diffusers/pr_12403/zh/_app/immutable/nodes/9.e1f70c93.js

import{s as qt,n as Pt,o as Ot}from"../chunks/scheduler.5c93273d.js";import{S as Kt,i as tl,g as a,s as d,r,A as ll,h as g,f as e,c as n,j as Gt,u as h,x as s,k as S,y as q,a as i,v as o,d as f,t as c,w as m}from"../chunks/index.e43dd92b.js";import{C as vt}from"../chunks/CodeBlock.6896320e.js";import{H as Y,E as el}from"../chunks/getInferenceSnippets.161194d2.js";function il(Ct){let p,P,H,O,w,K,J,Nt="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。",tt,R,lt,j,Wt="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。",et,x,$t="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。",it,G,Xt='<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>',dt,Z,At=`<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>`,nt,V,at,v,Et='<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>',gt,u,Ft='<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>',st,C,rt,M,kt="<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>",ht,N,Dt="一个辅助方法简化了与混合推理的交互。",ot,W,ft,$,ct,X,_t="让我们编码一张图像，然后解码以演示。",mt,y,Bt='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"/>',pt,T,z,St="代码",Zt,A,ut,I,Yt='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/remote_vae/decoded.png"/>',Mt,E,yt,F,zt="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！",Tt,U,Q,Qt="代码",Vt,k,It,b,Ht='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/remote_vae/fantasy_landscape.png"/>',Ut,D,bt,_,Lt='<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>',wt,B,Jt,L,Rt;return w=new Y({props:{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"}}),R=new Y({props:{title:"内存",local:"内存",headingTag:"h2"}}),V=new Y({props:{title:"可用 VAE",local:"可用-vae",headingTag:"h2"}}),C=new Y({props:{title:"代码",local:"代码",headingTag:"h2"}}),W=new vt({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy51dGlscy5yZW1vdGVfdXRpbHMlMjBpbXBvcnQlMjByZW1vdGVfZW5jb2Rl",highlighted:'<span class="hljs-keyword">from</span> diffusers.utils.remote_utils <span class="hljs-keyword">import</span> remote_encode',wrap:!1}}),$=new Y({props:{title:"基本示例",local:"基本示例",headingTag:"h3"}}),A=new vt({props:{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>,
)`,wrap:!1}}),E=new Y({props:{title:"生成",local:"生成",headingTag:"h3"}}),k=new vt({props:{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>)`,wrap:!1}}),D=new Y({props:{title:"集成",local:"集成",headingTag:"h2"}}),B=new 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