Update app.py

app.py

@@ -8,10 +8,8 @@ from PIL import Image
 from diffusers import StableDiffusionXLImg2ImgPipeline, EDMEulerScheduler, AutoencoderKL
 from huggingface_hub import hf_hub_download
 
-# Load the VAE
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 
-# Download and load the model
 pipe_edit = StableDiffusionXLImg2ImgPipeline.from_single_file(
     hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors"),
     num_in_channels=8,
@@ -20,11 +18,9 @@ pipe_edit = StableDiffusionXLImg2ImgPipeline.from_single_file(
     torch_dtype=torch.float16,
 )
 
-# Set the scheduler
 pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
 pipe_edit.to("cuda")
 
-# Load the refiner
 refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-refiner-1.0", 
     vae=vae, 
@@ -34,7 +30,6 @@ refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
 )
 refiner.to("cuda")
 
-# Patch for the scheduler
 def set_timesteps_patched(self, num_inference_steps: int, device=None):
     self.num_inference_steps = num_inference_steps
     ramp = np.linspace(0, 1, self.num_inference_steps)
@@ -48,7 +43,6 @@ def set_timesteps_patched(self, num_inference_steps: int, device=None):
 
 EDMEulerScheduler.set_timesteps = set_timesteps_patched
 
-# Function to perform image editing
 def king(input_image, instruction: str, negative_prompt: str = "", steps: int = 25, randomize_seed: bool = True, seed: int = 2404, guidance_scale: float = 6, progress=gr.Progress(track_tqdm=True)):
     input_image = Image.open(input_image).convert('RGB')
     if randomize_seed:
@@ -76,7 +70,6 @@ def king(input_image, instruction: str, negative_prompt: str = "", steps: int =
     ).images[0]  
     return seed, refine
 
-# CSS for the Gradio interface
 css = '''
 .gradio-container{max-width: 700px !important}
 h1{text-align:center}
@@ -85,13 +78,11 @@ footer {
 }
 '''
 
-# Examples for the Gradio interface
 examples = [
     ["./supercar.png", "make it red"],
     ["./red_car.png", "add some snow"],
 ]
 
-# Creating the Gradio interface
 with gr.Blocks(css=css) as demo:
     gr.Markdown("# Image Editing\n### Note: First image generation takes time")
     with gr.Row():
@@ -130,4 +121,4 @@ with gr.Blocks(css=css) as demo:
         outputs=[seed, input_image],
     )
 
-demo.queue(max_size=500).launch()
+demo.queue(max_size=500).launch()