import{s as Gt,o as Vt,n as mt}from"../chunks/scheduler.8c3d61f6.js";import{S as Bt,i as Wt,g as r,s,r as m,A as Ct,h as d,f as n,c as a,j as k,u as g,x as T,k as L,y as p,a as o,v as h,d as f,t as u,w as _}from"../chunks/index.da70eac4.js";import{T as $t}from"../chunks/Tip.1d9b8c37.js";import{D as ce}from"../chunks/Docstring.6b390b9a.js";import{C as we}from"../chunks/CodeBlock.00a903b3.js";import{E as Lt}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as ve,E as Rt}from"../chunks/EditOnGithub.1e64e623.js";function Nt(G){let l,v='Make sure to check out the Schedulers <a href="../../using-diffusers/schedulers.md">guide</a> to learn how to explore the tradeoff between scheduler speed and quality, and see the <a href="../../using-diffusers/loading.md#reuse-a-pipeline">reuse components across pipelines</a> section to learn how to efficiently load the same components into multiple pipelines.';return{c(){l=r("p"),l.innerHTML=v},l(M){l=d(M,"P",{"data-svelte-h":!0}),T(l)!=="svelte-w7r39y"&&(l.innerHTML=v)},m(M,c){o(M,l,c)},p:mt,d(M){M&&n(l)}}}function Ht(G){let l,v="Examples:",M,c,y;return c=new we({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> LTXPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = LTXPipeline.from_pretrained(<span class="hljs-string">&quot;Lightricks/LTX-Video&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>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>video = 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">704</span>,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">480</span>,
<span class="hljs-meta">... </span>    num_frames=<span class="hljs-number">161</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>).frames[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>export_to_video(video, <span class="hljs-string">&quot;output.mp4&quot;</span>, fps=<span class="hljs-number">24</span>)`,wrap:!1}}),{c(){l=r("p"),l.textContent=v,M=s(),m(c.$$.fragment)},l(i){l=d(i,"P",{"data-svelte-h":!0}),T(l)!=="svelte-kvfsh7"&&(l.textContent=v),M=a(i),g(c.$$.fragment,i)},m(i,b){o(i,l,b),o(i,M,b),h(c,i,b),y=!0},p:mt,i(i){y||(f(c.$$.fragment,i),y=!0)},o(i){u(c.$$.fragment,i),y=!1},d(i){i&&(n(l),n(M)),_(c,i)}}}function Pt(G){let l,v="Examples:",M,c,y;return c=new we({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> LTXImageToVideoPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video, load_image

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = LTXImageToVideoPipeline.from_pretrained(<span class="hljs-string">&quot;Lightricks/LTX-Video&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>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. Flames engulf the structure, with smoke billowing into the air. Firefighters in protective gear rush to the scene, a fire truck labeled &#x27;38&#x27; visible behind them. The girl&#x27;s neutral expression contrasts sharply with the chaos of the fire, creating a poignant and emotionally charged scene.&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>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">704</span>,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">480</span>,
<span class="hljs-meta">... </span>    num_frames=<span class="hljs-number">161</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>).frames[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>export_to_video(video, <span class="hljs-string">&quot;output.mp4&quot;</span>, fps=<span class="hljs-number">24</span>)`,wrap:!1}}),{c(){l=r("p"),l.textContent=v,M=s(),m(c.$$.fragment)},l(i){l=d(i,"P",{"data-svelte-h":!0}),T(l)!=="svelte-kvfsh7"&&(l.textContent=v),M=a(i),g(c.$$.fragment,i)},m(i,b){o(i,l,b),o(i,M,b),h(c,i,b),y=!0},p:mt,i(i){y||(f(c.$$.fragment,i),y=!0)},o(i){u(c.$$.fragment,i),y=!1},d(i){i&&(n(l),n(M)),_(c,i)}}}function Ft(G){let l,v,M,c,y,i,b,gt='<a href="https://huggingface.co/Lightricks/LTX-Video" rel="nofollow">LTX Video</a> is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. We provide a model for both text-to-video as well as image + text-to-video usecases.',Ue,V,je,R,ht="Available models:",Ze,N,ft='<thead><tr><th align="center">Model name</th> <th align="center">Recommended dtype</th></tr></thead> <tbody><tr><td align="center"><a href="https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors" rel="nofollow"><code>LTX Video 0.9.0</code></a></td> <td align="center"><code>torch.bfloat16</code></td></tr> <tr><td align="center"><a href="https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors" rel="nofollow"><code>LTX Video 0.9.1</code></a></td> <td align="center"><code>torch.bfloat16</code></td></tr></tbody>',Ie,H,ut="Note: The recommended dtype is for the transformer component. The VAE and text encoders can be either <code>torch.float32</code>, <code>torch.bfloat16</code> or <code>torch.float16</code> but the recommended dtype is <code>torch.bfloat16</code> as used in the original repository.",Xe,P,xe,F,_t="Loading the original LTX Video checkpoints is also possible with <code>~ModelMixin.from_single_file</code>. We recommend using <code>from_single_file</code> for the Lightricks series of models, as they plan to release multiple models in the future in the single file format.",ke,E,Le,z,Tt="Alternatively, the pipeline can be used to load the weights with <code>~FromSingleFileMixin.from_single_file</code>.",Ge,Y,Ve,Q,Mt='Loading <a href="https://huggingface.co/city96/LTX-Video-gguf" rel="nofollow">LTX GGUF checkpoints</a> are also supported:',Be,S,We,A,yt='Make sure to read the <a href="../../quantization/gguf">documentation on GGUF</a> to learn more about our GGUF support.',Ce,q,bt='Loading and running inference with <a href="https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors" rel="nofollow">LTX Video 0.9.1</a> weights.',$e,D,Re,O,wt='Refer to <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox#memory-optimization" rel="nofollow">this section</a> to learn more about optimizing memory consumption.',Ne,K,He,w,ee,De,me,Jt="Pipeline for text-to-video generation.",Oe,ge,vt='Reference: <a href="https://github.com/Lightricks/LTX-Video" rel="nofollow">https://github.com/Lightricks/LTX-Video</a>',Ke,j,te,et,he,Ut="Function invoked when calling the pipeline for generation.",tt,B,nt,W,ne,ot,fe,jt="Encodes the prompt into text encoder hidden states.",Pe,oe,Fe,J,se,st,ue,Zt="Pipeline for image-to-video generation.",at,_e,It='Reference: <a href="https://github.com/Lightricks/LTX-Video" rel="nofollow">https://github.com/Lightricks/LTX-Video</a>',it,Z,ae,lt,Te,Xt="Function invoked when calling the pipeline for generation.",rt,C,dt,$,ie,pt,Me,xt="Encodes the prompt into text encoder hidden states.",Ee,le,ze,X,re,ct,ye,kt="Output class for LTX pipelines.",Ye,de,Qe,Je,Se;return y=new ve({props:{title:"LTX Video",local:"ltx-video",headingTag:"h1"}}),V=new $t({props:{$$slots:{default:[Nt]},$$scope:{ctx:G}}}),P=new ve({props:{title:"Loading Single Files",local:"loading-single-files",headingTag:"h2"}}),E=new we({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> AutoencoderKLLTXVideo, LTXImageToVideoPipeline, LTXVideoTransformer3DModel

<span class="hljs-comment"># \`single_file_url\` could also be https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.1.safetensors</span>
single_file_url = <span class="hljs-string">&quot;https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors&quot;</span>
transformer = LTXVideoTransformer3DModel.from_single_file(
  single_file_url, torch_dtype=torch.bfloat16
)
vae = AutoencoderKLLTXVideo.from_single_file(single_file_url, torch_dtype=torch.bfloat16)
pipe = LTXImageToVideoPipeline.from_pretrained(
  <span class="hljs-string">&quot;Lightricks/LTX-Video&quot;</span>, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16
)

<span class="hljs-comment"># ... inference code ...</span>`,wrap:!1}}),Y=new we({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> LTXImageToVideoPipeline
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> T5EncoderModel, T5Tokenizer

single_file_url = <span class="hljs-string">&quot;https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors&quot;</span>
text_encoder = T5EncoderModel.from_pretrained(
  <span class="hljs-string">&quot;Lightricks/LTX-Video&quot;</span>, subfolder=<span class="hljs-string">&quot;text_encoder&quot;</span>, torch_dtype=torch.bfloat16
)
tokenizer = T5Tokenizer.from_pretrained(
  <span class="hljs-string">&quot;Lightricks/LTX-Video&quot;</span>, subfolder=<span class="hljs-string">&quot;tokenizer&quot;</span>, torch_dtype=torch.bfloat16
)
pipe = LTXImageToVideoPipeline.from_single_file(
  single_file_url, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=torch.bfloat16
)`,wrap:!1}}),S=new we({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LTXPipeline, LTXVideoTransformer3DModel, GGUFQuantizationConfig

ckpt_path = (
    <span class="hljs-string">&quot;https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf&quot;</span>
)
transformer = LTXVideoTransformer3DModel.from_single_file(
    ckpt_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
)
pipe = LTXPipeline.from_pretrained(
    <span class="hljs-string">&quot;Lightricks/LTX-Video&quot;</span>,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

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

video = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=<span class="hljs-number">704</span>,
    height=<span class="hljs-number">480</span>,
    num_frames=<span class="hljs-number">161</span>,
    num_inference_steps=<span class="hljs-number">50</span>,
).frames[<span class="hljs-number">0</span>]
export_to_video(video, <span class="hljs-string">&quot;output_gguf_ltx.mp4&quot;</span>, fps=<span class="hljs-number">24</span>)`,wrap:!1}}),D=new we({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> LTXPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = LTXPipeline.from_pretrained(<span class="hljs-string">&quot;a-r-r-o-w/LTX-Video-0.9.1-diffusers&quot;</span>, torch_dtype=torch.bfloat16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</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>
negative_prompt = <span class="hljs-string">&quot;worst quality, inconsistent motion, blurry, jittery, distorted&quot;</span>

video = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=<span class="hljs-number">768</span>,
    height=<span class="hljs-number">512</span>,
    num_frames=<span class="hljs-number">161</span>,
    decode_timestep=<span class="hljs-number">0.03</span>,
    decode_noise_scale=<span class="hljs-number">0.025</span>,
    num_inference_steps=<span class="hljs-number">50</span>,
).frames[<span class="hljs-number">0</span>]
export_to_video(video, <span class="hljs-string">&quot;output.mp4&quot;</span>, fps=<span class="hljs-number">24</span>)`,wrap:!1}}),K=new ve({props:{title:"LTXPipeline",local:"diffusers.LTXPipeline",headingTag:"h2"}}),ee=new ce({props:{name:"class diffusers.LTXPipeline",anchor:"diffusers.LTXPipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKLLTXVideo"},{name:"text_encoder",val:": T5EncoderModel"},{name:"tokenizer",val:": T5TokenizerFast"},{name:"transformer",val:": LTXVideoTransformer3DModel"}],parametersDescription:[{anchor:"diffusers.LTXPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/v0.32.1/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>) &#x2014;
Conditional Transformer architecture to denoise the encoded video latents.`,name:"transformer"},{anchor:"diffusers.LTXPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.32.1/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.LTXPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.32.1/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.LTXPipeline.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.LTXPipeline.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.LTXPipeline.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"}],source:"https://github.com/huggingface/diffusers/blob/v0.32.1/src/diffusers/pipelines/ltx/pipeline_ltx.py#L143"}}),te=new ce({props:{name:"__call__",anchor:"diffusers.LTXPipeline.__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 = 704"},{name:"num_frames",val:": int = 161"},{name:"frame_rate",val:": int = 25"},{name:"num_inference_steps",val:": int = 50"},{name:"timesteps",val:": typing.List[int] = None"},{name:"guidance_scale",val:": float = 3"},{name:"num_videos_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"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 = 128"}],parametersDescription:[{anchor:"diffusers.LTXPipeline.__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.LTXPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, 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.LTXPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, defaults to <code>704</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.LTXPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, defaults to <code>161</code>) &#x2014;
The number of video frames to generate`,name:"num_frames"},{anchor:"diffusers.LTXPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#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.LTXPipeline.__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.LTXPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, defaults to <code>3 </code>) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/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://arxiv.org/pdf/2205.11487.pdf" 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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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 image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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"},{anchor:"diffusers.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__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.LTXPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code> defaults to <code>128 </code>) &#x2014;
Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/v0.32.1/src/diffusers/pipelines/ltx/pipeline_ltx.py#L494",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 a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~pipelines.ltx.LTXPipelineOutput</code> or <code>tuple</code></p>
`}}),B=new Lt({props:{anchor:"diffusers.LTXPipeline.__call__.example",$$slots:{default:[Ht]},$$scope:{ctx:G}}}),ne=new ce({props:{name:"encode_prompt",anchor:"diffusers.LTXPipeline.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 = 128"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"dtype",val:": typing.Optional[torch.dtype] = None"}],parametersDescription:[{anchor:"diffusers.LTXPipeline.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.LTXPipeline.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.LTXPipeline.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.LTXPipeline.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.LTXPipeline.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.LTXPipeline.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.LTXPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>, <em>optional</em>):
torch device`,name:"device"},{anchor:"diffusers.LTXPipeline.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/v0.32.1/src/diffusers/pipelines/ltx/pipeline_ltx.py#L250"}}),oe=new ve({props:{title:"LTXImageToVideoPipeline",local:"diffusers.LTXImageToVideoPipeline",headingTag:"h2"}}),se=new ce({props:{name:"class diffusers.LTXImageToVideoPipeline",anchor:"diffusers.LTXImageToVideoPipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKLLTXVideo"},{name:"text_encoder",val:": T5EncoderModel"},{name:"tokenizer",val:": T5TokenizerFast"},{name:"transformer",val:": LTXVideoTransformer3DModel"}],parametersDescription:[{anchor:"diffusers.LTXImageToVideoPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/v0.32.1/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>) &#x2014;
Conditional Transformer architecture to denoise the encoded video latents.`,name:"transformer"},{anchor:"diffusers.LTXImageToVideoPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.32.1/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.LTXImageToVideoPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.32.1/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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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"}],source:"https://github.com/huggingface/diffusers/blob/v0.32.1/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py#L162"}}),ae=new ce({props:{name:"__call__",anchor:"diffusers.LTXImageToVideoPipeline.__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 = 704"},{name:"num_frames",val:": int = 161"},{name:"frame_rate",val:": int = 25"},{name:"num_inference_steps",val:": int = 50"},{name:"timesteps",val:": typing.List[int] = None"},{name:"guidance_scale",val:": float = 3"},{name:"num_videos_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"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 = 128"}],parametersDescription:[{anchor:"diffusers.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, 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.LTXImageToVideoPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, defaults to <code>704</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.LTXImageToVideoPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, defaults to <code>161</code>) &#x2014;
The number of video frames to generate`,name:"num_frames"},{anchor:"diffusers.LTXImageToVideoPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, defaults to <code>3 </code>) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/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://arxiv.org/pdf/2205.11487.pdf" 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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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 image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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"},{anchor:"diffusers.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__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.LTXImageToVideoPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code> defaults to <code>128 </code>) &#x2014;
Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/v0.32.1/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py#L553",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 a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~pipelines.ltx.LTXPipelineOutput</code> or <code>tuple</code></p>
`}}),C=new Lt({props:{anchor:"diffusers.LTXImageToVideoPipeline.__call__.example",$$slots:{default:[Pt]},$$scope:{ctx:G}}}),ie=new ce({props:{name:"encode_prompt",anchor:"diffusers.LTXImageToVideoPipeline.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 = 128"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"dtype",val:": typing.Optional[torch.dtype] = None"}],parametersDescription:[{anchor:"diffusers.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.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.LTXImageToVideoPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>, <em>optional</em>):
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