Create app.py
Browse files
app.py
ADDED
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import gradio as gr
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from ppdiffusers import StableDiffusionPipeline
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def generate_image(model, prompt, width, height, num_inference_steps, guidance_scale):
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if model == "shanshui_gen_style":
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pipe = StableDiffusionPipeline.from_pretrained("megemini/shanshui_gen_style", from_hf_hub=True)
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elif model == "shanshui_style":
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pipe = StableDiffusionPipeline.from_pretrained("megemini/shanshui_style", from_hf_hub=True)
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else:
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raise
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image = pipe(
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prompt,
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num_inference_steps=100,
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guidance_scale=7.5,
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height=height,
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width=width,).images[0]
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return image
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demo = gr.Blocks()
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with demo:
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gr.Markdown(
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r"### 【Hackathon】基于PaddleNLP PPDiffusers 训练 AIGC 趣味模型"
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)
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gr.Markdown(
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r"""
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[【Hackathon】基于PaddleNLP PPDiffusers 训练 AIGC 趣味模型](https://github.com/PaddlePaddle/community/blob/master/hackthon_4th/%E3%80%90PaddlePaddle%20Hackathon%204%E3%80%91%20%E6%A8%A1%E5%9E%8B%E5%A5%97%E4%BB%B6%E5%BC%80%E6%BA%90%E8%B4%A1%E7%8C%AE%E4%BB%BB%E5%8A%A1%E5%90%88%E9%9B%86.md#no105%E5%9F%BA%E4%BA%8Epaddlenlp-ppdiffusers-%E8%AE%AD%E7%BB%83-aigc-%E8%B6%A3%E5%91%B3%E6%A8%A1%E5%9E%8B-)
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"""
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)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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model = gr.Dropdown(["shanshui_gen_style", "shanshui_style"], label="Model", info="The model to generate image.")
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with gr.Row():
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prompt = gr.Textbox(label='prompt')
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with gr.Row():
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width = gr.Slider(128, 768, value=512, step=8, label="Width", info="The width of image.")
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height = gr.Slider(128, 768, value=512, step=8, label="Height", info="The height of image.")
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with gr.Row():
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num_inference_steps = gr.Textbox(label='num inference steps')
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guidance_scale = gr.Textbox(label='guidance scale')
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with gr.Row():
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btn = gr.Button(value="Run")
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with gr.Column():
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with gr.Row():
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output = gr.Image()
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gr.Examples(
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[
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[
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"shanshui_gen_style",
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"A fantasy landscape in <shanshui-gen-style>",
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512,
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288,
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100,
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7.5,
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],
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[
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"shanshui_style",
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"A fantasy landscape in <shanshui-style>",
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512,
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288,
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100,
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7.5,
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],
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],
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[model, prompt, width, height, num_inference_steps, guidance_scale]
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)
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btn.click(
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generate_image,
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[model, prompt, width, height, num_inference_steps, guidance_scale],
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output)
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if __name__ == "__main__":
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demo.launch(debug=True)
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