import{s as tt,o as nt,n as et}from"../chunks/scheduler.8c3d61f6.js";import{S as ot,i as st,g as l,s,r as m,A as it,h as r,f as n,c as i,j as oe,u,x as M,k as se,y as c,a as o,v as f,d as g,t as h,w as _}from"../chunks/index.da70eac4.js";import{T as at}from"../chunks/Tip.1d9b8c37.js";import{D as Ce}from"../chunks/Docstring.ee4b6913.js";import{C as ae}from"../chunks/CodeBlock.00a903b3.js";import{E as lt}from"../chunks/ExampleCodeBlock.f7bd2c1f.js";import{H as Ge,E as rt}from"../chunks/EditOnGithub.1e64e623.js";function dt(A){let a,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(){a=l("p"),a.innerHTML=v},l(p){a=r(p,"P",{"data-svelte-h":!0}),M(a)!=="svelte-w7r39y"&&(a.innerHTML=v)},m(p,w){o(p,a,w)},p:et,d(p){p&&n(a)}}}function pt(A){let a,v="Examples:",p,w,T;return w=new ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> CogVideoXPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = CogVideoXPipeline.from_pretrained(<span class="hljs-string">&quot;THUDM/CogVideoX-2b&quot;</span>, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;The panda&#x27;s fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;casting a gentle glow on the scene. The panda&#x27;s face is expressive, showing concentration and joy as it plays. &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;atmosphere of this unique musical performance.&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = pipe(prompt=prompt, guidance_scale=<span class="hljs-number">6</span>, num_inference_steps=<span class="hljs-number">50</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">8</span>)`,wrap:!1}}),{c(){a=l("p"),a.textContent=v,p=s(),m(w.$$.fragment)},l(d){a=r(d,"P",{"data-svelte-h":!0}),M(a)!=="svelte-kvfsh7"&&(a.textContent=v),p=i(d),u(w.$$.fragment,d)},m(d,J){o(d,a,J),o(d,p,J),f(w,d,J),T=!0},p:et,i(d){T||(g(w.$$.fragment,d),T=!0)},o(d){h(w.$$.fragment,d),T=!1},d(d){d&&(n(a),n(p)),_(w,d)}}}function ct(A){let a,v,p,w,T,d,J,Ne='<a href="https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf" rel="nofollow">CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer</a> from Tsinghua University &amp; ZhipuAI.',le,G,Pe="The abstract from the paper is:",re,V,ze='<em>We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at <a href="https://github.com/THUDM/CogVideo" rel="nofollow">https://github.com/THUDM/CogVideo</a>.</em>',de,x,pe,B,Fe='This pipeline was contributed by <a href="https://github.com/zRzRzRzRzRzRzR" rel="nofollow">zRzRzRzRzRzRzR</a>. The original codebase can be found <a href="https://huggingface.co/THUDM" rel="nofollow">here</a>. The original weights can be found under <a href="https://huggingface.co/THUDM" rel="nofollow">hf.co/THUDM</a>.',ce,X,me,W,Re='Use <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile" rel="nofollow"><code>torch.compile</code></a> to reduce the inference latency.',ue,$,Se="First, load the pipeline:",fe,k,ge,H,Qe="Then change the memory layout of the pipelines <code>transformer</code> and <code>vae</code> components to <code>torch.channels-last</code>:",he,Y,_e,N,Ee="Finally, compile the components and run inference:",Me,P,be,z,qe="The [benchmark](TODO: link) results on an 80GB A100 machine are:",we,F,ye,R,Te,b,S,Ve,O,Le="Pipeline for text-to-video generation using CogVideoX.",Be,K,De=`This model inherits from <a href="/docs/diffusers/v0.30.0/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,Xe,U,Q,We,ee,Ae="Function invoked when calling the pipeline for generation.",$e,Z,ke,C,E,He,te,Oe="Encodes the prompt into text encoder hidden states.",Je,q,ve,j,L,Ye,ne,Ke="Output class for CogVideo pipelines.",Ue,D,je,ie,Ie;return T=new Ge({props:{title:"CogVideoX",local:"cogvideox",headingTag:"h1"}}),x=new at({props:{$$slots:{default:[dt]},$$scope:{ctx:A}}}),X=new Ge({props:{title:"Inference",local:"inference",headingTag:"h2"}}),k=new ae({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> CogVideoXPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = CogVideoXPipeline.from_pretrained(<span class="hljs-string">&quot;THUDM/CogVideoX-2b&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
prompt = (
    <span class="hljs-string">&quot;A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. &quot;</span>
    <span class="hljs-string">&quot;The panda&#x27;s fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other &quot;</span>
    <span class="hljs-string">&quot;pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, &quot;</span>
    <span class="hljs-string">&quot;casting a gentle glow on the scene. The panda&#x27;s face is expressive, showing concentration and joy as it plays. &quot;</span>
    <span class="hljs-string">&quot;The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical &quot;</span>
    <span class="hljs-string">&quot;atmosphere of this unique musical performance.&quot;</span>
)
video = pipe(prompt=prompt, guidance_scale=<span class="hljs-number">6</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">8</span>)`,wrap:!1}}),Y=new ae({props:{code:"cGlwZWxpbmUudHJhbnNmb3JtZXIudG8obWVtb3J5X2Zvcm1hdCUzRHRvcmNoLmNoYW5uZWxzX2xhc3QpJTBBcGlwZWxpbmUudmFlLnRvKG1lbW9yeV9mb3JtYXQlM0R0b3JjaC5jaGFubmVsc19sYXN0KQ==",highlighted:`pipeline.transformer.to(memory_format=torch.channels_last)
pipeline.vae.to(memory_format=torch.channels_last)`,wrap:!1}}),P=new ae({props:{code:"cGlwZWxpbmUudHJhbnNmb3JtZXIlMjAlM0QlMjB0b3JjaC5jb21waWxlKHBpcGVsaW5lLnRyYW5zZm9ybWVyKSUwQXBpcGVsaW5lLnZhZS5kZWNvZGUlMjAlM0QlMjB0b3JjaC5jb21waWxlKHBpcGVsaW5lLnZhZS5kZWNvZGUpJTBBJTBBJTIzJTIwQ29nVmlkZW9YJTIwd29ya3MlMjB2ZXJ5JTIwd2VsbCUyMHdpdGglMjBsb25nJTIwYW5kJTIwd2VsbC1kZXNjcmliZWQlMjBwcm9tcHRzJTBBcHJvbXB0JTIwJTNEJTIwJTIyQSUyMHBhbmRhJTJDJTIwZHJlc3NlZCUyMGluJTIwYSUyMHNtYWxsJTJDJTIwcmVkJTIwamFja2V0JTIwYW5kJTIwYSUyMHRpbnklMjBoYXQlMkMlMjBzaXRzJTIwb24lMjBhJTIwd29vZGVuJTIwc3Rvb2wlMjBpbiUyMGElMjBzZXJlbmUlMjBiYW1ib28lMjBmb3Jlc3QuJTIwVGhlJTIwcGFuZGEncyUyMGZsdWZmeSUyMHBhd3MlMjBzdHJ1bSUyMGElMjBtaW5pYXR1cmUlMjBhY291c3RpYyUyMGd1aXRhciUyQyUyMHByb2R1Y2luZyUyMHNvZnQlMkMlMjBtZWxvZGljJTIwdHVuZXMuJTIwTmVhcmJ5JTJDJTIwYSUyMGZldyUyMG90aGVyJTIwcGFuZGFzJTIwZ2F0aGVyJTJDJTIwd2F0Y2hpbmclMjBjdXJpb3VzbHklMjBhbmQlMjBzb21lJTIwY2xhcHBpbmclMjBpbiUyMHJoeXRobS4lMjBTdW5saWdodCUyMGZpbHRlcnMlMjB0aHJvdWdoJTIwdGhlJTIwdGFsbCUyMGJhbWJvbyUyQyUyMGNhc3RpbmclMjBhJTIwZ2VudGxlJTIwZ2xvdyUyMG9uJTIwdGhlJTIwc2NlbmUuJTIwVGhlJTIwcGFuZGEncyUyMGZhY2UlMjBpcyUyMGV4cHJlc3NpdmUlMkMlMjBzaG93aW5nJTIwY29uY2VudHJhdGlvbiUyMGFuZCUyMGpveSUyMGFzJTIwaXQlMjBwbGF5cy4lMjBUaGUlMjBiYWNrZ3JvdW5kJTIwaW5jbHVkZXMlMjBhJTIwc21hbGwlMkMlMjBmbG93aW5nJTIwc3RyZWFtJTIwYW5kJTIwdmlicmFudCUyMGdyZWVuJTIwZm9saWFnZSUyQyUyMGVuaGFuY2luZyUyMHRoZSUyMHBlYWNlZnVsJTIwYW5kJTIwbWFnaWNhbCUyMGF0bW9zcGhlcmUlMjBvZiUyMHRoaXMlMjB1bmlxdWUlMjBtdXNpY2FsJTIwcGVyZm9ybWFuY2UuJTIyJTBBdmlkZW8lMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlM0Rwcm9tcHQlMkMlMjBndWlkYW5jZV9zY2FsZSUzRDYlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNENTApLmZyYW1lcyU1QjAlNUQ=",highlighted:`pipeline.transformer = torch.<span class="hljs-built_in">compile</span>(pipeline.transformer)
pipeline.vae.decode = torch.<span class="hljs-built_in">compile</span>(pipeline.vae.decode)

<span class="hljs-comment"># CogVideoX works very well with long and well-described prompts</span>
prompt = <span class="hljs-string">&quot;A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda&#x27;s fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda&#x27;s face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.&quot;</span>
video = pipeline(prompt=prompt, guidance_scale=<span class="hljs-number">6</span>, num_inference_steps=<span class="hljs-number">50</span>).frames[<span class="hljs-number">0</span>]`,wrap:!1}}),F=new ae({props:{code:"V2l0aG91dCUyMHRvcmNoLmNvbXBpbGUoKSUzQSUyMEF2ZXJhZ2UlMjBpbmZlcmVuY2UlMjB0aW1lJTNBJTIwVE9ETyUyMHNlY29uZHMuJTBBV2l0aCUyMHRvcmNoLmNvbXBpbGUoKSUzQSUyMEF2ZXJhZ2UlMjBpbmZlcmVuY2UlMjB0aW1lJTNBJTIwVE9ETyUyMHNlY29uZHMu",highlighted:`Without torch.compile(): Average inference <span class="hljs-built_in">time</span>: TODO <span class="hljs-built_in">seconds</span>.
With torch.compile(): Average inference <span class="hljs-built_in">time</span>: TODO <span class="hljs-built_in">seconds</span>.`,wrap:!1}}),R=new Ge({props:{title:"CogVideoXPipeline",local:"diffusers.CogVideoXPipeline",headingTag:"h2"}}),S=new Ce({props:{name:"class diffusers.CogVideoXPipeline",anchor:"diffusers.CogVideoXPipeline",parameters:[{name:"tokenizer",val:": T5Tokenizer"},{name:"text_encoder",val:": T5EncoderModel"},{name:"vae",val:": AutoencoderKLCogVideoX"},{name:"transformer",val:": CogVideoXTransformer3DModel"},{name:"scheduler",val:": Union"}],parametersDescription:[{anchor:"diffusers.CogVideoXPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.30.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.`,name:"vae"},{anchor:"diffusers.CogVideoXPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>T5EncoderModel</code>) &#x2014;
Frozen text-encoder. CogVideoX uses
<a href="https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel" rel="nofollow">T5</a>; specifically the
<a href="https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl" rel="nofollow">t5-v1_1-xxl</a> variant.`,name:"text_encoder"},{anchor:"diffusers.CogVideoXPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>T5Tokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer" rel="nofollow">T5Tokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.CogVideoXPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/v0.30.0/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>) &#x2014;
A text conditioned <code>CogVideoXTransformer3DModel</code> to denoise the encoded video latents.`,name:"transformer"},{anchor:"diffusers.CogVideoXPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.30.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>transformer</code> to denoise the encoded video latents.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.30.0/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py#L133"}}),Q=new Ce({props:{name:"__call__",anchor:"diffusers.CogVideoXPipeline.__call__",parameters:[{name:"prompt",val:": Union = None"},{name:"negative_prompt",val:": Union = None"},{name:"height",val:": int = 480"},{name:"width",val:": int = 720"},{name:"num_frames",val:": int = 48"},{name:"fps",val:": int = 8"},{name:"num_inference_steps",val:": int = 50"},{name:"timesteps",val:": Optional = None"},{name:"guidance_scale",val:": float = 6"},{name:"use_dynamic_cfg",val:": bool = False"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": Union = None"},{name:"latents",val:": Optional = None"},{name:"prompt_embeds",val:": Optional = None"},{name:"negative_prompt_embeds",val:": Optional = None"},{name:"output_type",val:": str = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": Union = None"},{name:"callback_on_step_end_tensor_inputs",val:": List = ['latents']"},{name:"max_sequence_length",val:": int = 226"}],parametersDescription:[{anchor:"diffusers.CogVideoXPipeline.__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.CogVideoXPipeline.__call__.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.CogVideoXPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image. This is set to 1024 by default for the best results.`,name:"height"},{anchor:"diffusers.CogVideoXPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image. This is set to 1024 by default for the best results.`,name:"width"},{anchor:"diffusers.CogVideoXPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, defaults to <code>48</code>) &#x2014;
Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
needs to be satisfied is that of divisibility mentioned above.`,name:"num_frames"},{anchor:"diffusers.CogVideoXPipeline.__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.CogVideoXPipeline.__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.CogVideoXPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.0) &#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.CogVideoXPipeline.__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.CogVideoXPipeline.__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.CogVideoXPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</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.CogVideoXPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</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.CogVideoXPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</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.CogVideoXPipeline.__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.CogVideoXPipeline.__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.stable_diffusion_xl.StableDiffusionXLPipelineOutput</code> instead
of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.CogVideoXPipeline.__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.CogVideoXPipeline.__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.CogVideoXPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, defaults to <code>226</code>) &#x2014;
Maximum sequence length in encoded prompt. Must be consistent with
<code>self.transformer.config.max_text_seq_length</code> otherwise may lead to poor results.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/v0.30.0/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py#L433",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/v0.30.0/en/api/pipelines/cogvideox#diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput"
>CogVideoXPipelineOutput</a> if <code>return_dict</code> is True, otherwise a
<code>tuple</code>. When returning a tuple, the first element is a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/v0.30.0/en/api/pipelines/cogvideox#diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput"
>CogVideoXPipelineOutput</a> or <code>tuple</code></p>
`}}),Z=new lt({props:{anchor:"diffusers.CogVideoXPipeline.__call__.example",$$slots:{default:[pt]},$$scope:{ctx:A}}}),E=new Ce({props:{name:"encode_prompt",anchor:"diffusers.CogVideoXPipeline.encode_prompt",parameters:[{name:"prompt",val:": Union"},{name:"negative_prompt",val:": Union = None"},{name:"do_classifier_free_guidance",val:": bool = True"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"prompt_embeds",val:": Optional = None"},{name:"negative_prompt_embeds",val:": Optional = None"},{name:"max_sequence_length",val:": int = 226"},{name:"device",val:": Optional = None"},{name:"dtype",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.CogVideoXPipeline.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.CogVideoXPipeline.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.CogVideoXPipeline.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.CogVideoXPipeline.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.CogVideoXPipeline.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.CogVideoXPipeline.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.
device &#x2014; (<code>torch.device</code>, <em>optional</em>):
torch device
dtype &#x2014; (<code>torch.dtype</code>, <em>optional</em>):
torch dtype`,name:"negative_prompt_embeds"}],source:"https://github.com/huggingface/diffusers/blob/v0.30.0/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py#L229"}}),q=new Ge({props:{title:"CogVideoXPipelineOutput",local:"diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput",headingTag:"h2"}}),L=new Ce({props:{name:"class diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput",anchor:"diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput",parameters:[{name:"frames",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput.frames",description:`<strong>frames</strong> (<code>torch.Tensor</code>, <code>np.ndarray</code>, or List[List[PIL.Image.Image]]) &#x2014;
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"}],source:"https://github.com/huggingface/diffusers/blob/v0.30.0/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py#L118"}}),D=new 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