoising_steps=[1000, 750],
             ),
         }
 
 
-class StreamDiffusionV2Config(BasePipelineConfig):
-    """Configuration for StreamDiffusion V2 pipeline.
+class LongLiveConfig(BasePipelineConfig):
+    """Configuration for LongLive pipeline.
 
-    StreamDiffusionV2 supports both text-to-video and video-to-video modes.
-    Default mode is video (V2V was the original training focus).
+    LongLive supports both text-to-video and video-to-video modes.
+    Default mode is text (T2V was the original training focus).
     """
 
-    pipeline_id: ClassVar[str] = "streamdiffusionv2"
-    pipeline_name: ClassVar[str] = "StreamDiffusion V2"
+    pipeline_id: ClassVar[str] = "longlive"
+    pipeline_name: ClassVar[str] = "LongLive"
     pipeline_description: ClassVar[str] = (
-        "Real-time video-to-video generation with temporal consistency"
+        "A streaming pipeline and autoregressive video diffusion model from Nvidia, MIT, HKUST, HKU and THU. "
+        "The model is trained using Self-Forcing on Wan2.1 1.3b with modifications to support smoother prompt "
+        "switching and improved quality over longer time periods while maintaining fast generation."
+    )
+    docs_url: ClassVar[str | None] = (
+        "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/longlive/docs/usage.md"
     )
+    estimated_vram_gb: ClassVar[float | None] = 20.0
+    requires_models: ClassVar[bool] = True
+    supports_lora: ClassVar[bool] = True
+    supports_vace: ClassVar[bool] = True
+
+    # UI capabilities
+    supports_cache_management: ClassVar[bool] = True
+    supports_quantization: ClassVar[bool] = True
+    min_dimension: ClassVar[int] = 16
+    modified: ClassVar[bool] = True
 
     # Mode support
     supported_modes: ClassVar[list[InputMode]] = ["text", "video"]
-    default_mode: ClassVar[InputMode] = "video"
+    default_mode: ClassVar[InputMode] = "text"
 
-    # StreamDiffusion V2 defaults (video mode baseline since it's the default)
-    height: int = Field(default=512, ge=1, description="Output height in pixels")
-    width: int = Field(default=512, ge=1, description="Output width in pixels")
+    # LongLive defaults (text mode baseline)
+    height: int = Field(default=320, ge=1, description="Output height in pixels")
+    width: int = Field(default=576, ge=1, description="Output width in pixels")
     denoising_steps: list[int] | None = Field(
-        default=[750, 250],
+        default=[1000, 750, 500, 250],
         description="Denoising step schedule for progressive generation",
     )
+    # noise_scale is None by default (text mode), overridden in video mode
     noise_scale: Annotated[float, Field(ge=0.0, le=1.0)] | None = Field(
-        default=0.7,
-        description="Amount of noise to add during video generation",
-    )
-    noise_controller: bool | None = Field(
-        default=True,
-        description="Enable dynamic noise control during generation",
-    )
-    input_size: int | None = Field(
-        default=4,
-        description="Expected input video frame count",
+        default=None,
+        description="Amount of noise to add during video generation (video mode only)",
     )
 
-    # VACE (optional reference image conditioning for text mode)
+    # VACE (optional reference image conditioning)
     ref_images: list[str] | None = Field(
         default=None,
-        description="List of reference image paths for VACE conditioning in text mode",
+        description="List of reference image paths for VACE conditioning",
     )
     vace_context_scale: float = Field(
         default=1.0,
@@ -301,20 +370,20 @@ class StreamDiffusionV2Config(BasePipelineConfig):
 
     @classmethod
     def get_mode_defaults(cls) -> dict[InputMode, ModeDefaults]:
-        """StreamDiffusionV2 mode-specific defaults."""
+        """LongLive mode-specific defaults."""
         return {
             "text": ModeDefaults(
-                # Text mode: distinct resolution, no video input, no noise controls
-                height=512,
-                width=512,
+                # Text mode: no video input, no noise controls
                 noise_scale=None,
                 noise_controller=None,
-                denoising_steps=[1000, 750],
             ),
             "video": ModeDefaults(
                 # Video mode: requires input frames, noise controls active
+                height=512,
+                width=512,
                 noise_scale=0.7,
                 noise_controller=True,
+                denoising_steps=[1000, 750],
             ),
         }
 
@@ -329,8 +398,29 @@ class KreaRealtimeVideoConfig(BasePipelineConfig):
     pipeline_id: ClassVar[str] = "krea-realtime-video"
     pipeline_name: ClassVar[str] = "Krea Realtime Video"
     pipeline_description: ClassVar[str] = (
-        "High-quality real-time video generation with 14B model"
+        "A streaming pipeline and autoregressive video diffusion model from Krea. "
+        "The model is trained using Self-Forcing on Wan2.1 14b."
+    )
+    docs_url: ClassVar[str | None] = (
+        "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/krea_realtime_video/docs/usage.md"
+    )
+    estimated_vram_gb: ClassVar[float | None] = 32.0
+    requires_models: ClassVar[bool] = True
+    supports_lora: ClassVar[bool] = True
+
+    # UI capabilities
+    supports_cache_management: ClassVar[bool] = True
+    supports_kv_cache_bias: ClassVar[bool] = True
+    supports_quantization: ClassVar[bool] = True
+    min_dimension: ClassVar[int] = 16
+    modified: ClassVar[bool] = True
+    # Recommend quantization for systems with <= 40GB VRAM
+    recommended_quantization_vram_threshold: ClassVar[float | None] = 40.0
+
+    default_temporal_interpolation_method: ClassVar[Literal["linear", "slerp"]] = (
+        "linear"
     )
+    default_temporal_interpolation_steps: ClassVar[int] = 4
 
     # Mode support
     supported_modes: ClassVar[list[InputMode]] = ["text", "video"]
@@ -379,8 +469,22 @@ class RewardForcingConfig(BasePipelineConfig):
     pipeline_id: ClassVar[str] = "reward-forcing"
     pipeline_name: ClassVar[str] = "RewardForcing"
     pipeline_description: ClassVar[str] = (
-        "Efficient streaming video generation with rewarded distribution matching distillation"
+        "A streaming pipeline and autoregressive video diffusion model from ZJU, Ant Group, SIAS-ZJU, HUST and SJTU. "
+        "The model is trained with Rewarded Distribution Matching Distillation using Wan2.1 1.3b as the base model."
+    )
+    docs_url: ClassVar[str | None] = (
+        "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/reward_forcing/docs/usage.md"
     )
+    estimated_vram_gb: ClassVar[float | None] = 20.0
+    requires_models: ClassVar[bool] = True
+    supports_lora: ClassVar[bool] = True
+    supports_vace: ClassVar[bool] = True
+
+    # UI capabilities
+    supports_cache_management: ClassVar[bool] = True
+    supports_quantization: ClassVar[bool] = True
+    min_dimension: ClassVar[int] = 16
+    modified: ClassVar[bool] = True
 
     # Mode support
     supported_modes: ClassVar[list[InputMode]] = ["text", "video"]
@@ -501,12 +605,17 @@ class PassthroughConfig(BasePipelineConfig):
 
     pipeline_id: ClassVar[str] = "passthrough"
     pipeline_name: ClassVar[str] = "Passthrough"
-    pipeline_description: ClassVar[str] = "Passthrough pipeline for testing"
+    pipeline_description: ClassVar[str] = (
+        "A pipeline that returns the input video without any processing that is useful for testing and debugging."
+    )
 
     # Mode support - video only
     supported_modes: ClassVar[list[InputMode]] = ["video"]
     default_mode: ClassVar[InputMode] = "video"
 
+    # Does not support prompts
+    supports_prompts: ClassVar[bool] = False
+
     # Passthrough defaults - requires video input (distinct from StreamDiffusionV2)
     height: int = Field(default=512, ge=1, description="Output height in pixels")
     width: int = Field(default=512, ge=1, description="Output width in pixels")
@@ -515,11 +624,20 @@ class PassthroughConfig(BasePipelineConfig):
         description="Expected input video frame count",
     )
 
+    @classmethod
+    def get_mode_defaults(cls) -> dict[InputMode, ModeDefaults]:
+        """Passthrough mode-specific defaults - no noise controls."""
+        return {
+            "video": ModeDefaults(
+                # No noise controls for passthrough - it just passes frames through
+            ),
+        }
+
 
 # Registry of pipeline config classes
 PIPELINE_CONFIGS: dict[str, type[BasePipelineConfig]] = {
-    "longlive": LongLiveConfig,
     "streamdiffusionv2": StreamDiffusionV2Config,
+    "longlive": LongLiveConfig,
     "krea-realtime-video": KreaRealtimeVideoConfig,
     "reward-forcing": RewardForcingConfig,
     "passthrough": PassthroughConfig,
diff --git a/src/scope/core/pipelines/streamdiffusionv2/pipeline.py b/src/scope/core/pipelines/streamdiffusionv2/pipeline.py
index ef8f63fc..93a4a8e1 100644
--- a/src/scope/core/pipelines/streamdiffusionv2/pipeline.py
+++ b/src/scope/core/pipelines/streamdiffusionv2/pipeline.py
@@ -22,7 +22,6 @@
 from ..wan2_1.vace import VACEEnabledPipeline
 from .components import StreamDiffusionV2WanVAEWrapper
 from .modular_blocks import StreamDiffusionV2Blocks
-from .modules.causal_model import CausalWanModel
 
 if TYPE_CHECKING:
     from ..schema import BasePipelineConfig
@@ -44,6 +43,8 @@ def __init__(
         device: torch.device | None = None,
         dtype: torch.dtype = torch.bfloat16,
     ):
+        from .modules.causal_model import CausalWanModel
+
         # Validate resolution requirements
         # VAE downsample (8) * patch embedding downsample (2) = 16
         validate_resolution(
diff --git a/src/scope/core/plugins/__init__.py b/src/scope/core/plugins/__init__.py
new file mode 100644
index 00000000..03b4ac6f
--- /dev/null
+++ b/src/scope/core/plugins/__init__.py
@@ -0,0 +1,6 @@
+"""Plugin system for Scope."""
+
+from .hookspecs import hookimpl
+from .manager import load_plugins, pm, register_plugin_pipelines
+
+__all__ = ["hookimpl", "load_plugins", "pm", "register_plugin_pipelines"]
diff --git a/src/scope/core/plugins/hookspecs.py b/src/scope/core/plugins/hookspecs.py
new file mode 100644
index 00000000..62ce4f67
--- /dev/null
+++ b/src/scope/core/plugins/hookspecs.py
@@ -0,0 +1,24 @@
+"""Hook specifications for the Scope plugin system."""
+
+import pluggy
+
+hookspec = pluggy.HookspecMarker("scope")
+hookimpl = pluggy.HookimplMarker("scope")
+
+
+class ScopeHookSpec:
+    """Hook specifications for Scope plugins."""
+
+    @hookspec
+    def register_pipelines(self, register):
+        """Register custom pipeline implementations.
+
+        Args:
+            register: Callback to register pipeline classes.
+                     Usage: register(PipelineClass)
+
+        Example:
+            @scope.core.hookimpl
+            def register_pipelines(register):
+                register(MyPipeline)
+        """
diff --git a/src/scope/core/plugins/manager.py b/src/scope/core/plugins/manager.py
new file mode 100644
index 00000000..9a724a42
--- /dev/null
+++ b/src/scope/core/plugins/manager.py
@@ -0,0 +1,36 @@
+"""Plugin manager for discovering and loading Scope plugins."""
+
+import logging
+
+import pluggy
+
+from .hookspecs import ScopeHookSpec
+
+logger = logging.getLogger(__name__)
+
+# Create the plugin manager singleton
+pm = pluggy.PluginManager("scope")
+pm.add_hookspecs(ScopeHookSpec)
+
+
+def load_plugins():
+    """Discover and load all plugins via entry points."""
+    pm.load_setuptools_entrypoints("scope")
+    logger.info(f"Loaded {len(pm.get_plugins())} plugin(s)")
+
+
+def register_plugin_pipelines(registry):
+    """Call register_pipelines hook for all plugins.
+
+    Args:
+        registry: PipelineRegistry to register pipelines with
+    """
+
+    def register_callback(pipeline_class):
+        """Callback function passed to plugins."""
+        config_class = pipeline_class.get_config_class()
+        pipeline_id = config_class.pipeline_id
+        registry.register(pipeline_id, pipeline_class)
+        logger.info(f"Registered plugin pipeline: {pipeline_id}")
+
+    pm.hook.register_pipelines(register=register_callback)
diff --git a/src/scope/server/app.py b/src/scope/server/app.py
index 16f0e453..344692ca 100644
--- a/src/scope/server/app.py
+++ b/src/scope/server/app.py
@@ -1,18 +1,22 @@
-import argparse
 import asyncio
+import contextlib
+import io
 import logging
 import os
 import subprocess
 import sys
 import threading
 import time
+import warnings
 import webbrowser
 from contextlib import asynccontextmanager
 from datetime import datetime
+from functools import wraps
 from importlib.metadata import version
 from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
+import click
 import torch
 import uvicorn
 from fastapi import Depends, FastAPI, HTTPException, Query, Request
@@ -117,6 +121,28 @@ def filter(self, record):
 logger = logging.getLogger(__name__)
 
 
+def suppress_init_output(func):
+    """Decorator to suppress all initialization output (logging, warnings, stdout/stderr)."""
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        with (
+            contextlib.redirect_stdout(io.StringIO()),
+            contextlib.redirect_stderr(io.StringIO()),
+            warnings.catch_warnings(),
+        ):
+            warnings.simplefilter("ignore")
+            # Temporarily disable all logging
+            logging.disable(logging.CRITICAL)
+            try:
+                return func(*args, **kwargs)
+            finally:
+                # Re-enable logging
+                logging.disable(logging.NOTSET)
+
+    return wrapper
+
+
 def get_git_commit_hash() -> str:
     """
     Get the current git commit hash.
@@ -346,15 +372,17 @@ async def get_pipeline_schemas():
 
     The frontend should use this as the source of truth for parameter defaults.
     """
-    from scope.core.pipelines.schema import PIPELINE_CONFIGS
+    from scope.core.pipelines.registry import PipelineRegistry
 
     pipelines: dict = {}
 
-    for pipeline_id, config_class in PIPELINE_CONFIGS.items():
-        # get_schema_with_metadata() now includes supported_modes, default_mode,
-        # and mode_defaults directly from the config class
-        schema_data = config_class.get_schema_with_metadata()
-        pipelines[pipeline_id] = schema_data
+    for pipeline_id in PipelineRegistry.list_pipelines():
+        config_class = PipelineRegistry.get_config_class(pipeline_id)
+        if config_class:
+            # get_schema_with_metadata() includes supported_modes, default_mode,
+            # and mode_defaults directly from the config class
+            schema_data = config_class.get_schema_with_metadata()
+            pipelines[pipeline_id] = schema_data
 
     return PipelineSchemasResponse(pipelines=pipelines)
 
@@ -845,40 +873,12 @@ def open_browser_when_ready(host: str, port: int, server):
         logger.info(f"🌐 UI is available at: {url}")
 
 
-def main():
-    """Main entry point for the daydream-scope command."""
-    parser = argparse.ArgumentParser(
-        description="A tool for running and customizing real-time, interactive generative AI pipelines and models"
-    )
-    parser.add_argument(
-        "--version",
-        action="store_true",
-        help="Show version information and exit",
-    )
-    parser.add_argument(
-        "--reload",
-        action="store_true",
-        help="Enable auto-reload for development (default: False)",
-    )
-    parser.add_argument(
-        "--host", default="0.0.0.0", help="Host to bind to (default: 0.0.0.0)"
-    )
-    parser.add_argument(
-        "--port", type=int, default=8000, help="Port to bind to (default: 8000)"
-    )
-    parser.add_argument(
-        "-N",
-        "--no-browser",
-        action="store_true",
-        help="Do not automatically open a browser window after the server starts",
-    )
-
-    args = parser.parse_args()
+def run_server(reload: bool, host: str, port: int, no_browser: bool):
+    """Run the Daydream Scope server."""
 
-    # Handle version flag
-    if args.version:
-        print_version_info()
-        sys.exit(0)
+    from scope.core.pipelines.registry import (
+        PipelineRegistry,  # noqa: F401 - imported for side effects (registry initialization)
+    )
 
     # Configure static file serving
     configure_static_files()
@@ -891,18 +891,18 @@ def main():
         # Create server instance for production mode
         config = uvicorn.Config(
             "scope.server.app:app",
-            host=args.host,
-            port=args.port,
-            reload=args.reload,
+            host=host,
+            port=port,
+            reload=reload,
             log_config=None,  # Use our logging config, don't override it
         )
         server = uvicorn.Server(config)
 
         # Start browser opening thread (unless disabled)
-        if not args.no_browser:
+        if not no_browser:
             browser_thread = threading.Thread(
                 target=open_browser_when_ready,
-                args=(args.host, args.port, server),
+                args=(host, port, server),
                 daemon=True,
             )
             browser_thread.start()
@@ -918,12 +918,148 @@ def main():
         # Development mode - just run normally
         uvicorn.run(
             "scope.server.app:app",
-            host=args.host,
-            port=args.port,
-            reload=args.reload,
+            host=host,
+            port=port,
+            reload=reload,
             log_config=None,  # Use our logging config, don't override it
         )
 
 
+@click.group(invoke_without_command=True)
+@click.option("--version", is_flag=True, help="Show version information and exit")
+@click.option(
+    "--reload", is_flag=True, help="Enable auto-reload for development (default: False)"
+)
+@click.option("--host", default="0.0.0.0", help="Host to bind to (default: 0.0.0.0)")
+@click.option("--port", default=8000, help="Port to bind to (default: 8000)")
+@click.option(
+    "-N",
+    "--no-browser",
+    is_flag=True,
+    help="Do not automatically open a browser window after the server starts",
+)
+@click.pass_context
+def main(ctx, version: bool, reload: bool, host: str, port: int, no_browser: bool):
+    # Handle version flag
+    if version:
+        print_version_info()
+        sys.exit(0)
+
+    # If no subcommand was invoked, run the server
+    if ctx.invoked_subcommand is None:
+        run_server(reload, host, port, no_browser)
+
+
+def _check_preview_flag():
+    """Check if the DAYDREAM_SCOPE_PREVIEW feature flag is enabled."""
+    if os.environ.get("DAYDREAM_SCOPE_PREVIEW", "").lower() not in ("1", "true", "yes"):
+        click.echo(
+            "Error: This command requires the DAYDREAM_SCOPE_PREVIEW feature flag.\n"
+            "Set DAYDREAM_SCOPE_PREVIEW=1 to enable preview features.",
+            err=True,
+        )
+        sys.exit(1)
+
+
+@main.command()
+def plugins():
+    """List all installed plugins."""
+    _check_preview_flag()
+
+    @suppress_init_output
+    def _load_plugins():
+        from scope.core.plugins import load_plugins, pm
+
+        load_plugins()
+        return pm.get_plugins()
+
+    plugin_list = _load_plugins()
+
+    if not plugin_list:
+        click.echo("No plugins installed.")
+        return
+
+    click.echo(f"{len(plugin_list)} plugin(s) installed:\n")
+
+    # List each plugin
+    for plugin in plugin_list:
+        plugin_name = plugin.__name__ if hasattr(plugin, "__name__") else str(plugin)
+        click.echo(f"  • {plugin_name}")
+
+
+@main.command()
+def pipelines():
+    """List all available pipelines."""
+    _check_preview_flag()
+
+    @suppress_init_output
+    def _load_pipelines():
+        from scope.core.pipelines.registry import PipelineRegistry
+
+        return PipelineRegistry.list_pipelines()
+
+    all_pipelines = _load_pipelines()
+
+    if not all_pipelines:
+        click.echo("No pipelines available.")
+        return
+
+    click.echo(f"{len(all_pipelines)} pipeline(s) available:\n")
+
+    # List all pipelines
+    for pipeline_id in all_pipelines:
+        click.echo(f"  • {pipeline_id}")
+
+
+@main.command()
+@click.argument("packages", nargs=-1, required=False)
+@click.option("--upgrade", is_flag=True, help="Upgrade packages to the latest version")
+@click.option(
+    "-e", "--editable", help="Install a project in editable mode from this path"
+)
+@click.option("--force-reinstall", is_flag=True, help="Force reinstall packages")
+@click.option("--no-cache-dir", is_flag=True, help="Disable the cache")
+@click.option(
+    "--pre", is_flag=True, help="Include pre-release and development versions"
+)
+def install(packages, upgrade, editable, force_reinstall, no_cache_dir, pre):
+    """Install a plugin."""
+    _check_preview_flag()
+    args = ["uv", "pip", "install"]
+    if upgrade:
+        args.append("--upgrade")
+    if editable:
+        args += ["--editable", editable]
+    if force_reinstall:
+        args.append("--force-reinstall")
+    if no_cache_dir:
+        args.append("--no-cache-dir")
+    if pre:
+        args.append("--pre")
+    args += list(packages)
+
+    result = subprocess.run(args, capture_output=False)
+
+    if result.returncode != 0:
+        sys.exit(result.returncode)
+
+
+@main.command()
+@click.argument("packages", nargs=-1, required=True)
+@click.option("-y", "--yes", is_flag=True, help="Don't ask for confirmation")
+def uninstall(packages, yes):
+    """Uninstall a plugin."""
+    _check_preview_flag()
+    args = ["uv", "pip", "uninstall"]
+    args += list(packages)
+    if yes:
+        args.append("-y")
+
+    result = subprocess.run(args, capture_output=False)
+
+    if result.returncode != 0:
+        sys.exit(result.returncode)
+
+
 if __name__ == "__main__":
     main()
diff --git a/src/scope/server/pipeline_manager.py b/src/scope/server/pipeline_manager.py
index 383eb995..2877150f 100644
--- a/src/scope/server/pipeline_manager.py
+++ b/src/scope/server/pipeline_manager.py
@@ -318,6 +318,27 @@ def _load_pipeline_implementation(
         self, pipeline_id: str, load_params: dict | None = None
     ):
         """Synchronous pipeline loading (runs in thread executor)."""
+        from scope.core.pipelines.registry import PipelineRegistry
+
+        # Check if pipeline is in registry
+        pipeline_class = PipelineRegistry.get(pipeline_id)
+
+        # List of built-in pipelines with custom initialization
+        BUILTIN_PIPELINES = {
+            "streamdiffusionv2",
+            "passthrough",
+            "longlive",
+            "krea-realtime-video",
+            "reward-forcing",
+        }
+
+        if pipeline_class is not None and pipeline_id not in BUILTIN_PIPELINES:
+            # Plugin pipeline - instantiate generically with load_params
+            logger.info(f"Loading plugin pipeline: {pipeline_id}")
+            load_params = load_params or {}
+            return pipeline_class(**load_params)
+
+        # Fall through to built-in pipeline initialization
         if pipeline_id == "streamdiffusionv2":
             from scope.core.pipelines import (
                 StreamDiffusionV2Pipeline,
diff --git a/uv.lock b/uv.lock
index 9a4b9d69..40645f25 100644
--- a/uv.lock
+++ b/uv.lock
@@ -532,14 +532,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.3.0"
+version = "8.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/46/61/de6cd827efad202d7057d93e0fed9294b96952e188f7384832791c7b2254/click-8.3.0.tar.gz", hash = "sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4", size = 276943, upload-time = "2025-09-18T17:32:23.696Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/db/d3/9dcc0f5797f070ec8edf30fbadfb200e71d9db6b84d211e3b2085a7589a0/click-8.3.0-py3-none-any.whl", hash = "sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc", size = 107295, upload-time = "2025-09-18T17:32:22.42Z" },
+    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
 ]
 
 [[package]]
@@ -623,6 +623,7 @@ source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "aiortc" },
+    { name = "click" },
     { name = "diffusers" },
     { name = "easydict" },
     { name = "einops" },
@@ -636,6 +637,7 @@ dependencies = [
     { name = "lmdb" },
     { name = "omegaconf" },
     { name = "peft" },
+    { name = "pluggy" },
     { name = "pyopengl", marker = "sys_platform == 'win32'" },
     { name = "safetensors" },
     { name = "sageattention", version = "2.2.0", source = { url = "https://github.com/daydreamlive/SageAttention/releases/download/v2.2.0-linux/sageattention-2.2.0-cp310-cp310-linux_x86_64.whl" }, marker = "sys_platform == 'linux'" },
@@ -668,6 +670,7 @@ dev = [
 requires-dist = [
     { name = "accelerate", specifier = ">=1.1.1" },
     { name = "aiortc", specifier = ">=1.13.0" },
+    { name = "click", specifier = ">=8.3.1" },
     { name = "diffusers", specifier = ">=0.31.0" },
     { name = "easydict", specifier = ">=1.13" },
     { name = "einops", specifier = ">=0.8.1" },
@@ -681,6 +684,7 @@ requires-dist = [
     { name = "lmdb", specifier = ">=1.7.3" },
     { name = "omegaconf", specifier = ">=2.3.0" },
     { name = "peft", specifier = ">=0.17.1" },
+    { name = "pluggy", specifier = ">=1.5.0" },
     { name = "pyopengl", marker = "sys_platform == 'win32'", specifier = ">=3.1.10" },
     { name = "safetensors", specifier = ">=0.6.2" },
     { name = "sageattention", marker = "sys_platform == 'linux'", url = "https://github.com/daydreamlive/SageAttention/releases/download/v2.2.0-linux/sageattention-2.2.0-cp310-cp310-linux_x86_64.whl" },