Update app.py

app.py

@@ -1,184 +1,34 @@
+import os
 import gradio as gr
 import numpy as np
 import random
-
-import spaces #[uncomment to use ZeroGPU]
+import spaces  # ZeroGPU integration
 from diffusers import DiffusionPipeline
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/stable-diffusion-3.5-large"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    # Seed Handling
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    # Generate Image
-    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]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-/* CSS Styling (remains unchanged from earlier examples) */
-"""
-
-# Higher Defaults for Advanced Settings
-DEFAULT_STEPS = 50
-DEFAULT_GUIDANCE = 7.5
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("<div id='header'><h1 id='title'>Veshon: Veshup's Image Generation AI</h1><p id='subtitle'>Create stunning images with just a prompt. Powered by cutting-edge AI technology.</p></div>")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Your Creative Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt here...",
-                container=False,
-            )
-
-            run_button = gr.Button("Generate Image", scale=0, variant="primary", elem_classes="gradio-button")
-
-        result = gr.Image(label="Generated Image", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative Prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt if needed",
-                visible=False,
-            )
-
-            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=768,  # Higher default resolution
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=768,  # Higher default resolution
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=0.0,
-                    maximum=15.0,
-                    step=0.1,
-                    value=DEFAULT_GUIDANCE,  # Higher guidance by default
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of Inference Steps",
-                    minimum=1,
-                    maximum=150,  # Increased maximum steps
-                    step=1,
-                    value=DEFAULT_STEPS,  # Higher inference steps for quality
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
-import gradio as gr
-import numpy as np
-import random
-
-import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+# Get Hugging Face token from environment variable
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None
+if not HF_TOKEN:
+    raise ValueError("Hugging Face token not found. Please set the 'HF_TOKEN' environment variable.")
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id = "stabilityai/sdxl-turbo"  # Replace with the model you would like to use
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = DiffusionPipeline.from_pretrained(
+    model_repo_id, torch_dtype=torch_dtype, use_auth_token=HF_TOKEN
+)
 pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-@spaces.GPU #[uncomment to use ZeroGPU]
+@spaces.GPU  # ZeroGPU decorator
 def infer(
     prompt,
     negative_prompt,