diffusers/pr_14045/zh/_app/immutable/nodes/52.aa4f6000.js

import{s as ga,n as ba,o as ua}from"../chunks/scheduler.e4ff9b64.js";import{S as Ia,i as da,e as M,s as t,c as J,h as Aa,a as p,d as a,b as n,f as ra,g as j,j as U,k as u,l as fa,m as e,n as y,t as c,o as T,p as i}from"../chunks/index.09f1bca0.js";import{C as Va,H as Al,E as Qa}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.815f09d4.js";import{C as m}from"../chunks/CodeBlock.739c5efd.js";import{D as Ra}from"../chunks/DocNotebookDropdown.02241b22.js";function Ba(Ss){let o,Ql,fl,Rl,I,Bl,d,Nl,A,kl,f,Ws='无条件图像生成是扩散模型最常见的应用之一，它会生成与训练数据集风格相似的图像。通常来说，在某个特定数据集上微调预训练模型能得到最好的结果。你可以在 <a href="https://huggingface.co/search/full-text?q=unconditional-image-generation&amp;type=model" rel="nofollow">Hub</a> 上找到很多现成检查点；如果找不到满意的，也完全可以自己训练一个！',Zl,V,Ys='这篇教程会教你如何在 <a href="https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset" rel="nofollow">Smithsonian Butterflies</a> 数据集的一个子集上，从零开始训练一个 <code>UNet2DModel</code>，生成属于你自己的 🦋 蝴蝶图像 🦋。',Gl,C,zs='<p>💡 这篇训练教程基于 <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb" rel="nofollow">Training with 🧨 Diffusers</a> notebook 编写。如果你想了解更多背景，例如扩散模型的工作原理，也推荐一起看看这个 notebook。</p>',Fl,Q,$s='开始之前，请确认已经安装了 🤗 Datasets，用来加载和预处理图像数据集；以及 🤗 Accelerate，用来简化任意数量 GPU 上的训练。下面这条命令也会安装 <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a> 来可视化训练指标（你也可以使用 <a href="https://docs.wandb.ai/" rel="nofollow">Weights &amp; Biases</a> 跟踪训练）。',El,R,_l,B,vs='我们也很鼓励你把模型分享给社区。为此，你需要登录自己的 Hugging Face 账号（如果还没有，可以在 <a href="https://hf.co/join" rel="nofollow">这里</a> 创建）。你可以在 notebook 中登录，系统会提示你输入 token。请确保这个 token 具有写入权限。',Xl,N,Sl,k,xs="或者在终端里登录：",Wl,Z,Yl,G,Os='由于模型检查点通常比较大，建议安装 <a href="https://git-lfs.com/" rel="nofollow">Git-LFS</a> 来管理这些大文件：',zl,F,$l,E,vl,_,Hs="为了方便起见，我们先创建一个 <code>TrainingConfig</code> 类，把训练超参数放在一起（你可以按需调整）：",xl,X,Ol,S,Hl,W,Ds='你可以很轻松地通过 🤗 Datasets 加载 <a href="https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset" rel="nofollow">Smithsonian Butterflies</a> 数据集：',Dl,Y,Ll,h,Ls='<p>💡 你也可以从 <a href="https://huggingface.co/huggan" rel="nofollow">HugGan Community Event</a> 找到更多数据集，或者通过本地 <a href="https://huggingface.co/docs/datasets/image_dataset#imagefolder" rel="nofollow"><code>ImageFolder</code></a> 使用自己的数据集。如果你使用 HugGan Community Event 里的数据集，把 <code>config.dataset_name</code> 设为对应数据集的 repository id；如果你使用自己的图像，就设为 <code>imagefolder</code>。</p>',ql,z,qs='🤗 Datasets 使用 <code>Image</code> 特性自动解码图像数据，并将其加载为 <a href="https://pillow.readthedocs.io/en/stable/reference/Image.html" rel="nofollow"><code>PIL.Image</code></a>，所以我们可以直接可视化：',Kl,$,Pl,w,Ks='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/butterflies_ds.png"/>',ls,v,Ps="不过这些图像的尺寸各不相同，所以你需要先做预处理：",ss,x,la="<li><code>Resize</code> 把图像缩放到 <code>config.image_size</code> 中定义的大小。</li> <li><code>RandomHorizontalFlip</code> 通过随机水平翻转图像来做数据增强。</li> <li><code>Normalize</code> 很重要，它会把像素值缩放到 <code>[-1, 1]</code> 区间，这是模型期望的输入范围。</li>",as,O,es,H,sa="使用 🤗 Datasets 的 <code>set_transform</code> 方法，在训练过程中按需应用 <code>preprocess</code> 函数：",ts,D,ns,L,aa='你也可以再次可视化图像，确认它们已经被调整到目标尺寸。接下来，就可以把数据集封装成一个 <a href="https://pytorch.org/docs/stable/data#torch.utils.data.DataLoader" rel="nofollow">DataLoader</a> 来训练了！',Ms,q,ps,K,Us,P,ea="在 🧨 Diffusers 中，可以很方便地通过模型类和参数创建预训练模型。例如，下面创建一个 <code>UNet2DModel</code>：",Js,ll,js,sl,ta="通常最好先快速检查一下，样本图像的形状和模型输出形状是否一致：",ys,al,cs,el,na="很好！接下来，你还需要一个调度器为图像添加噪声。",Ts,tl,is,nl,Ma="调度器在训练和推理中的行为不同。推理时，调度器会从噪声中生成图像；训练时，调度器会取扩散过程某一步的模型输出或样本，并根据<em>噪声日程</em>与<em>更新规则</em>对图像加噪。",ms,Ml,pa="我们先看看 <code>DDPMScheduler</code>，并使用 <code>add_noise</code> 方法给前面的 <code>sample_image</code> 添加一些随机噪声：",os,pl,Cs,r,Ua='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/noisy_butterfly.png"/>',hs,Ul,Ja="模型训练的目标，就是预测添加到图像中的噪声。当前步骤的损失可以这样计算：",ws,Jl,rs,jl,gs,yl,ja="到这里，启动训练所需的大部分组件都准备好了，剩下的就是把它们拼起来。",bs,cl,ya="首先，你需要一个优化器和一个学习率调度器：",us,Tl,Is,il,ca="接着，你还需要一种评估模型的方法。评估时，我们可以使用 <code>DDPMPipeline</code> 生成一批示例图像，并把它们保存成一个网格图：",ds,ml,As,ol,Ta="现在，你可以用 🤗 Accelerate 把这些组件包装进一个训练循环中，轻松实现 TensorBoard 日志记录、梯度累积和混合精度训练。为了把模型上传到 Hub，还需要写一个函数来创建仓库并将训练结果推送到 Hub。",fs,g,ia="<p>💡 下面的训练循环看起来可能有点长，也有点吓人，但等你真正只用一行代码启动训练时，就会觉得很值得！如果你现在只想快点开始生成图像，也可以先直接复制运行下面的代码，之后再回头仔细研究训练循环，比如等模型训练完成的时候。🤗</p>",Vs,Cl,Qs,hl,ma="呼，这段代码确实不少！不过现在你终于可以用 🤗 Accelerate 的 <code>notebook_launcher</code> 函数启动训练了。把训练循环函数、所有训练参数以及进程数（你可以改成自己可用 GPU 的数量）传进去即可：",Rs,wl,Bs,rl,oa="训练完成后，来看看你的扩散模型最终生成的 🦋 蝴蝶图像 🦋 吧！",Ns,gl,ks,b,Ca='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/butterflies_final.png"/>',Zs,bl,Gs,ul,ha='无条件图像生成只是可训练任务中的一个例子。你可以继续访问 <a href="../training/overview">🧨 Diffusers 训练示例</a> 页面，探索更多任务和训练技术。比如：',Fs,Il,wa='<li><a href="../training/text_inversion">Textual Inversion</a>：教会模型一个特定的视觉概念，并把它融入生成结果中。</li> <li><a href="../training/dreambooth">DreamBooth</a>：给定某个主体的若干输入图像，生成该主体的个性化图像。</li> <li><a href="../training/text2image">引导</a>：在你自己的数据集上微调 Stable Diffusion 模型。</li> <li><a href="../training/lora">引导</a>：使用 LoRA 这种更省内存的方法，更快地微调超大模型。</li>',Es,dl,_s,Vl,Xs;return I=new Va({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),d=new Ra({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/zh/basic_training.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/zh/pytorch/basic_training.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/zh/tensorflow/basic_training.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/zh/basic_training.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/zh/pytorch/basic_training.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/zh/tensorflow/basic_training.ipynb"}]}}),A=new Al({props:{title:"训练扩散模型",local:"训练扩散模型",headingTag:"h1"}}),R=new m({props:{code:"JTIzJTIwJUU1JUE2JTgyJUU2JTlFJTlDJUU0JUJEJUEwJUU1JTlDJUE4JTIwQ29sYWIlMjAlRTQlQjglQUQlRTglQkYlOTAlRTglQTElOEMlRUYlQkMlOEMlRTglQUYlQjclRTUlOEYlOTYlRTYlQjYlODglRTYlQjMlQTglRTklODclOEElRTYlOUQlQTUlRTUlQUUlODklRTglQTMlODUlRTYlODklODAlRTklOUMlODAlRTQlQkUlOUQlRTglQjUlOTYlMEElMjMhcGlwJTIwaW5zdGFsbCUyMGRpZmZ1c2VycyU1QnRyYWluaW5nJTVE",highlighted:`<span class="hljs-comment"># 如果你在 Colab 中运行，请取消注释来安装所需依赖</span>
<span class="hljs-comment">#!pip install diffusers[training]</span>`,lang:"py",wrap:!1}}),N=new m({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`,lang:"py",wrap:!1}}),Z=new m({props:{code:"aGYlMjBhdXRoJTIwbG9naW4=",highlighted:"hf auth login",lang:"bash",wrap:!1}}),F=new m({props:{code:"IXN1ZG8lMjBhcHQlMjAtcXElMjBpbnN0YWxsJTIwZ2l0LWxmcyUwQSFnaXQlMjBjb25maWclMjAtLWdsb2JhbCUyMGNyZWRlbnRpYWwuaGVscGVyJTIwc3RvcmU=",highlighted:`!sudo apt -qq install git-lfs
!git config --global credential.helper store`,lang:"bash",wrap:!1}}),E=new Al({props:{title:"训练配置",local:"训练配置",headingTag:"h2"}}),X=new m({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass

<span class="hljs-meta">&gt;&gt;&gt; </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">TrainingConfig</span>:
<span class="hljs-meta">... </span>    image_size = <span class="hljs-number">128</span>  <span class="hljs-comment"># 生成图像的分辨率</span>
<span class="hljs-meta">... </span>    train_batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">... </span>    eval_batch_size = <span class="hljs-number">16</span>  <span class="hljs-comment"># 评估时每次采样多少张图像</span>
<span class="hljs-meta">... </span>    num_epochs = <span class="hljs-number">50</span>
<span class="hljs-meta">... </span>    gradient_accumulation_steps = <span class="hljs-number">1</span>
<span class="hljs-meta">... </span>    learning_rate = <span class="hljs-number">1e-4</span>
<span class="hljs-meta">... </span>    lr_warmup_steps = <span class="hljs-number">500</span>
<span class="hljs-meta">... </span>    save_image_epochs = <span class="hljs-number">10</span>
<span class="hljs-meta">... </span>    save_model_epochs = <span class="hljs-number">30</span>
<span class="hljs-meta">... </span>    mixed_precision = <span class="hljs-string">&quot;fp16&quot;</span>  <span class="hljs-comment"># float32 用 \`no\`，自动混合精度用 \`fp16\`</span>
<span class="hljs-meta">... </span>    output_dir = <span class="hljs-string">&quot;ddpm-butterflies-128&quot;</span>  <span class="hljs-comment"># 本地和 HF Hub 上的模型名称</span>

<span class="hljs-meta">... </span>    push_to_hub = <span class="hljs-literal">True</span>  <span class="hljs-comment"># 是否将保存后的模型上传到 HF Hub</span>
<span class="hljs-meta">... </span>    hub_model_id = <span class="hljs-string">&quot;&lt;your-username&gt;/&lt;my-awesome-model&gt;&quot;</span>  <span class="hljs-comment"># 在 HF Hub 上创建的仓库名称</span>
<span class="hljs-meta">... </span>    hub_private_repo = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span>    overwrite_output_dir = <span class="hljs-literal">True</span>  <span class="hljs-comment"># 重新运行 notebook 时是否覆盖旧模型</span>
<span class="hljs-meta">... </span>    seed = <span class="hljs-number">0</span>


<span class="hljs-meta">&gt;&gt;&gt; </span>config = TrainingConfig()`,lang:"py",wrap:!1}}),S=new Al({props:{title:"加载数据集",local:"加载数据集",headingTag:"h2"}}),Y=new m({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBY29uZmlnLmRhdGFzZXRfbmFtZSUyMCUzRCUyMCUyMmh1Z2dhbiUyRnNtaXRoc29uaWFuX2J1dHRlcmZsaWVzX3N1YnNldCUyMiUwQWRhdGFzZXQlMjAlM0QlMjBsb2FkX2RhdGFzZXQoY29uZmlnLmRhdGFzZXRfbmFtZSUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>config.dataset_name = <span class="hljs-string">&quot;huggan/smithsonian_butterflies_subset&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(config.dataset_name, split=<span class="hljs-string">&quot;train&quot;</span>)`,lang:"py",wrap:!1}}),$=new m({props:{code:"aW1wb3J0JTIwbWF0cGxvdGxpYi5weXBsb3QlMjBhcyUyMHBsdCUwQSUwQWZpZyUyQyUyMGF4cyUyMCUzRCUyMHBsdC5zdWJwbG90cygxJTJDJTIwNCUyQyUyMGZpZ3NpemUlM0QoMTYlMkMlMjA0KSklMEFmb3IlMjBpJTJDJTIwaW1hZ2UlMjBpbiUyMGVudW1lcmF0ZShkYXRhc2V0JTVCJTNBNCU1RCU1QiUyMmltYWdlJTIyJTVEKSUzQSUwQSUyMCUyMCUyMCUyMGF4cyU1QmklNUQuaW1zaG93KGltYWdlKSUwQSUyMCUyMCUyMCUyMGF4cyU1QmklNUQuc2V0X2F4aXNfb2ZmKCklMEFmaWcuc2hvdygp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt

<span class="hljs-meta">&gt;&gt;&gt; </span>fig, axs = plt.subplots(<span class="hljs-number">1</span>, <span class="hljs-number">4</span>, figsize=(<span class="hljs-number">16</span>, <span class="hljs-number">4</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">&quot;image&quot;</span>]):
<span class="hljs-meta">... </span>    axs[i].imshow(image)
<span class="hljs-meta">... </span>    axs[i].set_axis_off()
<span class="hljs-meta">&gt;&gt;&gt; </span>fig.show()`,lang:"py",wrap:!1}}),O=new m({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torchvision <span class="hljs-keyword">import</span> transforms

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocess = transforms.Compose(
<span class="hljs-meta">... </span>    [
<span class="hljs-meta">... </span>        transforms.Resize((config.image_size, config.image_size)),
<span class="hljs-meta">... </span>        transforms.RandomHorizontalFlip(),
<span class="hljs-meta">... </span>        transforms.ToTensor(),
<span class="hljs-meta">... </span>        transforms.Normalize([<span class="hljs-number">0.5</span>], [<span class="hljs-number">0.5</span>]),
<span class="hljs-meta">... </span>    ]
<span class="hljs-meta">... </span>)`,lang:"py",wrap:!1}}),D=new m({props:{code:"ZGVmJTIwdHJhbnNmb3JtKGV4YW1wbGVzKSUzQSUwQSUyMCUyMCUyMCUyMGltYWdlcyUyMCUzRCUyMCU1QnByZXByb2Nlc3MoaW1hZ2UuY29udmVydCglMjJSR0IlMjIpKSUyMGZvciUyMGltYWdlJTIwaW4lMjBleGFtcGxlcyU1QiUyMmltYWdlJTIyJTVEJTVEJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwJTdCJTIyaW1hZ2VzJTIyJTNBJTIwaW1hZ2VzJTdEJTBBJTBBJTBBZGF0YXNldC5zZXRfdHJhbnNmb3JtKHRyYW5zZm9ybSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">transform</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    images = [preprocess(image.convert(<span class="hljs-string">&quot;RGB&quot;</span>)) <span class="hljs-keyword">for</span> image <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;image&quot;</span>]]
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;images&quot;</span>: images}


<span class="hljs-meta">&gt;&gt;&gt; </span>dataset.set_transform(transform)`,lang:"py",wrap:!1}}),q=new m({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEF0cmFpbl9kYXRhbG9hZGVyJTIwJTNEJTIwdG9yY2gudXRpbHMuZGF0YS5EYXRhTG9hZGVyKGRhdGFzZXQlMkMlMjBiYXRjaF9zaXplJTNEY29uZmlnLnRyYWluX2JhdGNoX3NpemUlMkMlMjBzaHVmZmxlJTNEVHJ1ZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=<span class="hljs-literal">True</span>)`,lang:"py",wrap:!1}}),K=new Al({props:{title:"创建 UNet2DModel",local:"创建-unet2dmodel",headingTag:"h2"}}),ll=new m({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = UNet2DModel(
<span class="hljs-meta">... </span>    sample_size=config.image_size,  <span class="hljs-comment"># 目标图像分辨率</span>
<span class="hljs-meta">... </span>    in_channels=<span class="hljs-number">3</span>,  <span class="hljs-comment"># 输入通道数，RGB 图像为 3</span>
<span class="hljs-meta">... </span>    out_channels=<span class="hljs-number">3</span>,  <span class="hljs-comment"># 输出通道数</span>
<span class="hljs-meta">... </span>    layers_per_block=<span class="hljs-number">2</span>,  <span class="hljs-comment"># 每个 UNet block 中使用多少个 ResNet 层</span>
<span class="hljs-meta">... </span>    block_out_channels=(<span class="hljs-number">128</span>, <span class="hljs-number">128</span>, <span class="hljs-number">256</span>, <span class="hljs-number">256</span>, <span class="hljs-number">512</span>, <span class="hljs-number">512</span>),  <span class="hljs-comment"># 每个 UNet block 的输出通道数</span>
<span class="hljs-meta">... </span>    down_block_types=(
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,  <span class="hljs-comment"># 标准的 ResNet 下采样块</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;AttnDownBlock2D&quot;</span>,  <span class="hljs-comment"># 带空间自注意力的 ResNet 下采样块</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,
<span class="hljs-meta">... </span>    ),
<span class="hljs-meta">... </span>    up_block_types=(
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,  <span class="hljs-comment"># 标准的 ResNet 上采样块</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;AttnUpBlock2D&quot;</span>,  <span class="hljs-comment"># 带空间自注意力的 ResNet 上采样块</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,
<span class="hljs-meta">... </span>    ),
<span class="hljs-meta">... </span>)`,lang:"py",wrap:!1}}),al=new m({props:{code:"c2FtcGxlX2ltYWdlJTIwJTNEJTIwZGF0YXNldCU1QjAlNUQlNUIlMjJpbWFnZXMlMjIlNUQudW5zcXVlZXplKDApJTBBcHJpbnQoJTIySW5wdXQlMjBzaGFwZSUzQSUyMiUyQyUyMHNhbXBsZV9pbWFnZS5zaGFwZSklMEElMEFwcmludCglMjJPdXRwdXQlMjBzaGFwZSUzQSUyMiUyQyUyMG1vZGVsKHNhbXBsZV9pbWFnZSUyQyUyMHRpbWVzdGVwJTNEMCkuc2FtcGxlLnNoYXBlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>sample_image = dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;images&quot;</span>].unsqueeze(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Input shape:&quot;</span>, sample_image.shape)
Input shape: torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">128</span>, <span class="hljs-number">128</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Output shape:&quot;</span>, model(sample_image, timestep=<span class="hljs-number">0</span>).sample.shape)
Output shape: torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">128</span>, <span class="hljs-number">128</span>])`,lang:"py",wrap:!1}}),tl=new Al({props:{title:"创建调度器",local:"创建调度器",headingTag:"h2"}}),pl=new m({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>noise_scheduler = DDPMScheduler(num_train_timesteps=<span class="hljs-number">1000</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>noise = torch.randn(sample_image.shape)
<span class="hljs-meta">&gt;&gt;&gt; </span>timesteps = torch.LongTensor([<span class="hljs-number">50</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)

<span class="hljs-meta">&gt;&gt;&gt; </span>Image.fromarray(((noisy_image.permute(<span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>) + <span class="hljs-number">1.0</span>) * <span class="hljs-number">127.5</span>).<span class="hljs-built_in">type</span>(torch.uint8).numpy()[<span class="hljs-number">0</span>])`,lang:"py",wrap:!1}}),Jl=new m({props:{code:"aW1wb3J0JTIwdG9yY2gubm4uZnVuY3Rpb25hbCUyMGFzJTIwRiUwQSUwQW5vaXNlX3ByZWQlMjAlM0QlMjBtb2RlbChub2lzeV9pbWFnZSUyQyUyMHRpbWVzdGVwcykuc2FtcGxlJTBBbG9zcyUyMCUzRCUyMEYubXNlX2xvc3Mobm9pc2VfcHJlZCUyQyUyMG5vaXNlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch.nn.functional <span class="hljs-keyword">as</span> F

<span class="hljs-meta">&gt;&gt;&gt; </span>noise_pred = model(noisy_image, timesteps).sample
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = F.mse_loss(noise_pred, noise)`,lang:"py",wrap:!1}}),jl=new Al({props:{title:"训练模型",local:"训练模型",headingTag:"h2"}}),Tl=new m({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.optimization <span class="hljs-keyword">import</span> get_cosine_schedule_with_warmup

<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
<span class="hljs-meta">&gt;&gt;&gt; </span>lr_scheduler = get_cosine_schedule_with_warmup(
<span class="hljs-meta">... </span>    optimizer=optimizer,
<span class="hljs-meta">... </span>    num_warmup_steps=config.lr_warmup_steps,
<span class="hljs-meta">... </span>    num_training_steps=(<span class="hljs-built_in">len</span>(train_dataloader) * config.num_epochs),
<span class="hljs-meta">... </span>)`,lang:"py",wrap:!1}}),ml=new m({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">evaluate</span>(<span class="hljs-params">config, epoch, pipeline</span>):
<span class="hljs-meta">... </span>    <span class="hljs-comment"># 从随机噪声采样图像（这就是反向扩散过程）</span>
<span class="hljs-meta">... </span>    <span class="hljs-comment"># 管道默认输出类型是 \`List[PIL.Image]\`</span>
<span class="hljs-meta">... </span>    images = pipeline(
<span class="hljs-meta">... </span>        batch_size=config.eval_batch_size,
<span class="hljs-meta">... </span>        generator=torch.Generator(device=<span class="hljs-string">&#x27;cpu&#x27;</span>).manual_seed(config.seed), <span class="hljs-comment"># 单独使用一个 torch generator，避免回退主训练循环的随机状态</span>
<span class="hljs-meta">... </span>    ).images

<span class="hljs-meta">... </span>    <span class="hljs-comment"># 把图像拼成网格</span>
<span class="hljs-meta">... </span>    image_grid = make_image_grid(images, rows=<span class="hljs-number">4</span>, cols=<span class="hljs-number">4</span>)

<span class="hljs-meta">... </span>    <span class="hljs-comment"># 保存图像</span>
<span class="hljs-meta">... </span>    test_dir = os.path.join(config.output_dir, <span class="hljs-string">&quot;samples&quot;</span>)
<span class="hljs-meta">... </span>    os.makedirs(test_dir, exist_ok=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span>    image_grid.save(<span class="hljs-string">f&quot;<span class="hljs-subst">{test_dir}</span>/<span class="hljs-subst">{epoch:04d}</span>.png&quot;</span>)`,lang:"py",wrap:!1}}),Cl=new m({props:{code:"from%20accelerate%20import%20Accelerator%0Afrom%20huggingface_hub%20import%20create_repo%2C%20upload_folder%0Afrom%20tqdm.auto%20import%20tqdm%0Afrom%20pathlib%20import%20Path%0Aimport%20os%0A%0Adef%20train_loop(config%2C%20model%2C%20noise_scheduler%2C%20optimizer%2C%20train_dataloader%2C%20lr_scheduler)%3A%0A%20%20%20%20%23%20%E5%88%9D%E5%A7%8B%E5%8C%96%20accelerator%20%E5%92%8C%20tensorboard%20%E6%97%A5%E5%BF%97%0A%20%20%20%20accelerator%20%3D%20Accelerator(%0A%20%20%20%20%20%20%20%20mixed_precision%3Dconfig.mixed_precision%2C%0A%20%20%20%20%20%20%20%20gradient_accumulation_steps%3Dconfig.gradient_accumulation_steps%2C%0A%20%20%20%20%20%20%20%20log_with%3D%22tensorboard%22%2C%0A%20%20%20%20%20%20%20%20project_dir%3Dos.path.join(config.output_dir%2C%20%22logs%22)%2C%0A%20%20%20%20)%0A%20%20%20%20if%20accelerator.is_main_process%3A%0A%20%20%20%20%20%20%20%20if%20config.output_dir%20is%20not%20None%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20os.makedirs(config.output_dir%2C%20exist_ok%3DTrue)%0A%20%20%20%20%20%20%20%20if%20config.push_to_hub%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20repo_id%20%3D%20create_repo(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20repo_id%3Dconfig.hub_model_id%20or%20Path(config.output_dir).name%2C%20exist_ok%3DTrue%0A%20%20%20%20%20%20%20%20%20%20%20%20).repo_id%0A%20%20%20%20%20%20%20%20accelerator.init_trackers(%22train_example%22)%0A%0A%20%20%20%20%23%20%E5%87%86%E5%A4%87%E6%89%80%E6%9C%89%E5%AF%B9%E8%B1%A1%0A%20%20%20%20%23%20%E4%B8%8D%E9%9C%80%E8%A6%81%E8%AE%B0%E4%BD%8F%E5%9B%BA%E5%AE%9A%E9%A1%BA%E5%BA%8F%EF%BC%8C%E5%8F%AA%E8%A6%81%E8%A7%A3%E5%8C%85%E6%97%B6%E5%92%8C%E4%BC%A0%E7%BB%99%20prepare%20%E7%9A%84%E9%A1%BA%E5%BA%8F%E4%B8%80%E8%87%B4%E5%8D%B3%E5%8F%AF%E3%80%82%0A%20%20%20%20model%2C%20optimizer%2C%20train_dataloader%2C%20lr_scheduler%20%3D%20accelerator.prepare(%0A%20%20%20%20%20%20%20%20model%2C%20optimizer%2C%20train_dataloader%2C%20lr_scheduler%0A%20%20%20%20)%0A%0A%20%20%20%20global_step%20%3D%200%0A%0A%20%20%20%20%23%20%E5%BC%80%E5%A7%8B%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B%0A%20%20%20%20for%20epoch%20in%20range(config.num_epochs)%3A%0A%20%20%20%20%20%20%20%20progress_bar%20%3D%20tqdm(total%3Dlen(train_dataloader)%2C%20disable%3Dnot%20accelerator.is_local_main_process)%0A%20%20%20%20%20%20%20%20progress_bar.set_description(f%22Epoch%20%7Bepoch%7D%22)%0A%0A%20%20%20%20%20%20%20%20for%20step%2C%20batch%20in%20enumerate(train_dataloader)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20clean_images%20%3D%20batch%5B%22images%22%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E4%B8%BA%E5%9B%BE%E5%83%8F%E9%87%87%E6%A0%B7%E5%99%AA%E5%A3%B0%0A%20%20%20%20%20%20%20%20%20%20%20%20noise%20%3D%20torch.randn(clean_images.shape%2C%20device%3Dclean_images.device)%0A%20%20%20%20%20%20%20%20%20%20%20%20bs%20%3D%20clean_images.shape%5B0%5D%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E4%B8%BA%E6%AF%8F%E5%BC%A0%E5%9B%BE%E5%83%8F%E9%9A%8F%E6%9C%BA%E9%87%87%E6%A0%B7%E4%B8%80%E4%B8%AA%E6%97%B6%E9%97%B4%E6%AD%A5%0A%20%20%20%20%20%20%20%20%20%20%20%20timesteps%20%3D%20torch.randint(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%2C%20noise_scheduler.config.num_train_timesteps%2C%20(bs%2C)%2C%20device%3Dclean_images.device%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20dtype%3Dtorch.int64%0A%20%20%20%20%20%20%20%20%20%20%20%20)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E6%8C%89%E7%85%A7%E6%AF%8F%E4%B8%AA%E6%97%B6%E9%97%B4%E6%AD%A5%E5%AF%B9%E5%BA%94%E7%9A%84%E5%99%AA%E5%A3%B0%E5%BC%BA%E5%BA%A6%E7%BB%99%E5%B9%B2%E5%87%80%E5%9B%BE%E5%83%8F%E5%8A%A0%E5%99%AA%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%EF%BC%88%E8%BF%99%E5%B0%B1%E6%98%AF%E5%89%8D%E5%90%91%E6%89%A9%E6%95%A3%E8%BF%87%E7%A8%8B%EF%BC%89%0A%20%20%20%20%20%20%20%20%20%20%20%20noisy_images%20%3D%20noise_scheduler.add_noise(clean_images%2C%20noise%2C%20timesteps)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20with%20accelerator.accumulate(model)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E9%A2%84%E6%B5%8B%E5%99%AA%E5%A3%B0%E6%AE%8B%E5%B7%AE%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20noise_pred%20%3D%20model(noisy_images%2C%20timesteps%2C%20return_dict%3DFalse)%5B0%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20loss%20%3D%20F.mse_loss(noise_pred%2C%20noise)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20accelerator.backward(loss)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20accelerator.sync_gradients%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20accelerator.clip_grad_norm_(model.parameters()%2C%201.0)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20optimizer.step()%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20lr_scheduler.step()%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20optimizer.zero_grad()%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20progress_bar.update(1)%0A%20%20%20%20%20%20%20%20%20%20%20%20logs%20%3D%20%7B%22loss%22%3A%20loss.detach().item()%2C%20%22lr%22%3A%20lr_scheduler.get_last_lr()%5B0%5D%2C%20%22step%22%3A%20global_step%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20progress_bar.set_postfix(**logs)%0A%20%20%20%20%20%20%20%20%20%20%20%20accelerator.log(logs%2C%20step%3Dglobal_step)%0A%20%20%20%20%20%20%20%20%20%20%20%20global_step%20%2B%3D%201%0A%0A%20%20%20%20%20%20%20%20%23%20%E6%AF%8F%E4%B8%AA%20epoch%20%E5%90%8E%E5%8F%AF%E4%BB%A5%E9%80%89%E6%8B%A9%E7%94%A8%20evaluate()%20%E9%87%87%E6%A0%B7%E4%B8%80%E4%BA%9B%E6%BC%94%E7%A4%BA%E5%9B%BE%E5%83%8F%EF%BC%8C%E5%B9%B6%E4%BF%9D%E5%AD%98%E6%A8%A1%E5%9E%8B%0A%20%20%20%20%20%20%20%20if%20accelerator.is_main_process%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20pipeline%20%3D%20DDPMPipeline(unet%3Daccelerator.unwrap_model(model)%2C%20scheduler%3Dnoise_scheduler)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20(epoch%20%2B%201)%20%25%20config.save_image_epochs%20%3D%3D%200%20or%20epoch%20%3D%3D%20config.num_epochs%20-%201%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20evaluate(config%2C%20epoch%2C%20pipeline)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20(epoch%20%2B%201)%20%25%20config.save_model_epochs%20%3D%3D%200%20or%20epoch%20%3D%3D%20config.num_epochs%20-%201%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20config.push_to_hub%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20upload_folder(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20repo_id%3Drepo_id%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20folder_path%3Dconfig.output_dir%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20commit_message%3Df%22Epoch%20%7Bepoch%7D%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20ignore_patterns%3D%5B%22step_*%22%2C%20%22epoch_*%22%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20pipeline.save_pretrained(config.output_dir)",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> Accelerator
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> create_repo, upload_folder
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">train_loop</span>(<span class="hljs-params">config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler</span>):
<span class="hljs-meta">... </span>    <span class="hljs-comment"># 初始化 accelerator 和 tensorboard 日志</span>
<span class="hljs-meta">... </span>    accelerator = Accelerator(
<span class="hljs-meta">... </span>        mixed_precision=config.mixed_precision,
<span class="hljs-meta">... </span>        gradient_accumulation_steps=config.gradient_accumulation_steps,
<span class="hljs-meta">... </span>        log_with=<span class="hljs-string">&quot;tensorboard&quot;</span>,
<span class="hljs-meta">... </span>        project_dir=os.path.join(config.output_dir, <span class="hljs-string">&quot;logs&quot;</span>),
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>    <span class="hljs-keyword">if</span> accelerator.is_main_process:
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> config.output_dir <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">None</span>:
<span class="hljs-meta">... </span>            os.makedirs(config.output_dir, exist_ok=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> config.push_to_hub:
<span class="hljs-meta">... </span>            repo_id = create_repo(
<span class="hljs-meta">... </span>                repo_id=config.hub_model_id <span class="hljs-keyword">or</span> Path(config.output_dir).name, exist_ok=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>            ).repo_id
<span class="hljs-meta">... </span>        accelerator.init_trackers(<span class="hljs-string">&quot;train_example&quot;</span>)

<span class="hljs-meta">... </span>    <span class="hljs-comment"># 准备所有对象</span>
<span class="hljs-meta">... </span>    <span class="hljs-comment"># 不需要记住固定顺序，只要解包时和传给 prepare 的顺序一致即可。</span>
<span class="hljs-meta">... </span>    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
<span class="hljs-meta">... </span>        model, optimizer, train_dataloader, lr_scheduler
<span class="hljs-meta">... </span>    )

<span class="hljs-meta">... </span>    global_step = <span class="hljs-number">0</span>

<span class="hljs-meta">... </span>    <span class="hljs-comment"># 开始训练模型</span>
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(config.num_epochs):
<span class="hljs-meta">... </span>        progress_bar = tqdm(total=<span class="hljs-built_in">len</span>(train_dataloader), disable=<span class="hljs-keyword">not</span> accelerator.is_local_main_process)
<span class="hljs-meta">... </span>        progress_bar.set_description(<span class="hljs-string">f&quot;Epoch <span class="hljs-subst">{epoch}</span>&quot;</span>)

<span class="hljs-meta">... </span>        <span class="hljs-keyword">for</span> step, batch <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(train_dataloader):
<span class="hljs-meta">... </span>            clean_images = batch[<span class="hljs-string">&quot;images&quot;</span>]
<span class="hljs-meta">... </span>            <span class="hljs-comment"># 为图像采样噪声</span>
<span class="hljs-meta">... </span>            noise = torch.randn(clean_images.shape, device=clean_images.device)
<span class="hljs-meta">... </span>            bs = clean_images.shape[<span class="hljs-number">0</span>]

<span class="hljs-meta">... </span>            <span class="hljs-comment"># 为每张图像随机采样一个时间步</span>
<span class="hljs-meta">... </span>            timesteps = torch.randint(
<span class="hljs-meta">... </span>                <span class="hljs-number">0</span>, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,
<span class="hljs-meta">... </span>                dtype=torch.int64
<span class="hljs-meta">... </span>            )

<span class="hljs-meta">... </span>            <span class="hljs-comment"># 按照每个时间步对应的噪声强度给干净图像加噪</span>
<span class="hljs-meta">... </span>            <span class="hljs-comment"># （这就是前向扩散过程）</span>
<span class="hljs-meta">... </span>            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)

<span class="hljs-meta">... </span>            <span class="hljs-keyword">with</span> accelerator.accumulate(model):
<span class="hljs-meta">... </span>                <span class="hljs-comment"># 预测噪声残差</span>
<span class="hljs-meta">... </span>                noise_pred = model(noisy_images, timesteps, return_dict=<span class="hljs-literal">False</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>                loss = F.mse_loss(noise_pred, noise)
<span class="hljs-meta">... </span>                accelerator.backward(loss)

<span class="hljs-meta">... </span>                <span class="hljs-keyword">if</span> accelerator.sync_gradients:
<span class="hljs-meta">... </span>                    accelerator.clip_grad_norm_(model.parameters(), <span class="hljs-number">1.0</span>)
<span class="hljs-meta">... </span>                optimizer.step()
<span class="hljs-meta">... </span>                lr_scheduler.step()
<span class="hljs-meta">... </span>                optimizer.zero_grad()

<span class="hljs-meta">... </span>            progress_bar.update(<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>            logs = {<span class="hljs-string">&quot;loss&quot;</span>: loss.detach().item(), <span class="hljs-string">&quot;lr&quot;</span>: lr_scheduler.get_last_lr()[<span class="hljs-number">0</span>], <span class="hljs-string">&quot;step&quot;</span>: global_step}
<span class="hljs-meta">... </span>            progress_bar.set_postfix(**logs)
<span class="hljs-meta">... </span>            accelerator.log(logs, step=global_step)
<span class="hljs-meta">... </span>            global_step += <span class="hljs-number">1</span>

<span class="hljs-meta">... </span>        <span class="hljs-comment"># 每个 epoch 后可以选择用 evaluate() 采样一些演示图像，并保存模型</span>
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> accelerator.is_main_process:
<span class="hljs-meta">... </span>            pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)

<span class="hljs-meta">... </span>            <span class="hljs-keyword">if</span> (epoch + <span class="hljs-number">1</span>) % config.save_image_epochs == <span class="hljs-number">0</span> <span class="hljs-keyword">or</span> epoch == config.num_epochs - <span class="hljs-number">1</span>:
<span class="hljs-meta">... </span>                evaluate(config, epoch, pipeline)

<span class="hljs-meta">... </span>            <span class="hljs-keyword">if</span> (epoch + <span class="hljs-number">1</span>) % config.save_model_epochs == <span class="hljs-number">0</span> <span class="hljs-keyword">or</span> epoch == config.num_epochs - <span class="hljs-number">1</span>:
<span class="hljs-meta">... </span>                <span class="hljs-keyword">if</span> config.push_to_hub:
<span class="hljs-meta">... </span>                    upload_folder(
<span class="hljs-meta">... </span>                        repo_id=repo_id,
<span class="hljs-meta">... </span>                        folder_path=config.output_dir,
<span class="hljs-meta">... </span>                        commit_message=<span class="hljs-string">f&quot;Epoch <span class="hljs-subst">{epoch}</span>&quot;</span>,
<span class="hljs-meta">... </span>                        ignore_patterns=[<span class="hljs-string">&quot;step_*&quot;</span>, <span class="hljs-string">&quot;epoch_*&quot;</span>],
<span class="hljs-meta">... </span>                    )
<span class="hljs-meta">... </span>                <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span>                    pipeline.save_pretrained(config.output_dir)`,lang:"py",wrap:!1}}),wl=new m({props:{code:"ZnJvbSUyMGFjY2VsZXJhdGUlMjBpbXBvcnQlMjBub3RlYm9va19sYXVuY2hlciUwQSUwQWFyZ3MlMjAlM0QlMjAoY29uZmlnJTJDJTIwbW9kZWwlMkMlMjBub2lzZV9zY2hlZHVsZXIlMkMlMjBvcHRpbWl6ZXIlMkMlMjB0cmFpbl9kYXRhbG9hZGVyJTJDJTIwbHJfc2NoZWR1bGVyKSUwQSUwQW5vdGVib29rX2xhdW5jaGVyKHRyYWluX2xvb3AlMkMlMjBhcmdzJTJDJTIwbnVtX3Byb2Nlc3NlcyUzRDEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> notebook_launcher

<span class="hljs-meta">&gt;&gt;&gt; </span>args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_launcher(train_loop, args, num_processes=<span class="hljs-number">1</span>)`,lang:"py",wrap:!1}}),gl=new m({props:{code:"aW1wb3J0JTIwZ2xvYiUwQSUwQXNhbXBsZV9pbWFnZXMlMjAlM0QlMjBzb3J0ZWQoZ2xvYi5nbG9iKGYlMjIlN0Jjb25maWcub3V0cHV0X2RpciU3RCUyRnNhbXBsZXMlMkYqLnBuZyUyMikpJTBBSW1hZ2Uub3BlbihzYW1wbGVfaW1hZ2VzJTVCLTElNUQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> glob

<span class="hljs-meta">&gt;&gt;&gt; </span>sample_images = <span class="hljs-built_in">sorted</span>(glob.glob(<span class="hljs-string">f&quot;<span class="hljs-subst">{config.output_dir}</span>/samples/*.png&quot;</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>Image.<span class="hljs-built_in">open</span>(sample_images[-<span class="hljs-number">1</span>])`,lang:"py",wrap:!1}}),bl=new Al({props:{title:"下一步",local:"下一步",headingTag:"h2"}}),dl=new 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