test gradio

app.py

@@ -17,21 +17,34 @@ login(token=token)
 
 # Model IDs for the base Stable Diffusion model and ControlNet variant
 model_id = "stabilityai/stable-diffusion-3.5-large-turbo"
-controlnet_id = "lllyasviel/control_v11p_sd15_inpaint"  # Make sure this ControlNet is compatible
+controlnet_id = "lllyasviel/control_v11p_sd15_inpaint"
 
-# Load ControlNet model and other components
+# Load each model component required by the pipeline
 controlnet = ControlNetModel.from_pretrained(controlnet_id, torch_dtype=torch.float16)
-pipeline = StableDiffusionControlNetPipeline.from_pretrained(
-    model_id,
+unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", torch_dtype=torch.float16)
+vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float16)
+feature_extractor = CLIPFeatureExtractor.from_pretrained(model_id)
+text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
+tokenizer = CLIPTokenizer.from_pretrained(model_id)
+
+# Initialize the pipeline with all components
+pipeline = StableDiffusionControlNetPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
     controlnet=controlnet,
-    torch_dtype=torch.float16
+    scheduler=UniPCMultistepScheduler.from_config({"name": "UniPCMultistepScheduler"}),
+    feature_extractor=feature_extractor,
+    torch_dtype=torch.float16,
 )
+
+# Set device for pipeline
 pipeline = pipeline.to("cuda") if torch.cuda.is_available() else pipeline
 
-# Enable CPU offloading for memory optimization
+# Enable model CPU offloading for memory optimization
 pipeline.enable_model_cpu_offload()
 
-
 # Gradio interface function
 def generate_image(prompt, reference_image):
     # Resize and prepare reference image
@@ -47,7 +60,6 @@ def generate_image(prompt, reference_image):
     ).images[0]
     return generated_image
 
-
 # Set up Gradio interface
 interface = gr.Interface(
     fn=generate_image,