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app.py
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# Install Gradio and dependencies
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# --- Imports ---
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from diffusers import StableDiffusionPipeline
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import torch
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import gradio as gr
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import uuid
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# --- Configuration ---
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class CGF:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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seed = 42
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generator = torch.Generator(device=device).manual_seed(seed)
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image_gen_model_id = "my_text_to_image_saved_sd_model"
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image_gen_steps = 50
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image_gen_guidance_scale = 10
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image_gen_size = (768, 768)
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# --- Load Model ---
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pipe = StableDiffusionPipeline.from_pretrained(
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CGF.image_gen_model_id,
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torch_dtype=torch.float16,
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use_safetensors=True,
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revision="fp16",
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).to(CGF.device)
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# --- Generation Function ---
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def generate_image(prompt):
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result = pipe(
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prompt,
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num_inference_steps=CGF.image_gen_steps,
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guidance_scale=CGF.image_gen_guidance_scale,
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generator=CGF.generator,
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).images[0]
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filename = f"{prompt.replace(' ', '_')}_{uuid.uuid4().hex[:6]}.png"
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result.save(filename)
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return result, filename
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# --- Gradio UI ---
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examples = [
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"A majestic mountain landscape during sunrise",
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"A futuristic cyberpunk city at night with neon lights",
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"A photorealistic portrait of a happy Indian woman in a green saree",
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"A fantasy dragon flying over a castle at sunset",
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"A cat astronaut floating in space",
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]
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("## 🎨 Text-to-Image Generator with Stable Diffusion 2.1")
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gr.Markdown("Type your prompt or select an example below to generate high-quality images.")
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with gr.Row():
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prompt = gr.Textbox(label="Enter your prompt", placeholder="e.g. A girl sitting on a hill in a summer breeze 🌼")
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generate_btn = gr.Button("🚀 Generate Image")
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with gr.Row():
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with gr.Column():
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gallery = gr.Image(label="Generated Image", type="filepath", interactive=False)
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download = gr.File(label="Download Image")
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with gr.Column():
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gr.Examples(examples=examples, inputs=prompt)
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generate_btn.click(fn=generate_image, inputs=prompt, outputs=[gallery, download])
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# Launch Gradio app with a public link
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# --- Launch App ---
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demo.launch(share=True)
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