diffusers/pr_11986/en/_app/immutable/nodes/5.d0871186.js

import{s as ht,o as ft,n as Le}from"../chunks/scheduler.8c3d61f6.js";import{S as mt,i as ut,g as p,s,r as f,A as gt,h as d,f as n,c as i,j,u as m,x as $,k as x,y as l,a as r,v as u,d as g,t as _,w as b}from"../chunks/index.da70eac4.js";import{D as Z}from"../chunks/Docstring.eabe339b.js";import{C as Ke}from"../chunks/CodeBlock.a9c4becf.js";import{E as ze}from"../chunks/ExampleCodeBlock.4ca28371.js";import{H as ge,E as _t}from"../chunks/getInferenceSnippets.366c2c95.js";function bt(W){let o,v="Example:",y,c,h;return c=new Ke({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQ29nVmlkZW9YUGlwZWxpbmUlMkMlMjBQeXJhbWlkQXR0ZW50aW9uQnJvYWRjYXN0Q29uZmlnJTBBJTBBcGlwZSUyMCUzRCUyMENvZ1ZpZGVvWFBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJUSFVETSUyRkNvZ1ZpZGVvWC01YiUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guYmZsb2F0MTYpJTBBcGlwZS50byglMjJjdWRhJTIyKSUwQSUwQWNvbmZpZyUyMCUzRCUyMFB5cmFtaWRBdHRlbnRpb25Ccm9hZGNhc3RDb25maWcoJTBBJTIwJTIwJTIwJTIwc3BhdGlhbF9hdHRlbnRpb25fYmxvY2tfc2tpcF9yYW5nZSUzRDIlMkMlMEElMjAlMjAlMjAlMjBzcGF0aWFsX2F0dGVudGlvbl90aW1lc3RlcF9za2lwX3JhbmdlJTNEKDEwMCUyQyUyMDgwMCklMkMlMEElMjAlMjAlMjAlMjBjdXJyZW50X3RpbWVzdGVwX2NhbGxiYWNrJTNEbGFtYmRhJTNBJTIwcGlwZS5jdXJyZW50X3RpbWVzdGVwJTJDJTBBKSUwQXBpcGUudHJhbnNmb3JtZXIuZW5hYmxlX2NhY2hlKGNvbmZpZyk=",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> diffusers <span class="hljs-keyword">import</span> CogVideoXPipeline, PyramidAttentionBroadcastConfig

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = CogVideoXPipeline.from_pretrained(<span class="hljs-string">&quot;THUDM/CogVideoX-5b&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>config = PyramidAttentionBroadcastConfig(
<span class="hljs-meta">... </span>    spatial_attention_block_skip_range=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    spatial_attention_timestep_skip_range=(<span class="hljs-number">100</span>, <span class="hljs-number">800</span>),
<span class="hljs-meta">... </span>    current_timestep_callback=<span class="hljs-keyword">lambda</span>: pipe.current_timestep,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.transformer.enable_cache(config)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,y=s(),f(c.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-11lpom8"&&(o.textContent=v),y=i(t),m(c.$$.fragment,t)},m(t,k){r(t,o,k),r(t,y,k),u(c,t,k),h=!0},p:Le,i(t){h||(g(c.$$.fragment,t),h=!0)},o(t){_(c.$$.fragment,t),h=!1},d(t){t&&(n(o),n(y)),b(c,t)}}}function yt(W){let o,v="Example:",y,c,h;return c=new Ke({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> diffusers <span class="hljs-keyword">import</span> CogVideoXPipeline, PyramidAttentionBroadcastConfig, apply_pyramid_attention_broadcast
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = CogVideoXPipeline.from_pretrained(<span class="hljs-string">&quot;THUDM/CogVideoX-5b&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>config = PyramidAttentionBroadcastConfig(
<span class="hljs-meta">... </span>    spatial_attention_block_skip_range=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    spatial_attention_timestep_skip_range=(<span class="hljs-number">100</span>, <span class="hljs-number">800</span>),
<span class="hljs-meta">... </span>    current_timestep_callback=<span class="hljs-keyword">lambda</span>: pipe.current_timestep,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>apply_pyramid_attention_broadcast(pipe.transformer, config)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,y=s(),f(c.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-11lpom8"&&(o.textContent=v),y=i(t),m(c.$$.fragment,t)},m(t,k){r(t,o,k),r(t,y,k),u(c,t,k),h=!0},p:Le,i(t){h||(g(c.$$.fragment,t),h=!0)},o(t){_(c.$$.fragment,t),h=!1},d(t){t&&(n(o),n(y)),b(c,t)}}}function kt(W){let o,v="Example:",y,c,h;return c=new Ke({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> diffusers <span class="hljs-keyword">import</span> CogVideoXPipeline, FasterCacheConfig, apply_faster_cache

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = CogVideoXPipeline.from_pretrained(<span class="hljs-string">&quot;THUDM/CogVideoX-5b&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>config = FasterCacheConfig(
<span class="hljs-meta">... </span>    spatial_attention_block_skip_range=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    spatial_attention_timestep_skip_range=(-<span class="hljs-number">1</span>, <span class="hljs-number">681</span>),
<span class="hljs-meta">... </span>    low_frequency_weight_update_timestep_range=(<span class="hljs-number">99</span>, <span class="hljs-number">641</span>),
<span class="hljs-meta">... </span>    high_frequency_weight_update_timestep_range=(-<span class="hljs-number">1</span>, <span class="hljs-number">301</span>),
<span class="hljs-meta">... </span>    spatial_attention_block_identifiers=[<span class="hljs-string">&quot;transformer_blocks&quot;</span>],
<span class="hljs-meta">... </span>    attention_weight_callback=<span class="hljs-keyword">lambda</span> _: <span class="hljs-number">0.3</span>,
<span class="hljs-meta">... </span>    tensor_format=<span class="hljs-string">&quot;BFCHW&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>apply_faster_cache(pipe.transformer, config)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,y=s(),f(c.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-11lpom8"&&(o.textContent=v),y=i(t),m(c.$$.fragment,t)},m(t,k){r(t,o,k),r(t,y,k),u(c,t,k),h=!0},p:Le,i(t){h||(g(c.$$.fragment,t),h=!0)},o(t){_(c.$$.fragment,t),h=!1},d(t){t&&(n(o),n(y)),b(c,t)}}}function wt(W){let o,v,y,c,h,t,k,Oe="Cache methods speedup diffusion transformers by storing and reusing intermediate outputs of specific layers, such as attention and feedforward layers, instead of recalculating them at each inference step.",_e,A,be,w,E,Ze,ae,et="A class for enable/disabling caching techniques on diffusion models.",We,se,tt="Supported caching techniques:",Ne,ie,nt='<li><a href="https://huggingface.co/papers/2408.12588" rel="nofollow">Pyramid Attention Broadcast</a></li> <li><a href="https://huggingface.co/papers/2410.19355" rel="nofollow">FasterCache</a></li> <li><a href="https://github.com/chengzeyi/ParaAttention/blob/7a266123671b55e7e5a2fe9af3121f07a36afc78/README.md#first-block-cache-our-dynamic-caching" rel="nofollow">FirstBlockCache</a></li>',Xe,N,P,Ge,oe,at="Context manager that provides additional methods for cache management.",Ie,J,Q,Re,re,st="Enable caching techniques on the model.",Ae,X,ye,Y,ke,B,V,Ee,le,it="Configuration for Pyramid Attention Broadcast.",we,T,D,Pe,ce,ot='Apply <a href="https://huggingface.co/papers/2408.12588" rel="nofollow">Pyramid Attention Broadcast</a> to a given pipeline.',Qe,pe,rt=`PAB is an attention approximation method that leverages the similarity in attention states between timesteps to
reduce the computational cost of attention computation. The key takeaway from the paper is that the attention
similarity in the cross-attention layers between timesteps is high, followed by less similarity in the temporal and
spatial layers. This allows for the skipping of attention computation in the cross-attention layers more frequently
than in the temporal and spatial layers. Applying PAB will, therefore, speedup the inference process.`,Ye,G,Ce,H,$e,q,S,Ve,de,lt='Configuration for <a href="https://huggingface.co/papers/2410.19355" rel="nofollow">FasterCache</a>.',ve,M,z,De,he,ct='Applies <a href="https://huggingface.co/papers/2410.19355" rel="nofollow">FasterCache</a> to a given pipeline.',He,I,Te,L,Me,F,K,Se,fe,pt=`Configuration for <a href="https://github.com/chengzeyi/ParaAttention/blob/7a266123671b55e7e5a2fe9af3121f07a36afc78/README.md#first-block-cache-our-dynamic-caching" rel="nofollow">First Block
Cache</a>.`,je,O,ee,xe,te,Je,ue,Be;return h=new ge({props:{title:"Caching methods",local:"caching-methods",headingTag:"h1"}}),A=new ge({props:{title:"CacheMixin",local:"diffusers.CacheMixin",headingTag:"h2"}}),E=new Z({props:{name:"class diffusers.CacheMixin",anchor:"diffusers.CacheMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11986/src/diffusers/models/cache_utils.py#L23"}}),P=new Z({props:{name:"cache_context",anchor:"diffusers.CacheMixin.cache_context",parameters:[{name:"name",val:": str"}],source:"https://github.com/huggingface/diffusers/blob/vr_11986/src/diffusers/models/cache_utils.py#L120"}}),Q=new Z({props:{name:"enable_cache",anchor:"diffusers.CacheMixin.enable_cache",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"diffusers.CacheMixin.enable_cache.config",description:`<strong>config</strong> (<code>Union[PyramidAttentionBroadcastConfig]</code>) &#x2014;
The configuration for applying the caching technique. Currently supported caching techniques are:<ul>
<li><a href="/docs/diffusers/pr_11986/en/api/cache#diffusers.PyramidAttentionBroadcastConfig">PyramidAttentionBroadcastConfig</a></li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/diffusers/blob/vr_11986/src/diffusers/models/cache_utils.py#L39"}}),X=new ze({props:{anchor:"diffusers.CacheMixin.enable_cache.example",$$slots:{default:[bt]},$$scope:{ctx:W}}}),Y=new ge({props:{title:"PyramidAttentionBroadcastConfig",local:"diffusers.PyramidAttentionBroadcastConfig",headingTag:"h2"}}),V=new Z({props:{name:"class diffusers.PyramidAttentionBroadcastConfig",anchor:"diffusers.PyramidAttentionBroadcastConfig",parameters:[{name:"spatial_attention_block_skip_range",val:": typing.Optional[int] = None"},{name:"temporal_attention_block_skip_range",val:": typing.Optional[int] = None"},{name:"cross_attention_block_skip_range",val:": typing.Optional[int] = None"},{name:"spatial_attention_timestep_skip_range",val:": typing.Tuple[int, int] = (100, 800)"},{name:"temporal_attention_timestep_skip_range",val:": typing.Tuple[int, int] = (100, 800)"},{name:"cross_attention_timestep_skip_range",val:": typing.Tuple[int, int] = (100, 800)"},{name:"spatial_attention_block_identifiers",val:": typing.Tuple[str, ...] = ('blocks', 'transformer_blocks', 'single_transformer_blocks')"},{name:"temporal_attention_block_identifiers",val:": typing.Tuple[str, ...] = ('temporal_transformer_blocks',)"},{name:"cross_attention_block_identifiers",val:": typing.Tuple[str, ...] = ('blocks', 'transformer_blocks')"},{name:"current_timestep_callback",val:": typing.Callable[[], int] = None"}],parametersDescription:[{anchor:"diffusers.PyramidAttentionBroadcastConfig.spatial_attention_block_skip_range",description:`<strong>spatial_attention_block_skip_range</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of times a specific spatial attention broadcast is skipped before computing the attention states
to re-use. If this is set to the value <code>N</code>, the attention computation will be skipped <code>N - 1</code> times (i.e.,
old attention states will be re-used) before computing the new attention states again.`,name:"spatial_attention_block_skip_range"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.temporal_attention_block_skip_range",description:`<strong>temporal_attention_block_skip_range</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of times a specific temporal attention broadcast is skipped before computing the attention
states to re-use. If this is set to the value <code>N</code>, the attention computation will be skipped <code>N - 1</code> times
(i.e., old attention states will be re-used) before computing the new attention states again.`,name:"temporal_attention_block_skip_range"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.cross_attention_block_skip_range",description:`<strong>cross_attention_block_skip_range</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of times a specific cross-attention broadcast is skipped before computing the attention states
to re-use. If this is set to the value <code>N</code>, the attention computation will be skipped <code>N - 1</code> times (i.e.,
old attention states will be re-used) before computing the new attention states again.`,name:"cross_attention_block_skip_range"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.spatial_attention_timestep_skip_range",description:`<strong>spatial_attention_timestep_skip_range</strong> (<code>Tuple[int, int]</code>, defaults to <code>(100, 800)</code>) &#x2014;
The range of timesteps to skip in the spatial attention layer. The attention computations will be
conditionally skipped if the current timestep is within the specified range.`,name:"spatial_attention_timestep_skip_range"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.temporal_attention_timestep_skip_range",description:`<strong>temporal_attention_timestep_skip_range</strong> (<code>Tuple[int, int]</code>, defaults to <code>(100, 800)</code>) &#x2014;
The range of timesteps to skip in the temporal attention layer. The attention computations will be
conditionally skipped if the current timestep is within the specified range.`,name:"temporal_attention_timestep_skip_range"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.cross_attention_timestep_skip_range",description:`<strong>cross_attention_timestep_skip_range</strong> (<code>Tuple[int, int]</code>, defaults to <code>(100, 800)</code>) &#x2014;
The range of timesteps to skip in the cross-attention layer. The attention computations will be
conditionally skipped if the current timestep is within the specified range.`,name:"cross_attention_timestep_skip_range"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.spatial_attention_block_identifiers",description:`<strong>spatial_attention_block_identifiers</strong> (<code>Tuple[str, ...]</code>, defaults to <code>(&quot;blocks&quot;, &quot;transformer_blocks&quot;)</code>) &#x2014;
The identifiers to match against the layer names to determine if the layer is a spatial attention layer.`,name:"spatial_attention_block_identifiers"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.temporal_attention_block_identifiers",description:`<strong>temporal_attention_block_identifiers</strong> (<code>Tuple[str, ...]</code>, defaults to <code>(&quot;temporal_transformer_blocks&quot;,)</code>) &#x2014;
The identifiers to match against the layer names to determine if the layer is a temporal attention layer.`,name:"temporal_attention_block_identifiers"},{anchor:"diffusers.PyramidAttentionBroadcastConfig.cross_attention_block_identifiers",description:`<strong>cross_attention_block_identifiers</strong> (<code>Tuple[str, ...]</code>, defaults to <code>(&quot;blocks&quot;, &quot;transformer_blocks&quot;)</code>) &#x2014;
The identifiers to match against the layer names to determine if the layer is a cross-attention layer.`,name:"cross_attention_block_identifiers"}],source:"https://github.com/huggingface/diffusers/blob/vr_11986/src/diffusers/hooks/pyramid_attention_broadcast.py#L37"}}),D=new Z({props:{name:"diffusers.apply_pyramid_attention_broadcast",anchor:"diffusers.apply_pyramid_attention_broadcast",parameters:[{name:"module",val:": Module"},{name:"config",val:": PyramidAttentionBroadcastConfig"}],parametersDescription:[{anchor:"diffusers.apply_pyramid_attention_broadcast.module",description:`<strong>module</strong> (<code>torch.nn.Module</code>) &#x2014;
The module to apply Pyramid Attention Broadcast to.`,name:"module"},{anchor:"diffusers.apply_pyramid_attention_broadcast.config",description:`<strong>config</strong> (<code>Optional[PyramidAttentionBroadcastConfig]</code>, <code>optional</code>, defaults to <code>None</code>) &#x2014;
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The timestep range within which the spatial attention computation can be skipped without a significant loss
in quality. This is to be determined by the user based on the underlying model. The first value in the
tuple is the lower bound and the second value is the upper bound. Typically, diffusion timesteps for
denoising are in the reversed range of 0 to 1000 (i.e. denoising starts at timestep 1000 and ends at
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The timestep range within which the temporal attention computation can be skipped without a significant
loss in quality. This is to be determined by the user based on the underlying model. The first value in the
tuple is the lower bound and the second value is the upper bound. Typically, diffusion timesteps for
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Process the unconditional branch every <code>N</code> iterations. If this is set to <code>N</code>, the unconditional branch
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The timestep range within which the unconditional branch computation can be skipped without a significant
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The identifiers to match the spatial attention blocks in the model. If the name of the block contains any
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The identifiers to match the temporal attention blocks in the model. If the name of the block contains any
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The callback function to determine the weight to scale the attention outputs by. This function should take
the attention module as input and return a float value. This is used to approximate the unconditional
branch from the conditional branch outputs. If not provided, the default weight is 0.5 for all timesteps.
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progresses. Users are encouraged to experiment and provide custom weight schedules that take into account
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higher threshold usually results in a forward pass through a lower number of layers and faster inference,
but might lead to poorer generation quality. A lower threshold may not result in significant generation
speedup. The threshold is compared against the absmean difference of the residuals between the current and
cached outputs from the first transformer block. If the difference is below the threshold, the forward pass
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