diffusers/main/zh/_app/immutable/nodes/9.jEhcNG5Q.js

import"../chunks/DsnmJJEf.js";import{i as G,h as j,C as Z,H as l,a as i,E as V,s as N}from"../chunks/DdZvggmf.js";import{p as W,o as X,s as t,f as x,a as u,b as F,d as y,c as I,r as T,n as v}from"../chunks/BbekZcyp.js";const A='{"title":"入门：使用混合推理进行 VAE 编码","local":"入门使用混合推理进行-vae-编码","sections":[{"title":"内存","local":"内存","sections":[],"depth":2},{"title":"可用 VAE","local":"可用-vae","sections":[],"depth":2},{"title":"代码","local":"代码","sections":[{"title":"基本示例","local":"基本示例","sections":[],"depth":3},{"title":"生成","local":"生成","sections":[],"depth":3}],"depth":2},{"title":"集成","local":"集成","sections":[],"depth":2}],"depth":1}';var k=I('<meta name="hf:doc:metadata"/>'),E=I(`<p></p> <!> <!> <p>VAE 编码用于训练、图像到图像和图像到视频——将图像或视频转换为潜在表示。</p> <!> <p>这些表格展示了在不同 GPU 上使用 SD v1 和 SD XL 进行 VAE 编码的 VRAM 需求。</p> <p>对于这些 GPU 中的大多数，内存使用百分比决定了其他模型（文本编码器、UNet/Transformer）必须被卸载，或者必须使用分块编码，这会增加时间并影响质量。</p> <details><summary>SD v1.5</summary> <table><thead><tr><th align="left">GPU</th><th align="left">分辨率</th><th align="right">时间（秒）</th><th align="right">内存（%）</th><th align="right">分块时间（秒）</th><th align="right">分块内存（%）</th></tr></thead><tbody><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">512x512</td><td align="right">0.015</td><td align="right">3.51901</td><td align="right">0.015</td><td align="right">3.51901</td></tr><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">256x256</td><td align="right">0.004</td><td align="right">1.3154</td><td align="right">0.005</td><td align="right">1.3154</td></tr><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">2048x2048</td><td align="right">0.402</td><td align="right">47.1852</td><td align="right">0.496</td><td align="right">3.51901</td></tr><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">1024x1024</td><td align="right">0.078</td><td align="right">12.2658</td><td align="right">0.094</td><td align="right">3.51901</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">512x512</td><td align="right">0.023</td><td align="right">5.30105</td><td align="right">0.023</td><td align="right">5.30105</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">256x256</td><td align="right">0.006</td><td align="right">1.98152</td><td align="right">0.006</td><td align="right">1.98152</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">2048x2048</td><td align="right">0.574</td><td align="right">71.08</td><td align="right">0.656</td><td align="right">5.30105</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">1024x1024</td><td align="right">0.111</td><td align="right">18.4772</td><td align="right">0.14</td><td align="right">5.30105</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">512x512</td><td align="right">0.032</td><td align="right">3.52782</td><td align="right">0.032</td><td align="right">3.52782</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">256x256</td><td align="right">0.01</td><td align="right">1.31869</td><td align="right">0.009</td><td align="right">1.31869</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">2048x2048</td><td align="right">0.742</td><td align="right">47.3033</td><td align="right">0.954</td><td align="right">3.52782</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">1024x1024</td><td align="right">0.136</td><td align="right">12.2965</td><td align="right">0.207</td><td align="right">3.52782</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">512x512</td><td align="right">0.036</td><td align="right">8.51761</td><td align="right">0.036</td><td align="right">8.51761</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">256x256</td><td align="right">0.01</td><td align="right">3.18387</td><td align="right">0.01</td><td align="right">3.18387</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">2048x2048</td><td align="right">0.863</td><td align="right">86.7424</td><td align="right">1.191</td><td align="right">8.51761</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">1024x1024</td><td align="right">0.157</td><td align="right">29.6888</td><td align="right">0.227</td><td align="right">8.51761</td></tr><tr><td align="left">NVIDIA GeForce RTX 3070</td><td align="left">512x512</td><td align="right">0.051</td><td align="right">10.6941</td><td align="right">0.051</td><td align="right">10.6941</td></tr><tr><td align="left">NVIDIA GeForce RTX 3070</td><td align="left">256x256</td><td align="right">0.015</td><td align="right"></td><td align="right"></td><td align="right"></td></tr><tr><td align="left">3.99743</td><td align="left">0.015</td><td align="right">3.99743</td><td align="right"></td><td align="right"></td><td align="right"></td></tr><tr><td align="left">NVIDIA GeForce RTX 3070</td><td align="left">2048x2048</td><td align="right">1.217</td><td align="right">96.054</td><td align="right">1.482</td><td align="right">10.6941</td></tr><tr><td align="left">NVIDIA GeForce RTX 3070</td><td align="left">1024x1024</td><td align="right">0.223</td><td align="right">37.2751</td><td align="right">0.327</td><td align="right">10.6941</td></tr></tbody></table></details> <details><summary>SDXL</summary> <table><thead><tr><th align="left">GPU</th><th align="left">Resolution</th><th align="right">Time (seconds)</th><th align="right">Memory Consumed (%)</th><th align="right">Tiled Time (seconds)</th><th align="right">Tiled Memory (%)</th></tr></thead><tbody><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">512x512</td><td align="right">0.029</td><td align="right">4.95707</td><td align="right">0.029</td><td align="right">4.95707</td></tr><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">256x256</td><td align="right">0.007</td><td align="right">2.29666</td><td align="right">0.007</td><td align="right">2.29666</td></tr><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">2048x2048</td><td align="right">0.873</td><td align="right">66.3452</td><td align="right">0.863</td><td align="right">15.5649</td></tr><tr><td align="left">NVIDIA GeForce RTX 4090</td><td align="left">1024x1024</td><td align="right">0.142</td><td align="right">15.5479</td><td align="right">0.143</td><td align="right">15.5479</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">512x512</td><td align="right">0.044</td><td align="right">7.46735</td><td align="right">0.044</td><td align="right">7.46735</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">256x256</td><td align="right">0.01</td><td align="right">3.4597</td><td align="right">0.01</td><td align="right">3.4597</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">2048x2048</td><td align="right">1.317</td><td align="right">87.1615</td><td align="right">1.291</td><td align="right">23.447</td></tr><tr><td align="left">NVIDIA GeForce RTX 4080 SUPER</td><td align="left">1024x1024</td><td align="right">0.213</td><td align="right">23.4215</td><td align="right">0.214</td><td align="right">23.4215</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">512x512</td><td align="right">0.058</td><td align="right">5.65638</td><td align="right">0.058</td><td align="right">5.65638</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">256x256</td><td align="right">0.016</td><td align="right">2.45081</td><td align="right">0.016</td><td align="right">2.45081</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">2048x2048</td><td align="right">1.755</td><td align="right">77.8239</td><td align="right">1.614</td><td align="right">18.4193</td></tr><tr><td align="left">NVIDIA GeForce RTX 3090</td><td align="left">1024x1024</td><td align="right">0.265</td><td align="right">18.4023</td><td align="right">0.265</td><td align="right">18.4023</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">512x512</td><td align="right">0.064</td><td align="right">13.6568</td><td align="right">0.064</td><td align="right">13.6568</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">256x256</td><td align="right">0.018</td><td align="right">5.91728</td><td align="right">0.018</td><td align="right">5.91728</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">2048x2048</td><td align="right">内存不足 (OOM)</td><td align="right">内存不足 (OOM)</td><td align="right">1.866</td><td align="right">44.4717</td></tr><tr><td align="left">NVIDIA GeForce RTX 3080</td><td align="left">1024x1024</td><td align="right">0.302</td><td align="right">44.4308</td><td align="right">0.302</td><td align="right">44.4308</td></tr><tr><td align="left">NVIDIA GeForce RTX 3070</td><td align="left">512x512</td><td align="right">0.093</td><td align="right">17.1465</td><td align="right">0.093</td><td align="right">17.1465</td></tr></tbody></table> <p>| NVIDIA GeForce R
| NVIDIA GeForce RTX 3070       | 256x256      |            0.025 |               7.42931 |                  0.026 |            7.42931 |
| NVIDIA GeForce RTX 3070       | 2048x2048    |          OOM     |             OOM       |                  2.674 |           55.8355  |
| NVIDIA GeForce RTX 3070       | 1024x1024    |            0.443 |              55.7841  |                  0.443 |           55.7841  |</p></details> <!> <table><thead><tr><th align="center"></th><th align="center"><strong>端点</strong></th><th align="center"><strong>模型</strong></th></tr></thead><tbody><tr><td align="center"><strong>Stable Diffusion v1</strong></td><td align="center"><a href="https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud" rel="nofollow">https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud</a></td><td align="center"><a href="https://hf.co/stabilityai/sd-vae-ft-mse" rel="nofollow"><code>stabilityai/sd-vae-ft-mse</code></a></td></tr><tr><td align="center"><strong>Stable Diffusion XL</strong></td><td align="center"><a href="https://xjqqhmyn62rog84g.us-east-1.aws.endpoints.huggingface.cloud" rel="nofollow">https://xjqqhmyn62rog84g.us-east-1.aws.endpoints.huggingface.cloud</a></td><td align="center"><a href="https://hf.co/madebyollin/sdxl-vae-fp16-fix" rel="nofollow"><code>madebyollin/sdxl-vae-fp16-fix</code></a></td></tr><tr><td align="center"><strong>Flux</strong></td><td align="center"><a href="https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud" rel="nofollow">https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud</a></td><td align="center"><a href="https://hf.co/black-forest-labs/FLUX.1-schnell" rel="nofollow"><code>black-forest-labs/FLUX.1-schnell</code></a></td></tr></tbody></table> <blockquote class="tip"><p>模型支持可以在此处请求：<a href="https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml" rel="nofollow">这里</a>。</p></blockquote> <!> <blockquote class="tip"><p>从 <code>main</code> 安装 <code>diffusers</code> 以运行代码：<code>pip install git+https://github.com/huggingface/diffusers@main</code></p></blockquote> <p>一个辅助方法简化了与混合推理的交互。</p> <!> <!> <p>让我们编码一张图像，然后解码以演示。</p> <figure class="image flex flex-col items-center justify-center text-center m-0 w-full"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"/></figure> <details><summary>代码</summary> <!></details> <figure class="image flex flex-col items-center justify-center text-center m-0 w-full"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/remote_vae/decoded.png"/></figure> <!> <p>现在让我们看一个生成示例，我们将编码图像，生成，然后远程解码！</p> <details><summary>代码</summary> <!></details> <figure class="image flex flex-col items-center justify-center text-center m-0 w-full"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/remote_vae/fantasy_landscape.png"/></figure> <!> <ul><li><strong><a href="https://github.com/vladmandic/sdnext" rel="nofollow">SD.Next</a>:</strong> 具有直接支持混合推理功能的一体化用户界面。</li> <li><strong><a href="https://github.com/kijai/ComfyUI-HFRemoteVae" rel="nofollow">ComfyUI-HFRemoteVae</a>:</strong> 用于混合推理的 ComfyUI 节点。</li></ul> <!> <p></p>`,1);function S(b,U){W(U,!1),X(()=>{new URLSearchParams(window.location.search).get("fw")}),G();var n=E();j("1wq4egn",m=>{var M=k();N(M,"content",A),u(m,M)});var e=t(x(n),2);Z(e,{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"});var g=t(e,2);l(g,{title:"入门：使用混合推理进行 VAE 编码",local:"入门使用混合推理进行-vae-编码",headingTag:"h1"});var r=t(g,4);l(r,{title:"内存",local:"内存",headingTag:"h2"});var s=t(r,10);l(s,{title:"可用 VAE",local:"可用-vae",headingTag:"h2"});var h=t(s,6);l(h,{title:"代码",local:"代码",headingTag:"h2"});var o=t(h,6);i(o,{code:"ZnJvbSUyMGRpZmZ1c2Vycy51dGlscy5yZW1vdGVfdXRpbHMlMjBpbXBvcnQlMjByZW1vdGVfZW5jb2Rl",highlighted:'<span class="hljs-keyword">from</span> diffusers.utils.remote_utils <span class="hljs-keyword">import</span> remote_encode',lang:"python",wrap:!1});var c=t(o,2);l(c,{title:"基本示例",local:"基本示例",headingTag:"h3"});var d=t(c,6),J=t(y(d),2);i(J,{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">from</span> diffusers.utils.remote_utils <span class="hljs-keyword">import</span> remote_decode

image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg?download=true&quot;</span>)

latent = remote_encode(
    endpoint=<span class="hljs-string">&quot;https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud/&quot;</span>,
    scaling_factor=<span class="hljs-number">0.3611</span>,
    shift_factor=<span class="hljs-number">0.1159</span>,
)

decoded = remote_decode(
    endpoint=<span class="hljs-string">&quot;https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/&quot;</span>,
    tensor=latent,
    scaling_factor=<span class="hljs-number">0.3611</span>,
    shift_factor=<span class="hljs-number">0.1159</span>,
)`,lang:"python",wrap:!1}),T(d);var p=t(d,4);l(p,{title:"生成",local:"生成",headingTag:"h3"});var a=t(p,4),w=t(y(a),2);i(w,{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionImg2ImgPip
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">from</span> diffusers.utils.remote_utils <span class="hljs-keyword">import</span> remote_decode, remote_encode

pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
    vae=<span class="hljs-literal">None</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

init_image = load_image(
    <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>
)
init_image = init_image.resize((<span class="hljs-number">768</span>, <span class="hljs-number">512</span>))

init_latent = remote_encode(
    endpoint=<span class="hljs-string">&quot;https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud/&quot;</span>,
    image=init_image,
    scaling_factor=<span class="hljs-number">0.18215</span>,
)

prompt = <span class="hljs-string">&quot;A fantasy landscape, trending on artstation&quot;</span>
latent = pipe(
    prompt=prompt,
    image=init_latent,
    strength=<span class="hljs-number">0.75</span>,
    output_type=<span class="hljs-string">&quot;latent&quot;</span>,
).images

image = remote_decode(
    endpoint=<span class="hljs-string">&quot;https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/&quot;</span>,
    tensor=latent,
    scaling_factor=<span class="hljs-number">0.18215</span>,
)
image.save(<span class="hljs-string">&quot;fantasy_landscape.jpg&quot;</span>)`,lang:"python",wrap:!1}),T(a);var f=t(a,4);l(f,{title:"集成",local:"集成",headingTag:"h2"});var R=t(f,4);V(R,{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/zh/hybrid_inference/vae_encode.md"}),v(2),u(b,n),F()}export{S as component};