import{S as Nn,i as Rn,s as Hn,e as s,k as l,w as v,t as i,M as qn,c as a,d as o,m as d,x as _,a as n,h as r,b as c,N as zn,f as Fn,G as t,g as f,y as b,q as y,o as w,B as T,v as Yn,L as Qn}from"../../../chunks/vendor-hf-doc-builder.js";import{T as On}from"../../../chunks/Tip-hf-doc-builder.js";import{D as se}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Uo}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as st}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Kn}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ei(We){let u,Z;return{c(){u=s("p"),Z=i("This pipeline is for research purposes only.")},l(x){u=a(x,"P",{});var E=n(u);Z=r(E,"This pipeline is for research purposes only."),E.forEach(o)},m(x,E){f(x,u,E),t(u,Z)},d(x){x&&o(u)}}}function ti(We){let u,Z,x,E,$;return E=new Uo({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> TextToVideoSDPipeline
<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 = TextToVideoSDPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;damo-vilab/text-to-video-ms-1.7b&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>
<span class="hljs-meta">... </span>)
<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;Spiderman is surfing&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>video_frames = pipe(prompt).frames
<span class="hljs-meta">&gt;&gt;&gt; </span>video_path = export_to_video(video_frames)
<span class="hljs-meta">&gt;&gt;&gt; </span>video_path`}}),{c(){u=s("p"),Z=i("Examples:"),x=l(),v(E.$$.fragment)},l(m){u=a(m,"P",{});var J=n(u);Z=r(J,"Examples:"),J.forEach(o),x=d(m),_(E.$$.fragment,m)},m(m,J){f(m,u,J),t(u,Z),f(m,x,J),b(E,m,J),$=!0},p:Qn,i(m){$||(y(E.$$.fragment,m),$=!0)},o(m){w(E.$$.fragment,m),$=!1},d(m){m&&o(u),m&&o(x),T(E,m)}}}function oi(We){let u,Z,x,E,$,m,J,ae,jo,at,Wo,Ht,S,L,nt,ne,Io,it,Co,qt,ie,re,Ao,Bo,Yt,Ie,Lo,Qt,Ce,rt,Xo,Ot,Ae,zo,Kt,V,lt,le,Fo,No,dt,de,Ro,Ho,pt,pe,qo,eo,U,X,ct,ce,Yo,ft,Qo,to,z,ht,j,mt,Oo,Ko,ut,es,ts,Be,os,ss,gt,W,vt,fe,as,ns,_t,bt,is,rs,Le,he,ls,oo,I,F,yt,me,ds,wt,ps,so,Xe,cs,ao,ue,no,ze,fs,io,Fe,hs,ro,ge,lo,N,ms,Tt,us,gs,po,Ne,vs,co,ve,fo,Re,_s,ho,He,_e,xt,R,bs,ys,ws,be,Ja,Ts,Et,H,xs,Es,$s,ye,Va,mo,C,q,$t,we,Ms,Mt,Zs,uo,Y,Zt,Te,Js,Vs,Jt,xe,Ds,go,A,Q,Vt,Ee,Ps,Dt,ks,vo,h,$e,Gs,Pt,Ss,Us,Me,js,qe,Ws,Is,Cs,D,Ze,As,kt,Bs,Ls,O,Xs,K,Je,zs,Ve,Fs,Gt,Ns,Rs,Hs,ee,De,qs,Pe,Ys,St,Qs,Os,Ks,te,ke,ea,M,ta,Ut,oa,sa,jt,aa,na,Wt,ia,ra,It,la,da,pa,oe,Ge,ca,B,fa,Ct,ha,ma,At,ua,ga,va,P,Se,_a,Bt,ba,ya,Lt,wa,Ta,k,Ue,xa,Xt,Ea,$a,zt,Ma,_o;return x=new On({props:{warning:!0,$$slots:{default:[ei]},$$scope:{ctx:We}}}),ae=new st({}),ne=new st({}),ce=new st({}),me=new st({}),ue=new Uo({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> DiffusionPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;damo-vilab/text-to-video-ms-1.7b&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;Spiderman is surfing&quot;</span>
video_frames = pipe(prompt).frames
video_path = export_to_video(video_frames)
video_path`}}),ge=new Uo({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> DiffusionPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;damo-vilab/text-to-video-ms-1.7b&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipe.enable_model_cpu_offload()

<span class="hljs-comment"># memory optimization</span>
pipe.enable_vae_slicing()

prompt = <span class="hljs-string">&quot;Darth Vader surfing a wave&quot;</span>
video_frames = pipe(prompt, num_frames=<span class="hljs-number">64</span>).frames
video_path = export_to_video(video_frames)
video_path`}}),ve=new Uo({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> DiffusionPipeline, DPMSolverMultistepScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;damo-vilab/text-to-video-ms-1.7b&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;Spiderman is surfing&quot;</span>
video_frames = pipe(prompt, num_inference_steps=<span class="hljs-number">25</span>).frames
video_path = export_to_video(video_frames)
video_path`}}),we=new st({}),Ee=new st({}),$e=new se({props:{name:"class diffusers.TextToVideoSDPipeline",anchor:"diffusers.TextToVideoSDPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet3DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"}],parametersDescription:[{anchor:"diffusers.TextToVideoSDPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.17.0/en/api/models#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.TextToVideoSDPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder. Same as Stable Diffusion 2.`,name:"text_encoder"},{anchor:"diffusers.TextToVideoSDPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.TextToVideoSDPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.17.0/en/api/models#diffusers.UNet3DConditionModel">UNet3DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded video latents.',name:"unet"},{anchor:"diffusers.TextToVideoSDPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.17.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.17.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.17.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py#L76"}}),Ze=new se({props:{name:"__call__",anchor:"diffusers.TextToVideoSDPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_frames",val:": int = 16"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 9.0"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'np'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.TextToVideoSDPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the video generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.TextToVideoSDPipeline.__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 video.`,name:"height"},{anchor:"diffusers.TextToVideoSDPipeline.__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 video.`,name:"width"},{anchor:"diffusers.TextToVideoSDPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video.`,name:"num_frames"},{anchor:"diffusers.TextToVideoSDPipeline.__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 videos at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.TextToVideoSDPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#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 videos that are closely linked to the text <code>prompt</code>,
usually at the expense of lower video quality.`,name:"guidance_scale"},{anchor:"diffusers.TextToVideoSDPipeline.__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 video 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.TextToVideoSDPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.TextToVideoSDPipeline.__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.TextToVideoSDPipeline.__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 video
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>. Latents should be of shape
<code>(batch_size, num_channel, num_frames, height, width)</code>.`,name:"latents"},{anchor:"diffusers.TextToVideoSDPipeline.__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.TextToVideoSDPipeline.__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.TextToVideoSDPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;np&quot;</code>) &#x2014;
The output format of the generate video. Choose between <code>torch.FloatTensor</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.TextToVideoSDPipeline.__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.TextToVideoSDPipelineOutput</code> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.TextToVideoSDPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that will be called every <code>callback_steps</code> steps during inference. The function will be
called with the following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.TextToVideoSDPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"},{anchor:"diffusers.TextToVideoSDPipeline.__call__.cross_attention_kwargs",description:`<strong>cross_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/cross_attention.py" rel="nofollow">diffusers.cross_attention</a>.`,name:"cross_attention_kwargs"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py#L486",returnDescription:`
<p><code>~pipelines.stable_diffusion.TextToVideoSDPipelineOutput</code> if <code>return_dict</code> is True, otherwise a \`tuple.
When returning a tuple, the first element is a list with the generated frames.</p>
`,returnType:`
<p><code>~pipelines.stable_diffusion.TextToVideoSDPipelineOutput</code> or <code>tuple</code></p>
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