import{S as ip,i as lp,s as pp,e as a,k as p,w as h,t as n,M as dp,c as r,d as o,m as d,a as s,x as m,h as i,b as l,N as cp,G as e,g as f,y as u,L as fp,q as _,o as g,B as v,v as hp}from"../../../chunks/vendor-hf-doc-builder.js";import{D as fr}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as V}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function mp(li){let q,ea,N,W,Ot,_e,hr,Mt,mr,ta,L,J,Rt,ge,ur,Ft,_r,oa,T,ve,gr,vr,be,br,wr,we,xr,yr,aa,ut,Pr,ra,k,Gt,Tr,$r,Ht,Er,kr,Ut,jr,sa,_t,Dr,na,gt,vt,pi,ia,bt,Cr,la,wt,Bt,Sr,pa,xt,Ar,da,j,Kt,xe,Vr,Ir,Yt,ye,qr,Nr,Wt,Pe,Lr,ca,Z,Q,Jt,Te,Zr,Qt,zr,fa,X,Xt,z,eo,Or,Mr,to,Rr,Fr,yt,Gr,Hr,oo,O,ao,$e,Ur,Br,ro,so,Kr,Yr,Pt,Ee,Wr,ha,M,ee,no,ke,Jr,io,Qr,ma,R,te,lo,je,Xr,po,es,ua,Tt,ts,_a,De,ga,$t,os,va,D,oe,as,Ce,rs,ss,co,$,fo,ns,is,ho,ls,ps,mo,ds,cs,uo,fs,hs,E,_o,ms,us,go,_s,gs,Se,vs,bs,vo,y,bo,ws,xs,wo,ys,Ps,xo,Ts,$s,yo,Es,ks,Po,js,Ds,Et,Cs,To,Ae,$o,Ss,As,Eo,Vs,ba,F,ae,ko,Ve,Is,jo,qs,wa,kt,Ie,Do,Ns,Ls,qe,xa,G,H,Co,Zs,zs,Ne,Os,Le,Ms,jt,Rs,Fs,Gs,Ze,ze,Hs,So,Us,Bs,Ks,Oe,ya,U,re,Ao,Me,Ys,Vo,Ws,Pa,se,Js,Re,Qs,Xs,Ta,B,ne,Io,Fe,en,qo,tn,$a,ie,on,Dt,an,rn,Ea,C,Ge,No,sn,nn,He,ln,Ue,Lo,pn,dn,Be,cn,Ke,Ye,fn,Zo,hn,mn,un,We,ka,K,le,zo,Je,_n,Oo,gn,ja,b,vn,Mo,bn,wn,Ro,xn,yn,Fo,Pn,Tn,Ct,$n,En,Qe,kn,jn,Xe,Dn,Cn,Da,S,et,Go,Sn,An,tt,Vn,ot,Ho,In,qn,at,Nn,rt,st,Ln,Uo,Zn,zn,On,nt,Ca,pe,Mn,it,Rn,Fn,Sa,Y,de,Bo,lt,Gn,Ko,Hn,Aa,w,pt,Un,Yo,Bn,Kn,dt,Yn,St,Wn,Jn,Qn,ce,ct,Xn,Wo,ei,ti,fe,ft,oi,Jo,ai,ri,he,ht,si,Qo,ni,Va;return _e=new I({}),ge=new I({}),Te=new I({}),ke=new I({}),je=new I({}),De=new V({props:{code:`import torch
import imageio
from diffusers import TextToVideoZeroPipeline

model_id = "runwayml/stable-diffusion-v1-5"
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

prompt = "A panda is playing guitar on times square"
result = pipe(prompt=prompt).images
result = [(r * 255).astype("uint8") for r in result]
imageio.mimsave("video.mp4", result, fps=4)`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> imageio
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> TextToVideoZeroPipeline

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A panda is playing guitar on times square&quot;</span>
result = pipe(prompt=prompt).images
result = [(r * <span class="hljs-number">255</span>).astype(<span class="hljs-string">&quot;uint8&quot;</span>) <span class="hljs-keyword">for</span> r <span class="hljs-keyword">in</span> result]
imageio.mimsave(<span class="hljs-string">&quot;video.mp4&quot;</span>, result, fps=<span class="hljs-number">4</span>)`}}),Ve=new I({}),qe=new V({props:{code:`from huggingface_hub import hf_hub_download

filename = "__assets__/poses_skeleton_gifs/dance1_corr.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)`,highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

filename = <span class="hljs-string">&quot;__assets__/poses_skeleton_gifs/dance1_corr.mp4&quot;</span>
repo_id = <span class="hljs-string">&quot;PAIR/Text2Video-Zero&quot;</span>
video_path = hf_hub_download(repo_type=<span class="hljs-string">&quot;space&quot;</span>, repo_id=repo_id, filename=filename)`}}),Ne=new V({props:{code:`from PIL import Image
import imageio

reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]`,highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> imageio

reader = imageio.get_reader(video_path, <span class="hljs-string">&quot;ffmpeg&quot;</span>)
frame_count = <span class="hljs-number">8</span>
pose_images = [Image.fromarray(reader.get_data(i)) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(frame_count)]`}}),Oe=new V({props:{code:`import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor

model_id = "runwayml/stable-diffusion-v1-5"
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    model_id, controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")

# Set the attention processor
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))

# fix latents for all frames
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)

prompt = "Darth Vader dancing in a desert"
result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
imageio.mimsave("video.mp4", result, fps=4)`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionControlNetPipeline, ControlNetModel
<span class="hljs-keyword">from</span> diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero <span class="hljs-keyword">import</span> CrossFrameAttnProcessor

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
controlnet = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;lllyasviel/sd-controlnet-openpose&quot;</span>, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    model_id, controlnet=controlnet, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># Set the attention processor</span>
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=<span class="hljs-number">2</span>))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=<span class="hljs-number">2</span>))

<span class="hljs-comment"># fix latents for all frames</span>
latents = torch.randn((<span class="hljs-number">1</span>, <span class="hljs-number">4</span>, <span class="hljs-number">64</span>, <span class="hljs-number">64</span>), device=<span class="hljs-string">&quot;cuda&quot;</span>, dtype=torch.float16).repeat(<span class="hljs-built_in">len</span>(pose_images), <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>)

prompt = <span class="hljs-string">&quot;Darth Vader dancing in a desert&quot;</span>
result = pipe(prompt=[prompt] * <span class="hljs-built_in">len</span>(pose_images), image=pose_images, latents=latents).images
imageio.mimsave(<span class="hljs-string">&quot;video.mp4&quot;</span>, result, fps=<span class="hljs-number">4</span>)`}}),Me=new I({}),Fe=new I({}),He=new V({props:{code:`from huggingface_hub import hf_hub_download

filename = "__assets__/pix2pix video/camel.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)`,highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

filename = <span class="hljs-string">&quot;__assets__/pix2pix video/camel.mp4&quot;</span>
repo_id = <span class="hljs-string">&quot;PAIR/Text2Video-Zero&quot;</span>
video_path = hf_hub_download(repo_type=<span class="hljs-string">&quot;space&quot;</span>, repo_id=repo_id, filename=filename)`}}),Be=new V({props:{code:`from PIL import Image
import imageio

reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]`,highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> imageio

reader = imageio.get_reader(video_path, <span class="hljs-string">&quot;ffmpeg&quot;</span>)
frame_count = <span class="hljs-number">8</span>
video = [Image.fromarray(reader.get_data(i)) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(frame_count)]`}}),We=new V({props:{code:`import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor

model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))

prompt = "make it Van Gogh Starry Night style"
result = pipe(prompt=[prompt] * len(video), image=video).images
imageio.mimsave("edited_video.mp4", result, fps=4)`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionInstructPix2PixPipeline
<span class="hljs-keyword">from</span> diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero <span class="hljs-keyword">import</span> CrossFrameAttnProcessor

model_id = <span class="hljs-string">&quot;timbrooks/instruct-pix2pix&quot;</span>
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=<span class="hljs-number">3</span>))

prompt = <span class="hljs-string">&quot;make it Van Gogh Starry Night style&quot;</span>
result = pipe(prompt=[prompt] * <span class="hljs-built_in">len</span>(video), image=video).images
imageio.mimsave(<span class="hljs-string">&quot;edited_video.mp4&quot;</span>, result, fps=<span class="hljs-number">4</span>)`}}),Je=new I({}),tt=new V({props:{code:`from huggingface_hub import hf_hub_download

filename = "__assets__/canny_videos_mp4/girl_turning.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)`,highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

filename = <span class="hljs-string">&quot;__assets__/canny_videos_mp4/girl_turning.mp4&quot;</span>
repo_id = <span class="hljs-string">&quot;PAIR/Text2Video-Zero&quot;</span>
video_path = hf_hub_download(repo_type=<span class="hljs-string">&quot;space&quot;</span>, repo_id=repo_id, filename=filename)`}}),at=new V({props:{code:`from PIL import Image
import imageio

reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]`,highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> imageio

reader = imageio.get_reader(video_path, <span class="hljs-string">&quot;ffmpeg&quot;</span>)
frame_count = <span class="hljs-number">8</span>
video = [Image.fromarray(reader.get_data(i)) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(frame_count)]`}}),nt=new V({props:{code:`import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor

# set model id to custom model
model_id = "PAIR/text2video-zero-controlnet-canny-avatar"
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    model_id, controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")

# Set the attention processor
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))

# fix latents for all frames
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)

prompt = "oil painting of a beautiful girl avatar style"
result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
imageio.mimsave("video.mp4", result, fps=4)`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionControlNetPipeline, ControlNetModel
<span class="hljs-keyword">from</span> diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero <span class="hljs-keyword">import</span> CrossFrameAttnProcessor

<span class="hljs-comment"># set model id to custom model</span>
model_id = <span class="hljs-string">&quot;PAIR/text2video-zero-controlnet-canny-avatar&quot;</span>
controlnet = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;lllyasviel/sd-controlnet-canny&quot;</span>, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    model_id, controlnet=controlnet, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># Set the attention processor</span>
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=<span class="hljs-number">2</span>))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=<span class="hljs-number">2</span>))

<span class="hljs-comment"># fix latents for all frames</span>
latents = torch.randn((<span class="hljs-number">1</span>, <span class="hljs-number">4</span>, <span class="hljs-number">64</span>, <span class="hljs-number">64</span>), device=<span class="hljs-string">&quot;cuda&quot;</span>, dtype=torch.float16).repeat(<span class="hljs-built_in">len</span>(pose_images), <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>)

prompt = <span class="hljs-string">&quot;oil painting of a beautiful girl avatar style&quot;</span>
result = pipe(prompt=[prompt] * <span class="hljs-built_in">len</span>(pose_images), image=pose_images, latents=latents).images
imageio.mimsave(<span class="hljs-string">&quot;video.mp4&quot;</span>, result, fps=<span class="hljs-number">4</span>)`}}),lt=new I({}),pt=new fr({props:{name:"class diffusers.TextToVideoZeroPipeline",anchor:"diffusers.TextToVideoZeroPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.TextToVideoZeroPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.16.0/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.TextToVideoZeroPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder. Stable Diffusion uses the text portion of
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIP</a>, specifically
the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.TextToVideoZeroPipeline.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.TextToVideoZeroPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.TextToVideoZeroPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.16.0/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/v0.16.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.16.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.16.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.TextToVideoZeroPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionSafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow">model card</a> for details.`,name:"safety_checker"},{anchor:"diffusers.TextToVideoZeroPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L188"}}),ct=new fr({props:{name:"__call__",anchor:"diffusers.TextToVideoZeroPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"video_length",val:": typing.Optional[int] = 8"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 7.5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_videos_per_prompt",val:": typing.Optional[int] = 1"},{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:"motion_field_strength_x",val:": float = 12"},{name:"motion_field_strength_y",val:": float = 12"},{name:"output_type",val:": typing.Optional[str] = 'tensor'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": typing.Optional[int] = 1"},{name:"t0",val:": int = 44"},{name:"t1",val:": int = 47"}],parametersDescription:[{anchor:"diffusers.TextToVideoZeroPipeline.__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.TextToVideoZeroPipeline.__call__.video_length",description:"<strong>video_length</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014; The number of generated video frames",name:"video_length"},{anchor:"diffusers.TextToVideoZeroPipeline.__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.`,name:"height"},{anchor:"diffusers.TextToVideoZeroPipeline.__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.`,name:"width"},{anchor:"diffusers.TextToVideoZeroPipeline.__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.TextToVideoZeroPipeline.__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 images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.TextToVideoZeroPipeline.__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.TextToVideoZeroPipeline.__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.TextToVideoZeroPipeline.__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/v0.16.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.TextToVideoZeroPipeline.__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>
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<p>The output contains a ndarray of the generated images, when output_type != \u2018latent\u2019, otherwise a latent
codes of generated image, and a list of <code>bool</code>s denoting whether the corresponding generated image
likely represents \u201Cnot-safe-for-work\u201D (nsfw) content, according to the <code>safety_checker</code>.</p>
`,returnType:`
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The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.
extra_step_kwargs &#x2014; extra_step_kwargs.
cross_attention_kwargs &#x2014; cross_attention_kwargs.
num_warmup_steps &#x2014; number of warmup steps.`,name:"callback_steps"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L250",returnDescription:`
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Andranik Movsisyan,
Vahram Tadevosyan,
Roberto Henschel,
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Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object.
Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing.
As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.`),ki.forEach(o),Ei.forEach(o),pa=d(t),xt=r(t,"P",{});var ji=s(xt);Ar=i(ji,"Resources:"),ji.forEach(o),da=d(t),j=r(t,"UL",{});var Vt=s(j);Kt=r(Vt,"LI",{});var Di=s(Kt);xe=r(Di,"A",{href:!0,rel:!0});var Ci=s(xe);Vr=i(Ci,"Project Page"),Ci.forEach(o),Di.forEach(o),Ir=d(Vt),Yt=r(Vt,"LI",{});var Si=s(Yt);ye=r(Si,"A",{href:!0,rel:!0});var Ai=s(ye);qr=i(Ai,"Paper"),Ai.forEach(o),Si.forEach(o),Nr=d(Vt),Wt=r(Vt,"LI",{});var Vi=s(Wt);Pe=r(Vi,"A",{href:!0,rel:!0});var Ii=s(Pe);Lr=i(Ii,"Original Code"),Ii.forEach(o),Vi.forEach(o),Vt.forEach(o),ca=d(t),Z=r(t,"H2",{class:!0});var Na=s(Z);Q=r(Na,"A",{id:!0,class:!0,href:!0});var qi=s(Q);Jt=r(qi,"SPAN",{});var Ni=s(Jt);m(Te.$$.fragment,Ni),Ni.forEach(o),qi.forEach(o),Zr=d(Na),Qt=r(Na,"SPAN",{});var Li=s(Qt);zr=i(Li,"Available Pipelines:"),Li.forEach(o),Na.forEach(o),fa=d(t),X=r(t,"TABLE",{});var La=s(X);Xt=r(La,"THEAD",{});var Zi=s(Xt);z=r(Zi,"TR",{});var It=s(z);eo=r(It,"TH",{});var zi=s(eo);Or=i(zi,"Pipeline"),zi.forEach(o),Mr=d(It),to=r(It,"TH",{});var Oi=s(to);Rr=i(Oi,"Tasks"),Oi.forEach(o),Fr=d(It),yt=r(It,"TH",{align:!0});var Mi=s(yt);Gr=i(Mi,"Demo"),Mi.forEach(o),It.forEach(o),Zi.forEach(o),Hr=d(La),oo=r(La,"TBODY",{});var Ri=s(oo);O=r(Ri,"TR",{});var qt=s(O);ao=r(qt,"TD",{});var Fi=s(ao);$e=r(Fi,"A",{href:!0,rel:!0});var Gi=s($e);Ur=i(Gi,"TextToVideoZeroPipeline"),Gi.forEach(o),Fi.forEach(o),Br=d(qt),ro=r(qt,"TD",{});var Hi=s(ro);so=r(Hi,"EM",{});var Ui=s(so);Kr=i(Ui,"Zero-shot Text-to-Video Generation"),Ui.forEach(o),Hi.forEach(o),Yr=d(qt),Pt=r(qt,"TD",{align:!0});var Bi=s(Pt);Ee=r(Bi,"A",{href:!0,rel:!0});var Ki=s(Ee);Wr=i(Ki,"\u{1F917} Space"),Ki.forEach(o),Bi.forEach(o),qt.forEach(o),Ri.forEach(o),La.forEach(o),ha=d(t),M=r(t,"H2",{class:!0});var Za=s(M);ee=r(Za,"A",{id:!0,class:!0,href:!0});var Yi=s(ee);no=r(Yi,"SPAN",{});var Wi=s(no);m(ke.$$.fragment,Wi),Wi.forEach(o),Yi.forEach(o),Jr=d(Za),io=r(Za,"SPAN",{});var Ji=s(io);Qr=i(Ji,"Usage example"),Ji.forEach(o),Za.forEach(o),ma=d(t),R=r(t,"H3",{class:!0});var za=s(R);te=r(za,"A",{id:!0,class:!0,href:!0});var Qi=s(te);lo=r(Qi,"SPAN",{});var Xi=s(lo);m(je.$$.fragment,Xi),Xi.forEach(o),Qi.forEach(o),Xr=d(za),po=r(za,"SPAN",{});var el=s(po);es=i(el,"Text-To-Video"),el.forEach(o),za.forEach(o),ua=d(t),Tt=r(t,"P",{});var tl=s(Tt);ts=i(tl,"To generate a video from prompt, run the following python command"),tl.forEach(o),_a=d(t),m(De.$$.fragment,t),ga=d(t),$t=r(t,"P",{});var ol=s($t);os=i(ol,"You can change these parameters in the pipeline call:"),ol.forEach(o),va=d(t),D=r(t,"UL",{});var Nt=s(D);oe=r(Nt,"LI",{});var Xo=s(oe);as=i(Xo,"Motion field strength (see the "),Ce=r(Xo,"A",{href:!0,rel:!0});var al=s(Ce);rs=i(al,"paper"),al.forEach(o),ss=i(Xo,", Sect. 3.3.1):"),co=r(Xo,"UL",{});var rl=s(co);$=r(rl,"LI",{});var me=s($);fo=r(me,"CODE",{});var sl=s(fo);ns=i(sl,"motion_field_strength_x"),sl.forEach(o),is=i(me," and "),ho=r(me,"CODE",{});var nl=s(ho);ls=i(nl,"motion_field_strength_y"),nl.forEach(o),ps=i(me,". Default: "),mo=r(me,"CODE",{});var il=s(mo);ds=i(il,"motion_field_strength_x=12"),il.forEach(o),cs=i(me,", "),uo=r(me,"CODE",{});var ll=s(uo);fs=i(ll,"motion_field_strength_y=12"),ll.forEach(o),me.forEach(o),rl.forEach(o),Xo.forEach(o),hs=d(Nt),E=r(Nt,"LI",{});var ue=s(E);_o=r(ue,"CODE",{});var pl=s(_o);ms=i(pl,"T"),pl.forEach(o),us=i(ue," and "),go=r(ue,"CODE",{});var dl=s(go);_s=i(dl,"T'"),dl.forEach(o),gs=i(ue," (see the "),Se=r(ue,"A",{href:!0,rel:!0});var cl=s(Se);vs=i(cl,"paper"),cl.forEach(o),bs=i(ue,", Sect. 3.3.1)"),vo=r(ue,"UL",{});var fl=s(vo);y=r(fl,"LI",{});var A=s(y);bo=r(A,"CODE",{});var hl=s(bo);ws=i(hl,"t0"),hl.forEach(o),xs=i(A," and "),wo=r(A,"CODE",{});var ml=s(wo);ys=i(ml,"t1"),ml.forEach(o),Ps=i(A," in the range "),xo=r(A,"CODE",{});var ul=s(xo);Ts=i(ul,"{0, ..., num_inference_steps}"),ul.forEach(o),$s=i(A,". Default: "),yo=r(A,"CODE",{});var _l=s(yo);Es=i(_l,"t0=45"),_l.forEach(o),ks=i(A,", "),Po=r(A,"CODE",{});var gl=s(Po);js=i(gl,"t1=48"),gl.forEach(o),A.forEach(o),fl.forEach(o),ue.forEach(o),Ds=d(Nt),Et=r(Nt,"LI",{});var ii=s(Et);Cs=i(ii,"Video length:"),To=r(ii,"UL",{});var vl=s(To);Ae=r(vl,"LI",{});var Oa=s(Ae);$o=r(Oa,"CODE",{});var bl=s($o);Ss=i(bl,"video_length"),bl.forEach(o),As=i(Oa,", the number of frames video_length to be generated. Default: "),Eo=r(Oa,"CODE",{});var wl=s(Eo);Vs=i(wl,"video_length=8"),wl.forEach(o),Oa.forEach(o),vl.forEach(o),ii.forEach(o),Nt.forEach(o),ba=d(t),F=r(t,"H3",{class:!0});var Ma=s(F);ae=r(Ma,"A",{id:!0,class:!0,href:!0});var xl=s(ae);ko=r(xl,"SPAN",{});var yl=s(ko);m(Ve.$$.fragment,yl),yl.forEach(o),xl.forEach(o),Is=d(Ma),jo=r(Ma,"SPAN",{});var Pl=s(jo);qs=i(Pl,"Text-To-Video with Pose Control"),Pl.forEach(o),Ma.forEach(o),wa=i(t,`

To generate a video from prompt with additional pose control
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can run with custom `),Ct=r(x,"A",{href:!0});var Ul=s(Ct);$n=i(Ul,"DreamBooth"),Ul.forEach(o),En=i(x,` models, as shown below for
`),Qe=r(x,"A",{href:!0,rel:!0});var Bl=s(Qe);kn=i(Bl,"Canny edge ControlNet model"),Bl.forEach(o),jn=i(x,` and
`),Xe=r(x,"A",{href:!0,rel:!0});var Kl=s(Xe);Dn=i(Kl,"Avatar style DreamBooth"),Kl.forEach(o),Cn=i(x," model"),x.forEach(o),Da=d(t),S=r(t,"OL",{});var zt=s(S);et=r(zt,"LI",{});var or=s(et);Go=r(or,"P",{});var Yl=s(Go);Sn=i(Yl,"Download a demo video"),Yl.forEach(o),An=d(or),m(tt.$$.fragment,or),or.forEach(o),Vn=d(zt),ot=r(zt,"LI",{});var ar=s(ot);Ho=r(ar,"P",{});var Wl=s(Ho);In=i(Wl,"Read video from path"),Wl.forEach(o),qn=d(ar),m(at.$$.fragment,ar),ar.forEach(o),Nn=d(zt),rt=r(zt,"LI",{});var rr=s(rt);st=r(rr,"P",{});var sr=s(st);Ln=i(sr,"Run "),Uo=r(sr,"CODE",{});var Jl=s(Uo);Zn=i(Jl,"StableDiffusionControlNetPipeline"),Jl.forEach(o),zn=i(sr," with custom trained DreamBooth model"),sr.forEach(o),On=d(rr),m(nt.$$.fragment,rr),rr.forEach(o),zt.forEach(o),Ca=d(t),pe=r(t,"P",{});var nr=s(pe);Mn=i(nr,"You can filter out some available DreamBooth-trained models with "),it=r(nr,"A",{href:!0,rel:!0});var Ql=s(it);Rn=i(Ql,"this link"),Ql.forEach(o),Fn=i(nr,"."),nr.forEach(o),Sa=d(t),Y=r(t,"H2",{class:!0});var ir=s(Y);de=r(ir,"A",{id:!0,class:!0,href:!0});var Xl=s(de);Bo=r(Xl,"SPAN",{});var ep=s(Bo);m(lt.$$.fragment,ep),ep.forEach(o),Xl.forEach(o),Gn=d(ir),Ko=r(ir,"SPAN",{});var tp=s(Ko);Hn=i(tp,"TextToVideoZeroPipeline"),tp.forEach(o),ir.forEach(o),Aa=d(t),w=r(t,"DIV",{class:!0});var P=s(w);m(pt.$$.fragment,P),Un=d(P),Yo=r(P,"P",{});var op=s(Yo);Bn=i(op,"Pipeline for zero-shot text-to-video generation using Stable Diffusion."),op.forEach(o),Kn=d(P),dt=r(P,"P",{});var lr=s(dt);Yn=i(lr,"This model inherits from "),St=r(lr,"A",{href:!0});var ap=s(St);Wn=i(ap,"StableDiffusionPipeline"),ap.forEach(o),Jn=i(lr,`. Check the superclass documentation for the generic methods
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