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| # pip install transformers gradio scipy ftfy "ipywidgets>=7,<8" datasets diffusers | |
| import random | |
| import gradio as gr | |
| import torch | |
| from torch import autocast | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline | |
| model_id = "hakurei/waifu-diffusion" | |
| # torch.backends.cudnn.benchmark = True | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def __def_helper(): | |
| StableDiffusionPipeline.__call__() | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, | |
| resume_download=True, # 模型文件断点续传 | |
| torch_dtype=torch.float16, | |
| revision='fp16') | |
| pipe = pipe.to(device) | |
| def infer(prompt, width, height, nums, steps, guidance_scale, seed): | |
| print(prompt) | |
| print(width, height, nums, steps, guidance_scale, seed) | |
| if prompt is not None and prompt != "": | |
| if seed is None or seed == '' or seed == -1: | |
| seed = int(random.randrange(4294967294)) | |
| with autocast("cuda"): | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| images = pipe([prompt] * nums, | |
| height=height, | |
| width=width, | |
| num_inference_steps=steps, | |
| generator=generator, | |
| guidance_scale=guidance_scale | |
| )["sample"] | |
| return images | |
| description = """ | |
| prompt 素材：[https://lexica.art](https://lexica.art) \n | |
| seed：为空会使用随机seed | |
| """ | |
| # with block as demo: | |
| def run(): | |
| _app = gr.Interface( | |
| fn=infer, | |
| title="Waifu Diffusion", | |
| description=description, | |
| inputs=[ | |
| gr.Textbox(label="输入 prompt"), | |
| gr.Slider(512, 1024, 512, step=64, label="width"), | |
| gr.Slider(512, 1024, 512, step=64, label="height"), | |
| gr.Slider(1, 4, 1, step=1, label="Number of Images"), | |
| gr.Slider(10, 150, step=1, value=50, | |
| label="num_inference_steps:\n" | |
| "去噪步骤的数量。更多的去噪步骤通常会导致更高质量的图像，但会降低推理速度。"), | |
| gr.Slider(0, 20, 7.5, step=0.5, | |
| label="guidance_scale:\n" + | |
| "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"), | |
| gr.Textbox(label="随机 seed", | |
| placeholder="Random Seed", | |
| lines=1), | |
| ], | |
| outputs=[ | |
| gr.Gallery(label="Generated images") | |
| ]) | |
| return _app | |
| app = run() | |
| app.launch(debug=True) | |