test

app.py

@@ -9,14 +9,6 @@ import uuid
 import io
 import os
 
-# Load the base & refiner pipelines
-base = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", 
-    torch_dtype=torch.float16, 
-    variant="fp16",
-    use_safetensors=True
-)
-base.to("cuda:0")
 
 # Load your model
 pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -25,6 +17,7 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
     use_safetensors=True, 
     variant="fp16"
 )
+
 pipe.to("cuda:0")
 
 
@@ -45,19 +38,6 @@ def generate_and_save_image(prompt, negative_prompt=''):
     # Return the path of the saved image to display in Gradio interface
     return image_path
 
-def generate_image_with_refinement(prompt):
-    n_steps = 40
-    high_noise_frac = 0.8
-
-    # run both experts
-    image = base(prompt=prompt).images[0]
-    # Save the image as before
-    unique_id = str(uuid.uuid4())
-    image_path = f"generated_images_refined/{unique_id}.jpeg"
-    os.makedirs('generated_images_refined', exist_ok=True)
-    image.save(image_path, format='JPEG')
-
-    return image_path
 
 # Start of the Gradio Blocks interface
 with gr.Blocks() as demo:
@@ -83,25 +63,6 @@ with gr.Blocks() as demo:
             outputs=output_image1
         )
 
-    with gr.Column():
-        gr.Markdown("## SDXL 1.0")
-        gr.Markdown("Enter a prompt to generate an image.")
-        
-        # Input field for the prompt
-        prompt2 = gr.Textbox(label="Enter prompt for refined generation")
-        
-        # Button for generating the refined image
-        generate_button2 = gr.Button("Generate Refined Image")
-        
-        # Output refined image display, set to a larger default size
-        output_image2 = gr.Image(label="Generated Refined Image")
-
-        # Click event for the generate button
-        generate_button2.click(
-            generate_image_with_refinement, 
-            inputs=[prompt2], 
-            outputs=output_image2
-        )
 
 # Launch the combined Gradio app
 demo.launch()