Update app.py

app.py

@@ -1,39 +1,50 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
 import torch
+import os
 
+# Retrieve Hugging Face access token from environment variables
+access_token = os.getenv("HF_ACCESS_TOKEN")
+
+# Set device
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+# Load the Stable Diffusion model
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe = StableDiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-3-medium",
+        torch_dtype=torch.float16,
+        use_auth_token=access_token  # Use the token here
+    )
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+else:
+    pipe = StableDiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-3-medium",
+        use_auth_token=access_token  # Use the token here
+    )
     pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
     generator = torch.Generator().manual_seed(seed)
-    
+
     image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
     
     return image
 
@@ -43,20 +54,16 @@ examples = [
     "A delicious ceviche cheesecake slice",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
 }
 """
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
+power_device = "GPU" if torch.cuda.is_available() else "CPU"
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
         # Text-to-Image Gradio Template
@@ -64,28 +71,24 @@ with gr.Blocks(css=css) as demo:
         """)
         
         with gr.Row():
-            
-            prompt = gr.Text(
+            prompt = gr.Textbox(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
+            negative_prompt = gr.Textbox(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
                 visible=False,
             )
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -93,11 +96,9 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -105,7 +106,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=512,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -115,7 +115,6 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
@@ -123,7 +122,6 @@ with gr.Blocks(css=css) as demo:
                     step=0.1,
                     value=0.0,
                 )
-                
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -133,14 +131,14 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            examples=examples,
+            inputs=[prompt]
         )
 
     run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
     )
 
-demo.queue().launch()
+demo.queue().launch()