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Create app.py
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app.py
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import gradio as gr
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import torch
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from PIL import Image
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from diffusers import DiffusionPipeline
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import os
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# Constants
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#SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
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# Initialize the model
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pipe = DiffusionPipeline.from_pretrained(
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"playgroundai/playground-v2.5-1024px-aesthetic",
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torch_dtype=torch.float16,
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variant="fp16",
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).to("cuda")
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# Safety Checker (if necessary)
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#if SAFETY_CHECKER:
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# Implement or import the safety checker code here
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def generate_image(prompt, num_inference_steps=50, guidance_scale=7):
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# Generate image
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results = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)
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# Safety check (if necessary)
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if SAFETY_CHECKER:
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# Implement the safety check logic here
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pass
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return results.images[0]
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# Gradio Interface
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with gr.Blocks() as demo:
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gr.Markdown("## Playground-V2.5 Demo")
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with gr.Row():
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prompt = gr.Textbox(label='Enter your image prompt')
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num_inference_steps = gr.Slider(minimum=1, maximum=75, step=1, label='Number of Inference Steps', value=50)
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guidance_scale = gr.Slider(minimum=1, maximum=20, step=0.1, label='Guidance Scale', value=7)
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submit = gr.Button('Generate Image')
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img = gr.Image(label='Generated Image')
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submit.click(
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fn=generate_image,
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inputs=[prompt, num_inference_steps, guidance_scale],
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outputs=img,
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)
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demo.queue().launch()
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