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import{s as Ft,n as gt,o as Yt}from"../chunks/scheduler.5c93273d.js";import{S as vt,i as Nt,g as a,s as n,r as M,A as $t,h as p,f as t,c as i,j as Vt,u as J,x as o,k as St,y as Et,a as s,v as d,d as T,t as c,w as m}from"../chunks/index.e43dd92b.js";import{C as U}from"../chunks/CodeBlock.6896320e.js";import{H as y,E as Lt}from"../chunks/getInferenceSnippets.22672bbf.js";function Dt(Pe){let f,Vl,kl,Sl,u,Fl,r,Ke="模块化Diffusers是一个快速构建灵活和可定制管道的框架。模块化Diffusers的核心是<code>ModularPipelineBlocks</code>，可以与其他块组合以适应新的工作流程。这些块被转换为<code>ModularPipeline</code>，一个开发者可以使用的友好用户界面。",gl,w,Oe='本文档将向您展示如何使用模块化框架实现<a href="https://differential-diffusion.github.io/" rel="nofollow">Differential Diffusion</a>管道。',Yl,Z,vl,I,lt="<code>ModularPipelineBlocks</code>是<em>定义</em>，指定管道中单个步骤的组件、输入、输出和计算逻辑。有四种类型的块。",Nl,b,et="<li><code>ModularPipelineBlocks</code>是最基本的单一步骤块。</li> <li><code>SequentialPipelineBlocks</code>是一个多块，线性组合其他块。一个块的输出是下一个块的输入。</li> <li><code>LoopSequentialPipelineBlocks</code>是一个多块，迭代运行，专为迭代工作流程设计。</li> <li><code>AutoPipelineBlocks</code>是一个针对不同工作流程的块集合，它根据输入选择运行哪个块。它旨在方便地将多个工作流程打包到单个管道中。</li>",$l,j,tt='<a href="https://differential-diffusion.github.io/" rel="nofollow">Differential Diffusion</a>是一个图像到图像的工作流程。从<code>IMAGE2IMAGE_BLOCKS</code>预设开始，这是一个用于图像到图像生成的<code>ModularPipelineBlocks</code>集合。',El,G,Ll,R,Dl,C,st="模块化Diffusers使用<em>状态</em>在块之间通信数据。有两种类型的状态。",Ql,_,nt="<li><code>PipelineState</code>是一个全局状态，可用于跟踪所有块的所有输入和输出。</li> <li><code>BlockState</code>是<code>PipelineState</code>中相关变量的局部视图，用于单个块。</li>",Al,h,Hl,X,it='<a href="https://differential-diffusion.github.io/" rel="nofollow">Differential Diffusion</a> 与标准的图像到图像转换在其 <code>prepare_latents</code> 和 <code>denoise</code> 块上有所不同。所有其他块都可以重用，但你需要修改这两个。',zl,B,at="通过复制和修改现有的块，为 <code>prepare_latents</code> 和 <code>denoise</code> 创建占位符 <code>ModularPipelineBlocks</code>。",ql,k,pt="打印 <code>denoise</code> 块，可以看到它由 <code>LoopSequentialPipelineBlocks</code> 组成，包含三个子块，<code>before_denoiser</code>、<code>denoiser</code> 和 <code>after_denoiser</code>。只需要修改 <code>before_denoiser</code> 子块，根据变化图为去噪器准备潜在输入。",xl,W,Pl,V,ot="用新的 <code>SDXLDiffDiffLoopBeforeDenoiser</code> 块替换 <code>StableDiffusionXLLoopBeforeDenoiser</code> 子块。",Kl,S,Ol,F,le,g,Mt="<code>prepare_latents</code> 块需要进行以下更改。",ee,Y,Jt="<li>一个处理器来处理变化图</li> <li>一个新的 <code>inputs</code> 来接受用户提供的变化图，<code>timestep</code> 用于预计算所有潜在变量和 <code>num_inference_steps</code> 来创建更新图像区域的掩码</li> <li>更新 <code>__call__</code> 方法中的计算，用于处理变化图和创建掩码，并将其存储在 <code>BlockState</code> 中</li>",te,v,se,N,ne,$,dt="<code>before_denoiser</code> 子块需要进行以下更改。",ie,E,Tt="<li>新的 <code>inputs</code> 以接受 <code>denoising_start</code> 参数，<code>original_latents</code> 和 <code>diffdiff_masks</code> 来自 <code>prepare_latents</code> 块</li> <li>更新 <code>__call__</code> 方法中的计算以应用 Differential Diffusion</li>",ae,L,pe,D,oe,Q,ct="此时，您应该拥有创建 <code>ModularPipeline</code> 所需的所有块。",Me,A,mt="复制现有的 <code>IMAGE2IMAGE_BLOCKS</code> 预设，对于 <code>set_timesteps</code> 块，使用 <code>TEXT2IMAGE_BLOCKS</code> 中的 <code>set_timesteps</code>，因为 Differential Diffusion 不需要 <code>strength</code> 参数。",Je,H,Ut="将 <code>prepare_latents</code> 和 <code>denoise</code> 块设置为您刚刚修改的 <code>SDXLDiffDiffPrepareLatentsStep</code> 和 <code>SDXLDiffDiffDenoiseStep</code> 块。",de,z,yt="调用 <code>SequentialPipelineBlocks.from_blocks_dict</code> 在块上创建一个 <code>SequentialPipelineBlocks</code>。",Te,q,ce,x,me,P,ft="将 <code>SequentialPipelineBlocks</code> 转换为 <code>ModularPipeline</code>，使用 <code>ModularPipeline.init_pipeline</code> 方法。这会初始化从 <code>modular_model_index.json</code> 文件加载的预期组件。通过调用 <code>ModularPipeline.load_defau lt_components</code>。",Ue,K,ut="初始化<code>ComponentManager</code>时传入pipeline是一个好主意，以帮助管理不同的组件。一旦调用<code>load_default_components()</code>，组件就会被注册到<code>ComponentManager</code>中，并且可以在工作流之间共享。下面的例子使用<code>collection</code>参数为组件分配了一个<code>&quot;diffdiff&quot;</code>标签，以便更好地组织。",ye,O,fe,ll,ue,el,rt="可以向<code>ModularPipeline</code>添加其他工作流以支持更多功能，而无需从头重写整个pipeline。",re,tl,wt="本节演示如何添加IP-Adapter或ControlNet。",we,sl,Ze,nl,Zt="Stable Diffusion XL已经有一个预设的IP-Adapter块，你可以使用，并且不需要对现有的Differential Diffusion pipeline进行任何更改。",Ie,il,be,al,It="使用<code>sub_blocks.insert</code>方法将其插入到<code>ModularPipeline</code>中。下面的例子在位置<code>0</code>插入了<code>ip_adapter_block</code>。打印pipeline可以看到<code>ip_adapter_block</code>被添加了，并且它需要一个<code>ip_adapter_image</code>。这也向pipeline添加了两个组件，<code>image_encoder</code>和<code>feature_extractor</code>。",je,pl,Ge,ol,bt="调用<code>~ModularPipeline.init_pipeline</code>来初始化一个<code>ModularPipeline</code>，并使用<code>load_default_components()</code>加载模型组件。加载并设置IP-Adapter以运行pipeline。",Re,Ml,Ce,Jl,_e,dl,jt="Stable Diffusion XL 已经预设了一个可以立即使用的 ControlNet 块。",he,Tl,Xe,cl,Gt="然而，它需要修改 <code>denoise</code> 块，因为那是 ControlNet 将控制信息注入到 UNet 的地方。",Be,ml,Rt="通过将 <code>StableDiffusionXLLoopDenoiser</code> 子块替换为 <code>StableDiffusionXLControlNetLoopDenoiser</code> 来修改 <code>denoise</code> 块。",ke,Ul,We,yl,Ct="插入 <code>controlnet_input</code> 块并用新的 <code>controlnet_denoise_block</code> 替换 <code>denoise</code> 块。初始化一个 <code>ModularPipeline</code> 并将 <code>load_default_components()</code> 加载到其中。",Ve,fl,Se,ul,Fe,rl,_t="差分扩散、IP-Adapter 和 ControlNet 工作流可以通过使用 <code>AutoPipelineBlocks</code> 捆绑到一个单一的 <code>ModularPipeline</code> 中。这允许根据输入如 <code>control_image</code> 或 <code>ip_adapter_image</code> 自动选择要运行的子块。如果没有传递这些输入，则默认为差分扩散。",ge,wl,ht="使用 <code>block_trigger_inputs</code> 仅在提供 <code>control_image</code> 输入时运行 <code>SDXLDiffDiffControlNetDenoiseStep</code> 块。否则，使用 <code>SDXLDiffDiffDenoiseStep</code>。",Ye,Zl,ve,Il,Xt="添加 <code>ip_adapter</code> 和 <code>controlnet_input</code> 块。",Ne,bl,$e,jl,Bt="调用 <code>SequentialPipelineBlocks.from_blocks_dict</code> 来创建一个 <code>SequentialPipelineBlocks</code> 并创建一个 <code>ModularPipeline</code> 并加载模型组件以运行。",Ee,Gl,Le,Rl,De,Cl,kt="使用 <code>save_pretrained()</code> 将您的 <code>ModularPipeline</code> 添加到 Hub，并将 <code>push_to_hub</code> 参数设置为 <code>True</code>。",Qe,_l,Ae,hl,Wt="其他用户可以使用 <code>from_pretrained()</code> 加载 <code>ModularPipeline</code>。",He,Xl,ze,Bl,qe,Wl,xe;return u=new y({props:{title:"快速入门",local:"快速入门",headingTag:"h1"}}),Z=new y({props:{title:"ModularPipelineBlocks",local:"modularpipelineblocks",headingTag:"h2"}}),G=new U({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers.modular_pipelines.stable_diffusion_xl <span class="hljs-keyword">import</span> IMAGE2IMAGE_BLOCKS
IMAGE2IMAGE_BLOCKS = InsertableDict([
 (<span class="hljs-string">&quot;text_encoder&quot;</span>, StableDiffusionXLTextEncoderStep),
 (<span class="hljs-string">&quot;image_encoder&quot;</span>, StableDiffusionXLVaeEncoderStep),
 (<span class="hljs-string">&quot;input&quot;</span>, StableDiffusionXLInputStep),
 (<span class="hljs-string">&quot;set_timesteps&quot;</span>, StableDiffusionXLImg2ImgSetTimestepsStep),
 (<span class="hljs-string">&quot;prepare_latents&quot;</span>, StableDiffusionXLImg2ImgPrepareLatentsStep),
 (<span class="hljs-string">&quot;prepare_add_cond&quot;</span>, StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep),
 (<span class="hljs-string">&quot;denoise&quot;</span>, StableDiffusionXLDenoiseStep),
 (<span class="hljs-string">&quot;decode&quot;</span>, StableDiffusionXLDecodeStep)
])`,wrap:!1}}),R=new y({props:{title:"管道和块状态",local:"管道和块状态",headingTag:"h2"}}),h=new y({props:{title:"自定义块",local:"自定义块",headingTag:"h2"}}),W=new U({props:{code:"ZGVub2lzZV9ibG9ja3MlMjAlM0QlMjBJTUFHRTJJTUFHRV9CTE9DS1MlNUIlMjJkZW5vaXNlJTIyJTVEKCklMEFwcmludChkZW5vaXNlX2Jsb2Nrcyk=",highlighted:`denoise_blocks = IMAGE2IMAGE_BLOCKS[<span class="hljs-string">&quot;denoise&quot;</span>]()
<span class="hljs-built_in">print</span>(denoise_blocks)`,wrap:!1}}),S=new U({props:{code:"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",highlighted:`<span class="hljs-comment"># 复制现有块作为占位符</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">SDXLDiffDiffPrepareLatentsStep</span>(<span class="hljs-title class_ inherited__">ModularPipelineBlocks</span>):
 <span class="hljs-string">&quot;&quot;&quot;Copied from StableDiffusionXLImg2ImgPrepareLatentsStep - will modify later&quot;&quot;&quot;</span>
 <span class="hljs-comment"># ... 与 StableDiffusionXLImg2ImgPrepareLatentsStep 相同的实现</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">SDXLDiffDiffDenoiseStep</span>(<span class="hljs-title class_ inherited__">StableDiffusionXLDenoiseLoopWrapper</span>):
 block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLLoopDenoiser, StableDiffusionXLLoopAfterDenoiser]
 block_names = [<span class="hljs-string">&quot;before_denoiser&quot;</span>, <span class="hljs-string">&quot;denoiser&quot;</span>, <span class="hljs-string">&quot;after_denoiser&quot;</span>]`,wrap:!1}}),F=new y({props:{title:"prepare_latents",local:"preparelatents",headingTag:"h3"}}),v=new U({props:{code:"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",highlighted:`class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks):
 @property
 def expected_components(self) -&gt; List[ComponentSpec]:
 return [
 ComponentSpec(&quot;vae&quot;, AutoencoderKL),
 ComponentSpec(&quot;scheduler&quot;, EulerDiscreteScheduler),
<span class="hljs-addition">+ ComponentSpec(&quot;mask_processor&quot;, VaeImageProcessor, config=FrozenDict({&quot;do_normalize&quot;: False, &quot;do_convert_grayscale&quot;: True}))</span>
 ]
 @property
 def inputs(self) -&gt; List[Tuple[str, Any]]:
 return [
 InputParam(&quot;generator&quot;),
<span class="hljs-addition">+ InputParam(&quot;diffdiff_map&quot;, required=True),</span>
<span class="hljs-deletion">- InputParam(&quot;latent_timestep&quot;, required=True, type_hint=torch.Tensor),</span>
<span class="hljs-addition">+ InputParam(&quot;timesteps&quot;, type_hint=torch.Tensor),</span>
<span class="hljs-addition">+ InputParam(&quot;num_inference_steps&quot;, type_hint=int),</span>
 ]
 @property
 def intermediate_outputs(self) -&gt; List[OutputParam]:
 return [
<span class="hljs-addition">+ OutputParam(&quot;original_latents&quot;, type_hint=torch.Tensor),</span>
<span class="hljs-addition">+ OutputParam(&quot;diffdiff_masks&quot;, type_hint=torch.Tensor),</span>
 ]
 def __call__(self, components, state: PipelineState):
 # ... existing logic ...
<span class="hljs-addition">+ # Process change map and create masks</span>
<span class="hljs-addition">+ diffdiff_map = components.mask_processor.preprocess(block_state.diffdiff_map, height=latent_height, width=latent_width)</span>
<span class="hljs-addition">+ thresholds = torch.arange(block_state.num_inference_steps, dtype=diffdiff_map.dtype) / block_state.num_inference_steps</span>
<span class="hljs-addition">+ block_state.diffdiff_masks = diffdiff_map &gt; (thresholds + (block_state.denoising_start or 0))</span>
<span class="hljs-addition">+ block_state.original_latents = block_state.latents</span>`,wrap:!1}}),N=new y({props:{title:"去噪",local:"去噪",headingTag:"h3"}}),L=new U({props:{code:"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",highlighted:`class SDXLDiffDiffLoopBeforeDenoiser(ModularPipelineBlocks):
 @property
 def description(self) -&gt; str:
 return (
 &quot;Step within the denoising loop for differential diffusion that prepare the latent input for the denoiser&quot;
 )
 @property
 def inputs(self) -&gt; List[str]:
 return [
 InputParam(&quot;latents&quot;, required=True, type_hint=torch.Tensor),
<span class="hljs-addition">+ InputParam(&quot;denoising_start&quot;),</span>
<span class="hljs-addition">+ InputParam(&quot;original_latents&quot;, type_hint=torch.Tensor),</span>
<span class="hljs-addition">+ InputParam(&quot;diffdiff_masks&quot;, type_hint=torch.Tensor),</span>
 ]
 def __call__(self, components, block_state, i, t):
<span class="hljs-addition">+ # Apply differential diffusion logic</span>
<span class="hljs-addition">+ if i == 0 and block_state.denoising_start is None:</span>
<span class="hljs-addition">+ block_state.latents = block_state.original_latents[:1]</span>
<span class="hljs-addition">+ else:</span>
<span class="hljs-addition">+ block_state.mask = block_state.diffdiff_masks[i].unsqueeze(0).unsqueeze(1)</span>
<span class="hljs-addition">+ block_state.latents = block_state.original_latents[i] * block_state.mask + block_state.latents * (1 - block_state.mask)</span>
 # ... rest of existing logic ...`,wrap:!1}}),D=new y({props:{title:"组装块",local:"组装块",headingTag:"h2"}}),q=new U({props:{code:"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",highlighted:`DIFFDIFF_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
DIFFDIFF_BLOCKS[<span class="hljs-string">&quot;set_timesteps&quot;</span>] = TEXT2IMAGE_BLOCKS[<span class="hljs-string">&quot;set_timesteps&quot;</span>]
DIFFDIFF_BLOCKS[<span class="hljs-string">&quot;prepare_latents&quot;</span>] = SDXLDiffDiffPrepareLatentsStep
DIFFDIFF_BLOCKS[<span class="hljs-string">&quot;denoise&quot;</span>] = SDXLDiffDiffDenoiseStep
dd_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_BLOCKS)
<span class="hljs-built_in">print</span>(dd_blocks)`,wrap:!1}}),x=new y({props:{title:"ModularPipeline",local:"modularpipeline",headingTag:"h2"}}),O=new U({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy5tb2R1bGFyX3BpcGVsaW5lcyUyMGltcG9ydCUyMENvbXBvbmVudHNNYW5hZ2VyJTBBJTBBY29tcG9uZW50cyUyMCUzRCUyMENvbXBvbmVudE1hbmFnZXIoKSUwQSUwQWRkX3BpcGVsaW5lJTIwJTNEJTIwZGRfYmxvY2tzLmluaXRfcGlwZWxpbmUoJTIyWWlZaVh1JTJGbW9kdWxhci1kZW1vLWF1dG8lMjIlMkMlMjBjb21wb25lbnRzX21hbmFnZXIlM0Rjb21wb25lbnRzJTJDJTIwY29sbGVjdGlvbiUzRCUyMmRpZmZkaWZmJTIyKSUwQWRkX3BpcGVsaW5lLmxvYWRfZGVmYXVsdF9jb21wb25lbmV0cyh0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpJTBBZGRfcGlwZWxpbmUudG8oJTIyY3VkYSUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers.modular_pipelines <span class="hljs-keyword">import</span> ComponentsManager
components = ComponentManager()
dd_pipeline = dd_blocks.init_pipeline(<span class="hljs-string">&quot;YiYiXu/modular-demo-auto&quot;</span>, components_manager=components, collection=<span class="hljs-string">&quot;diffdiff&quot;</span>)
dd_pipeline.load_default_componenets(torch_dtype=torch.float16)
dd_pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),ll=new y({props:{title:"添加工作流",local:"添加工作流",headingTag:"h2"}}),sl=new y({props:{title:"IP-Adapter",local:"ip-adapter",headingTag:"h3"}}),il=new U({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy5tb2R1bGFyX3BpcGVsaW5lcy5zdGFibGVfZGlmZnVzaW9uX3hsLmVuY29kZXJzJTIwaW1wb3J0JTIwU3RhYmxlRGlmZnVzaW9uWExBdXRvSVBBZGFwdGVyU3RlcCUwQSUwQWlwX2FkYXB0ZXJfYmxvY2slMjAlM0QlMjBTdGFibGVEaWZmdXNpb25YTEF1dG9JUEFkYXB0ZXJTdGVwKCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers.modular_pipelines.stable_diffusion_xl.encoders <span class="hljs-keyword">import</span> StableDiffusionXLAutoIPAdapterStep
ip_adapter_block = StableDiffusionXLAutoIPAdapterStep()`,wrap:!1}}),pl=new U({props:{code:"ZGRfYmxvY2tzLnN1Yl9ibG9ja3MuaW5zZXJ0KCUyMmlwX2FkYXB0ZXIlMjIlMkMlMjBpcF9hZGFwdGVyX2Jsb2NrJTJDJTIwMCk=",highlighted:'dd_blocks.sub_blocks.insert(<span class="hljs-string">&quot;ip_adapter&quot;</span>, ip_adapter_block, <span class="hljs-number">0</span>)',wrap:!1}}),Ml=new U({props:{code:"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",highlighted:`dd_pipeline = dd_blocks.init_pipeline(<span class="hljs-string">&quot;YiYiXu/modular-demo-auto&quot;</span>, collection=<span class="hljs-string">&quot;diffdiff&quot;</span>)
dd_pipeline.load_default_components(torch_dtype=torch.float16)
dd_pipeline.loader.load_ip_adapter(<span class="hljs-string">&quot;h94/IP-Adapter&quot;</span>, subfolder=<span class="hljs-string">&quot;sdxl_models&quot;</span>, weight_name=<span class="hljs-string">&quot;ip-adapter_sdxl.bin&quot;</span>)
dd_pipeline.loader.set_ip_adapter_scale(<span class="hljs-number">0.6</span>)
dd_pipeline = dd_pipeline.to(device)
ip_adapter_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_orange.jpeg&quot;</span>)
image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true&quot;</span>)
mask = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true&quot;</span>)
prompt = <span class="hljs-string">&quot;a green pear&quot;</span>
negative_prompt = <span class="hljs-string">&quot;blurry&quot;</span>
generator = torch.Generator(device=device).manual_seed(<span class="hljs-number">42</span>)
image = dd_pipeline(
 prompt=prompt,
 negative_prompt=negative_prompt,
 num_inference_steps=<span class="hljs-number">25</span>,
 generator=generator,
 ip_adapter_image=ip_adapter_image,
 diffdiff_map=mask,
 image=image,
output=<span class="hljs-string">&quot;images&quot;</span>
)[<span class="hljs-number">0</span>]`,wrap:!1}}),Jl=new y({props:{title:"ControlNet",local:"controlnet",headingTag:"h3"}}),Tl=new U({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy5tb2R1bGFyX3BpcGVsaW5lcy5zdGFibGVfZGlmZnVzaW9uX3hsLm1vZHVsYXJfYmxvY2tzJTIwaW1wb3J0JTIwU3RhYmxlRGlmZnVzaW9uWExBdXRvQ29udHJvbE5ldElucHV0U3RlcCUwQSUwQWNvbnRyb2xfaW5wdXRfYmxvY2slMjAlM0QlMjBTdGFibGVEaWZmdXNpb25YTEF1dG9Db250cm9sTmV0SW5wdXRTdGVwKCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers.modular_pipelines.stable_diffusion_xl.modular_blocks <span class="hljs-keyword">import</span> StableDiffusionXLAutoControlNetInputStep
control_input_block = StableDiffusionXLAutoControlNetInputStep()`,wrap:!1}}),Ul=new U({props:{code:"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",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">SDXLDiffDiffControlNetDenoiseStep</span>(<span class="hljs-title class_ inherited__">StableDiffusionXLDenoiseLoopWrapper</span>):
 block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLControlNetLoopDenoiser, StableDiffusionXLDenoiseLoopAfterDenoiser]
 block_names = [<span class="hljs-string">&quot;before_denoiser&quot;</span>, <span class="hljs-string">&quot;denoiser&quot;</span>, <span class="hljs-string">&quot;after_denoiser&quot;</span>]
controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep()`,wrap:!1}}),fl=new U({props:{code:"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",highlighted:`dd_blocks.sub_blocks.insert(<span class="hljs-string">&quot;controlnet_input&quot;</span>, control_input_block, <span class="hljs-number">7</span>)
dd_blocks.sub_blocks[<span class="hljs-string">&quot;denoise&quot;</span>] = controlnet_denoise_block
dd_pipeline = dd_blocks.init_pipeline(<span class="hljs-string">&quot;YiYiXu/modular-demo-auto&quot;</span>, collection=<span class="hljs-string">&quot;diffdiff&quot;</span>)
dd_pipeline.load_default_components(torch_dtype=torch.float16)
dd_pipeline = dd_pipeline.to(device)
control_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg&quot;</span>)
image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true&quot;</span>)
mask = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true&quot;</span>)
prompt = <span class="hljs-string">&quot;a green pear&quot;</span>
negative_prompt = <span class="hljs-string">&quot;blurry&quot;</span>
generator = torch.Generator(device=device).manual_seed(<span class="hljs-number">42</span>)
image = dd_pipeline(
 prompt=prompt,
 negative_prompt=negative_prompt,
 num_inference_steps=<span class="hljs-number">25</span>,
 generator=generator,
 control_image=control_image,
 controlnet_conditioning_scale=<span class="hljs-number">0.5</span>,
 diffdiff_map=mask,
 image=image,
 output=<span class="hljs-string">&quot;images&quot;</span>
)[<span class="hljs-number">0</span>]`,wrap:!1}}),ul=new y({props:{title:"AutoPipelineBlocks",local:"autopipelineblocks",headingTag:"h3"}}),Zl=new U({props:{code:"Y2xhc3MlMjBTRFhMRGlmZkRpZmZBdXRvRGVub2lzZVN0ZXAoQXV0b1BpcGVsaW5lQmxvY2tzKSUzQSUwQSUyMCUyMCUyMCUyMGJsb2NrX2NsYXNzZXMlMjAlM0QlMjAlNUJTRFhMRGlmZkRpZmZDb250cm9sTmV0RGVub2lzZVN0ZXAlMkMlMjBTRFhMRGlmZkRpZmZEZW5vaXNlU3RlcCU1RCUwQSUyMCUyMCUyMCUyMGJsb2NrX25hbWVzJTIwJTNEJTIwJTVCJTIyY29udHIlMEFvbG5ldF9kZW5vaXNlJTIyJTJDJTIwJTIyZGVub2lzZSUyMiU1RCUwQWJsb2NrX3RyaWdnZXJfaW5wdXRzJTIwJTNEJTIwJTVCJTIyY29udHJvbG5ldF9jb25kJTIyJTJDJTIwTm9uZSU1RA==",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">SDXLDiffDiffAutoDenoiseStep</span>(<span class="hljs-title class_ inherited__">AutoPipelineBlocks</span>):
 block_classes = [SDXLDiffDiffControlNetDenoiseStep, SDXLDiffDiffDenoiseStep]
 block_names = [<span class="hljs-string">&quot;contr
olnet_denoise&quot;</span>, <span class="hljs-string">&quot;denoise&quot;</span>]
block_trigger_inputs = [<span class="hljs-string">&quot;controlnet_cond&quot;</span>, <span class="hljs-literal">None</span>]`,wrap:!1}}),bl=new U({props:{code:"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",highlighted:`DIFFDIFF_AUTO_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
DIFFDIFF_AUTO_BLOCKS[<span class="hljs-string">&quot;prepare_latents&quot;</span>] = SDXLDiffDiffPrepareLatentsStep
DIFFDIFF_AUTO_BLOCKS[<span class="hljs-string">&quot;set_timesteps&quot;</span>] = TEXT2IMAGE_BLOCKS[<span class="hljs-string">&quot;set_timesteps&quot;</span>]
DIFFDIFF_AUTO_BLOCKS[<span class="hljs-string">&quot;denoise&quot;</span>] = SDXLDiffDiffAutoDenoiseStep
DIFFDIFF_AUTO_BLOCKS.insert(<span class="hljs-string">&quot;ip_adapter&quot;</span>, StableDiffusionXLAutoIPAdapterStep, <span class="hljs-number">0</span>)
DIFFDIFF_AUTO_BLOCKS.insert(<span class="hljs-string">&quot;controlnet_input&quot;</span>,StableDiffusionXLControlNetAutoInput, <span class="hljs-number">7</span>)`,wrap:!1}}),Gl=new U({props:{code:"ZGRfYXV0b19ibG9ja3MlMjAlM0QlMjBTZXF1ZW50aWFsUGlwZWxpbmVCbG9ja3MuZnJvbV9ibG9ja3NfZGljdChESUZGRElGRl9BVVRPX0JMT0NLUyklMEFkZF9waXBlbGluZSUyMCUzRCUyMGRkX2F1dG9fYmxvY2tzLmluaXRfcGlwZWxpbmUoJTIyWWlZaVh1JTJGbW9kdWxhci1kZW1vLWF1dG8lMjIlMkMlMjBjb2xsZWN0aW9uJTNEJTIyZGlmZmRpZmYlMjIpJTBBZGRfcGlwZWxpbmUubG9hZF9kZWZhdWx0X2NvbXBvbmVudHModG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KQ==",highlighted:`dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS)
dd_pipeline = dd_auto_blocks.init_pipeline(<span class="hljs-string">&quot;YiYiXu/modular-demo-auto&quot;</span>, collection=<span class="hljs-string">&quot;diffdiff&quot;</span>)
dd_pipeline.load_default_components(torch_dtype=torch.float16)`,wrap:!1}}),Rl=new y({props:{title:"分享",local:"分享",headingTag:"h2"}}),_l=new U({props:{code:"ZGRfcGlwZWxpbmUuc2F2ZV9wcmV0cmFpbmVkKCUyMllpWWlYdSUyRnRlc3RfbW9kdWxhcl9kb2MlMjIlMkMlMjBwdXNoX3RvX2h1YiUzRFRydWUp",highlighted:'dd_pipeline.save_pretrained(<span class="hljs-string">&quot;YiYiXu/test_modular_doc&quot;</span>, push_to_hub=<span class="hljs-literal">True</span>)',wrap:!1}}),Xl=new U({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers.modular_pipelines <span class="hljs-keyword">import</span> ModularPipeline, ComponentsManager
components = ComponentsManager()
diffdiff_pipeline = ModularPipeline.from_pretrained(<span class="hljs-string">&quot;YiYiXu/modular-diffdiff-0704&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>, components_manager=components, collection=<span class="hljs-string">&quot;diffdiff&quot;</span>)
diffdiff_pipeline.load_default_components(torch_dtype=torch.float16)`,wrap:!1}}),Bl=new 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