test

app.py

@@ -27,18 +27,9 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 )
 pipe.to("cuda:0")
 
-refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=base.text_encoder_2,
-    torch_dtype=torch.float16, 
-    vae=base.vae,
-    use_safetensors=True,
-    variant="fp16",
-)
-refiner.to("cuda:0")
 
 
-@spaces.GPU  # Apply the GPU decorator
+@spaces.GPU 
 def generate_and_save_image(prompt, negative_prompt=''):
     # Generate image using the provided prompts
     image = pipe(prompt=prompt, negative_prompt=negative_prompt).images[0]
@@ -59,19 +50,7 @@ def generate_image_with_refinement(prompt):
     high_noise_frac = 0.8
 
     # run both experts
-    image = base(
-        prompt=prompt,
-        num_inference_steps=n_steps,
-        denoising_end=high_noise_frac,
-        output_type="latent",
-    ).images
-    image = refiner(
-        prompt=prompt,
-        num_inference_steps=n_steps,
-        denoising_start=high_noise_frac,
-        image=image,
-    ).images[0]
-
+    image = pipe(prompt=prompt).images[0]
     # Save the image as before
     unique_id = str(uuid.uuid4())
     image_path = f"generated_images_refined/{unique_id}.jpeg"
@@ -105,8 +84,8 @@ with gr.Blocks() as demo:
         )
 
     with gr.Column():
-        gr.Markdown("## Refined Image Generation")
-        gr.Markdown("Enter a prompt to generate a refined image.")
+        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")