建设一个网站的操作流程300字seo搜索引擎优化视频

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关于 Stable Diffusion

A latent text-to-image diffusion model

Stable Diffusion 是一个文本到图像的潜在扩散模型，由CompVis、Stability AI和LAION的研究人员和工程师创建。
它使用来自LAION-5B数据库子集的512x512图像进行训练。使用这个模型，可以生成包括人脸在内的任何图像，因为有开源的预训练模型，所以我们也可以在自己的机器上运行它。

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• github : https://github.com/CompVis/stable-diffusion
• stability.ai 公司主页：https://stability.ai
• 官方创作平台：http://dreamstudio.ai/ 付费/可试用
  https://beta.dreamstudio.ai
• hugginface : https://huggingface.co/CompVis/stable-diffusion

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教程资源

• 数据派THU : Stable Diffusion的入门介绍和使用教程
  https://mp.weixin.qq.com/s/Y3XsytE4_ewCc6yVp6G43A
• 35张图，直观理解Stable Diffusion
  https://mp.weixin.qq.com/s/9ngMKHLYKjMASUYeashDJw
• 当下最强的 AI art 生成模型 Stable Diffusion 最全面介绍
  https://mp.weixin.qq.com/s/Q4ZYjUxt22Jsx2W2179C8Q

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Lexica

• Lexica: The Stable Diffusion search engine
  https://lexica.art

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代码实现

这里我使用 colab 运行，colab 使用操作可参考：
https://blog.csdn.net/lovechris00/article/details/123960622

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Stable Diffusion 操作方法可详见：
https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb#scrollTo=AAVZStIokTVv

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安装依赖库

!pip install "ipywidgets>=7,<8"
!pip install diffusers==0.12.1 
!pip install  transformers scipy ftfy
!pip install accelerate
!pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 

如果 diffusers 版本较低（如，小于等于 0.4.0），后面可能会报如下错误：

getattr(): attribute name must be string

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查看信息

import diffusers
print(diffusers.__version__)!nvidia-smi

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from google.colab import output
output.enable_custom_widget_manager()  

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登陆 huggingface

from huggingface_hub import notebook_login
notebook_login()

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查看 huggingface token

settings --> tokens
https://huggingface.co/settings/tokens
你的huggingface 如果还没有激活邮箱，需要激活后操作。

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登陆成功后，会返回下述结果：

Token is valid.
Your token has been saved in your configured git credential helpers (store).
Your token has been saved to /root/.cache/huggingface/token
Login successful

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下载模型

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True)   

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pip 内容如下：

StableDiffusionPipeline {"_class_name": "StableDiffusionPipeline","_diffusers_version": "0.12.1","feature_extractor": ["transformers","CLIPFeatureExtractor"],"requires_safety_checker": true,"safety_checker": ["stable_diffusion","StableDiffusionSafetyChecker"],"scheduler": ["diffusers","PNDMScheduler"],"text_encoder": ["transformers","CLIPTextModel"],"tokenizer": ["transformers","CLIPTokenizer"],"unet": ["diffusers","UNet2DConditionModel"],"vae": ["diffusers","AutoencoderKL"]
}

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计算生成

from torch import autocast # 移动到 cuda 
pipe = pipe.to('cuda')prompt = "a photo of an astronut riding a horse"
with autocast("cuda"):ret = pipe(prompt)

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ret 的内容是：

StableDiffusionPipelineOutput(images=[<PIL.Image.Image image mode=RGB size=512x512 at 0x7F99A7332310>], nsfw_content_detected=[False])

有的demo会使用 sample 键，我这里没有，所以如果调用 sample 会报错

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查看图片文件

ret.images[0]

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设置宽高

宽高必须被 8 整除

# `height` and `width` have to be divisible by 8 but are 300 and 200.
with autocast("cuda"):ret = pipe(prompt, width=400, height=640)ret.images[0]

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测试迭代次数

设置随机种子

 # 先快速生成低质量图片看效果，然后调高画质
generator = torch.Generator("cuda").manual_seed(1024)with autocast("cuda"):ret = pipe(prompt, generator=generator)ret.images[0]

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# 调整迭代次数
with autocast("cuda"):ret = pipe(prompt, generator=generator, num_inference_steps=15)ret.images[0]

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生成多列图片

from PIL import Imagedef image_grid(imgs, rows, cols):assert len(imgs) == rows * colsw, h = imgs[0].sizegrid = Image.new("RGB", size=(cols*w, rows*h) )grid_w, grid_h = grid.sizefor i, img in enumerate(imgs):grid.paste(img, box=(i%cols * w, i // cols * h) )return grid  

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num_imgs = 3
prompts = [prompt] * num_imgswith autocast("cuda"):ret = pipe(prompts, generator=generator, num_inference_steps=15)

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ret 内容如下，包含三张图片

StableDiffusionPipelineOutput(images=[<PIL.Image.Image image mode=RGB size=512x512 at 0x7F99A7150820>, <PIL.Image.Image image mode=RGB size=512x512 at 0x7F99A7103280>, <PIL.Image.Image image mode=RGB size=512x512 at 0x7F99A7103520>], nsfw_content_detected=[False, False, False])

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显示图片

grid = image_grid(ret.images, rows=1, cols=3)

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2023-02-15（情人节快乐）