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import{s as sn,o as an,n as vs}from"../chunks/scheduler.5c93273d.js";import{S as nn,i as pn,g as i,s as a,r as c,A as Mn,h as p,f as t,c as n,j as tn,u as o,x as M,k as Ze,y as Jn,a as s,v as m,d as y,t as T,w as r}from"../chunks/index.e43dd92b.js";import{T as Gs}from"../chunks/Tip.3538f9e3.js";import{C as u}from"../chunks/CodeBlock.6896320e.js";import{H as Be,E as cn}from"../chunks/getInferenceSnippets.161194d2.js";function on(I){let J,U='在模型训练过程中查看推理样本有助于评估训练进度。我们的<a href="https://github.com/huggingface/diffusers/tree/main/examples/" rel="nofollow">训练脚本</a>支持此功能，并额外提供TensorBoard和Weights &amp; Biases日志记录功能。';return{c(){J=i("p"),J.innerHTML=U},l(d){J=p(d,"P",{"data-svelte-h":!0}),M(J)!=="svelte-11fkf2z"&&(J.innerHTML=U)},m(d,w){s(d,J,w)},p:vs,d(d){d&&t(J)}}}function mn(I){let J,U="该分数存在固有局限性：训练数据中的标题是从网络爬取，并提取自图片关联的<code>alt</code>等标签。这些描述未必符合人类描述图像的方式，因此我们需要人工”设计”部分提示词。";return{c(){J=i("p"),J.innerHTML=U},l(d){J=p(d,"P",{"data-svelte-h":!0}),M(J)!=="svelte-1kd06m1"&&(J.innerHTML=U)},m(d,w){s(d,J,w)},p:vs,d(d){d&&t(J)}}}function yn(I){let J,U="CLIP分数和CLIP方向相似度都依赖CLIP模型，可能导致评估结果存在偏差。";return{c(){J=i("p"),J.textContent=U},l(d){J=p(d,"P",{"data-svelte-h":!0}),M(J)!=="svelte-deyo40"&&(J.textContent=U)},m(d,w){s(d,J,w)},p:vs,d(d){d&&t(J)}}}function Tn(I){let J,U="FID结果往往具有脆弱性，因为它依赖于许多因素：",d,w,C="<li>计算过程中使用的特定Inception模型</li> <li>计算实现的准确性</li> <li>图像格式（PNG和JPG的起点不同）</li>",Z,j,We="需要注意的是，FID通常在比较相似实验时最有用，但除非作者仔细公开FID测量代码，否则很难复现论文结果。",B,h,ge="这些注意事项同样适用于其他相关指标，如KID和IS。";return{c(){J=i("p"),J.textContent=U,d=a(),w=i("ul"),w.innerHTML=C,Z=a(),j=i("p"),j.textContent=We,B=a(),h=i("p"),h.textContent=ge},l(f){J=p(f,"P",{"data-svelte-h":!0}),M(J)!=="svelte-fax4tc"&&(J.textContent=U),d=n(f),w=p(f,"UL",{"data-svelte-h":!0}),M(w)!=="svelte-40toec"&&(w.innerHTML=C),Z=n(f),j=p(f,"P",{"data-svelte-h":!0}),M(j)!=="svelte-1lq2320"&&(j.textContent=We),B=n(f),h=p(f,"P",{"data-svelte-h":!0}),M(h)!=="svelte-1zrqq3"&&(h.textContent=ge)},m(f,b){s(f,J,b),s(f,d,b),s(f,w,b),s(f,Z,b),s(f,j,b),s(f,B,b),s(f,h,b)},p:vs,d(f){f&&(t(J),t(d),t(w),t(Z),t(j),t(B),t(h))}}}function rn(I){let J,U,d,w,C,Z,j,We='<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"/>',B,h,ge='<p>鉴于当前已出现针对图像生成Diffusion模型的成熟评估框架（如<a href="https://crfm.stanford.edu/helm/heim/latest/" rel="nofollow">HEIM</a>、<a href="https://huggingface.co/papers/2307.06350" rel="nofollow">T2I-Compbench</a>、<a href="https://huggingface.co/papers/2310.11513" rel="nofollow">GenEval</a>），本文档部分内容已过时。</p>',f,b,Vs='像 <a href="https://huggingface.co/docs/diffusers/stable_diffusion" rel="nofollow">Stable Diffusion</a> 这类生成模型的评估本质上是主观的。但作为开发者和研究者，我们经常需要在众多可能性中做出审慎选择。那么当面对不同生成模型（如 GANs、Diffusion 等）时，该如何决策？',Ge,k,ks="定性评估容易产生偏差，可能导致错误结论；而定量指标又未必能准确反映图像质量。因此，通常需要结合定性与定量评估来获得更可靠的模型选择依据。",ve,x,xs="本文档将系统介绍扩散模型的定性与定量评估方法（非穷尽列举）。对于定量方法，我们将重点演示如何结合 <code>diffusers</code> 库实现这些评估。",Ve,X,Xs='文档所示方法同样适用于评估不同<a href="https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview" rel="nofollow">噪声调度器</a>在固定生成模型下的表现差异。',ke,R,xe,N,Rs="我们涵盖以下Diffusion模型管线的评估：",Xe,F,Ns='<li>文本引导图像生成（如 <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img" rel="nofollow"><code>StableDiffusionPipeline</code></a>）</li> <li>基于文本和输入图像的引导生成（如 <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img" rel="nofollow"><code>StableDiffusionImg2ImgPipeline</code></a> 和 <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix" rel="nofollow"><code>StableDiffusionInstructPix2PixPipeline</code></a>）</li> <li>类别条件图像生成模型(如 <a href="https://huggingface.co/docs/diffusers/main/en/api/pipe" rel="nofollow"><code>DiTPipeline</code></a>)</li>',Re,H,Ne,E,Fs='定性评估通常涉及对生成图像的人工评判。评估维度包括构图质量、图文对齐度和空间关系等方面。标准化的提示词能为这些主观指标提供统一基准。DrawBench和PartiPrompts是常用的定性评估提示词数据集，分别由<a href="https://imagen.research.google/" rel="nofollow">Imagen</a>和<a href="https://parti.research.google/" rel="nofollow">Parti</a>团队提出。',Fe,Y,Hs='根据<a href="https://parti.research.google/" rel="nofollow">Parti官方网站</a>说明：',He,$,Es="<p>PartiPrompts (P2)是我们发布的包含1600多个英文提示词的丰富集合，可用于测量模型在不同类别和挑战维度上的能力。</p>",Ee,Q,Ys='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts.png" alt="parti-prompts"/>',Ye,A,$s="PartiPrompts包含以下字段：",$e,z,Qs="<li>Prompt（提示词）</li> <li>Category（类别，如”抽象”、“世界知识”等）</li> <li>Challenge（难度等级，如”基础”、“复杂”、“文字与符号”等）</li>",Qe,S,As="这些基准测试支持对不同图像生成模型进行并排人工对比评估。为此，🧨 Diffusers团队构建了<strong>Open Parti Prompts</strong>——一个基于Parti Prompts的社区驱动型定性评估基准，用于比较顶尖开源diffusion模型：",Ae,L,zs='<li><a href="https://huggingface.co/spaces/OpenGenAI/open-parti-prompts" rel="nofollow">Open Parti Prompts游戏</a>：展示10个parti提示词对应的4张生成图像，用户选择最符合提示的图片</li> <li><a href="https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard" rel="nofollow">Open Parti Prompts排行榜</a>：对比当前最优开源diffusion模型的性能榜单</li>',ze,P,Ss="为进行手动图像对比，我们演示如何使用<code>diffusers</code>处理部分PartiPrompts提示词。",Se,D,Ls='以下是从不同挑战维度（基础、复杂、语言结构、想象力、文字与符号）采样的提示词示例（使用<a href="https://huggingface.co/datasets/nateraw/parti-prompts" rel="nofollow">PartiPrompts作为数据集</a>）：',Le,q,Pe,K,Ps='现在我们可以使用Stable Diffusion（<a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" rel="nofollow">v1-4 checkpoint</a>）生成这些提示词对应的图像：',De,O,qe,ll,Ds='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-14.png" alt="parti-prompts-14"/>',Ke,el,qs='我们也可以通过设置<code>num_images_per_prompt</code>参数来比较同一提示词生成的不同图像。使用不同检查点(<a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">v1-5</a>)运行相同流程后，结果如下：',Oe,tl,Ks='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png" alt="parti-prompts-15"/>',lt,sl,Os="当使用多个待评估模型为所有提示词生成若干图像后，这些结果将提交给人类评估员进行打分。有关DrawBench和PartiPrompts基准测试的更多细节，请参阅各自的论文。",et,W,tt,al,st,nl,la="本节将指导您如何评估三种不同的扩散流程，使用以下指标：",at,il,ea="<li>CLIP分数</li> <li>CLIP方向相似度</li> <li>FID（弗雷歇起始距离）</li>",nt,pl,it,Ml,ta='<a href="https://huggingface.co/papers/2104.08718" rel="nofollow">CLIP分数</a>用于衡量图像-标题对的匹配程度。CLIP分数越高表明匹配度越高🔼。该分数是对”匹配度”这一定性概念的量化测量，也可以理解为图像与标题之间的语义相似度。研究发现CLIP分数与人类判断具有高度相关性。',pt,Jl,sa="首先加载<code>StableDiffusionPipeline</code>：",Mt,cl,Jt,ol,aa="使用多个提示词生成图像：",ct,ml,ot,yl,na="然后计算CLIP分数：",mt,Tl,yt,rl,ia="上述示例中，我们为每个提示生成一张图像。如果为每个提示生成多张图像，则需要计算每个提示生成图像的平均分数。",Tt,dl,pa='当需要比较两个兼容<code>StableDiffusionPipeline</code>的检查点时，应在调用管道时传入生成器。首先使用<a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" rel="nofollow">v1-4 Stable Diffusion检查点</a>以固定种子生成图像：',rt,ul,dt,fl,Ma='然后加载<a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">v1-5检查点</a>生成图像：',ut,wl,ft,jl,Ja="最后比较两者的CLIP分数：",wt,Ul,jt,hl,ca='结果表明<a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">v1-5</a>检查点性能优于前代。但需注意，我们用于计算CLIP分数的提示词数量较少。实际评估时应使用更多样化且数量更大的提示词集。',Ut,g,ht,bl,bt,Il,oa="这种情况下，生成管道同时接受输入图像和文本提示作为条件。以<code>StableDiffusionInstructPix2PixPipeline</code>为例，该管道接收编辑指令作为输入提示，并接受待编辑的输入图像。",It,Cl,ma="示例图示：",Ct,Zl,ya='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png" alt="编辑指令"/>',Zt,Bl,Ta='评估此类模型的策略之一是测量两幅图像间变化的连贯性（通过<a href="https://huggingface.co/docs/transformers/model_doc/clip" rel="nofollow">CLIP</a>定义）中两个图像之间的变化与两个图像描述之间的变化的一致性（如论文<a href="https://huggingface.co/papers/2108.00946" rel="nofollow">《CLIP-Guided Domain Adaptation of Image Generators》</a>所示）。这被称为“<strong>CLIP方向相似度</strong>”。',Bt,Wl,ra="<li><strong>描述1</strong>对应输入图像（图像1），即待编辑的图像。</li> <li><strong>描述2</strong>对应编辑后的图像（图像2），应反映编辑指令。</li>",Wt,gl,da="以下是示意图：",gt,_l,ua='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-consistency.png" alt="edit-consistency"/>',_t,Gl,fa="我们准备了一个小型数据集来实现该指标。首先加载数据集：",Gt,vl,vt,Vl,Vt,kl,wa="数据字段说明：",kt,xl,ja="<li><code>input</code>：与<code>image</code>对应的原始描述。</li> <li><code>edit</code>：编辑指令。</li> <li><code>output</code>：反映<code>edit</code>指令的修改后描述。</li>",xt,Xl,Ua="查看一个样本：",Xt,Rl,Rt,Nl,Nt,Fl,ha="对应的图像：",Ft,Hl,Ht,El,ba='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-dataset.png" alt="edit-dataset"/>',Et,Yl,Ia="我们将根据编辑指令修改数据集中的图像，并计算方向相似度。",Yt,$l,Ca="首先加载<code>StableDiffusionInstructPix2PixPipeline</code>：",$t,Ql,Qt,Al,Za="执行编辑操作：",At,zl,zt,Sl,Ba="为测量方向相似度，我们首先加载CLIP的图像和文本编码器：",St,Ll,Lt,Pl,Wa='注意我们使用的是特定CLIP检查点——<code>openai/clip-vit-large-patch14</code>，因为Stable Diffusion预训练正是基于此CLIP变体。详见<a href="https://huggingface.co/docs/transformers/model_doc/clip" rel="nofollow">文档</a>。',Pt,Dl,ga="接着准备计算方向相似度的PyTorch <code>nn.Module</code>：",Dt,ql,qt,Kl,_a="现在让我们使用<code>DirectionalSimilarity</code>模块：",Kt,Ol,Ot,le,Ga="与CLIP分数类似，CLIP方向相似度数值越高越好。",ls,ee,va="需要注意的是，<code>StableDiffusionInstructPix2PixPipeline</code>提供了两个控制参数<code>image_guidance_scale</code>和<code>guidance_scale</code>来调节最终编辑图像的质量。建议您尝试调整这两个参数，观察它们对方向相似度的影响。",es,te,Va="我们可以扩展这个度量标准来评估原始图像与编辑版本的相似度，只需计算<code>F.cosine_similarity(img_feat_two, img_feat_one)</code>。对于这类编辑任务，我们仍希望尽可能保留图像的主要语义特征（即保持较高的相似度分数）。",ts,se,ka='该度量方法同样适用于类似流程，例如<a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline" rel="nofollow"><code>StableDiffusionPix2PixZeroPipeline</code></a>。',ss,_,as,ae,xa='<strong><em>扩展IS、FID（后文讨论）或KID等指标存在困难</em></strong>，当被评估模型是在大型图文数据集（如<a href="https://laion.ai/blog/laion-5b/" rel="nofollow">LAION-5B数据集</a>）上预训练时。因为这些指标的底层都使用了在ImageNet-1k数据集上预训练的InceptionNet来提取图像特征。Stable Diffusion的预训练数据集与InceptionNet的预训练数据集可能重叠有限，因此不适合作为特征提取器。',ns,ne,Xa=`<strong><em>上述指标更适合评估类别条件模型</em></strong>，例如<a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit" rel="nofollow">DiT</a>。该模型是在ImageNet-1k类别条件下预训练的。
这是9篇文档中的第8部分。`,is,ie,ps,pe,Ra='基于类别的生成模型通常是在带有类别标签的数据集（如<a href="https://huggingface.co/datasets/imagenet-1k" rel="nofollow">ImageNet-1k</a>）上进行预训练的。评估这些模型的常用指标包括Fréchet Inception Distance（FID）、Kernel Inception Distance（KID）和Inception Score（IS）。本文档重点介绍FID（<a href="https://huggingface.co/papers/1706.08500" rel="nofollow">Heusel等人</a>），并展示如何使用<a href="https://huggingface.co/docs/diffusers/api/pipelines/dit" rel="nofollow"><code>DiTPipeline</code></a>计算该指标，该管道底层使用了<a href="https://huggingface.co/papers/2212.09748" rel="nofollow">DiT模型</a>。',Ms,Me,Na='FID旨在衡量两组图像数据集的相似程度。根据<a href="https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid" rel="nofollow">此资源</a>：',Js,Je,Fa="<p>Fréchet Inception Distance是衡量两组图像数据集相似度的指标。研究表明其与人类对视觉质量的主观判断高度相关，因此最常用于评估生成对抗网络（GAN）生成样本的质量。FID通过计算Inception网络特征表示所拟合的两个高斯分布之间的Fréchet距离来实现。</p>",cs,ce,Ha="这两个数据集本质上是真实图像数据集和生成图像数据集（本例中为人工生成的图像）。FID通常基于两个大型数据集计算，但本文档将使用两个小型数据集进行演示。",os,oe,Ea="首先下载ImageNet-1k训练集中的部分图像：",ms,me,ys,ye,Ts,Te,Ya="这些是来自以下ImageNet-1k类别的10张图像：“cassette_player”、“chain_saw”（2张）、“church”、“gas_pump”（3张）、“parachute”（2张）和”tench”。",rs,G,$a='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/real-images.png" alt="真实图像"/><br/> <em>真实图像</em>',ds,re,Qa="加载图像后，我们对其进行轻量级预处理以便用于FID计算：",us,de,fs,ue,Aa='我们现在加载<a href="https://huggingface.co/docs/diffusers/api/pipelines/dit" rel="nofollow"><code>DiTPipeline</code></a>来生成基于上述类别的条件图像。',ws,fe,js,we,za='现在，我们可以使用<a href="https://torchmetrics.readthedocs.io/" rel="nofollow"><code>torchmetrics</code></a>计算FID分数。',Us,je,hs,Ue,Sa="FID分数越低越好。以下因素会影响FID结果：",bs,he,La="<li>图像数量（包括真实图像和生成图像）</li> <li>扩散过程中引入的随机性</li> <li>扩散过程的推理步数</li> <li>扩散过程中使用的调度器</li>",Is,be,Pa="对于最后两点，最佳实践是使用不同的随机种子和推理步数进行多次评估，然后报告平均结果。",Cs,v,Zs,Ie,Da="最后，让我们可视化检查这些<code>fake_images</code>。",Bs,V,qa='<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/fake-images.png" alt="生成图像"/><br/> <em>生成图像示例</em>',Ws,Ce,gs,_e,_s;return C=new Be({props:{title:"Diffusion模型评估指南",local:"diffusion模型评估指南",headingTag:"h1"}}),R=new Be({props:{title:"评估场景",local:"评估场景",headingTag:"h2"}}),H=new Be({props:{title:"定性评估",local:"定性评估",headingTag:"h2"}}),q=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBJTIzJTIwcHJvbXB0cyUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJuYXRlcmF3JTJGcGFydGktcHJvbXB0cyUyMiUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIpJTBBJTIzJTIwcHJvbXB0cyUyMCUzRCUyMHByb21wdHMuc2h1ZmZsZSgpJTBBJTIzJTIwc2FtcGxlX3Byb21wdHMlMjAlM0QlMjAlNUJwcm9tcHRzJTVCaSU1RCU1QiUyMlByb21wdCUyMiU1RCUyMGZvciUyMGklMjBpbiUyMHJhbmdlKDUpJTVEJTBBJTBBJTIzJTIwRml4aW5nJTIwdGhlc2UlMjBzYW1wbGUlMjBwcm9tcHRzJTIwaW4lMjB0aGUlMjBpbnRlcmVzdCUyMG9mJTIwcmVwcm9kdWNpYmlsaXR5LiUwQXNhbXBsZV9wcm9tcHRzJTIwJTNEJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIyYSUyMGNvcmdpJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyYSUyMGhvdCUyMGFpciUyMGJhbGxvb24lMjB3aXRoJTIwYSUyMHlpbi15YW5nJTIwc3ltYm9sJTJDJTIwd2l0aCUyMHRoZSUyMG1vb24lMjB2aXNpYmxlJTIwaW4lMjB0aGUlMjBkYXl0aW1lJTIwc2t5JTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyYSUyMGNhciUyMHdpdGglMjBubyUyMHdpbmRvd3MlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJhJTIwY3ViZSUyMG1hZGUlMjBvZiUyMHBvcmN1cGluZSUyMiUyQyUwQSUyMCUyMCUyMCUyMCdUaGUlMjBzYXlpbmclMjAlMjJCRSUyMEVYQ0VMTEVOVCUyMFRPJTIwRUFDSCUyME9USEVSJTIyJTIwd3JpdHRlbiUyMG9uJTIwYSUyMHJlZCUyMGJyaWNrJTIwd2FsbCUyMHdpdGglMjBhJTIwZ3JhZmZpdGklMjBpbWFnZSUyMG9mJTIwYSUyMGdyZWVuJTIwYWxpZW4lMjB3ZWFyaW5nJTIwYSUyMHR1eGVkby4lMjBBJTIweWVsbG93JTIwZmlyZSUyMGh5ZHJhbnQlMjBpcyUyMG9uJTIwYSUyMHNpZGV3YWxrJTIwaW4lMjB0aGUlMjBmb3JlZ3JvdW5kLiclMkMlMEElNUQ=",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-comment"># prompts = load_dataset(&quot;nateraw/parti-prompts&quot;, split=&quot;train&quot;)</span>
<span class="hljs-comment"># prompts = prompts.shuffle()</span>
<span class="hljs-comment"># sample_prompts = [prompts[i][&quot;Prompt&quot;] for i in range(5)]</span>
<span class="hljs-comment"># Fixing these sample prompts in the interest of reproducibility.</span>
sample_prompts = [
 <span class="hljs-string">&quot;a corgi&quot;</span>,
 <span class="hljs-string">&quot;a hot air balloon with a yin-yang symbol, with the moon visible in the daytime sky&quot;</span>,
 <span class="hljs-string">&quot;a car with no windows&quot;</span>,
 <span class="hljs-string">&quot;a cube made of porcupine&quot;</span>,
 <span class="hljs-string">&#x27;The saying &quot;BE EXCELLENT TO EACH OTHER&quot; written on a red brick wall with a graffiti image of a green alien wearing a tuxedo. A yellow fire hydrant is on a sidewalk in the foreground.&#x27;</span>,
]`,wrap:!1}}),O=new u({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEFzZWVkJTIwJTNEJTIwMCUwQWdlbmVyYXRvciUyMCUzRCUyMHRvcmNoLm1hbnVhbF9zZWVkKHNlZWQpJTBBJTBBaW1hZ2VzJTIwJTNEJTIwc2RfcGlwZWxpbmUoc2FtcGxlX3Byb21wdHMlMkMlMjBudW1faW1hZ2VzX3Blcl9wcm9tcHQlM0QxJTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yKS5pbWFnZXM=",highlighted:`<span class="hljs-keyword">import</span> torch
seed = <span class="hljs-number">0</span>
generator = torch.manual_seed(seed)
images = sd_pipeline(sample_prompts, num_images_per_prompt=<span class="hljs-number">1</span>, generator=generator).images`,wrap:!1}}),W=new Gs({props:{$$slots:{default:[on]},$$scope:{ctx:I}}}),al=new Be({props:{title:"定量评估",local:"定量评估",headingTag:"h2"}}),pl=new Be({props:{title:"文本引导图像生成",local:"文本引导图像生成",headingTag:"h3"}}),cl=new u({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFtb2RlbF9ja3B0JTIwJTNEJTIwJTIyQ29tcFZpcyUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNCUyMiUwQXNkX3BpcGVsaW5lJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKG1vZGVsX2NrcHQlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch
model_ckpt = <span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
sd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),ml=new u({props:{code:"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",highlighted:`prompts = [
 <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>,
 <span class="hljs-string">&quot;A high tech solarpunk utopia in the Amazon rainforest&quot;</span>,
 <span class="hljs-string">&quot;A pikachu fine dining with a view to the Eiffel Tower&quot;</span>,
 <span class="hljs-string">&quot;A mecha robot in a favela in expressionist style&quot;</span>,
 <span class="hljs-string">&quot;an insect robot preparing a delicious meal&quot;</span>,
 <span class="hljs-string">&quot;A small cabin on top of a snowy mountain in the style of Disney, artstation&quot;</span>,
]
images = sd_pipeline(prompts, num_images_per_prompt=<span class="hljs-number">1</span>, output_type=<span class="hljs-string">&quot;np&quot;</span>).images
<span class="hljs-built_in">print</span>(images.shape)
<span class="hljs-comment"># (6, 512, 512, 3)</span>`,wrap:!1}}),Tl=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> torchmetrics.functional.multimodal <span class="hljs-keyword">import</span> clip_score
<span class="hljs-keyword">from</span> functools <span class="hljs-keyword">import</span> partial
clip_score_fn = partial(clip_score, model_name_or_path=<span class="hljs-string">&quot;openai/clip-vit-base-patch16&quot;</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">calculate_clip_score</span>(<span class="hljs-params">images, prompts</span>):
 images_int = (images * <span class="hljs-number">255</span>).astype(<span class="hljs-string">&quot;uint8&quot;</span>)
 clip_score = clip_score_fn(torch.from_numpy(images_int).permute(<span class="hljs-number">0</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>), prompts).detach()
 <span class="hljs-keyword">return</span> <span class="hljs-built_in">round</span>(<span class="hljs-built_in">float</span>(clip_score), <span class="hljs-number">4</span>)
sd_clip_score = calculate_clip_score(images, prompts)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;CLIP分数: <span class="hljs-subst">{sd_clip_score}</span>&quot;</span>)
<span class="hljs-comment"># CLIP分数: 35.7038</span>`,wrap:!1}}),ul=new u({props:{code:"c2VlZCUyMCUzRCUyMDAlMEFnZW5lcmF0b3IlMjAlM0QlMjB0b3JjaC5tYW51YWxfc2VlZChzZWVkKSUwQSUwQWltYWdlcyUyMCUzRCUyMHNkX3BpcGVsaW5lKHByb21wdHMlMkMlMjBudW1faW1hZ2VzX3Blcl9wcm9tcHQlM0QxJTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTJDJTIwb3V0cHV0X3R5cGUlM0QlMjJucCUyMikuaW1hZ2Vz",highlighted:`seed = <span class="hljs-number">0</span>
generator = torch.manual_seed(seed)
images = sd_pipeline(prompts, num_images_per_prompt=<span class="hljs-number">1</span>, generator=generator, output_type=<span class="hljs-string">&quot;np&quot;</span>).images`,wrap:!1}}),wl=new u({props:{code:"bW9kZWxfY2twdF8xXzUlMjAlM0QlMjAlMjJzdGFibGUtZGlmZnVzaW9uLXYxLTUlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMEFzZF9waXBlbGluZV8xXzUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQobW9kZWxfY2twdF8xXzUlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpLnRvKCUyMmN1ZGElMjIpJTBBJTBBaW1hZ2VzXzFfNSUyMCUzRCUyMHNkX3BpcGVsaW5lXzFfNShwcm9tcHRzJTJDJTIwbnVtX2ltYWdlc19wZXJfcHJvbXB0JTNEMSUyQyUyMGdlbmVyYXRvciUzRGdlbmVyYXRvciUyQyUyMG91dHB1dF90eXBlJTNEJTIybnAlMjIpLmltYWdlcw==",highlighted:`model_ckpt_1_5 = <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>
sd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
images_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=<span class="hljs-number">1</span>, generator=generator, output_type=<span class="hljs-string">&quot;np&quot;</span>).images`,wrap:!1}}),Ul=new u({props:{code:"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",highlighted:`sd_clip_score_1_4 = calculate_clip_score(images, prompts)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;v-1-4版本的CLIP分数: <span class="hljs-subst">{sd_clip_score_1_4}</span>&quot;</span>)
<span class="hljs-comment"># v-1-4版本的CLIP分数: 34.9102</span>
sd_clip_score_1_5 = calculate_clip_score(images_1_5, prompts)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;v-1-5版本的CLIP分数: <span class="hljs-subst">{sd_clip_score_1_5}</span>&quot;</span>)
<span class="hljs-comment"># v-1-5版本的CLIP分数: 36.2137</span>`,wrap:!1}}),g=new Gs({props:{warning:!0,$$slots:{default:[mn]},$$scope:{ctx:I}}}),bl=new Be({props:{title:"图像条件式文本生成图像",local:"图像条件式文本生成图像",headingTag:"h3"}}),vl=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJzYXlha3BhdWwlMkZpbnN0cnVjdHBpeDJwaXgtZGVtbyUyMiUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIpJTBBZGF0YXNldC5mZWF0dXJlcw==",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
dataset = load_dataset(<span class="hljs-string">&quot;sayakpaul/instructpix2pix-demo&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
dataset.features`,wrap:!1}}),Vl=new u({props:{code:"JTdCJ2lucHV0JyUzQSUyMFZhbHVlKGR0eXBlJTNEJ3N0cmluZyclMkMlMjBpZCUzRE5vbmUpJTJDJTBBJTIwJ2VkaXQnJTNBJTIwVmFsdWUoZHR5cGUlM0Qnc3RyaW5nJyUyQyUyMGlkJTNETm9uZSklMkMlMEElMjAnb3V0cHV0JyUzQSUyMFZhbHVlKGR0eXBlJTNEJ3N0cmluZyclMkMlMjBpZCUzRE5vbmUpJTJDJTBBJTIwJ2ltYWdlJyUzQSUyMEltYWdlKGRlY29kZSUzRFRydWUlMkMlMjBpZCUzRE5vbmUpJTdE",highlighted:`{<span class="hljs-string">&#x27;input&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=None),
 <span class="hljs-string">&#x27;edit&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=None),
 <span class="hljs-string">&#x27;output&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=None),
 <span class="hljs-string">&#x27;image&#x27;</span>: Image(decode=True, <span class="hljs-built_in">id</span>=None)}`,wrap:!1}}),Rl=new u({props:{code:"aWR4JTIwJTNEJTIwMCUwQXByaW50KGYlMjJPcmlnaW5hbCUyMGNhcHRpb24lM0ElMjAlN0JkYXRhc2V0JTVCaWR4JTVEJTVCJ2lucHV0JyU1RCU3RCUyMiklMEFwcmludChmJTIyRWRpdCUyMGluc3RydWN0aW9uJTNBJTIwJTdCZGF0YXNldCU1QmlkeCU1RCU1QidlZGl0JyU1RCU3RCUyMiklMEFwcmludChmJTIyTW9kaWZpZWQlMjBjYXB0aW9uJTNBJTIwJTdCZGF0YXNldCU1QmlkeCU1RCU1QidvdXRwdXQnJTVEJTdEJTIyKQ==",highlighted:`idx = <span class="hljs-number">0</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Original caption: <span class="hljs-subst">{dataset[idx][<span class="hljs-string">&#x27;input&#x27;</span>]}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Edit instruction: <span class="hljs-subst">{dataset[idx][<span class="hljs-string">&#x27;edit&#x27;</span>]}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Modified caption: <span class="hljs-subst">{dataset[idx][<span class="hljs-string">&#x27;output&#x27;</span>]}</span>&quot;</span>)`,wrap:!1}}),Nl=new u({props:{code:"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",highlighted:`Original caption: 2. FAROE ISLANDS: An archipelago of 18 mountainous isles <span class="hljs-keyword">in</span> the North Atlantic Ocean between Norway and Iceland, the Faroe Islands has <span class="hljs-string">&#x27;everything you could hope for&#x27;</span>, according to Big 7 Travel. It boasts <span class="hljs-string">&#x27;crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills&#x27;</span>
Edit instruction: make the isles all white marble
Modified caption: 2. WHITE MARBLE ISLANDS: An archipelago of 18 mountainous white marble isles <span class="hljs-keyword">in</span> the North Atlantic Ocean between Norway and Iceland, the White Marble Islands has <span class="hljs-string">&#x27;everything you could hope for&#x27;</span>, according to Big 7 Travel. It boasts <span class="hljs-string">&#x27;crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills&#x27;</span>`,wrap:!1}}),Hl=new u({props:{code:"ZGF0YXNldCU1QmlkeCU1RCU1QiUyMmltYWdlJTIyJTVE",highlighted:'dataset[idx][<span class="hljs-string">&quot;image&quot;</span>]',wrap:!1}}),Ql=new u({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvbkluc3RydWN0UGl4MlBpeFBpcGVsaW5lJTBBJTBBaW5zdHJ1Y3RfcGl4MnBpeF9waXBlbGluZSUyMCUzRCUyMFN0YWJsZURpZmZ1c2lvbkluc3RydWN0UGl4MlBpeFBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJ0aW1icm9va3MlMkZpbnN0cnVjdC1waXgycGl4JTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionInstructPix2PixPipeline
instruct_pix2pix_pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
 <span class="hljs-string">&quot;timbrooks/instruct-pix2pix&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),zl=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">def</span> <span class="hljs-title function_">edit_image</span>(<span class="hljs-params">input_image, instruction</span>):
 image = instruct_pix2pix_pipeline(
 instruction,
 image=input_image,
 output_type=<span class="hljs-string">&quot;np&quot;</span>,
 generator=generator,
 ).images[<span class="hljs-number">0</span>]
 <span class="hljs-keyword">return</span> image
input_images = []
original_captions = []
modified_captions = []
edited_images = []
<span class="hljs-keyword">for</span> idx <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(dataset)):
 input_image = dataset[idx][<span class="hljs-string">&quot;image&quot;</span>]
 edit_instruction = dataset[idx][<span class="hljs-string">&quot;edit&quot;</span>]
 edited_image = edit_image(input_image, edit_instruction)
 input_images.append(np.array(input_image))
 original_captions.append(dataset[idx][<span class="hljs-string">&quot;input&quot;</span>])
 modified_captions.append(dataset[idx][<span class="hljs-string">&quot;output&quot;</span>])
 edited_images.append(edited_image)`,wrap:!1}}),Ll=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
 CLIPTokenizer,
 CLIPTextModelWithProjection,
 CLIPVisionModelWithProjection,
 CLIPImageProcessor,
)
clip_id = <span class="hljs-string">&quot;openai/clip-vit-large-patch14&quot;</span>
tokenizer = CLIPTokenizer.from_pretrained(clip_id)
text_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to(<span class="hljs-string">&quot;cuda&quot;</span>)
image_processor = CLIPImageProcessor.from_pretrained(clip_id)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),ql=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn
<span class="hljs-keyword">import</span> torch.nn.functional <span class="hljs-keyword">as</span> F
<span class="hljs-keyword">class</span> <span class="hljs-title class_">DirectionalSimilarity</span>(nn.Module):
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, tokenizer, text_encoder, image_processor, image_encoder</span>):
 <span class="hljs-built_in">super</span>().__init__()
 self.tokenizer = tokenizer
 self.text_encoder = text_encoder
 self.image_processor = image_processor
 self.image_encoder = image_encoder
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_image</span>(<span class="hljs-params">self, image</span>):
 image = self.image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;pixel_values&quot;</span>]
 <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;pixel_values&quot;</span>: image.to(<span class="hljs-string">&quot;cuda&quot;</span>)}
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_text</span>(<span class="hljs-params">self, text</span>):
 inputs = self.tokenizer(
 text,
 max_length=self.tokenizer.model_max_length,
 padding=<span class="hljs-string">&quot;max_length&quot;</span>,
 truncation=<span class="hljs-literal">True</span>,
 return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
 )
 <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;input_ids&quot;</span>: inputs.input_ids.to(<span class="hljs-string">&quot;cuda&quot;</span>)}
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">encode_image</span>(<span class="hljs-params">self, image</span>):
 preprocessed_image = self.preprocess_image(image)
 image_features = self.image_encoder(**preprocessed_image).image_embeds
 image_features = image_features / image_features.norm(dim=<span class="hljs-number">1</span>, keepdim=<span class="hljs-literal">True</span>)
 <span class="hljs-keyword">return</span> image_features
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">encode_text</span>(<span class="hljs-params">self, text</span>):
 tokenized_text = self.tokenize_text(text)
 text_features = self.text_encoder(**tokenized_text).text_embeds
 text_features = text_features / text_features.norm(dim=<span class="hljs-number">1</span>, keepdim=<span class="hljs-literal">True</span>)
 <span class="hljs-keyword">return</span> text_features
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_directional_similarity</span>(<span class="hljs-params">self, img_feat_one, img_feat_two, text_feat_one, text_feat_two</span>):
 sim_direction = F.cosine_similarity(img_feat_two - img_feat_one, text_feat_two - text_feat_one)
 <span class="hljs-keyword">return</span> sim_direction
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, image_one, image_two, caption_one, caption_two</span>):
 img_feat_one = self.encode_image(image_one)
 img_feat_two = self.encode_image(image_two)
 text_feat_one = self.encode_text(caption_one)
 text_feat_two = self.encode_text(caption_two)
 directional_similarity = self.compute_directional_similarity(
 img_feat_one, img_feat_two, text_feat_one, text_feat_two
 )
 <span class="hljs-keyword">return</span> directional_similarity`,wrap:!1}}),Ol=new u({props:{code:"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",highlighted:`dir_similarity = DirectionalSimilarity(tokenizer, text_encoder, image_processor, image_encoder)
scores = []
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(input_images)):
 original_image = input_images[i]
 original_caption = original_captions[i]
 edited_image = edited_images[i]
 modified_caption = modified_captions[i]
 similarity_score = dir_similarity(original_image, edited_image, original_caption, modified_caption)
 scores.append(<span class="hljs-built_in">float</span>(similarity_score.detach().cpu()))
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;CLIP方向相似度: <span class="hljs-subst">{np.mean(scores)}</span>&quot;</span>)
<span class="hljs-comment"># CLIP方向相似度: 0.0797976553440094</span>`,wrap:!1}}),_=new Gs({props:{$$slots:{default:[yn]},$$scope:{ctx:I}}}),ie=new Be({props:{title:"基于类别的图像生成",local:"基于类别的图像生成",headingTag:"h3"}}),me=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> zipfile <span class="hljs-keyword">import</span> ZipFile
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">def</span> <span class="hljs-title function_">download</span>(<span class="hljs-params">url, local_filepath</span>):
 r = requests.get(url)
 <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(local_filepath, <span class="hljs-string">&quot;wb&quot;</span>) <span class="hljs-keyword">as</span> f:
 f.write(r.content)
 <span class="hljs-keyword">return</span> local_filepath
dummy_dataset_url = <span class="hljs-string">&quot;https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/sample-imagenet-images.zip&quot;</span>
local_filepath = download(dummy_dataset_url, dummy_dataset_url.split(<span class="hljs-string">&quot;/&quot;</span>)[-<span class="hljs-number">1</span>])
<span class="hljs-keyword">with</span> ZipFile(local_filepath, <span class="hljs-string">&quot;r&quot;</span>) <span class="hljs-keyword">as</span> zipper:
 zipper.extractall(<span class="hljs-string">&quot;.&quot;</span>)`,wrap:!1}}),ye=new u({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwb3MlMEFpbXBvcnQlMjBudW1weSUyMGFzJTIwbnAlMEElMEFkYXRhc2V0X3BhdGglMjAlM0QlMjAlMjJzYW1wbGUtaW1hZ2VuZXQtaW1hZ2VzJTIyJTBBaW1hZ2VfcGF0aHMlMjAlM0QlMjBzb3J0ZWQoJTVCb3MucGF0aC5qb2luKGRhdGFzZXRfcGF0aCUyQyUyMHgpJTIwZm9yJTIweCUyMGluJTIwb3MubGlzdGRpcihkYXRhc2V0X3BhdGgpJTVEKSUwQSUwQXJlYWxfaW1hZ2VzJTIwJTNEJTIwJTVCbnAuYXJyYXkoSW1hZ2Uub3BlbihwYXRoKS5jb252ZXJ0KCUyMlJHQiUyMikpJTIwZm9yJTIwcGF0aCUyMGluJTIwaW1hZ2VfcGF0aHMlNUQ=",highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> os
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
dataset_path = <span class="hljs-string">&quot;sample-imagenet-images&quot;</span>
image_paths = <span class="hljs-built_in">sorted</span>([os.path.join(dataset_path, x) <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> os.listdir(dataset_path)])
real_images = [np.array(Image.<span class="hljs-built_in">open</span>(path).convert(<span class="hljs-string">&quot;RGB&quot;</span>)) <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> image_paths]`,wrap:!1}}),de=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> torchvision.transforms <span class="hljs-keyword">import</span> functional <span class="hljs-keyword">as</span> F
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_image</span>(<span class="hljs-params">image</span>):
 image = torch.tensor(image).unsqueeze(<span class="hljs-number">0</span>)
 image = image.permute(<span class="hljs-number">0</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>) / <span class="hljs-number">255.0</span>
 <span class="hljs-keyword">return</span> F.center_crop(image, (<span class="hljs-number">256</span>, <span class="hljs-number">256</span>))
real_images = torch.stack([dit_pipeline.preprocess_image(image) <span class="hljs-keyword">for</span> image <span class="hljs-keyword">in</span> real_images])
<span class="hljs-built_in">print</span>(real_images.shape)
<span class="hljs-comment"># torch.Size([10, 3, 256, 256])</span>`,wrap:!1}}),fe=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiTPipeline, DPMSolverMultistepScheduler
dit_pipeline = DiTPipeline.from_pretrained(<span class="hljs-string">&quot;facebook/DiT-XL-2-256&quot;</span>, torch_dtype=torch.float16)
dit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_pipeline.scheduler.config)
dit_pipeline = dit_pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)
seed = <span class="hljs-number">0</span>
generator = torch.manual_seed(seed)
words = [
 <span class="hljs-string">&quot;cassette player&quot;</span>,
 <span class="hljs-string">&quot;chainsaw&quot;</span>,
 <span class="hljs-string">&quot;chainsaw&quot;</span>,
 <span class="hljs-string">&quot;church&quot;</span>,
 <span class="hljs-string">&quot;gas pump&quot;</span>,
 <span class="hljs-string">&quot;gas pump&quot;</span>,
 <span class="hljs-string">&quot;gas pump&quot;</span>,
 <span class="hljs-string">&quot;parachute&quot;</span>,
 <span class="hljs-string">&quot;parachute&quot;</span>,
 <span class="hljs-string">&quot;tench&quot;</span>,
]
class_ids = dit_pipeline.get_label_ids(words)
output = dit_pipeline(class_labels=class_ids, generator=generator, output_type=<span class="hljs-string">&quot;np&quot;</span>)
fake_images = output.images
fake_images = torch.tensor(fake_images)
fake_images = fake_images.permute(<span class="hljs-number">0</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>)
<span class="hljs-built_in">print</span>(fake_images.shape)
<span class="hljs-comment"># torch.Size([10, 3, 256, 256])</span>`,wrap:!1}}),je=new u({props:{code:"ZnJvbSUyMHRvcmNobWV0cmljcy5pbWFnZS5maWQlMjBpbXBvcnQlMjBGcmVjaGV0SW5jZXB0aW9uRGlzdGFuY2UlMEElMEFmaWQlMjAlM0QlMjBGcmVjaGV0SW5jZXB0aW9uRGlzdGFuY2Uobm9ybWFsaXplJTNEVHJ1ZSklMEFmaWQudXBkYXRlKHJlYWxfaW1hZ2VzJTJDJTIwcmVhbCUzRFRydWUpJTBBZmlkLnVwZGF0ZShmYWtlX2ltYWdlcyUyQyUyMHJlYWwlM0RGYWxzZSklMEElMEFwcmludChmJTIyRklEJUU1JTg4JTg2JUU2JTk1JUIwJTNBJTIwJTdCZmxvYXQoZmlkLmNvbXB1dGUoKSklN0QlMjIpJTBBJTIzJTIwRklEJUU1JTg4JTg2JUU2JTk1JUIwJTNBJTIwMTc3LjcxNDcyMTY3OTY4NzU=",highlighted:`<span class="hljs-keyword">from</span> torchmetrics.image.fid <span class="hljs-keyword">import</span> FrechetInceptionDistance
fid = FrechetInceptionDistance(normalize=<span class="hljs-literal">True</span>)
fid.update(real_images, real=<span class="hljs-literal">True</span>)
fid.update(fake_images, real=<span class="hljs-literal">False</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;FID分数: <span class="hljs-subst">{<span class="hljs-built_in">float</span>(fid.compute())}</span>&quot;</span>)
<span class="hljs-comment"># FID分数: 177.7147216796875</span>`,wrap:!1}}),v=new Gs({props:{warning:!0,$$slots:{default:[Tn]},$$scope:{ctx:I}}}),Ce=new 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