diffusers/pr_13745/en/_app/immutable/nodes/118.a82abc52.js

import{s as Qn,o as Ln,n as K}from"../chunks/scheduler.53228c21.js";import{S as qn,i as Dn,e as f,s as a,c as g,q as xt,H as Vn,h as Sn,a as h,d as o,b as l,f as x,g as y,j as b,r as kt,u as En,k as j,w as Hn,l as i,m as p,n as _,t as w,o as M,p as v}from"../chunks/index.cac5d66a.js";import{C as Xn}from"../chunks/CopyLLMTxtMenu.4912207d.js";import{D as P}from"../chunks/Docstring.1e7ac4f3.js";import{C as ee}from"../chunks/CodeBlock.606cbaf4.js";import{E as Pn}from"../chunks/ExampleCodeBlock.ccf7d2a9.js";import{H as O,E as Yn}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.323ee77a.js";import{H as rn,a as Xe}from"../chunks/HfOption.6b51ddef.js";function On(A){let n,r;return n=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AnyFlowPipeline
<span class="hljs-keyword">from</span> diffusers.hooks <span class="hljs-keyword">import</span> apply_group_offloading

pipe = AnyFlowPipeline.from_pretrained(
    <span class="hljs-string">&quot;nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
)
apply_group_offloading(pipe.transformer, onload_device=<span class="hljs-string">&quot;cuda&quot;</span>, offload_type=<span class="hljs-string">&quot;leaf_level&quot;</span>)
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()`,lang:"py",wrap:!1}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p:K,i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function Kn(A){let n,r;return n=new ee({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQW55Rmxvd1BpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyMEFueUZsb3dQaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIybnZpZGlhJTJGQW55Rmxvdy1XYW4yLjEtVDJWLTE0Qi1EaWZmdXNlcnMlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmJmbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGUudHJhbnNmb3JtZXIlMjAlM0QlMjB0b3JjaC5jb21waWxlKHBpcGUudHJhbnNmb3JtZXIlMkMlMjBtb2RlJTNEJTIybWF4LWF1dG90dW5lLW5vLWN1ZGFncmFwaHMlMjIp",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AnyFlowPipeline

pipe = AnyFlowPipeline.from_pretrained(
    <span class="hljs-string">&quot;nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.transformer = torch.<span class="hljs-built_in">compile</span>(pipe.transformer, mode=<span class="hljs-string">&quot;max-autotune-no-cudagraphs&quot;</span>)`,lang:"py",wrap:!1}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p:K,i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function es(A){let n,r,t,u;return n=new Xe({props:{id:"optimization",option:"memory",$$slots:{default:[On]},$$scope:{ctx:A}}}),t=new Xe({props:{id:"optimization",option:"inference speed",$$slots:{default:[Kn]},$$scope:{ctx:A}}}),{c(){g(n.$$.fragment),r=a(),g(t.$$.fragment)},l(c){y(n.$$.fragment,c),r=l(c),y(t.$$.fragment,c)},m(c,d){_(n,c,d),p(c,r,d),_(t,c,d),u=!0},p(c,d){const m={};d&2&&(m.$$scope={dirty:d,ctx:c}),n.$set(m);const $={};d&2&&($.$$scope={dirty:d,ctx:c}),t.$set($)},i(c){u||(w(n.$$.fragment,c),w(t.$$.fragment,c),u=!0)},o(c){M(n.$$.fragment,c),M(t.$$.fragment,c),u=!1},d(c){c&&o(r),v(n,c),v(t,c)}}}function ts(A){let n,r;return n=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AnyFlowPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = AnyFlowPipeline.from_pretrained(
    <span class="hljs-string">&quot;nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A red panda eating bamboo in a forest, cinematic lighting&quot;</span>
video = pipe(prompt, num_inference_steps=<span class="hljs-number">4</span>, num_frames=<span class="hljs-number">33</span>).frames[<span class="hljs-number">0</span>]
export_to_video(video, <span class="hljs-string">&quot;out.mp4&quot;</span>, fps=<span class="hljs-number">16</span>)`,lang:"py",wrap:!1}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p:K,i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function ns(A){let n,r;return n=new Xe({props:{id:"anyflow-bidi",option:"usage",$$slots:{default:[ts]},$$scope:{ctx:A}}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p(t,u){const c={};u&2&&(c.$$scope={dirty:u,ctx:t}),n.$set(c)},i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function ss(A){let n,r;return n=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AnyFlowFARPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = AnyFlowFARPipeline.from_pretrained(
    <span class="hljs-string">&quot;nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

video = pipe(
    prompt=<span class="hljs-string">&quot;A cat surfing a wave, sunset&quot;</span>,
    num_inference_steps=<span class="hljs-number">4</span>,
    num_frames=<span class="hljs-number">81</span>,
).frames[<span class="hljs-number">0</span>]
export_to_video(video, <span class="hljs-string">&quot;out.mp4&quot;</span>, fps=<span class="hljs-number">16</span>)`,lang:"py",wrap:!1}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p:K,i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function os(A){let n,r;return n=new ee({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">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AnyFlowFARPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video, load_image

pipe = AnyFlowFARPipeline.from_pretrained(
    <span class="hljs-string">&quot;nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># Wrap the conditioning image as a one-frame video tensor: (1, 1, 3, H, W) in [0, 1].</span>
first_frame = load_image(<span class="hljs-string">&quot;path/to/first_frame.png&quot;</span>).resize((<span class="hljs-number">832</span>, <span class="hljs-number">480</span>))
arr = np.asarray(first_frame).astype(<span class="hljs-string">&quot;float32&quot;</span>) / <span class="hljs-number">255.0</span>  <span class="hljs-comment"># (480, 832, 3)</span>
context_tensor = torch.from_numpy(arr).permute(<span class="hljs-number">2</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>).unsqueeze(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">1</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

video = pipe(
    prompt=<span class="hljs-string">&quot;a cat walks across a sunlit lawn&quot;</span>,
    video=context_tensor,
    num_inference_steps=<span class="hljs-number">4</span>,
    num_frames=<span class="hljs-number">81</span>,
).frames[<span class="hljs-number">0</span>]
export_to_video(video, <span class="hljs-string">&quot;out.mp4&quot;</span>, fps=<span class="hljs-number">16</span>)`,lang:"py",wrap:!1}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p:K,i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function as(A){let n,r;return n=new ee({props:{code:"aW1wb3J0JTIwbnVtcHklMjBhcyUyMG5wJTBBaW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQW55Rmxvd0ZBUlBpcGVsaW5lJTBBZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGV4cG9ydF90b192aWRlbyUyQyUyMGxvYWRfdmlkZW8lMEElMEFwaXBlJTIwJTNEJTIwQW55Rmxvd0ZBUlBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJudmlkaWElMkZBbnlGbG93LUZBUi1XYW4yLjEtMS4zQi1EaWZmdXNlcnMlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmJmbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKSUwQSUwQSUyMyUyMENvbnRleHQlMjBjbGlwJTIwJUUyJTgwJTk0JTIwOSUyMHJhdyUyMGZyYW1lcyUyMG1hcCUyMHRvJTIwMyUyMGxhdGVudCUyMGZyYW1lcyUyMCg5JTIwJTNEJTIwNCVDMiVCNzIlMjAlMkIlMjAxJTJDJTIwMyUyMCUzRCUyMDIlMjAlMkIlMjAxKS4lMEFjb250ZXh0X2ZyYW1lcyUyMCUzRCUyMGxvYWRfdmlkZW8oJTIycGF0aCUyRnRvJTJGY29udGV4dC5tcDQlMjIpJTVCJTNBOSU1RCUwQWFyciUyMCUzRCUyMG5wLnN0YWNrKCU1Qm5wLmFzYXJyYXkoZi5yZXNpemUoKDgzMiUyQyUyMDQ4MCkpKSUyMGZvciUyMGYlMjBpbiUyMGNvbnRleHRfZnJhbWVzJTVEKS5hc3R5cGUoJTIyZmxvYXQzMiUyMiklMjAlMkYlMjAyNTUuMCUwQSUyMyUyMG5wLnN0YWNrJTIwZ2l2ZXMlMjAoVCUyQyUyMEglMkMlMjBXJTJDJTIwQyklMjAlM0QlMjAoOSUyQyUyMDQ4MCUyQyUyMDgzMiUyQyUyMDMpJTIwJUUyJTg2JTkyJTIwcGVybXV0ZSUyMHRvJTIwKFQlMkMlMjBDJTJDJTIwSCUyQyUyMFcpJTIwdGhlbiUyMGFkZCUyMGJhdGNoLiUwQWNvbnRleHRfdGVuc29yJTIwJTNEJTIwdG9yY2guZnJvbV9udW1weShhcnIpLnBlcm11dGUoMCUyQyUyMDMlMkMlMjAxJTJDJTIwMikudW5zcXVlZXplKDApLnRvKCUyMmN1ZGElMjIpJTIwJTIwJTIzJTIwKDElMkMlMjA5JTJDJTIwMyUyQyUyMDQ4MCUyQyUyMDgzMiklMEElMEF2aWRlbyUyMCUzRCUyMHBpcGUoJTBBJTIwJTIwJTIwJTIwcHJvbXB0JTNEJTIyY29udGludWUlMjB0aGUlMjBzdG9yeSUyMiUyQyUwQSUyMCUyMCUyMCUyMHZpZGVvJTNEY29udGV4dF90ZW5zb3IlMkMlMEElMjAlMjAlMjAlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNENCUyQyUwQSUyMCUyMCUyMCUyMG51bV9mcmFtZXMlM0Q4MSUyQyUwQSUyMCUyMCUyMCUyMCUyMyUyME92ZXJyaWRlJTIwY2h1bmtfcGFydGl0aW9uJTIwc28lMjB0aGUlMjBmaXJzdCUyMGNodW5rJTIwY292ZXJzJTIwZXhhY3RseSUyMHRoZSUyMDMlMjBsYXRlbnQlMjBjb250ZXh0JTIwZnJhbWVzLiUwQSUyMCUyMCUyMCUyMGNodW5rX3BhcnRpdGlvbiUzRCU1QjMlMkMlMjAzJTJDJTIwMyUyQyUyMDMlMkMlMjAzJTJDJTIwMyUyQyUyMDMlNUQlMkMlMEEpLmZyYW1lcyU1QjAlNUQlMEFleHBvcnRfdG9fdmlkZW8odmlkZW8lMkMlMjAlMjJvdXQubXA0JTIyJTJDJTIwZnBzJTNEMTYp",highlighted:`<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AnyFlowFARPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video, load_video

pipe = AnyFlowFARPipeline.from_pretrained(
    <span class="hljs-string">&quot;nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># Context clip — 9 raw frames map to 3 latent frames (9 = 4·2 + 1, 3 = 2 + 1).</span>
context_frames = load_video(<span class="hljs-string">&quot;path/to/context.mp4&quot;</span>)[:<span class="hljs-number">9</span>]
arr = np.stack([np.asarray(f.resize((<span class="hljs-number">832</span>, <span class="hljs-number">480</span>))) <span class="hljs-keyword">for</span> f <span class="hljs-keyword">in</span> context_frames]).astype(<span class="hljs-string">&quot;float32&quot;</span>) / <span class="hljs-number">255.0</span>
<span class="hljs-comment"># np.stack gives (T, H, W, C) = (9, 480, 832, 3) → permute to (T, C, H, W) then add batch.</span>
context_tensor = torch.from_numpy(arr).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>).unsqueeze(<span class="hljs-number">0</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)  <span class="hljs-comment"># (1, 9, 3, 480, 832)</span>

video = pipe(
    prompt=<span class="hljs-string">&quot;continue the story&quot;</span>,
    video=context_tensor,
    num_inference_steps=<span class="hljs-number">4</span>,
    num_frames=<span class="hljs-number">81</span>,
    <span class="hljs-comment"># Override chunk_partition so the first chunk covers exactly the 3 latent context frames.</span>
    chunk_partition=[<span class="hljs-number">3</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>],
).frames[<span class="hljs-number">0</span>]
export_to_video(video, <span class="hljs-string">&quot;out.mp4&quot;</span>, fps=<span class="hljs-number">16</span>)`,lang:"py",wrap:!1}}),{c(){g(n.$$.fragment)},l(t){y(n.$$.fragment,t)},m(t,u){_(n,t,u),r=!0},p:K,i(t){r||(w(n.$$.fragment,t),r=!0)},o(t){M(n.$$.fragment,t),r=!1},d(t){v(n,t)}}}function ls(A){let n,r,t,u,c,d;return n=new Xe({props:{id:"anyflow-far",option:"t2v",$$slots:{default:[ss]},$$scope:{ctx:A}}}),t=new Xe({props:{id:"anyflow-far",option:"i2v",$$slots:{default:[os]},$$scope:{ctx:A}}}),c=new Xe({props:{id:"anyflow-far",option:"v2v",$$slots:{default:[as]},$$scope:{ctx:A}}}),{c(){g(n.$$.fragment),r=a(),g(t.$$.fragment),u=a(),g(c.$$.fragment)},l(m){y(n.$$.fragment,m),r=l(m),y(t.$$.fragment,m),u=l(m),y(c.$$.fragment,m)},m(m,$){_(n,m,$),p(m,r,$),_(t,m,$),p(m,u,$),_(c,m,$),d=!0},p(m,$){const te={};$&2&&(te.$$scope={dirty:$,ctx:m}),n.$set(te);const W={};$&2&&(W.$$scope={dirty:$,ctx:m}),t.$set(W);const ne={};$&2&&(ne.$$scope={dirty:$,ctx:m}),c.$set(ne)},i(m){d||(w(n.$$.fragment,m),w(t.$$.fragment,m),w(c.$$.fragment,m),d=!0)},o(m){M(n.$$.fragment,m),M(t.$$.fragment,m),M(c.$$.fragment,m),d=!1},d(m){m&&(o(r),o(u)),v(n,m),v(t,m),v(c,m)}}}function is(A){let n,r="Examples:",t,u,c;return u=new ee({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> AnyFlowPipeline
<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 = AnyFlowPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A red panda eating bamboo in a forest, cinematic lighting&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>video = pipe(prompt, num_inference_steps=<span class="hljs-number">4</span>, num_frames=<span class="hljs-number">33</span>).frames[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>export_to_video(video, <span class="hljs-string">&quot;anyflow_t2v.mp4&quot;</span>, fps=<span class="hljs-number">16</span>)`,lang:"python",wrap:!1}}),{c(){n=f("p"),n.textContent=r,t=a(),g(u.$$.fragment)},l(d){n=h(d,"P",{"data-svelte-h":!0}),b(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=l(d),y(u.$$.fragment,d)},m(d,m){p(d,n,m),p(d,t,m),_(u,d,m),c=!0},p:K,i(d){c||(w(u.$$.fragment,d),c=!0)},o(d){M(u.$$.fragment,d),c=!1},d(d){d&&(o(n),o(t)),v(u,d)}}}function rs(A){let n,r="Examples:",t,u,c;return u=new ee({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<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> AnyFlowFARPipeline
<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, load_image

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AnyFlowFARPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers&quot;</span>, torch_dtype=torch.bfloat16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Single-frame I2V: wrap the conditioning image as a (1, 1, 3, H, W) tensor in [0, 1].</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>first_frame = load_image(<span class="hljs-string">&quot;path/to/first_frame.png&quot;</span>).resize((<span class="hljs-number">832</span>, <span class="hljs-number">480</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>arr = np.asarray(first_frame).astype(<span class="hljs-string">&quot;float32&quot;</span>) / <span class="hljs-number">255.0</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>context = torch.from_numpy(arr).permute(<span class="hljs-number">2</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>).unsqueeze(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">1</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>video = pipe(
<span class="hljs-meta">... </span>    prompt=<span class="hljs-string">&quot;a cat walks across a sunlit lawn&quot;</span>,
<span class="hljs-meta">... </span>    video=context,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span>    num_frames=<span class="hljs-number">81</span>,
<span class="hljs-meta">... </span>).frames[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>export_to_video(video, <span class="hljs-string">&quot;anyflow_far.mp4&quot;</span>, fps=<span class="hljs-number">16</span>)`,lang:"python",wrap:!1}}),{c(){n=f("p"),n.textContent=r,t=a(),g(u.$$.fragment)},l(d){n=h(d,"P",{"data-svelte-h":!0}),b(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=l(d),y(u.$$.fragment,d)},m(d,m){p(d,n,m),p(d,t,m),_(u,d,m),c=!0},p:K,i(d){c||(w(u.$$.fragment,d),c=!0)},o(d){M(u.$$.fragment,d),c=!1},d(d){d&&(o(n),o(t)),v(u,d)}}}function ps(A){let n,r,t,u,c,d,m,$='<div class="flex flex-wrap space-x-1"><a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py"><img alt="LoRA" src="https://img.shields.io/badge/LoRA-supported-green"/></a></div>',te,W,ne,se,pn='<a href="https://huggingface.co/papers/2605.13724" rel="nofollow">AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation</a> by Yuchao Gu, Guian Fang and collaborators at <a href="https://sites.google.com/view/showlab" rel="nofollow">NUS ShowLab</a> in collaboration with NVIDIA.',Oe,oe,cn="<em>Few-step video generation has been significantly advanced by consistency models. However, their performance often degrades in any-step video diffusion models due to the fixed-point formulation. To address this limitation, we present AnyFlow, the first any-step video diffusion distillation framework built on flow maps. Instead of learning only the mapping z_t → z_0, AnyFlow learns transitions z_t → z_r over arbitrary time intervals, enabling a single model to adapt to different inference budgets. We design an improved forward flow map training recipe that fine-tunes pretrained video diffusion models into flow map models, and introduce Flow Map Backward Simulation to enable on-policy distillation for flow map models. Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B, on text-to-video and image-to-video tasks demonstrate that AnyFlow outperforms consistency-based baselines while preserving high fidelity and flexible sampling under varying step budgets.</em>",Ke,ae,dn='The original training code is at <a href="https://github.com/NVlabs/AnyFlow" rel="nofollow"><code>NVlabs/AnyFlow</code></a>. The project page is at <a href="https://nvlabs.github.io/AnyFlow" rel="nofollow">nvlabs.github.io/AnyFlow</a>.',et,le,mn="The following AnyFlow checkpoints are supported:",tt,ie,un='<thead><tr><th>Checkpoint</th> <th>Backbone</th> <th>Description</th></tr></thead> <tbody><tr><td><a href="https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers" rel="nofollow"><code>nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers</code></a></td> <td>Wan2.1 1.3B</td> <td>Bidirectional T2V, lightweight</td></tr> <tr><td><a href="https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers" rel="nofollow"><code>nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers</code></a></td> <td>Wan2.1 14B</td> <td>Bidirectional T2V, full quality</td></tr> <tr><td><a href="https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers" rel="nofollow"><code>nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers</code></a></td> <td>FAR + Wan2.1 1.3B</td> <td>Causal T2V / I2V / V2V</td></tr> <tr><td><a href="https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers" rel="nofollow"><code>nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers</code></a></td> <td>FAR + Wan2.1 14B</td> <td>Causal T2V / I2V / V2V</td></tr></tbody>',nt,re,fn='All four are grouped under the <a href="https://huggingface.co/collections/nvidia/anyflow" rel="nofollow"><code>nvidia/anyflow</code></a> Hugging Face collection.',st,N,hn="<p>Choose <code>AnyFlowPipeline</code> for traditional bidirectional text-to-video generation. Choose <code>AnyFlowFARPipeline</code> for streaming I2V, video continuation (V2V), or any setup that benefits from frame-by-frame autoregressive sampling.</p>",ot,z,gn="<p>AnyFlow supports any-step sampling: a single distilled checkpoint can be evaluated at 1, 2, 4, 8, 16… NFE without retraining. Quality scales monotonically with steps in our benchmarks.</p>",at,pe,lt,Q,it,ce,rt,L,pt,de,ct,me,yn=`The causal pipeline selects between T2V / I2V / V2V via the <code>video</code> (or <code>video_latents</code>) argument:
omit both for plain text-to-video, or pass <code>video=&lt;tensor&gt;</code> of shape <code>(B, T, C, H, W)</code> in <code>[0, 1]</code>
with <code>T = 4n + 1</code> to condition on existing frames. Use a single conditioning frame for I2V and a longer
clip for V2V continuation. If you already have pre-encoded latents in the model layout, pass them via
<code>video_latents=&lt;tensor&gt;</code> to skip VAE encoding. <code>video</code> and <code>video_latents</code> are mutually exclusive.`,dt,q,_n=`<p><code>AnyFlowFARPipeline.default_chunk_partition = [1, 3, 3, 3, 3, 3, 3, 2]</code> (sum 21) is matched to the
released checkpoints’ canonical 81 raw frames (21 latent frames at the VAE temporal stride of 4). When
you change <code>num_frames</code>, you must also pass a matching <code>chunk_partition</code> summing to
<code>(num_frames - 1) // 4 + 1</code>, otherwise the pipeline raises an <code>AssertionError</code>.</p>`,mt,D,ut,ue,ft,fe,wn='<li>Classifier-free guidance is fused into the released checkpoints, so inference does not run a second guided forward pass. Keep the default <code>guidance_scale=1.0</code> unless your own checkpoint requires otherwise.</li> <li><code>FlowMapEulerDiscreteScheduler</code> is general-purpose. You can attach it to any flow-map-distilled checkpoint via <code>from_pretrained(..., scheduler=FlowMapEulerDiscreteScheduler.from_config(...))</code>.</li> <li><code>AnyFlowPipeline</code> uses <a href="../models/anyflow_transformer3d"><code>AnyFlowTransformer3DModel</code></a> (bidirectional). <code>AnyFlowFARPipeline</code> uses <a href="../models/anyflow_far_transformer3d"><code>AnyFlowFARTransformer3DModel</code></a>, which adds a compressed-frame patch embedding and the FAR causal block-mask.</li> <li>LoRA loading is supported via <code>WanLoraLoaderMixin</code>, the same mixin used by the upstream Wan pipelines.</li> <li>For training recipes (forward flow-map training and on-policy distillation), refer to the original AnyFlow training framework at <a href="https://github.com/NVlabs/AnyFlow" rel="nofollow"><code>NVlabs/AnyFlow</code></a>; training is out of scope for diffusers.</li>',ht,he,gt,F,ge,Ct,je,Mn=`Bidirectional text-to-video generation pipeline for AnyFlow flow-map-distilled checkpoints, introduced in
<a href="https://huggingface.co/papers/2605.13724" rel="nofollow">AnyFlow</a> by Yuchao Gu, Guian Fang et al.`,Wt,k,Gt,yt,Nn='<span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>z</mi><mi>t</mi></msub><mo>→</mo><msub><mi>z</mi><mi>r</mi></msub></mrow><annotation encoding="application/x-tex">z_t \\to z_r</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.044em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">→</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.044em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.02778em;">r</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span>',_t,wt,zn='<span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>z</mi><mi>t</mi></msub><mo>→</mo><msub><mi>z</mi><mn>0</mn></msub></mrow><annotation encoding="application/x-tex">z_t \\to z_0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.044em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">→</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.044em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span>',Mt,xe,vn="AnyFlowFARPipeline",It,Zt,ke,bn=`Sampling is plain Euler in mean-velocity form (<code>z_r = z_t - (t - r) * u</code>) with no re-noising. The released NVIDIA
checkpoints fold classifier-free guidance into the model weights, so the default <code>guidance_scale=1.0</code> is the
recommended setting.`,Bt,Ce,Tn=`This model inherits from [<em>DiffusionPipeline</em>]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,Rt,G,ye,Vt,We,An="The call function to the pipeline for generation.",Et,S,Pt,H,_e,Nt,Ge,Fn="Encodes the prompt into text encoder hidden states.",zt,I,we,Qt,Ie,Un="Encode a pixel-space video into AnyFlow’s latent layout.",Lt,Ze,Jn=`Mirrors the single-helper convention of other diffusers pipelines (cf.
<code>WanImageToVideoPipeline.encode_image</code>): wraps preprocessing, VAE encoding, and latent normalization into one
call. Output layout is <code>(B, T_latent, C, H, W)</code>, which is what the AnyFlow transformer expects for
conditioning frames.`,vt,Me,bt,T,ve,qt,Be,$n=`Causal (FAR-based) text-to-video / image-to-video / video-to-video pipeline for AnyFlow checkpoints, introduced in
<a href="https://huggingface.co/papers/2605.13724" rel="nofollow">AnyFlow</a> by Yuchao Gu, Guian Fang et al.`,Dt,Re,jn=`The pipeline drives a frame-level autoregressive sampling loop over chunks: each chunk is denoised with flow-map
steps while attending only to past chunks via block-sparse causal attention, and intermediate KV cache is reused
across chunks.`,St,Ve,xn="The task mode (T2V / I2V / V2V) is selected by which conditioning argument is passed to <code>__call__</code>:",Ht,Ee,kn=`<li>both <code>video=None</code> and <code>video_latents=None</code> — pure text-to-video.</li> <li><code>video=&lt;tensor of shape (B, T, C, H, W) in [0, 1] with T = 4n + 1&gt;</code> — pre-VAE conditioning frames; the pipeline
VAE-encodes them. Pass a single-frame video for I2V or a multi-frame clip for V2V.</li> <li><code>video_latents=&lt;latent tensor of shape (B, T_latent, C, H_latent, W_latent)&gt;</code> — already-encoded latents in the
FAR layout (skips the VAE encode step).</li>`,Xt,Pe,Cn=`The FAR backbone is the causal Wan2.1 variant introduced by FAR (Gu et al., 2025; arXiv:2503.19325). Inference is
plain Euler in mean-velocity form per chunk with no re-noising. Joint T2V / I2V / V2V is supported by a single
distilled model.`,Yt,Ne,Wn=`This model inherits from [<em>DiffusionPipeline</em>]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,Ot,Z,be,Kt,ze,Gn="The call function to the pipeline for generation.",en,X,tn,Y,Te,nn,Qe,In="Encodes the prompt into text encoder hidden states.",sn,B,Ae,on,Le,Zn="Encode a pixel-space video into AnyFlow-FAR’s latent layout.",an,qe,Bn="Mirrors the single-helper convention of other diffusers pipelines. Output layout is <code>(B, T_latent, C, H_latent, W_latent)</code> — the per-frame layout the FAR rollout consumes.",Tt,Fe,At,R,Ue,ln,De,Rn="Output class for AnyFlow pipelines.",Ft,Je,Ut,Ye,Jt;return c=new Xn({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),W=new O({props:{title:"AnyFlow",local:"anyflow",headingTag:"h1"}}),pe=new O({props:{title:"Optimizing Memory and Inference Speed",local:"optimizing-memory-and-inference-speed",headingTag:"h3"}}),Q=new rn({props:{id:"optimization",options:["memory","inference speed"],$$slots:{default:[es]},$$scope:{ctx:A}}}),ce=new O({props:{title:"Generation with AnyFlow (Bidirectional T2V)",local:"generation-with-anyflow-bidirectional-t2v",headingTag:"h3"}}),L=new rn({props:{id:"anyflow-bidi",options:["usage"],$$slots:{default:[ns]},$$scope:{ctx:A}}}),de=new O({props:{title:"Generation with AnyFlow (FAR Causal)",local:"generation-with-anyflow-far-causal",headingTag:"h3"}}),D=new rn({props:{id:"anyflow-far",options:["t2v","i2v","v2v"],$$slots:{default:[ls]},$$scope:{ctx:A}}}),ue=new O({props:{title:"Notes",local:"notes",headingTag:"h2"}}),he=new O({props:{title:"AnyFlowPipeline",local:"diffusers.AnyFlowPipeline",headingTag:"h2"}}),ge=new P({props:{name:"class diffusers.AnyFlowPipeline",anchor:"diffusers.AnyFlowPipeline",parameters:[{name:"tokenizer",val:": AutoTokenizer"},{name:"text_encoder",val:": UMT5EncoderModel"},{name:"transformer",val:": AnyFlowTransformer3DModel"},{name:"vae",val:": AutoencoderKLWan"},{name:"scheduler",val:": FlowMapEulerDiscreteScheduler"}],parametersDescription:[{anchor:"diffusers.AnyFlowPipeline.tokenizer",description:`<strong>tokenizer</strong> ([<em>AutoTokenizer</em>]) &#x2014;
Tokenizer from <a href="https://huggingface.co/google/umt5-xxl" rel="nofollow">google/umt5-xxl</a>.`,name:"tokenizer"},{anchor:"diffusers.AnyFlowPipeline.text_encoder",description:`<strong>text_encoder</strong> ([<em>UMT5EncoderModel</em>]) &#x2014;
<a href="https://huggingface.co/google/umt5-xxl" rel="nofollow">google/umt5-xxl</a> text encoder.`,name:"text_encoder"},{anchor:"diffusers.AnyFlowPipeline.transformer",description:`<strong>transformer</strong> ([<em>AnyFlowTransformer3DModel</em>]) &#x2014;
Bidirectional flow-map 3D Transformer.`,name:"transformer"},{anchor:"diffusers.AnyFlowPipeline.vae",description:`<strong>vae</strong> ([<em>AutoencoderKLWan</em>]) &#x2014;
VAE that encodes/decodes videos to and from latent representations.`,name:"vae"},{anchor:"diffusers.AnyFlowPipeline.scheduler",description:`<strong>scheduler</strong> ([<em>FlowMapEulerDiscreteScheduler</em>]) &#x2014;
Flow-map sampler. The pipeline drives <code>scheduler.step(..., timestep, sample, r_timestep)</code> per inference
step.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/pipelines/anyflow/pipeline_anyflow.py#L80"}}),ye=new P({props:{name:"__call__",anchor:"diffusers.AnyFlowPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"video",val:": typing.Optional[torch.Tensor] = None"},{name:"video_latents",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": int = 480"},{name:"width",val:": int = 832"},{name:"num_frames",val:": int = 81"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 1.0"},{name:"num_videos_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'np'"},{name:"return_dict",val:": bool = True"},{name:"attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"callback_on_step_end",val:": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 512"},{name:"use_mean_velocity",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.AnyFlowPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the video generation. If not defined, pass <code>prompt_embeds</code> instead.`,name:"prompt"},{anchor:"diffusers.AnyFlowPipeline.__call__.video",description:`<strong>video</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-VAE conditioning frames of shape <code>(B, T, C, H, W)</code> in <code>[0, 1]</code>. When provided, the pipeline
VAE-encodes them and keeps the corresponding latent prefix fixed during sampling. Mutually exclusive
with <code>video_latents</code>.`,name:"video"},{anchor:"diffusers.AnyFlowPipeline.__call__.video_latents",description:`<strong>video_latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-encoded VAE latents in the AnyFlow layout <code>(B, T_latent, C, H_latent, W_latent)</code>. Skips VAE
encoding on the pipeline side. Mutually exclusive with <code>video</code>.`,name:"video_latents"},{anchor:"diffusers.AnyFlowPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to avoid during video generation. Ignored when not using guidance
(<code>guidance_scale &lt; 1</code>).`,name:"negative_prompt"},{anchor:"diffusers.AnyFlowPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, defaults to <code>480</code>) &#x2014;
The height in pixels of the generated video.`,name:"height"},{anchor:"diffusers.AnyFlowPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, defaults to <code>832</code>) &#x2014;
The width in pixels of the generated video.`,name:"width"},{anchor:"diffusers.AnyFlowPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, defaults to <code>81</code>) &#x2014;
The number of frames in the generated video. Must satisfy <code>(num_frames - 1) % vae_scale_factor_temporal == 0</code>.`,name:"num_frames"},{anchor:"diffusers.AnyFlowPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, defaults to <code>50</code>) &#x2014;
The number of denoising steps. Distilled AnyFlow checkpoints support any-step sampling, so values as
low as <code>1</code>, <code>2</code>, <code>4</code>, or <code>8</code> are typical.`,name:"num_inference_steps"},{anchor:"diffusers.AnyFlowPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, defaults to <code>1.0</code>) &#x2014;
Classifier-free guidance scale. The released AnyFlow checkpoints fuse CFG into the weights during
training; keep at <code>1.0</code> unless you know your checkpoint expects otherwise.`,name:"guidance_scale"},{anchor:"diffusers.AnyFlowPipeline.__call__.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1</code>) &#x2014;
The number of videos to generate per prompt.`,name:"num_videos_per_prompt"},{anchor:"diffusers.AnyFlowPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.AnyFlowPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents to use as inputs. If not provided, latents are sampled from the supplied
<code>generator</code>.`,name:"latents"},{anchor:"diffusers.AnyFlowPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to tweak text inputs (e.g., prompt weighting). If not
provided, embeddings are generated from <code>prompt</code>.`,name:"prompt_embeds"},{anchor:"diffusers.AnyFlowPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AnyFlowPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;np&quot;</code>) &#x2014;
The output format. One of <code>&quot;pil&quot;</code>, <code>&quot;np&quot;</code>, <code>&quot;pt&quot;</code>, or <code>&quot;latent&quot;</code>.`,name:"output_type"},{anchor:"diffusers.AnyFlowPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to return an <code>AnyFlowPipelineOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.AnyFlowPipeline.__call__.attention_kwargs",description:`<strong>attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow">diffusers.models.attention_processor</a>.`,name:"attention_kwargs"},{anchor:"diffusers.AnyFlowPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function or <code>PipelineCallback</code> called at the end of each inference step. See
<a href="../callbacks"><code>callbacks</code></a> for details.`,name:"callback_on_step_end"},{anchor:"diffusers.AnyFlowPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List[str]</code>, <em>optional</em>, defaults to <code>[&quot;latents&quot;]</code>) &#x2014;
The tensor inputs forwarded to the callback. Must be a subset of <code>self._callback_tensor_inputs</code>.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.AnyFlowPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, defaults to <code>512</code>) &#x2014;
The maximum text-encoder sequence length. Longer prompts are truncated.`,name:"max_sequence_length"},{anchor:"diffusers.AnyFlowPipeline.__call__.use_mean_velocity",description:`<strong>use_mean_velocity</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
When <code>True</code>, the flow-map model is conditioned on both the source timestep <code>t</code> and the target timestep
<code>r</code> to predict a mean velocity, matching the training-time behavior. Disable to mirror raw Euler
stepping (<code>r = t</code>).`,name:"use_mean_velocity"}],source:"https://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/pipelines/anyflow/pipeline_anyflow.py#L379",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <code>AnyFlowPipelineOutput</code> is returned, otherwise a <code>tuple</code> whose first
element is the generated video.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~AnyFlowPipelineOutput</code> or <code>tuple</code></p>
`}}),S=new Pn({props:{anchor:"diffusers.AnyFlowPipeline.__call__.example",$$slots:{default:[is]},$$scope:{ctx:A}}}),_e=new P({props:{name:"encode_prompt",anchor:"diffusers.AnyFlowPipeline.encode_prompt",parameters:[{name:"prompt",val:": str | list[str]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"do_classifier_free_guidance",val:": bool = True"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"prompt_embeds",val:": torch.Tensor | None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor | None = None"},{name:"max_sequence_length",val:": int = 226"},{name:"device",val:": torch.device | None = None"},{name:"dtype",val:": torch.dtype | None = None"}],parametersDescription:[{anchor:"diffusers.AnyFlowPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
prompt to be encoded`,name:"prompt"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use classifier free guidance or not.`,name:"do_classifier_free_guidance"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on`,name:"num_videos_per_prompt"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>, <em>optional</em>):
torch device`,name:"device"},{anchor:"diffusers.AnyFlowPipeline.encode_prompt.dtype",description:`<strong>dtype</strong> &#x2014; (<code>torch.dtype</code>, <em>optional</em>):
torch dtype`,name:"dtype"}],source:"https://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/pipelines/anyflow/pipeline_anyflow.py#L179"}}),we=new P({props:{name:"encode_video",anchor:"diffusers.AnyFlowPipeline.encode_video",parameters:[{name:"video",val:": Tensor"},{name:"height",val:": int"},{name:"width",val:": int"}],source:"https://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/pipelines/anyflow/pipeline_anyflow.py#L359"}}),Me=new O({props:{title:"AnyFlowFARPipeline",local:"diffusers.AnyFlowFARPipeline",headingTag:"h2"}}),ve=new P({props:{name:"class diffusers.AnyFlowFARPipeline",anchor:"diffusers.AnyFlowFARPipeline",parameters:[{name:"tokenizer",val:": AutoTokenizer"},{name:"text_encoder",val:": UMT5EncoderModel"},{name:"transformer",val:": AnyFlowFARTransformer3DModel"},{name:"vae",val:": AutoencoderKLWan"},{name:"scheduler",val:": FlowMapEulerDiscreteScheduler"}],parametersDescription:[{anchor:"diffusers.AnyFlowFARPipeline.tokenizer",description:`<strong>tokenizer</strong> ([<em>AutoTokenizer</em>]) &#x2014;
Tokenizer from <a href="https://huggingface.co/google/umt5-xxl" rel="nofollow">google/umt5-xxl</a>.`,name:"tokenizer"},{anchor:"diffusers.AnyFlowFARPipeline.text_encoder",description:`<strong>text_encoder</strong> ([<em>UMT5EncoderModel</em>]) &#x2014;
<a href="https://huggingface.co/google/umt5-xxl" rel="nofollow">google/umt5-xxl</a> text encoder.`,name:"text_encoder"},{anchor:"diffusers.AnyFlowFARPipeline.transformer",description:`<strong>transformer</strong> ([<em>AnyFlowFARTransformer3DModel</em>]) &#x2014;
FAR causal flow-map 3D Transformer.`,name:"transformer"},{anchor:"diffusers.AnyFlowFARPipeline.vae",description:`<strong>vae</strong> ([<em>AutoencoderKLWan</em>]) &#x2014;
VAE that encodes/decodes videos to and from latent representations.`,name:"vae"},{anchor:"diffusers.AnyFlowFARPipeline.scheduler",description:`<strong>scheduler</strong> ([<em>FlowMapEulerDiscreteScheduler</em>]) &#x2014;
Flow-map sampler.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/pipelines/anyflow/pipeline_anyflow_far.py#L92"}}),be=new P({props:{name:"__call__",anchor:"diffusers.AnyFlowFARPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"video",val:": typing.Optional[torch.Tensor] = None"},{name:"video_latents",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": int = 480"},{name:"width",val:": int = 832"},{name:"num_frames",val:": int = 81"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 1.0"},{name:"num_videos_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'np'"},{name:"return_dict",val:": bool = True"},{name:"attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"callback_on_step_end",val:": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 512"},{name:"use_mean_velocity",val:": bool = True"},{name:"use_kv_cache",val:": bool = True"},{name:"chunk_partition",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"diffusers.AnyFlowFARPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the video generation. If not defined, pass <code>prompt_embeds</code> instead.`,name:"prompt"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.video",description:`<strong>video</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-VAE conditioning frames of shape <code>(B, T, C, H, W)</code> in <code>[0, 1]</code> (<code>T = 4n + 1</code>). When provided, the
pipeline VAE-encodes them and keeps the corresponding latent prefix fixed during sampling. Mutually
exclusive with <code>video_latents</code>.`,name:"video"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.video_latents",description:`<strong>video_latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-encoded VAE latents in the FAR layout <code>(B, T_latent, C, H_latent, W_latent)</code>. Skips VAE encoding on
the pipeline side. Mutually exclusive with <code>video</code>.`,name:"video_latents"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to avoid during video generation. Ignored when not using guidance
(<code>guidance_scale &lt; 1</code>).`,name:"negative_prompt"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, defaults to <code>480</code>) &#x2014;
The height in pixels of the generated video.`,name:"height"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, defaults to <code>832</code>) &#x2014;
The width in pixels of the generated video.`,name:"width"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, defaults to <code>81</code>) &#x2014;
The number of frames in the generated video. Must satisfy <code>(num_frames - 1) % vae_scale_factor_temporal == 0</code>.`,name:"num_frames"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, defaults to <code>50</code>) &#x2014;
The number of denoising steps per chunk. Distilled AnyFlow-FAR checkpoints support any-step sampling
(1, 2, 4, 8, &#x2026;).`,name:"num_inference_steps"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, defaults to <code>1.0</code>) &#x2014;
Classifier-free guidance scale. The released AnyFlow checkpoints fuse CFG into the weights during
training; keep at <code>1.0</code> unless the checkpoint requires otherwise.`,name:"guidance_scale"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1</code>) &#x2014;
The number of videos to generate per prompt.`,name:"num_videos_per_prompt"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
Generator used to seed sampling.`,name:"generator"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents. If not provided, latents are sampled from the supplied <code>generator</code>.`,name:"latents"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. If not provided, embeddings are generated from <code>prompt</code>.`,name:"prompt_embeds"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;np&quot;</code>) &#x2014;
Output format. One of <code>&quot;pil&quot;</code>, <code>&quot;np&quot;</code>, <code>&quot;pt&quot;</code>, or <code>&quot;latent&quot;</code>.`,name:"output_type"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to return an <code>AnyFlowPipelineOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.attention_kwargs",description:`<strong>attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow">diffusers.models.attention_processor</a>.`,name:"attention_kwargs"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
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Tensor inputs forwarded to the callback. Must be a subset of <code>self._callback_tensor_inputs</code>.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.AnyFlowFARPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, defaults to <code>512</code>) &#x2014;
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<p>If <code>return_dict</code> is <code>True</code>, an <code>AnyFlowPipelineOutput</code> is returned, otherwise a <code>tuple</code> whose first
element is the generated video.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~AnyFlowPipelineOutput</code> or <code>tuple</code></p>
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