Add Qwen Image Edit 2511 model support

Adds full support for the Qwen Image Edit 2511 model architecture,
including both the diffusers version (Qwen/Qwen-Image-Edit-2511) and
GGUF quantized versions (unsloth/Qwen-Image-Edit-2511-GGUF).

Backend changes:
- Add QwenImageEdit base model type to taxonomy
- Add diffusers and GGUF model config classes with detection logic
- Add model loader for diffusers and GGUF formats
- Add 5 invocation nodes: model loader, text/vision encoder, denoise,
  image-to-latents, latents-to-image
- Add QwenVLEncoderField for Qwen2.5-VL vision-language encoder
- Add QwenImageEditConditioningInfo and conditioning field
- Add generation modes and step callback support
- Add 5 starter models (full diffusers + Q2_K, Q4_K_M, Q6_K, Q8_0 GGUF)

Frontend changes:
- Add graph builder for linear UI generation
- Register in canvas and generate enqueue hooks
- Update type definitions, optimal dimensions, grid sizes
- Add readiness validation, model picker grouping, clip skip config
- Regenerate OpenAPI schema

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

fix: use AutoProcessor.from_pretrained to load Qwen VL processor correctly

Co-authored-by: lstein <111189+lstein@users.noreply.github.com>
Agent-Logs-Url: https://github.com/lstein/InvokeAI/sessions/4d4417be-0f61-4faa-a21c-16e9ce81fec7

chore: bump diffusers==0.37.1

Co-authored-by: lstein <111189+lstein@users.noreply.github.com>
Agent-Logs-Url: https://github.com/lstein/InvokeAI/sessions/38a76809-d9a3-40f1-b5b3-fb56342e8e90

fix: handle multiple reference images

feature: add text encoder selection to advanced section for Qwen Image Edit

feat: complete Qwen Image Edit pipeline with LoRA, GGUF, quantization, and UI support

Major additions:
- LoRA support: loader invocation, config detection, conversion utils, prefix
  constants, and LayerPatcher integration in denoise with sidecar patching for
  GGUF models
- Lightning LoRA: starter models (4-step and 8-step bf16), shift override
  parameter for the distilled sigma schedule
- GGUF fixes: correct base class (ModelLoader), zero_cond_t=True, correct
  in_channels (no /4 division)
- Denoise: use FlowMatchEulerDiscreteScheduler directly, proper CFG gating
  (skip negative when cfg<=1), reference latent pixel-space resize
- I2L: resize reference image to generation dimensions before VAE encoding
- Graph builder: wire LoRAs via collection loader, VAE-encode reference image
  as latents for spatial conditioning, pass shift/quantization params
- Frontend: shift override (checkbox+slider), LoRA graph wiring, scheduler
  hidden for Qwen Image Edit, model switching cleanup
- Starter model bundle for Qwen Image Edit
- LoRA config registered in discriminated union (factory.py)
- Downgrade transformers requirement back to >=4.56.0

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: lstein

invokeai/app/api/dependencies.py

@@ -50,6 +50,7 @@
     CogView4ConditioningInfo,
     ConditioningFieldData,
     FLUXConditioningInfo,
+    QwenImageEditConditioningInfo,
     SD3ConditioningInfo,
     SDXLConditioningInfo,
     ZImageConditioningInfo,
@@ -140,6 +141,7 @@ def initialize(
                     SD3ConditioningInfo,
                     CogView4ConditioningInfo,
                     ZImageConditioningInfo,
+                    QwenImageEditConditioningInfo,
                 ],
                 ephemeral=True,
             ),

invokeai/app/invocations/fields.py

@@ -171,6 +171,8 @@ class FieldDescriptions:
     sd3_model = "SD3 model (MMDiTX) to load"
     cogview4_model = "CogView4 model (Transformer) to load"
     z_image_model = "Z-Image model (Transformer) to load"
+    qwen_image_edit_model = "Qwen Image Edit model (Transformer) to load"
+    qwen_vl_encoder = "Qwen2.5-VL tokenizer, processor and text/vision encoder"
     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"
@@ -340,6 +342,12 @@ class ZImageConditioningField(BaseModel):
     )
 
 
+class QwenImageEditConditioningField(BaseModel):
+    """A Qwen Image Edit conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/metadata.py

@@ -166,6 +166,10 @@ def invoke(self, context: InvocationContext) -> MetadataOutput:
     "z_image_img2img",
     "z_image_inpaint",
     "z_image_outpaint",
+    "qwen_image_edit_txt2img",
+    "qwen_image_edit_img2img",
+    "qwen_image_edit_inpaint",
+    "qwen_image_edit_outpaint",
 ]
 
 

invokeai/app/invocations/model.py

@@ -72,6 +72,13 @@ class GlmEncoderField(BaseModel):
     text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
 
 
+class QwenVLEncoderField(BaseModel):
+    """Field for Qwen2.5-VL encoder used by Qwen Image Edit models."""
+
+    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
+
+
 class Qwen3EncoderField(BaseModel):
     """Field for Qwen3 text encoder used by Z-Image models."""
 

invokeai/app/invocations/primitives.py

@@ -24,6 +24,7 @@
     InputField,
     LatentsField,
     OutputField,
+    QwenImageEditConditioningField,
     SD3ConditioningField,
     TensorField,
     UIComponent,
@@ -473,6 +474,17 @@ def build(cls, conditioning_name: str) -> "ZImageConditioningOutput":
         return cls(conditioning=ZImageConditioningField(conditioning_name=conditioning_name))
 
 
+@invocation_output("qwen_image_edit_conditioning_output")
+class QwenImageEditConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a Qwen Image Edit conditioning tensor."""
+
+    conditioning: QwenImageEditConditioningField = OutputField(description=FieldDescriptions.cond)
+
+    @classmethod
+    def build(cls, conditioning_name: str) -> "QwenImageEditConditioningOutput":
+        return cls(conditioning=QwenImageEditConditioningField(conditioning_name=conditioning_name))
+
+
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""