Create app.py
Browse files
app.py
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import gradio as gr
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import torch
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from diffusers import StableDiffusionXLPipeline
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from datetime import datetime
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import os
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class RafayyAI:
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def __init__(self):
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self.model = StableDiffusionXLPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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torch_dtype=torch.float16,
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use_safetensors=True,
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variant="fp16"
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)
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if torch.cuda.is_available():
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self.model = self.model.to("cuda")
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def generate_image(self, prompt, negative_prompt=""):
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# Generate unique filename
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"generated_{timestamp}.png"
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# Generate image
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image = self.model(
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prompt=prompt,
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negative_prompt=negative_prompt,
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num_inference_steps=30,
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guidance_scale=7.5
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).images[0]
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# Save image
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image.save(filename)
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return filename
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# Initialize the AI
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rafayy = RafayyAI()
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# Create Gradio interface
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def generate(prompt, negative_prompt=""):
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return rafayy.generate_image(prompt, negative_prompt)
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demo = gr.Interface(
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fn=generate,
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inputs=[
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gr.Textbox(label="Prompt", placeholder="Describe the image you want to generate..."),
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gr.Textbox(label="Negative Prompt (Optional)", placeholder="What you don't want in the image...")
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],
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outputs=gr.Image(label="Generated Image"),
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title="Rafayy AI Image Generator",
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description="Generate unique images from text descriptions",
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examples=[
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["A beautiful sunset over mountains", "blur, low quality"],
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["A futuristic city at night", "dark, blurry"],
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["A cute cat playing with yarn", "ugly, distorted"]
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]
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)
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# Launch the app
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if __name__ == "__main__":
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demo.launch()
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