Fix: Use DiffusionPipeline for custom model support (ZImagePipeline)

codai/api/images.py

@@ -314,15 +314,20 @@ async def create_image_generation(request: ImageGenerationRequest, http_request:
             # Try to load the model
             load_error = None
             try:
-                if is_xl:
-                    # Load SDXL
-                    pipeline = StableDiffusionXLPipeline.from_pretrained(
-                        model_to_use,
-                        torch_dtype=dtype,
-                    )
+                # Use DiffusionPipeline.from_pretrained which auto-detects the correct pipeline class
+                # from model_index.json (supports custom pipelines like ZImagePipeline)
+                from diffusers import DiffusionPipeline
+                
+                print(f"Loading diffusers model: {model_to_use}")
+                
+                # Determine compute type
+                if torch.cuda.is_available():
+                    dtype = torch.float16
                 else:
-                    # Load SD 1.5
-                    pipeline = StableDiffusionPipeline.from_pretrained(
+                    dtype = torch.float32
+                
+                # Use DiffusionPipeline for auto-detection of pipeline class
+                pipeline = DiffusionPipeline.from_pretrained(
                     model_to_use,
                     torch_dtype=dtype,
                 )
@@ -341,13 +346,14 @@ async def create_image_generation(request: ImageGenerationRequest, http_request:
                 
                 # Try with default resolution
                 try:
+                    from diffusers import DiffusionPipeline
                     if is_xl:
                         pipeline = StableDiffusionXLPipeline.from_pretrained(
                             model_to_use,
                             torch_dtype=dtype,
                         )
                     else:
-                        pipeline = StableDiffusionPipeline.from_pretrained(
+                        pipeline = DiffusionPipeline.from_pretrained(
                             model_to_use,
                             torch_dtype=dtype,
                         )
@@ -361,7 +367,7 @@ async def create_image_generation(request: ImageGenerationRequest, http_request:
                             safety_checker=None,
                         )
                     else:
-                        pipeline = StableDiffusionPipeline.from_pretrained(
+                        pipeline = DiffusionPipeline.from_pretrained(
                             model_to_use,
                             torch_dtype=dtype,
                             safety_checker=None,