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import gradio as gr |
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import spaces |
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from panna import SD3 |
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model = SD3("stabilityai/stable-diffusion-3-medium-diffusers") |
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title = ("# [Stable Diffusion 3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)\n" |
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"The demo is part of [panna](https://github.com/asahi417/panna) project.") |
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examples = [ |
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", |
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"A female model, high quality, fashion, Paris, Vogue, Maison Margiela, 8k", |
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] |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 580px; |
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} |
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""" |
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@spaces.GPU |
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def infer(prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps): |
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return model.text2image( |
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prompt=[prompt], |
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negative_prompt=[negative_prompt], |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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width=width, |
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height=height, |
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seed=seed |
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)[0] |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(title) |
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with gr.Row(): |
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prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Text(label="Negative Prompt", max_lines=1, placeholder="Enter a negative prompt") |
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seed = gr.Slider(label="Seed", minimum=0, maximum=1_000_000, step=1, value=0) |
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with gr.Row(): |
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width = gr.Slider(label="Width", minimum=256, maximum=1344, step=64, value=1024) |
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height = gr.Slider(label="Height", minimum=256, maximum=1344, step=64, value=1024) |
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with gr.Row(): |
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guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7) |
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num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=28) |
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gr.Examples(examples=examples, inputs=[prompt]) |
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gr.on( |
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triggers=[run_button.click, prompt.submit, negative_prompt.submit], |
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fn=infer, |
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inputs=[prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps], |
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outputs=[result] |
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) |
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demo.launch(server_name="0.0.0.0") |
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