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app.py
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# pip install transformers gradio scipy ftfy "ipywidgets>=7,<8" datasets diffusers
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import gradio as gr
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import torch
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from torch import autocast
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from diffusers import StableDiffusionPipeline
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model_id = "hakurei/waifu-diffusion"
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device = "cpu"
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# pip install transformers gradio scipy ftfy "ipywidgets>=7,<8" datasets diffusers
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import random
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import gradio as gr
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import torch
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from torch import autocast
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
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model_id = "hakurei/waifu-diffusion"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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def __def_helper():
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StableDiffusionPipeline.__call__()
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pipe = StableDiffusionPipeline.from_pretrained(model_id,
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resume_download=True, # 模型文件断点续传
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torch_dtype=torch.float16,
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revision='fp16')
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pipe = pipe.to(device)
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def infer(prompt, width, height, nums, steps, guidance_scale, seed):
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print(prompt, width, height, nums, steps, guidance_scale, seed)
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if prompt is not None and prompt != "":
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if seed is None or seed == '' or seed == -1:
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seed = int(random.randrange(4294967294))
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with autocast("cuda"):
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generator = torch.Generator("cuda").manual_seed(seed)
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images = pipe([prompt] * nums,
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height=height,
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width=width,
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num_inference_steps=steps,
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generator=generator,
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guidance_scale=guidance_scale
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)["sample"]
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return images
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description = """
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prompt 素材:[https://lexica.art](https://lexica.art) \n
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seed:为空会使用随机seed
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"""
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# with block as demo:
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def run():
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_app = gr.Interface(
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fn=infer,
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title="Waifu Diffusion",
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description=description,
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inputs=[
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gr.Textbox(label="输入 prompt"),
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gr.Slider(512, 1024, 512, step=64, label="width"),
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gr.Slider(512, 1024, 512, step=64, label="height"),
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gr.Slider(1, 4, 1, step=1, label="Number of Images"),
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gr.Slider(10, 150, step=1, value=50,
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label="num_inference_steps"),
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gr.Slider(0, 20, 7.5, step=0.5,
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label="guidance_scale:\n" +
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"较高的引导比例鼓励生成与文本“提示”密切相关的图像,通常以降低图像质量为代价"),
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gr.Textbox(label="随机 send",
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placeholder="Random Seed",
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lines=1),
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],
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outputs=[
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gr.Gallery(label="Generated images")
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])
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return _app
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app = run()
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app.launch(debug=True)
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app2.py
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# pip install transformers gradio scipy ftfy "ipywidgets>=7,<8" datasets diffusers
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import gradio as gr
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import torch
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from torch import autocast
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from diffusers import StableDiffusionPipeline
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model_id = "hakurei/waifu-diffusion"
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device = "cpu"
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# pipe = StableDiffusionPipeline.from_pretrained(model_id,
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# resume_download=True, # 模型文件断点续传
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# torch_dtype=torch.float16,
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# revision='fp16')
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# pipe = pipe.to(device)
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block = gr.Blocks(css=".container { max-width: 800px; margin: auto; }")
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num_samples = 2
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def infer(prompt):
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print(prompt)
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return prompt
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# with autocast("cuda"):
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# images = pipe([prompt] * num_samples,
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# hight=111,
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# width=100,
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#
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# guidance_scale=7.5)["sample"]
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# return images
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with block as demo:
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gr.Markdown("<h1><center>Waifu Diffusion</center></h1>")
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gr.Markdown(
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"waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning."
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)
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with gr.Group():
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with gr.Box():
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with gr.Column().style(mobile_collapse=False, equal_height=True):
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text = gr.Textbox(
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label="Enter your prompt",
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show_label=False,
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max_lines=1
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).style(
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border=(True, False, True, True),
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rounded=(True, False, False, True),
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container=False,
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)
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slider = gr.Slider(0, 1000, 10)
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btn = gr.Button("Run").style(
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margin=False,
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rounded=(False, True, True, False),
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)
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gallery = gr.Gallery(
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label="Generated images",
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show_label=False
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).style(
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grid=[2],
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height="auto"
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)
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gr.Interface
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text.submit(infer,
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inputs=[text,slider] ,
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outputs=gallery)
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btn.click(infer, inputs=[text], outputs=gallery)
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gr.Markdown(
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"""___
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<p style='text-align: center
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'>
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Created by https://huggingface.co/hakurei
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<br/>
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</p>"""
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)
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demo.launch(debug=True)
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