Update app.py

app.py

@@ -1,9 +1,7 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces
 import torch
-import os
 import time
 from diffusers import DiffusionPipeline
 
@@ -17,14 +15,13 @@ except ImportError:
 dtype = torch.float16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load the diffusion pipeline without requiring an API token
+# Load the diffusion pipeline
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-@spaces.GPU()
-def infer(prompt, negative_prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, guidance_scale=7.5, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, num_inference_steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
     start_time = time.time()
 
     if width > MAX_IMAGE_SIZE or height > MAX_IMAGE_SIZE:
@@ -78,38 +75,42 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 placeholder="Enter things to avoid in the image",
                 lines=2
             )
-            run_button = gr.Button("Generate Image")
-        with gr.Column(scale=3):
+            run_button = gr.Button("Generate Image", variant="primary")
+
+        with gr.Column(scale=2):
             result = gr.Image(label="Generated Image")
+            seed_output = gr.Number(label="Seed Used")
 
     with gr.Accordion("Advanced Settings", open=False):
-        with gr.Row():
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
         with gr.Row():
             width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
             height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+
         with gr.Row():
             num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=4)
             guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, step=0.5, value=7.5)
 
     gr.Examples(
         examples=examples,
-        fn=infer,
         inputs=[prompt, negative_prompt],
-        outputs=[result, seed],
+        outputs=[result, seed_output],
+        fn=infer,
         cache_examples=True
     )
 
     run_button.click(
         fn=infer,
         inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, num_inference_steps, guidance_scale],
-        outputs=[result, seed],
+        outputs=[result, seed_output]
     )
 
     gr.Markdown("""
     ## Save Your Image
-    Right-click on the image and select 'Save As' to download the generated image.
+    Right-click on the generated image and select 'Save image as' to download it.
     """)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()