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hf-doc-build/doc-dev / diffusers /pr_13171 /en /_app /immutable /nodes /163.7f090f4a.js

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	import{s as an,o as ln,n as Wt}from"../chunks/scheduler.53228c21.js";import{S as on,i as rn,e as r,s,c as m,h as pn,a as p,d as t,b as a,f as Z,g as u,j as M,k as U,l as o,m as i,n as h,t as g,o as f,p as _}from"../chunks/index.100fac89.js";import{D as G}from"../chunks/Docstring.2d2c05b8.js";import{C as Ce}from"../chunks/CodeBlock.d30a6509.js";import{E as kt}from"../chunks/ExampleCodeBlock.a6b08f6c.js";import{H as Te,E as dn}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.ae0a5c00.js";function cn(R){let d,j="Examples:",y,c,T;return c=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LTX2Pipeline
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video

	<span class="hljs-meta">>>> </span>pipe = LTX2Pipeline.from_pretrained(<span class="hljs-string">"Lightricks/LTX-2"</span>, torch_dtype=torch.bfloat16)
	<span class="hljs-meta">>>> </span>pipe.enable_model_cpu_offload()

	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"</span>
	<span class="hljs-meta">>>> </span>negative_prompt = <span class="hljs-string">"worst quality, inconsistent motion, blurry, jittery, distorted"</span>

	<span class="hljs-meta">>>> </span>frame_rate = <span class="hljs-number">24.0</span>
	<span class="hljs-meta">>>> </span>video, audio = pipe(
	<span class="hljs-meta">... </span> prompt=prompt,
	<span class="hljs-meta">... </span> negative_prompt=negative_prompt,
	<span class="hljs-meta">... </span> width=<span class="hljs-number">768</span>,
	<span class="hljs-meta">... </span> height=<span class="hljs-number">512</span>,
	<span class="hljs-meta">... </span> num_frames=<span class="hljs-number">121</span>,
	<span class="hljs-meta">... </span> frame_rate=frame_rate,
	<span class="hljs-meta">... </span> num_inference_steps=<span class="hljs-number">40</span>,
	<span class="hljs-meta">... </span> guidance_scale=<span class="hljs-number">4.0</span>,
	<span class="hljs-meta">... </span> output_type=<span class="hljs-string">"np"</span>,
	<span class="hljs-meta">... </span> return_dict=<span class="hljs-literal">False</span>,
	<span class="hljs-meta">... </span>)

	<span class="hljs-meta">>>> </span>encode_video(
	<span class="hljs-meta">... </span> video[<span class="hljs-number">0</span>],
	<span class="hljs-meta">... </span> fps=frame_rate,
	<span class="hljs-meta">... </span> audio=audio[<span class="hljs-number">0</span>].<span class="hljs-built_in">float</span>().cpu(),
	<span class="hljs-meta">... </span> audio_sample_rate=pipe.vocoder.config.output_sampling_rate, <span class="hljs-comment"># should be 24000</span>
	<span class="hljs-meta">... </span> output_path=<span class="hljs-string">"video.mp4"</span>,
	<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){d=r("p"),d.textContent=j,y=s(),m(c.$$.fragment)},l(l){d=p(l,"P",{"data-svelte-h":!0}),M(d)!=="svelte-kvfsh7"&&(d.textContent=j),y=a(l),u(c.$$.fragment,l)},m(l,w){i(l,d,w),i(l,y,w),h(c,l,w),T=!0},p:Wt,i(l){T\|\|(g(c.$$.fragment,l),T=!0)},o(l){f(c.$$.fragment,l),T=!1},d(l){l&&(t(d),t(y)),_(c,l)}}}function mn(R){let d,j="Examples:",y,c,T;return c=new Ce({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwTFRYMkltYWdlVG9WaWRlb1BpcGVsaW5lJTBBZnJvbSUyMGRpZmZ1c2Vycy5waXBlbGluZXMubHR4Mi5leHBvcnRfdXRpbHMlMjBpbXBvcnQlMjBlbmNvZGVfdmlkZW8lMEFmcm9tJTIwZGlmZnVzZXJzLnV0aWxzJTIwaW1wb3J0JTIwbG9hZF9pbWFnZSUwQSUwQXBpcGUlMjAlM0QlMjBMVFgySW1hZ2VUb1ZpZGVvUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMkxpZ2h0cmlja3MlMkZMVFgtMiUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guYmZsb2F0MTYpJTBBcGlwZS5lbmFibGVfbW9kZWxfY3B1X29mZmxvYWQoKSUwQSUwQWltYWdlJTIwJTNEJTIwbG9hZF9pbWFnZSglMEElMjAlMjAlMjAlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZhLXItci1vLXclMkZ0aW55LW1lbWUtZGF0YXNldC1jYXB0aW9uZWQlMkZyZXNvbHZlJTJGbWFpbiUyRmltYWdlcyUyRjgucG5nJTIyJTBBKSUwQXByb21wdCUyMCUzRCUyMCUyMkElMjB5b3VuZyUyMGdpcmwlMjBzdGFuZHMlMjBjYWxtbHklMjBpbiUyMHRoZSUyMGZvcmVncm91bmQlMkMlMjBsb29raW5nJTIwZGlyZWN0bHklMjBhdCUyMHRoZSUyMGNhbWVyYSUyQyUyMGFzJTIwYSUyMGhvdXNlJTIwZmlyZSUyMHJhZ2VzJTIwaW4lMjB0aGUlMjBiYWNrZ3JvdW5kLiUyMiUwQW5lZ2F0aXZlX3Byb21wdCUyMCUzRCUyMCUyMndvcnN0JTIwcXVhbGl0eSUyQyUyMGluY29uc2lzdGVudCUyMG1vdGlvbiUyQyUyMGJsdXJyeSUyQyUyMGppdHRlcnklMkMlMjBkaXN0b3J0ZWQlMjIlMEElMEFmcmFtZV9yYXRlJTIwJTNEJTIwMjQuMCUwQXZpZGVvJTJDJTIwYXVkaW8lMjAlM0QlMjBwaXBlKCUwQSUyMCUyMCUyMCUyMGltYWdlJTNEaW1hZ2UlMkMlMEElMjAlMjAlMjAlMjBwcm9tcHQlM0Rwcm9tcHQlMkMlMEElMjAlMjAlMjAlMjBuZWdhdGl2ZV9wcm9tcHQlM0RuZWdhdGl2ZV9wcm9tcHQlMkMlMEElMjAlMjAlMjAlMjB3aWR0aCUzRDc2OCUyQyUwQSUyMCUyMCUyMCUyMGhlaWdodCUzRDUxMiUyQyUwQSUyMCUyMCUyMCUyMG51bV9mcmFtZXMlM0QxMjElMkMlMEElMjAlMjAlMjAlMjBmcmFtZV9yYXRlJTNEZnJhbWVfcmF0ZSUyQyUwQSUyMCUyMCUyMCUyMG51bV9pbmZlcmVuY2Vfc3RlcHMlM0Q0MCUyQyUwQSUyMCUyMCUyMCUyMGd1aWRhbmNlX3NjYWxlJTNENC4wJTJDJTBBJTIwJTIwJTIwJTIwb3V0cHV0X3R5cGUlM0QlMjJucCUyMiUyQyUwQSUyMCUyMCUyMCUyMHJldHVybl9kaWN0JTNERmFsc2UlMkMlMEEpJTBBJTBBZW5jb2RlX3ZpZGVvKCUwQSUyMCUyMCUyMCUyMHZpZGVvJTVCMCU1RCUyQyUwQSUyMCUyMCUyMCUyMGZwcyUzRGZyYW1lX3JhdGUlMkMlMEElMjAlMjAlMjAlMjBhdWRpbyUzRGF1ZGlvJTVCMCU1RC5mbG9hdCgpLmNwdSgpJTJDJTBBJTIwJTIwJTIwJTIwYXVkaW9fc2FtcGxlX3JhdGUlM0RwaXBlLnZvY29kZXIuY29uZmlnLm91dHB1dF9zYW1wbGluZ19yYXRlJTJDJTIwJTIwJTIzJTIwc2hvdWxkJTIwYmUlMjAyNDAwMCUwQSUyMCUyMCUyMCUyMG91dHB1dF9wYXRoJTNEJTIydmlkZW8ubXA0JTIyJTJDJTBBKQ==",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LTX2ImageToVideoPipeline
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

	<span class="hljs-meta">>>> </span>pipe = LTX2ImageToVideoPipeline.from_pretrained(<span class="hljs-string">"Lightricks/LTX-2"</span>, torch_dtype=torch.bfloat16)
	<span class="hljs-meta">>>> </span>pipe.enable_model_cpu_offload()

	<span class="hljs-meta">>>> </span>image = load_image(
	<span class="hljs-meta">... </span> <span class="hljs-string">"https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"</span>
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background."</span>
	<span class="hljs-meta">>>> </span>negative_prompt = <span class="hljs-string">"worst quality, inconsistent motion, blurry, jittery, distorted"</span>

	<span class="hljs-meta">>>> </span>frame_rate = <span class="hljs-number">24.0</span>
	<span class="hljs-meta">>>> </span>video, audio = pipe(
	<span class="hljs-meta">... </span> image=image,
	<span class="hljs-meta">... </span> prompt=prompt,
	<span class="hljs-meta">... </span> negative_prompt=negative_prompt,
	<span class="hljs-meta">... </span> width=<span class="hljs-number">768</span>,
	<span class="hljs-meta">... </span> height=<span class="hljs-number">512</span>,
	<span class="hljs-meta">... </span> num_frames=<span class="hljs-number">121</span>,
	<span class="hljs-meta">... </span> frame_rate=frame_rate,
	<span class="hljs-meta">... </span> num_inference_steps=<span class="hljs-number">40</span>,
	<span class="hljs-meta">... </span> guidance_scale=<span class="hljs-number">4.0</span>,
	<span class="hljs-meta">... </span> output_type=<span class="hljs-string">"np"</span>,
	<span class="hljs-meta">... </span> return_dict=<span class="hljs-literal">False</span>,
	<span class="hljs-meta">... </span>)

	<span class="hljs-meta">>>> </span>encode_video(
	<span class="hljs-meta">... </span> video[<span class="hljs-number">0</span>],
	<span class="hljs-meta">... </span> fps=frame_rate,
	<span class="hljs-meta">... </span> audio=audio[<span class="hljs-number">0</span>].<span class="hljs-built_in">float</span>().cpu(),
	<span class="hljs-meta">... </span> audio_sample_rate=pipe.vocoder.config.output_sampling_rate, <span class="hljs-comment"># should be 24000</span>
	<span class="hljs-meta">... </span> output_path=<span class="hljs-string">"video.mp4"</span>,
	<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){d=r("p"),d.textContent=j,y=s(),m(c.$$.fragment)},l(l){d=p(l,"P",{"data-svelte-h":!0}),M(d)!=="svelte-kvfsh7"&&(d.textContent=j),y=a(l),u(c.$$.fragment,l)},m(l,w){i(l,d,w),i(l,y,w),h(c,l,w),T=!0},p:Wt,i(l){T\|\|(g(c.$$.fragment,l),T=!0)},o(l){f(c.$$.fragment,l),T=!1},d(l){l&&(t(d),t(y)),_(c,l)}}}function un(R){let d,j="Examples:",y,c,T;return c=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LTX2ImageToVideoPipeline, LTX2LatentUpsamplePipeline
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.latent_upsampler <span class="hljs-keyword">import</span> LTX2LatentUpsamplerModel
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

	<span class="hljs-meta">>>> </span>pipe = LTX2ImageToVideoPipeline.from_pretrained(<span class="hljs-string">"Lightricks/LTX-2"</span>, torch_dtype=torch.bfloat16)
	<span class="hljs-meta">>>> </span>pipe.enable_model_cpu_offload()

	<span class="hljs-meta">>>> </span>image = load_image(
	<span class="hljs-meta">... </span> <span class="hljs-string">"https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"</span>
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background."</span>
	<span class="hljs-meta">>>> </span>negative_prompt = <span class="hljs-string">"worst quality, inconsistent motion, blurry, jittery, distorted"</span>

	<span class="hljs-meta">>>> </span>frame_rate = <span class="hljs-number">24.0</span>
	<span class="hljs-meta">>>> </span>video, audio = pipe(
	<span class="hljs-meta">... </span> image=image,
	<span class="hljs-meta">... </span> prompt=prompt,
	<span class="hljs-meta">... </span> negative_prompt=negative_prompt,
	<span class="hljs-meta">... </span> width=<span class="hljs-number">768</span>,
	<span class="hljs-meta">... </span> height=<span class="hljs-number">512</span>,
	<span class="hljs-meta">... </span> num_frames=<span class="hljs-number">121</span>,
	<span class="hljs-meta">... </span> frame_rate=frame_rate,
	<span class="hljs-meta">... </span> num_inference_steps=<span class="hljs-number">40</span>,
	<span class="hljs-meta">... </span> guidance_scale=<span class="hljs-number">4.0</span>,
	<span class="hljs-meta">... </span> output_type=<span class="hljs-string">"pil"</span>,
	<span class="hljs-meta">... </span> return_dict=<span class="hljs-literal">False</span>,
	<span class="hljs-meta">... </span>)

	<span class="hljs-meta">>>> </span>latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
	<span class="hljs-meta">... </span> <span class="hljs-string">"Lightricks/LTX-2"</span>, subfolder=<span class="hljs-string">"latent_upsampler"</span>, torch_dtype=torch.bfloat16
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler)
	<span class="hljs-meta">>>> </span>upsample_pipe.vae.enable_tiling()
	<span class="hljs-meta">>>> </span>upsample_pipe.to(device=<span class="hljs-string">"cuda"</span>, dtype=torch.bfloat16)

	<span class="hljs-meta">>>> </span>video = upsample_pipe(
	<span class="hljs-meta">... </span> video=video,
	<span class="hljs-meta">... </span> width=<span class="hljs-number">768</span>,
	<span class="hljs-meta">... </span> height=<span class="hljs-number">512</span>,
	<span class="hljs-meta">... </span> output_type=<span class="hljs-string">"np"</span>,
	<span class="hljs-meta">... </span> return_dict=<span class="hljs-literal">False</span>,
	<span class="hljs-meta">... </span>)[<span class="hljs-number">0</span>]

	<span class="hljs-meta">>>> </span>encode_video(
	<span class="hljs-meta">... </span> video[<span class="hljs-number">0</span>],
	<span class="hljs-meta">... </span> fps=frame_rate,
	<span class="hljs-meta">... </span> audio=audio[<span class="hljs-number">0</span>].<span class="hljs-built_in">float</span>().cpu(),
	<span class="hljs-meta">... </span> audio_sample_rate=pipe.vocoder.config.output_sampling_rate, <span class="hljs-comment"># should be 24000</span>
	<span class="hljs-meta">... </span> output_path=<span class="hljs-string">"video.mp4"</span>,
	<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){d=r("p"),d.textContent=j,y=s(),m(c.$$.fragment)},l(l){d=p(l,"P",{"data-svelte-h":!0}),M(d)!=="svelte-kvfsh7"&&(d.textContent=j),y=a(l),u(c.$$.fragment,l)},m(l,w){i(l,d,w),i(l,y,w),h(c,l,w),T=!0},p:Wt,i(l){T\|\|(g(c.$$.fragment,l),T=!0)},o(l){f(c.$$.fragment,l),T=!1},d(l){l&&(t(d),t(y)),_(c,l)}}}function hn(R){let d,j,y,c,T,l,w,Lt='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',Ne,Y,Rt="LTX-2 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution.",Ee,H,Ct='You can find all the original LTX-Video checkpoints under the <a href="https://huggingface.co/Lightricks" rel="nofollow">Lightricks</a> organization.',Fe,$,Nt='The original codebase for LTX-2 can be found <a href="https://github.com/Lightricks/LTX-2" rel="nofollow">here</a>.',Qe,P,Ae,z,Et="Recommended pipeline to achieve production quality generation, this pipeline is composed of two stages:",Se,D,Ft="<li>Stage 1: Generate a video at the target resolution using diffusion sampling with classifier-free guidance (CFG). This stage produces a coherent low-noise video sequence that respects the text/image conditioning.</li> <li>Stage 2: Upsample the Stage 1 output by 2 and refine details using a distilled LoRA model to improve fidelity and visual quality. Stage 2 may apply lighter CFG to preserve the structure from Stage 1 while enhancing texture and sharpness.</li>",Ye,q,Qt="Sample usage of text-to-video two stages pipeline",He,K,$e,O,Pe,ee,At="Fastest two-stages generation pipeline using a distilled checkpoint.",ze,te,De,ne,qe,J,se,dt,ye,St="Pipeline for text-to-video generation.",ct,we,Yt='Reference: <a href="https://github.com/Lightricks/LTX-Video" rel="nofollow">https://github.com/Lightricks/LTX-Video</a>',mt,x,ae,ut,be,Ht="Function invoked when calling the pipeline for generation.",ht,C,gt,N,le,ft,Je,$t="Encodes the prompt into text encoder hidden states.",Ke,oe,Oe,b,ie,_t,Ue,Pt="Pipeline for image-to-video generation.",Mt,je,zt='Reference: <a href="https://github.com/Lightricks/LTX-Video" rel="nofollow">https://github.com/Lightricks/LTX-Video</a>',Tt,ve,Dt="TODO",yt,V,re,wt,Xe,qt="Function invoked when calling the pipeline for generation.",bt,E,Jt,F,pe,Ut,Ze,Kt="Encodes the prompt into text encoder hidden states.",et,de,tt,v,ce,jt,B,me,vt,Ie,Ot="Function invoked when calling the pipeline for generation.",Xt,Q,Zt,A,ue,It,Ge,en=`Applies Adaptive Instance Normalization (AdaIN) to a latent tensor based on statistics from a reference latent
	tensor.`,Gt,k,he,xt,xe,tn=`Applies a non-linear tone-mapping function to latent values to reduce their dynamic range in a perceptually
	smooth way using a sigmoid-based compression.`,Vt,Ve,nn=`This is useful for regularizing high-variance latents or for conditioning outputs during generation, especially
	when controlling dynamic behavior with a <code>compression</code> factor.`,nt,ge,st,W,fe,Bt,Be,sn="Output class for LTX pipelines.",at,_e,lt,Re,ot;return T=new Te({props:{title:"LTX-2",local:"ltx-2",headingTag:"h1"}}),P=new Te({props:{title:"Two-stages Generation",local:"two-stages-generation",headingTag:"h2"}}),K=new Ce({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> FlowMatchEulerDiscreteScheduler
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2 <span class="hljs-keyword">import</span> LTX2Pipeline, LTX2LatentUpsamplePipeline
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.latent_upsampler <span class="hljs-keyword">import</span> LTX2LatentUpsamplerModel
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.utils <span class="hljs-keyword">import</span> STAGE_2_DISTILLED_SIGMA_VALUES
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video

	device = <span class="hljs-string">"cuda:0"</span>
	width = <span class="hljs-number">768</span>
	height = <span class="hljs-number">512</span>

	pipe = LTX2Pipeline.from_pretrained(
	<span class="hljs-string">"Lightricks/LTX-2"</span>, torch_dtype=torch.bfloat16
	)
	pipe.enable_sequential_cpu_offload(device=device)

	prompt = <span class="hljs-string">"A beautiful sunset over the ocean"</span>
	negative_prompt = <span class="hljs-string">"shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static."</span>

	<span class="hljs-comment"># Stage 1 default (non-distilled) inference</span>
	frame_rate = <span class="hljs-number">24.0</span>
	video_latent, audio_latent = pipe(
	prompt=prompt,
	negative_prompt=negative_prompt,
	width=width,
	height=height,
	num_frames=<span class="hljs-number">121</span>,
	frame_rate=frame_rate,
	num_inference_steps=<span class="hljs-number">40</span>,
	sigmas=<span class="hljs-literal">None</span>,
	guidance_scale=<span class="hljs-number">4.0</span>,
	output_type=<span class="hljs-string">"latent"</span>,
	return_dict=<span class="hljs-literal">False</span>,
	)

	latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
	<span class="hljs-string">"Lightricks/LTX-2"</span>,
	subfolder=<span class="hljs-string">"latent_upsampler"</span>,
	torch_dtype=torch.bfloat16,
	)
	upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler)
	upsample_pipe.enable_model_cpu_offload(device=device)
	upscaled_video_latent = upsample_pipe(
	latents=video_latent,
	output_type=<span class="hljs-string">"latent"</span>,
	return_dict=<span class="hljs-literal">False</span>,
	)[<span class="hljs-number">0</span>]

	<span class="hljs-comment"># Load Stage 2 distilled LoRA</span>
	pipe.load_lora_weights(
	<span class="hljs-string">"Lightricks/LTX-2"</span>, adapter_name=<span class="hljs-string">"stage_2_distilled"</span>, weight_name=<span class="hljs-string">"ltx-2-19b-distilled-lora-384.safetensors"</span>
	)
	pipe.set_adapters(<span class="hljs-string">"stage_2_distilled"</span>, <span class="hljs-number">1.0</span>)
	<span class="hljs-comment"># VAE tiling is usually necessary to avoid OOM error when VAE decoding</span>
	pipe.vae.enable_tiling()
	<span class="hljs-comment"># Change scheduler to use Stage 2 distilled sigmas as is</span>
	new_scheduler = FlowMatchEulerDiscreteScheduler.from_config(
	pipe.scheduler.config, use_dynamic_shifting=<span class="hljs-literal">False</span>, shift_terminal=<span class="hljs-literal">None</span>
	)
	pipe.scheduler = new_scheduler
	<span class="hljs-comment"># Stage 2 inference with distilled LoRA and sigmas</span>
	video, audio = pipe(
	latents=upscaled_video_latent,
	audio_latents=audio_latent,
	prompt=prompt,
	negative_prompt=negative_prompt,
	num_inference_steps=<span class="hljs-number">3</span>,
	noise_scale=STAGE_2_DISTILLED_SIGMA_VALUES[<span class="hljs-number">0</span>], <span class="hljs-comment"># renoise with first sigma value https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py#L218</span>
	sigmas=STAGE_2_DISTILLED_SIGMA_VALUES,
	guidance_scale=<span class="hljs-number">1.0</span>,
	output_type=<span class="hljs-string">"np"</span>,
	return_dict=<span class="hljs-literal">False</span>,
	)

	encode_video(
	video[<span class="hljs-number">0</span>],
	fps=frame_rate,
	audio=audio[<span class="hljs-number">0</span>].<span class="hljs-built_in">float</span>().cpu(),
	audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
	output_path=<span class="hljs-string">"ltx2_lora_distilled_sample.mp4"</span>,
	)`,wrap:!1}}),O=new Te({props:{title:"Distilled checkpoint generation",local:"distilled-checkpoint-generation",headingTag:"h2"}}),te=new Ce({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2 <span class="hljs-keyword">import</span> LTX2Pipeline, LTX2LatentUpsamplePipeline
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.latent_upsampler <span class="hljs-keyword">import</span> LTX2LatentUpsamplerModel
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.utils <span class="hljs-keyword">import</span> DISTILLED_SIGMA_VALUES, STAGE_2_DISTILLED_SIGMA_VALUES
	<span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video

	device = <span class="hljs-string">"cuda"</span>
	width = <span class="hljs-number">768</span>
	height = <span class="hljs-number">512</span>
	random_seed = <span class="hljs-number">42</span>
	generator = torch.Generator(device).manual_seed(random_seed)
	model_path = <span class="hljs-string">"rootonchair/LTX-2-19b-distilled"</span>

	pipe = LTX2Pipeline.from_pretrained(
	model_path, torch_dtype=torch.bfloat16
	)
	pipe.enable_sequential_cpu_offload(device=device)

	prompt = <span class="hljs-string">"A beautiful sunset over the ocean"</span>
	negative_prompt = <span class="hljs-string">"shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static."</span>

	frame_rate = <span class="hljs-number">24.0</span>
	video_latent, audio_latent = pipe(
	prompt=prompt,
	negative_prompt=negative_prompt,
	width=width,
	height=height,
	num_frames=<span class="hljs-number">121</span>,
	frame_rate=frame_rate,
	num_inference_steps=<span class="hljs-number">8</span>,
	sigmas=DISTILLED_SIGMA_VALUES,
	guidance_scale=<span class="hljs-number">1.0</span>,
	generator=generator,
	output_type=<span class="hljs-string">"latent"</span>,
	return_dict=<span class="hljs-literal">False</span>,
	)

	latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
	model_path,
	subfolder=<span class="hljs-string">"latent_upsampler"</span>,
	torch_dtype=torch.bfloat16,
	)
	upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler)
	upsample_pipe.enable_model_cpu_offload(device=device)
	upscaled_video_latent = upsample_pipe(
	latents=video_latent,
	output_type=<span class="hljs-string">"latent"</span>,
	return_dict=<span class="hljs-literal">False</span>,
	)[<span class="hljs-number">0</span>]

	video, audio = pipe(
	latents=upscaled_video_latent,
	audio_latents=audio_latent,
	prompt=prompt,
	negative_prompt=negative_prompt,
	num_inference_steps=<span class="hljs-number">3</span>,
	noise_scale=STAGE_2_DISTILLED_SIGMA_VALUES[<span class="hljs-number">0</span>], <span class="hljs-comment"># renoise with first sigma value https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/distilled.py#L178</span>
	sigmas=STAGE_2_DISTILLED_SIGMA_VALUES,
	generator=generator,
	guidance_scale=<span class="hljs-number">1.0</span>,
	output_type=<span class="hljs-string">"np"</span>,
	return_dict=<span class="hljs-literal">False</span>,
	)

	encode_video(
	video[<span class="hljs-number">0</span>],
	fps=frame_rate,
	audio=audio[<span class="hljs-number">0</span>].<span class="hljs-built_in">float</span>().cpu(),
	audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
	output_path=<span class="hljs-string">"ltx2_distilled_sample.mp4"</span>,
	)`,wrap:!1}}),ne=new Te({props:{title:"LTX2Pipeline",local:"diffusers.LTX2Pipeline",headingTag:"h2"}}),se=new G({props:{name:"class diffusers.LTX2Pipeline",anchor:"diffusers.LTX2Pipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKLLTX2Video"},{name:"audio_vae",val:": AutoencoderKLLTX2Audio"},{name:"text_encoder",val:": Gemma3ForConditionalGeneration"},{name:"tokenizer",val:": transformers.models.gemma.tokenization_gemma.GemmaTokenizer \| transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast"},{name:"connectors",val:": LTX2TextConnectors"},{name:"transformer",val:": LTX2VideoTransformer3DModel"},{name:"vocoder",val:": LTX2Vocoder"}],parametersDescription:[{anchor:"diffusers.LTX2Pipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/pr_13171/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>) —
	Conditional Transformer architecture to denoise the encoded video latents.`,name:"transformer"},{anchor:"diffusers.LTX2Pipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_13171/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) —
	A scheduler to be used in combination with <code>transformer</code> to denoise the encoded image latents.`,name:"scheduler"},{anchor:"diffusers.LTX2Pipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/pr_13171/en/api/models/autoencoderkl_ltx_video#diffusers.AutoencoderKLLTXVideo">AutoencoderKLLTXVideo</a>) —
	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.LTX2Pipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>T5EncoderModel</code>) —
	<a href="https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel" rel="nofollow">T5</a>, specifically
	the <a href="https://huggingface.co/google/t5-v1_1-xxl" rel="nofollow">google/t5-v1_1-xxl</a> variant.`,name:"text_encoder"},{anchor:"diffusers.LTX2Pipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) —
	Tokenizer of class
	<a href="https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.LTX2Pipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>T5TokenizerFast</code>) —
	Second Tokenizer of class
	<a href="https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast" rel="nofollow">T5TokenizerFast</a>.`,name:"tokenizer"},{anchor:"diffusers.LTX2Pipeline.connectors",description:`<strong>connectors</strong> (<code>LTX2TextConnectors</code>) —
	Text connector stack used to adapt text encoder hidden states for the video and audio branches.`,name:"connectors"}],source:"https://github.com/huggingface/diffusers/blob/vr_13171/src/diffusers/pipelines/ltx2/pipeline_ltx2.py#L185"}}),ae=new G({props:{name:"__call__",anchor:"diffusers.LTX2Pipeline.__call__",parameters:[{name:"prompt",val:": str \| list[str] = None"},{name:"negative_prompt",val:": str \| list[str] \| None = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 768"},{name:"num_frames",val:": int = 121"},{name:"frame_rate",val:": float = 24.0"},{name:"num_inference_steps",val:": int = 40"},{name:"sigmas",val:": list[float] \| None = None"},{name:"timesteps",val:": list = None"},{name:"guidance_scale",val:": float = 4.0"},{name:"guidance_rescale",val:": float = 0.0"},{name:"noise_scale",val:": float = 0.0"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator \| list[torch._C.Generator] \| None = None"},{name:"latents",val:": torch.Tensor \| None = None"},{name:"audio_latents",val:": torch.Tensor \| None = None"},{name:"prompt_embeds",val:": torch.Tensor \| None = None"},{name:"prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor \| None = None"},{name:"negative_prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"decode_timestep",val:": float \| list[float] = 0.0"},{name:"decode_noise_scale",val:": float \| list[float] \| None = None"},{name:"output_type",val:": str = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"attention_kwargs",val:": dict[str, typing.Any] \| None = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"},{name:"max_sequence_length",val:": int = 1024"}],parametersDescription:[{anchor:"diffusers.LTX2Pipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) —
	The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
	instead.`,name:"prompt"},{anchor:"diffusers.LTX2Pipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>512</code>) —
	The height in pixels of the generated image. This is set to 480 by default for the best results.`,name:"height"},{anchor:"diffusers.LTX2Pipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to <code>768</code>) —
	The width in pixels of the generated image. This is set to 848 by default for the best results.`,name:"width"},{anchor:"diffusers.LTX2Pipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, <em>optional</em>, defaults to <code>121</code>) —
	The number of video frames to generate`,name:"num_frames"},{anchor:"diffusers.LTX2Pipeline.__call__.frame_rate",description:`<strong>frame_rate</strong> (<code>float</code>, <em>optional</em>, defaults to <code>24.0</code>) —
	The frames per second (FPS) of the generated video.`,name:"frame_rate"},{anchor:"diffusers.LTX2Pipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 40) —
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.LTX2Pipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) —
	Custom sigmas to use for the denoising process with schedulers which support a <code>sigmas</code> argument in
	their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is passed
	will be used.`,name:"sigmas"},{anchor:"diffusers.LTX2Pipeline.__call__.timesteps",description:`<strong>timesteps</strong> (<code>list[int]</code>, <em>optional</em>) —
	Custom timesteps to use for the denoising process with schedulers which support a <code>timesteps</code> argument
	in their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is
	passed will be used. Must be in descending order.`,name:"timesteps"},{anchor:"diffusers.LTX2Pipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to <code>4.0</code>) —
	Guidance scale as defined in <a href="https://huggingface.co/papers/2207.12598" rel="nofollow">Classifier-Free Diffusion
	Guidance</a>. <code>guidance_scale</code> is defined as <code>w</code> of equation 2.
	of <a href="https://huggingface.co/papers/2205.11487" rel="nofollow">Imagen Paper</a>. Guidance scale is enabled by setting
	<code>guidance_scale > 1</code>. Higher guidance scale encourages to generate images that are closely linked to
	the text <code>prompt</code>, usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.LTX2Pipeline.__call__.guidance_rescale",description:`<strong>guidance_rescale</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
	Guidance rescale factor proposed by <a href="https://huggingface.co/papers/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and Sample Steps are
	Flawed</a> <code>guidance_scale</code> is defined as <code>φ</code> in equation 16. of
	<a href="https://huggingface.co/papers/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and Sample Steps are
	Flawed</a>. Guidance rescale factor should fix overexposure when
	using zero terminal SNR.`,name:"guidance_rescale"},{anchor:"diffusers.LTX2Pipeline.__call__.noise_scale",description:`<strong>noise_scale</strong> (<code>float</code>, <em>optional</em>, defaults to <code>0.0</code>) —
	The interpolation factor between random noise and denoised latents at each timestep. Applying noise to
	the <code>latents</code> and <code>audio_latents</code> before continue denoising.`,name:"noise_scale"},{anchor:"diffusers.LTX2Pipeline.__call__.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
	The number of videos to generate per prompt.`,name:"num_videos_per_prompt"},{anchor:"diffusers.LTX2Pipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>list[torch.Generator]</code>, <em>optional</em>) —
	One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
	to make generation deterministic.`,name:"generator"},{anchor:"diffusers.LTX2Pipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will be generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.LTX2Pipeline.__call__.audio_latents",description:`<strong>audio_latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for audio
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will be generated by sampling using the supplied random <code>generator</code>.`,name:"audio_latents"},{anchor:"diffusers.LTX2Pipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	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.LTX2Pipeline.__call__.prompt_attention_mask",description:`<strong>prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	Pre-generated attention mask for text embeddings.`,name:"prompt_attention_mask"},{anchor:"diffusers.LTX2Pipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) —
	Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
	provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.LTX2Pipeline.__call__.negative_prompt_attention_mask",description:`<strong>negative_prompt_attention_mask</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) —
	Pre-generated attention mask for negative text embeddings.`,name:"negative_prompt_attention_mask"},{anchor:"diffusers.LTX2Pipeline.__call__.decode_timestep",description:`<strong>decode_timestep</strong> (<code>float</code>, defaults to <code>0.0</code>) —
	The timestep at which generated video is decoded.`,name:"decode_timestep"},{anchor:"diffusers.LTX2Pipeline.__call__.decode_noise_scale",description:`<strong>decode_noise_scale</strong> (<code>float</code>, defaults to <code>None</code>) —
	The interpolation factor between random noise and denoised latents at the decode timestep.`,name:"decode_noise_scale"},{anchor:"diffusers.LTX2Pipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"pil"</code>) —
	The output format of the generate image. Choose between
	<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.LTX2Pipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
	Whether or not to return a <code>~pipelines.ltx.LTX2PipelineOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.LTX2Pipeline.__call__.attention_kwargs",description:`<strong>attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) —
	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.LTX2Pipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) —
	A function that calls at the end of each denoising steps during the inference. The function is called
	with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
	<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.LTX2Pipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>, defaults to <code>["latents"]</code>) —
	The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
	will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
	<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.LTX2Pipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1024</code>) —
	Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_13171/src/diffusers/pipelines/ltx2/pipeline_ltx2.py#L780",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p>If <code>return_dict</code> is <code>True</code>, <code>~pipelines.ltx.LTX2PipelineOutput</code> is returned, otherwise a <code>tuple</code> is
	returned where the first element is a list with the generated images.</p>
	`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><code>~pipelines.ltx.LTX2PipelineOutput</code> or <code>tuple</code></p>
	`}}),C=new kt({props:{anchor:"diffusers.LTX2Pipeline.__call__.example",$$slots:{default:[cn]},$$scope:{ctx:R}}}),le=new G({props:{name:"encode_prompt",anchor:"diffusers.LTX2Pipeline.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:"prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"negative_prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"max_sequence_length",val:": int = 1024"},{name:"scale_factor",val:": int = 8"},{name:"device",val:": torch.device \| None = None"},{name:"dtype",val:": torch.dtype \| None = None"}],parametersDescription:[{anchor:"diffusers.LTX2Pipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) —
	prompt to be encoded`,name:"prompt"},{anchor:"diffusers.LTX2Pipeline.encode_prompt.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) —
	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.LTX2Pipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
	Whether to use classifier free guidance or not.`,name:"do_classifier_free_guidance"},{anchor:"diffusers.LTX2Pipeline.encode_prompt.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
	Number of videos that should be generated per prompt. torch device to place the resulting embeddings on`,name:"num_videos_per_prompt"},{anchor:"diffusers.LTX2Pipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	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.LTX2Pipeline.encode_prompt.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	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.LTX2Pipeline.encode_prompt.device",description:`<strong>device</strong> — (<code>torch.device</code>, <em>optional</em>):
	torch device`,name:"device"},{anchor:"diffusers.LTX2Pipeline.encode_prompt.dtype",description:`<strong>dtype</strong> — (<code>torch.dtype</code>, <em>optional</em>):
	torch dtype`,name:"dtype"}],source:"https://github.com/huggingface/diffusers/blob/vr_13171/src/diffusers/pipelines/ltx2/pipeline_ltx2.py#L411"}}),oe=new Te({props:{title:"LTX2ImageToVideoPipeline",local:"diffusers.LTX2ImageToVideoPipeline",headingTag:"h2"}}),ie=new G({props:{name:"class diffusers.LTX2ImageToVideoPipeline",anchor:"diffusers.LTX2ImageToVideoPipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKLLTX2Video"},{name:"audio_vae",val:": AutoencoderKLLTX2Audio"},{name:"text_encoder",val:": Gemma3ForConditionalGeneration"},{name:"tokenizer",val:": transformers.models.gemma.tokenization_gemma.GemmaTokenizer \| transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast"},{name:"connectors",val:": LTX2TextConnectors"},{name:"transformer",val:": LTX2VideoTransformer3DModel"},{name:"vocoder",val:": LTX2Vocoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_13171/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py#L205"}}),re=new G({props:{name:"__call__",anchor:"diffusers.LTX2ImageToVideoPipeline.__call__",parameters:[{name:"image",val:": PIL.Image.Image \| numpy.ndarray \| torch.Tensor \| list[PIL.Image.Image] \| list[numpy.ndarray] \| list[torch.Tensor] = None"},{name:"prompt",val:": str \| list[str] = None"},{name:"negative_prompt",val:": str \| list[str] \| None = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 768"},{name:"num_frames",val:": int = 121"},{name:"frame_rate",val:": float = 24.0"},{name:"num_inference_steps",val:": int = 40"},{name:"sigmas",val:": list[float] \| None = None"},{name:"timesteps",val:": list[int] \| None = None"},{name:"guidance_scale",val:": float = 4.0"},{name:"guidance_rescale",val:": float = 0.0"},{name:"noise_scale",val:": float = 0.0"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator \| list[torch._C.Generator] \| None = None"},{name:"latents",val:": torch.Tensor \| None = None"},{name:"audio_latents",val:": torch.Tensor \| None = None"},{name:"prompt_embeds",val:": torch.Tensor \| None = None"},{name:"prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor \| None = None"},{name:"negative_prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"decode_timestep",val:": float \| list[float] = 0.0"},{name:"decode_noise_scale",val:": float \| list[float] \| None = None"},{name:"output_type",val:": str = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"attention_kwargs",val:": dict[str, typing.Any] \| None = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"},{name:"max_sequence_length",val:": int = 1024"}],parametersDescription:[{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.image",description:`<strong>image</strong> (<code>PipelineImageInput</code>) —
	The input image to condition the generation on. Must be an image, a list of images or a <code>torch.Tensor</code>.`,name:"image"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) —
	The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
	instead.`,name:"prompt"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>512</code>) —
	The height in pixels of the generated image. This is set to 480 by default for the best results.`,name:"height"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to <code>768</code>) —
	The width in pixels of the generated image. This is set to 848 by default for the best results.`,name:"width"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, <em>optional</em>, defaults to <code>121</code>) —
	The number of video frames to generate`,name:"num_frames"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.frame_rate",description:`<strong>frame_rate</strong> (<code>float</code>, <em>optional</em>, defaults to <code>24.0</code>) —
	The frames per second (FPS) of the generated video.`,name:"frame_rate"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 40) —
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) —
	Custom sigmas to use for the denoising process with schedulers which support a <code>sigmas</code> argument in
	their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is passed
	will be used.`,name:"sigmas"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>) —
	Custom timesteps to use for the denoising process with schedulers which support a <code>timesteps</code> argument
	in their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is
	passed will be used. Must be in descending order.`,name:"timesteps"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to <code>4.0</code>) —
	Guidance scale as defined in <a href="https://huggingface.co/papers/2207.12598" rel="nofollow">Classifier-Free Diffusion
	Guidance</a>. <code>guidance_scale</code> is defined as <code>w</code> of equation 2.
	of <a href="https://huggingface.co/papers/2205.11487" rel="nofollow">Imagen Paper</a>. Guidance scale is enabled by setting
	<code>guidance_scale > 1</code>. Higher guidance scale encourages to generate images that are closely linked to
	the text <code>prompt</code>, usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.guidance_rescale",description:`<strong>guidance_rescale</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
	Guidance rescale factor proposed by <a href="https://huggingface.co/papers/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and Sample Steps are
	Flawed</a> <code>guidance_scale</code> is defined as <code>φ</code> in equation 16. of
	<a href="https://huggingface.co/papers/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and Sample Steps are
	Flawed</a>. Guidance rescale factor should fix overexposure when
	using zero terminal SNR.`,name:"guidance_rescale"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.noise_scale",description:`<strong>noise_scale</strong> (<code>float</code>, <em>optional</em>, defaults to <code>0.0</code>) —
	The interpolation factor between random noise and denoised latents at each timestep. Applying noise to
	the <code>latents</code> and <code>audio_latents</code> before continue denoising.`,name:"noise_scale"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
	The number of videos to generate per prompt.`,name:"num_videos_per_prompt"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>list[torch.Generator]</code>, <em>optional</em>) —
	One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
	to make generation deterministic.`,name:"generator"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will be generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.audio_latents",description:`<strong>audio_latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for audio
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will be generated by sampling using the supplied random <code>generator</code>.`,name:"audio_latents"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	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.LTX2ImageToVideoPipeline.__call__.prompt_attention_mask",description:`<strong>prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	Pre-generated attention mask for text embeddings.`,name:"prompt_attention_mask"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) —
	Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
	provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.negative_prompt_attention_mask",description:`<strong>negative_prompt_attention_mask</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) —
	Pre-generated attention mask for negative text embeddings.`,name:"negative_prompt_attention_mask"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.decode_timestep",description:`<strong>decode_timestep</strong> (<code>float</code>, defaults to <code>0.0</code>) —
	The timestep at which generated video is decoded.`,name:"decode_timestep"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.decode_noise_scale",description:`<strong>decode_noise_scale</strong> (<code>float</code>, defaults to <code>None</code>) —
	The interpolation factor between random noise and denoised latents at the decode timestep.`,name:"decode_noise_scale"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"pil"</code>) —
	The output format of the generate image. Choose between
	<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
	Whether or not to return a <code>~pipelines.ltx.LTX2PipelineOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.attention_kwargs",description:`<strong>attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) —
	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.LTX2ImageToVideoPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) —
	A function that calls at the end of each denoising steps during the inference. The function is called
	with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
	<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) —
	The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
	will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
	<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1024</code>) —
	Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_13171/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py#L827",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p>If <code>return_dict</code> is <code>True</code>, <code>~pipelines.ltx.LTX2PipelineOutput</code> is returned, otherwise a <code>tuple</code> is
	returned where the first element is a list with the generated images.</p>
	`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><code>~pipelines.ltx.LTX2PipelineOutput</code> or <code>tuple</code></p>
	`}}),E=new kt({props:{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.example",$$slots:{default:[mn]},$$scope:{ctx:R}}}),pe=new G({props:{name:"encode_prompt",anchor:"diffusers.LTX2ImageToVideoPipeline.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:"prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"negative_prompt_attention_mask",val:": torch.Tensor \| None = None"},{name:"max_sequence_length",val:": int = 1024"},{name:"scale_factor",val:": int = 8"},{name:"device",val:": torch.device \| None = None"},{name:"dtype",val:": torch.dtype \| None = None"}],parametersDescription:[{anchor:"diffusers.LTX2ImageToVideoPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) —
	prompt to be encoded`,name:"prompt"},{anchor:"diffusers.LTX2ImageToVideoPipeline.encode_prompt.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) —
	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.LTX2ImageToVideoPipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
	Whether to use classifier free guidance or not.`,name:"do_classifier_free_guidance"},{anchor:"diffusers.LTX2ImageToVideoPipeline.encode_prompt.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
	Number of videos that should be generated per prompt. torch device to place the resulting embeddings on`,name:"num_videos_per_prompt"},{anchor:"diffusers.LTX2ImageToVideoPipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	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.LTX2ImageToVideoPipeline.encode_prompt.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) —
	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.LTX2ImageToVideoPipeline.encode_prompt.device",description:`<strong>device</strong> — (<code>torch.device</code>, <em>optional</em>):
	torch device`,name:"device"},{anchor:"diffusers.LTX2ImageToVideoPipeline.encode_prompt.dtype",description:`<strong>dtype</strong> — (<code>torch.dtype</code>, <em>optional</em>):
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Xet Storage Details

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