diffusers/pr_11636/en/_app/immutable/nodes/160.c1e34b06.js

import{s as Ct,o as Pt,n as ht}from"../chunks/scheduler.53228c21.js";import{S as Vt,i as $t,e as l,s as a,c as m,h as Nt,a as r,d as s,b as o,f as x,g,j as b,k as X,l as n,m as d,n as u,t as f,o as h,p as _}from"../chunks/index.100fac89.js";import{D as L}from"../chunks/Docstring.07ca7ce7.js";import{C as _t}from"../chunks/CodeBlock.d30a6509.js";import{E as ft}from"../chunks/ExampleCodeBlock.672157f9.js";import{H as Xe,E as Wt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.83a5c0e1.js";function Rt(V){let p,J="Examples:",y,c,T;return c=new _t({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> LTX2Pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video

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

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;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&#x27;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&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>negative_prompt = <span class="hljs-string">&quot;worst quality, inconsistent motion, blurry, jittery, distorted&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>frame_rate = <span class="hljs-number">24.0</span>
<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;np&quot;</span>,
<span class="hljs-meta">... </span>    return_dict=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = (video * <span class="hljs-number">255</span>).<span class="hljs-built_in">round</span>().astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = torch.from_numpy(video)

<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;video.mp4&quot;</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){p=l("p"),p.textContent=J,y=a(),m(c.$$.fragment)},l(t){p=r(t,"P",{"data-svelte-h":!0}),b(p)!=="svelte-kvfsh7"&&(p.textContent=J),y=o(t),g(c.$$.fragment,t)},m(t,w){d(t,p,w),d(t,y,w),u(c,t,w),T=!0},p:ht,i(t){T||(f(c.$$.fragment,t),T=!0)},o(t){h(c.$$.fragment,t),T=!1},d(t){t&&(s(p),s(y)),_(c,t)}}}function Et(V){let p,J="Examples:",y,c,T;return c=new _t({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> LTX2Pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>frame_rate = <span class="hljs-number">24.0</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>video = 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">&quot;np&quot;</span>,
<span class="hljs-meta">... </span>    return_dict=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = (video * <span class="hljs-number">255</span>).<span class="hljs-built_in">round</span>().astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = torch.from_numpy(video)

<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;video.mp4&quot;</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){p=l("p"),p.textContent=J,y=a(),m(c.$$.fragment)},l(t){p=r(t,"P",{"data-svelte-h":!0}),b(p)!=="svelte-kvfsh7"&&(p.textContent=J),y=o(t),g(c.$$.fragment,t)},m(t,w){d(t,p,w),d(t,y,w),u(c,t,w),T=!0},p:ht,i(t){T||(f(c.$$.fragment,t),T=!0)},o(t){h(c.$$.fragment,t),T=!1},d(t){t&&(s(p),s(y)),_(c,t)}}}function Ft(V){let p,J="Examples:",y,c,T;return c=new _t({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> LTX2ImageToVideoPipeline, LTX2LatentUpsamplePipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.export_utils <span class="hljs-keyword">import</span> encode_video
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.pipelines.ltx2.latent_upsampler <span class="hljs-keyword">import</span> LTX2LatentUpsamplerModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>frame_rate = <span class="hljs-number">24.0</span>
<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;pil&quot;</span>,
<span class="hljs-meta">... </span>    return_dict=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;np&quot;</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">&gt;&gt;&gt; </span>video = (video * <span class="hljs-number">255</span>).<span class="hljs-built_in">round</span>().astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = torch.from_numpy(video)

<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;video.mp4&quot;</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){p=l("p"),p.textContent=J,y=a(),m(c.$$.fragment)},l(t){p=r(t,"P",{"data-svelte-h":!0}),b(p)!=="svelte-kvfsh7"&&(p.textContent=J),y=o(t),g(c.$$.fragment,t)},m(t,w){d(t,p,w),d(t,y,w),u(c,t,w),T=!0},p:ht,i(t){T||(f(c.$$.fragment,t),T=!0)},o(t){h(c.$$.fragment,t),T=!1},d(t){t&&(s(p),s(y)),_(c,t)}}}function Qt(V){let p,J,y,c,T,t,w,Tt="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.",Ie,A,yt='You can find all the original LTX-Video checkpoints under the <a href="https://huggingface.co/Lightricks" rel="nofollow">Lightricks</a> organization.',Le,S,bt='The original codebase for LTX-2 can be found <a href="https://github.com/Lightricks/LTX-2" rel="nofollow">here</a>.',Ze,z,ke,M,Y,Ae,de,wt="Pipeline for text-to-video generation.",Se,ce,vt='Reference: <a href="https://github.com/Lightricks/LTX-Video" rel="nofollow">https://github.com/Lightricks/LTX-Video</a>',ze,Z,H,Ye,me,Mt="Function invoked when calling the pipeline for generation.",He,$,De,N,D,qe,ge,Jt="Encodes the prompt into text encoder hidden states.",Ge,q,Be,v,O,Oe,ue,jt="Pipeline for image-to-video generation.",Ke,fe,Ut='Reference: <a href="https://github.com/Lightricks/LTX-Video" rel="nofollow">https://github.com/Lightricks/LTX-Video</a>',et,he,xt="TODO",tt,k,K,nt,_e,Xt="Function invoked when calling the pipeline for generation.",st,W,at,R,ee,ot,Te,It="Encodes the prompt into text encoder hidden states.",Ce,te,Pe,j,ne,it,G,se,lt,ye,Lt="Function invoked when calling the pipeline for generation.",rt,E,pt,F,ae,dt,be,Zt=`Applies Adaptive Instance Normalization (AdaIN) to a latent tensor based on statistics from a reference latent
tensor.`,ct,B,oe,mt,we,kt=`Applies a non-linear tone-mapping function to latent values to reduce their dynamic range in a perceptually
smooth way using a sigmoid-based compression.`,gt,ve,Gt=`This is useful for regularizing high-variance latents or for conditioning outputs during generation, especially
when controlling dynamic behavior with a <code>compression</code> factor.`,Ve,ie,$e,C,le,ut,Me,Bt="Output class for LTX pipelines.",Ne,re,We,xe,Re;return T=new Xe({props:{title:"LTX-2",local:"ltx-2",headingTag:"h1"}}),z=new Xe({props:{title:"LTX2Pipeline",local:"diffusers.LTX2Pipeline",headingTag:"h2"}}),Y=new L({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:": typing.Union[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_11636/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>) &#x2014;
Conditional Transformer architecture to denoise the encoded video latents.`,name:"transformer"},{anchor:"diffusers.LTX2Pipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_11636/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) &#x2014;
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_11636/en/api/models/autoencoderkl_ltx_video#diffusers.AutoencoderKLLTXVideo">AutoencoderKLLTXVideo</a>) &#x2014;
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>) &#x2014;
<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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2.py#L187"}}),H=new L({props:{name:"__call__",anchor:"diffusers.LTX2Pipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = 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:"timesteps",val:": typing.List[int] = None"},{name:"guidance_scale",val:": float = 4.0"},{name:"guidance_rescale",val:": float = 0.0"},{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:"audio_latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decode_timestep",val:": typing.Union[float, typing.List[float]] = 0.0"},{name:"decode_noise_scale",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{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.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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) &#x2014;
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__.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
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>) &#x2014;
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 &gt; 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) &#x2014;
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>&#x3C6;</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__.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#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.LTX2Pipeline.__call__.prompt_attention_mask",description:`<strong>prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
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>) &#x2014;
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be &quot;&quot;. 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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>&quot;pil&quot;</code>) &#x2014;
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>) &#x2014;
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>) &#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.LTX2Pipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
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>[&quot;latents&quot;]</code>) &#x2014;
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>) &#x2014;
Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2.py#L742",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>
`}}),$=new ft({props:{anchor:"diffusers.LTX2Pipeline.__call__.example",$$slots:{default:[Rt]},$$scope:{ctx:V}}}),D=new L({props:{name:"encode_prompt",anchor:"diffusers.LTX2Pipeline.encode_prompt",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"do_classifier_free_guidance",val:": bool = True"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"max_sequence_length",val:": int = 1024"},{name:"scale_factor",val:": int = 8"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"dtype",val:": typing.Optional[torch.dtype] = None"}],parametersDescription:[{anchor:"diffusers.LTX2Pipeline.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.LTX2Pipeline.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.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>) &#x2014;
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) &#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.LTX2Pipeline.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.LTX2Pipeline.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.LTX2Pipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>, <em>optional</em>):
torch device`,name:"device"},{anchor:"diffusers.LTX2Pipeline.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_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2.py#L413"}}),q=new Xe({props:{title:"LTX2ImageToVideoPipeline",local:"diffusers.LTX2ImageToVideoPipeline",headingTag:"h2"}}),O=new L({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:": typing.Union[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_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py#L207"}}),K=new L({props:{name:"__call__",anchor:"diffusers.LTX2ImageToVideoPipeline.__call__",parameters:[{name:"image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"},{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = 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:"timesteps",val:": typing.List[int] = None"},{name:"guidance_scale",val:": float = 4.0"},{name:"guidance_rescale",val:": float = 0.0"},{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:"audio_latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decode_timestep",val:": typing.Union[float, typing.List[float]] = 0.0"},{name:"decode_noise_scale",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{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.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 1024"}],parametersDescription:[{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.image",description:`<strong>image</strong> (<code>PipelineImageInput</code>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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) &#x2014;
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__.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
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>) &#x2014;
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 &gt; 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) &#x2014;
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>&#x3C6;</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__.num_videos_per_prompt",description:`<strong>num_videos_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>) &#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.LTX2ImageToVideoPipeline.__call__.prompt_attention_mask",description:`<strong>prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
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>) &#x2014;
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be &quot;&quot;. 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>) &#x2014;
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>) &#x2014;
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>) &#x2014;
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>&quot;pil&quot;</code>) &#x2014;
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>) &#x2014;
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>) &#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.LTX2ImageToVideoPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
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>) &#x2014;
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>) &#x2014;
Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py#L802",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>
`}}),W=new ft({props:{anchor:"diffusers.LTX2ImageToVideoPipeline.__call__.example",$$slots:{default:[Et]},$$scope:{ctx:V}}}),ee=new L({props:{name:"encode_prompt",anchor:"diffusers.LTX2ImageToVideoPipeline.encode_prompt",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"do_classifier_free_guidance",val:": bool = True"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"max_sequence_length",val:": int = 1024"},{name:"scale_factor",val:": int = 8"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"dtype",val:": typing.Optional[torch.dtype] = None"}],parametersDescription:[{anchor:"diffusers.LTX2ImageToVideoPipeline.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.LTX2ImageToVideoPipeline.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.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>) &#x2014;
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) &#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.LTX2ImageToVideoPipeline.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.LTX2ImageToVideoPipeline.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.LTX2ImageToVideoPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>, <em>optional</em>):
torch device`,name:"device"},{anchor:"diffusers.LTX2ImageToVideoPipeline.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_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py#L419"}}),te=new Xe({props:{title:"LTX2LatentUpsamplePipeline",local:"diffusers.LTX2LatentUpsamplePipeline",headingTag:"h2"}}),ne=new L({props:{name:"class diffusers.LTX2LatentUpsamplePipeline",anchor:"diffusers.LTX2LatentUpsamplePipeline",parameters:[{name:"vae",val:": AutoencoderKLLTX2Video"},{name:"latent_upsampler",val:": LTX2LatentUpsamplerModel"}],source:"https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2_latent_upsample.py#L107"}}),se=new L({props:{name:"__call__",anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__",parameters:[{name:"video",val:": typing.Optional[typing.List[typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]]]] = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 768"},{name:"num_frames",val:": int = 121"},{name:"spatial_patch_size",val:": int = 1"},{name:"temporal_patch_size",val:": int = 1"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"latents_normalized",val:": bool = False"},{name:"decode_timestep",val:": typing.Union[float, typing.List[float]] = 0.0"},{name:"decode_noise_scale",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"adain_factor",val:": float = 0.0"},{name:"tone_map_compression_ratio",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.video",description:`<strong>video</strong> (<code>List[PipelineImageInput]</code>, <em>optional</em>) &#x2014;
The video to be upsampled (such as a LTX 2.0 first stage output). If not supplied, <code>latents</code> should be
supplied.`,name:"video"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>512</code>) &#x2014;
The height in pixels of the input video (not the generated video, which will have a larger resolution).`,name:"height"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to <code>768</code>) &#x2014;
The width in pixels of the input video (not the generated video, which will have a larger resolution).`,name:"width"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, <em>optional</em>, defaults to <code>121</code>) &#x2014;
The number of frames in the input video.`,name:"num_frames"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.spatial_patch_size",description:`<strong>spatial_patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1</code>) &#x2014;
The spatial patch size of the video latents. Used when <code>latents</code> is supplied if unpacking is necessary.`,name:"spatial_patch_size"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.temporal_patch_size",description:`<strong>temporal_patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1</code>) &#x2014;
The temporal patch size of the video latents. Used when <code>latents</code> is supplied if unpacking is
necessary.`,name:"temporal_patch_size"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated video latents. This can be supplied in place of the <code>video</code> argument. Can either be a
patch sequence of shape <code>(batch_size, seq_len, hidden_dim)</code> or a video latent of shape <code>(batch_size, latent_channels, latent_frames, latent_height, latent_width)</code>.`,name:"latents"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.latents_normalized",description:`<strong>latents_normalized</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>latents</code> are supplied, whether the <code>latents</code> are normalized using the VAE latent mean and std. If
<code>True</code>, the <code>latents</code> will be denormalized before being supplied to the latent upsampler.`,name:"latents_normalized"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.decode_timestep",description:`<strong>decode_timestep</strong> (<code>float</code>, defaults to <code>0.0</code>) &#x2014;
The timestep at which generated video is decoded.`,name:"decode_timestep"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.decode_noise_scale",description:`<strong>decode_noise_scale</strong> (<code>float</code>, defaults to <code>None</code>) &#x2014;
The interpolation factor between random noise and denoised latents at the decode timestep.`,name:"decode_noise_scale"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.adain_factor",description:`<strong>adain_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>0.0</code>) &#x2014;
Adaptive Instance Normalization (AdaIN) blending factor between the upsampled and original latents.
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The compression strength for tone mapping, which will reduce the dynamic range of the latent values.
This is useful for regularizing high-variance latents or for conditioning outputs during generation.
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the full compression effect.`,name:"tone_map_compression_ratio"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
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.LTX2LatentUpsamplePipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
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.LTX2LatentUpsamplePipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <code>~pipelines.ltx.LTXPipelineOutput</code> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2_latent_upsample.py#L278",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>~pipelines.ltx.LTXPipelineOutput</code> or <code>tuple</code></p>
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The reference latents providing style statistics.`,name:"reference_latents"},{anchor:"diffusers.LTX2LatentUpsamplePipeline.adain_filter_latent.factor",description:`<strong>factor</strong> (<code>float</code>) &#x2014;
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<p>The transformed latent tensor</p>
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<p>torch.Tensor</p>
`}}),oe=new L({props:{name:"tone_map_latents",anchor:"diffusers.LTX2LatentUpsamplePipeline.tone_map_latents",parameters:[{name:"latents",val:": Tensor"},{name:"compression",val:": float"}],parametersDescription:[{anchor:"diffusers.LTX2LatentUpsamplePipeline.tone_map_latents.latents",description:`<strong>latents</strong>  &#x2014; torch.Tensor
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Compression strength in the range [0, 1].
<ul>
<li>0.0: No tone-mapping (identity transform)</li>
<li>1.0: Full compression effect</li>
</ul>`,name:"compression"}],source:"https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/pipelines/ltx2/pipeline_ltx2_latent_upsample.py#L199",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>torch.Tensor
The tone-mapped latent tensor of the same shape as input.</p>
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List of video outputs - It can be a nested list of length <code>batch_size,</code> with each sub-list containing
denoised PIL image sequences of length <code>num_frames.</code> It can also be a NumPy array or Torch tensor of shape
<code>(batch_size, num_frames, channels, height, width)</code>.`,name:"frames"},{anchor:"diffusers.pipelines.ltx2.pipeline_output.LTX2PipelineOutput.audio",description:`<strong>audio</strong> (<code>torch.Tensor</code>, <code>np.ndarray</code>) &#x2014;
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