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e2bc0a8
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Parent(s):
407a5fa
Refactor app.py for improved error handling and simplify Gradio interface; downgrade gradio version in requirements.txt
Browse files- app.py +70 -113
- requirements.txt +1 -1
app.py
CHANGED
@@ -30,27 +30,28 @@ except Exception as e:
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from diffusers import DiffusionPipeline
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import torch
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model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
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logger.info(f"Using device: {device}")
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logger.info(f"Loading model: {model_repo_id}")
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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try:
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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logger.info("Model loaded successfully")
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except Exception as e:
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logger.error(f"Error
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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# @spaces.GPU #[uncomment to use ZeroGPU]
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def infer(
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@@ -88,113 +89,69 @@ def infer(
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return image, seed
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except Exception as e:
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logger.error(f"Error in inference: {str(e)}")
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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"A delicious ceviche cheesecake slice",
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]
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#col-container {
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margin: 0 auto;
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max-width: 640px;
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}
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"""
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try:
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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(" # Text-to-Image Gradio Template")
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with gr.Row():
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)
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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(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=True, # 改为可见
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=2,
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)
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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],
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fn=infer,
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inputs=[
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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],
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outputs=[result, seed],
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)
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if __name__ == "__main__":
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try:
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logger.info("Starting Gradio app")
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from diffusers import DiffusionPipeline
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import torch
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# 使用 try/except 避免在导入模块时出错
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try:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "stabilityai/sdxl-turbo"
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logger.info(f"Using device: {device}")
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logger.info(f"Loading model: {model_repo_id}")
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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logger.info("Model loaded successfully")
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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except Exception as e:
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logger.error(f"Error during setup: {str(e)}")
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# 不立即抛出异常,让 Gradio 界面可以加载
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# @spaces.GPU #[uncomment to use ZeroGPU]
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def infer(
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return image, seed
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except Exception as e:
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logger.error(f"Error in inference: {str(e)}")
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return None, seed # 返回 None 而不是抛出异常
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# 定义示例
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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"A delicious ceviche cheesecake slice",
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]
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# 简化 CSS
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css = "#col-container { margin: 0 auto; max-width: 640px; }"
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# 创建简化版的 Gradio 界面
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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("# Text-to-Image Generator")
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# 主输入区域
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prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt")
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run_button = gr.Button("Generate Image")
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# 结果显示
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result = gr.Image(label="Generated Image")
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seed_text = gr.Number(label="Seed Used")
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# 高级设置(折叠)
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What to exclude from the image")
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# 种子设置
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
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randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
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# 尺寸设置
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with gr.Row():
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width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
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height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
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# 生成参数
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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=0.0)
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num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=2)
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# 示例
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gr.Examples(examples, inputs=prompt)
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# 绑定事件处理
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run_button.click(
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fn=infer,
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inputs=[
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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],
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outputs=[result, seed_text],
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)
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# 启动应用
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if __name__ == "__main__":
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try:
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logger.info("Starting Gradio app")
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requirements.txt
CHANGED
@@ -4,4 +4,4 @@ invisible_watermark
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torch
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transformers
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xformers
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gradio==3.
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torch
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transformers
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xformers
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gradio==3.34.0
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