Update app.py

app.py

@@ -1,12 +1,11 @@
 import gradio as gr
 from transformers import pipeline
-from diffusers import StableDiffusion3Pipeline 
+from diffusers import StableDiffusionPipeline
 from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/stable-diffusion-3.5-medium"
-image_style = "pixel art"
+model_id = "CompVis/stable-diffusion-v1-4"
 torch_dtype = torch.float32
 
 if torch.cuda.is_available():
@@ -17,20 +16,18 @@ def generate_description(image):
     return model(image)[0]['generated_text']
 
 def generate_image_by_description(description):
-    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
     pipe = pipe.to(device)
+    pipe.enable_attention_slicing()
+
     prompt = (
-        f"Generate a high-quality, detailed image of a {image_style} of a pigeon. "
+        f"Generate a image of a pigeon for my profile avatar. "
         f"The description of the pigeon is: {description}. "
-        "Make it visually appealing with clear textures and distinct colors."
-    )
-    image = pipe(
-        prompt,
-        num_inference_steps=40,
-        guidance_scale=4.5,
-    ).images[0]
+        )
+    image = pipe(prompt).images[0]  
     return image
 
+
 with gr.Blocks() as demo:
     selected_image = gr.Image(type="filepath", label="Upload an Image of the Pigeon")
     generate_button = gr.Button("Generate Avatar", variant="primary")