Feature: Add Z-Image-Turbo regional guidance

invokeai/app/api/dependencies.py

@@ -49,6 +49,7 @@
     FLUXConditioningInfo,
     SD3ConditioningInfo,
     SDXLConditioningInfo,
+    ZImageConditioningInfo,
 )
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
@@ -129,6 +130,7 @@ def initialize(
                     FLUXConditioningInfo,
                     SD3ConditioningInfo,
                     CogView4ConditioningInfo,
+                    ZImageConditioningInfo,
                 ],
                 ephemeral=True,
             ),

invokeai/app/invocations/fields.py

@@ -154,6 +154,7 @@ class FieldDescriptions:
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
     glm_encoder = "GLM (THUDM) tokenizer and text encoder"
+    qwen3_encoder = "Qwen3 tokenizer and text encoder"
     clip_embed_model = "CLIP Embed loader"
     clip_g_model = "CLIP-G Embed loader"
     unet = "UNet (scheduler, LoRAs)"
@@ -169,6 +170,7 @@ class FieldDescriptions:
     flux_model = "Flux model (Transformer) to load"
     sd3_model = "SD3 model (MMDiTX) to load"
     cogview4_model = "CogView4 model (Transformer) to load"
+    z_image_model = "Z-Image model (Transformer) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -321,6 +323,17 @@ class CogView4ConditioningField(BaseModel):
     conditioning_name: str = Field(description="The name of conditioning tensor")
 
 
+class ZImageConditioningField(BaseModel):
+    """A Z-Image conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+    mask: Optional[TensorField] = Field(
+        default=None,
+        description="The mask associated with this conditioning tensor for regional prompting. "
+        "Excluded regions should be set to False, included regions should be set to True.",
+    )
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/metadata.py

@@ -158,6 +158,10 @@ def invoke(self, context: InvocationContext) -> MetadataOutput:
     "cogview4_img2img",
     "cogview4_inpaint",
     "cogview4_outpaint",
+    "z_image_txt2img",
+    "z_image_img2img",
+    "z_image_inpaint",
+    "z_image_outpaint",
 ]
 
 

invokeai/app/invocations/model.py

@@ -72,6 +72,14 @@ class GlmEncoderField(BaseModel):
     text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
 
 
+class Qwen3EncoderField(BaseModel):
+    """Field for Qwen3 text encoder used by Z-Image models."""
+
+    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
+    loras: List[LoRAField] = Field(default_factory=list, description="LoRAs to apply on model loading")
+
+
 class VAEField(BaseModel):
     vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')

invokeai/app/invocations/primitives.py

@@ -27,6 +27,7 @@
     SD3ConditioningField,
     TensorField,
     UIComponent,
+    ZImageConditioningField,
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -461,6 +462,17 @@ def build(cls, conditioning_name: str) -> "CogView4ConditioningOutput":
         return cls(conditioning=CogView4ConditioningField(conditioning_name=conditioning_name))
 
 
+@invocation_output("z_image_conditioning_output")
+class ZImageConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a Z-Image text conditioning tensor."""
+
+    conditioning: ZImageConditioningField = OutputField(description=FieldDescriptions.cond)
+
+    @classmethod
+    def build(cls, conditioning_name: str) -> "ZImageConditioningOutput":
+        return cls(conditioning=ZImageConditioningField(conditioning_name=conditioning_name))
+
+
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""