Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusion3Pipeline
+import os
+
+# Retrieve the API token from the environment variable
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+if huggingface_token is None:
+    raise ValueError("HUGGINGFACE_TOKEN environment variable is not set.")
+
+# Check if CUDA is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the Stable Diffusion model
+repo = "stabilityai/stable-diffusion-3-medium-diffusers"
+image_gen = StableDiffusion3Pipeline.from_pretrained(repo, text_encoder_3=None, tokenizer_3=None, use_auth_token=huggingface_token)
+image_gen = image_gen.to(device)
+
+def generate_image(prompt, num_inference_steps=50, guidance_scale=7.5):
+    # Generate the image
+    result = image_gen(
+        prompt=prompt,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        negative_prompt="blurred, ugly, watermark, low resolution, blurry",
+        height=512,
+        width=512
+    )
+    # Get the generated image
+    image = result.images[0]
+    return image
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Enter a prompt"),
+        gr.Slider(label="Number of inference steps", minimum=1, maximum=100, value=50),
+        gr.Slider(label="Guidance scale", minimum=1.0, maximum=20.0, value=7.5)
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Stable Diffusion Image Generator",
+    description="Enter a prompt to generate an image using the Stable Diffusion model."
+)
+
+# Launch the Gradio app
+iface.launch()