import{S as ui,i as _i,s as yi,e as s,k as d,w as m,t as l,M as gi,c as n,d as o,m as c,a,x as f,h as r,b as i,N as vi,G as t,g as h,y as u,L as bi,q as _,o as y,B as g,v as wi}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Bo}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as U}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as F}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Ti(Vl){let B,No,N,A,Wt,me,Ys,Vt,Fs,Go,Z,fe,As,Hs,ue,Ls,qs,_e,Os,Ks,Co,ct,en,Ro,I,Et,tn,on,Bt,sn,nn,Nt,an,Xo,ht,ln,Po,mt,ft,El,$o,ut,rn,So,_t,Gt,pn,Qo,J,dn,ye,cn,hn,ge,mn,fn,ve,un,_n,zo,G,H,Ct,be,yn,Rt,gn,Do,C,L,Xt,we,vn,Pt,bn,Yo,yt,wn,Fo,Te,Ao,gt,Tn,Ho,k,q,Jn,Je,Mn,Zn,$t,x,St,xn,jn,Qt,Un,In,zt,kn,Wn,Dt,Vn,En,j,Yt,Bn,Nn,Ft,Gn,Cn,Me,Rn,Xn,At,T,Ht,Pn,$n,Lt,Sn,Qn,qt,zn,Dn,Ot,Yn,Fn,Kt,An,Hn,vt,Ln,eo,Ze,to,qn,On,oo,Kn,Lo,bt,ea,qo,xe,Oo,R,O,so,je,ta,no,oa,Ko,wt,Ue,ao,sa,na,Ie,es,X,P,lo,aa,la,ke,ra,We,ia,Tt,pa,da,ca,Ve,Ee,ha,ro,ma,fa,ua,Be,ts,$,K,io,Ne,_a,po,ya,os,ee,ga,Ge,va,ba,ss,S,te,co,Ce,wa,ho,Ta,ns,oe,Ja,Jt,Ma,Za,as,W,Re,mo,xa,ja,Xe,Ua,Pe,fo,Ia,ka,$e,Wa,Se,Qe,Va,uo,Ea,Ba,Na,ze,ls,Q,se,_o,De,Ga,yo,Ca,rs,v,Ra,go,Xa,Pa,vo,$a,Sa,bo,Qa,za,Mt,Da,Ya,Ye,Fa,Aa,Fe,Ha,La,is,V,Ae,wo,qa,Oa,He,Ka,Le,To,el,tl,qe,ol,Oe,Ke,sl,Jo,nl,al,ll,et,ps,ne,rl,tt,il,pl,ds,z,ae,Mo,ot,dl,Zo,cl,cs,b,st,hl,xo,ml,fl,nt,ul,Zt,_l,yl,gl,le,at,vl,jo,bl,wl,re,lt,Tl,Uo,Jl,Ml,ie,rt,Zl,Io,xl,hs,D,pe,ko,it,jl,Wo,Ul,ms,Y,pt,Il,Vo,kl,fs;return me=new F({}),be=new F({}),we=new F({}),Te=new U({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjBpbWFnZWlvJTBBZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFRleHRUb1ZpZGVvWmVyb1BpcGVsaW5lJTBBJTBBbW9kZWxfaWQlMjAlM0QlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUwQXBpcGUlMjAlM0QlMjBUZXh0VG9WaWRlb1plcm9QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQobW9kZWxfaWQlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpLnRvKCUyMmN1ZGElMjIpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyQSUyMHBhbmRhJTIwaXMlMjBwbGF5aW5nJTIwZ3VpdGFyJTIwb24lMjB0aW1lcyUyMHNxdWFyZSUyMiUwQXJlc3VsdCUyMCUzRCUyMHBpcGUocHJvbXB0JTNEcHJvbXB0KS5pbWFnZXMlMEFyZXN1bHQlMjAlM0QlMjAlNUIociUyMColMjAyNTUpLmFzdHlwZSglMjJ1aW50OCUyMiklMjBmb3IlMjByJTIwaW4lMjByZXN1bHQlNUQlMEFpbWFnZWlvLm1pbXNhdmUoJTIydmlkZW8ubXA0JTIyJTJDJTIwcmVzdWx0JTJDJTIwZnBzJTNENCk=",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>)`}}),xe=new U({props:{code:"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",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
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

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>)
seed = <span class="hljs-number">0</span>
video_length = <span class="hljs-number">8</span>
chunk_size = <span class="hljs-number">4</span>
prompt = <span class="hljs-string">&quot;A panda is playing guitar on times square&quot;</span>

<span class="hljs-comment"># Generate the video chunk-by-chunk</span>
result = []
chunk_ids = np.arange(<span class="hljs-number">0</span>, video_length, chunk_size - <span class="hljs-number">1</span>)
generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(chunk_ids)):
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Processing chunk <span class="hljs-subst">{i + <span class="hljs-number">1</span>}</span> / <span class="hljs-subst">{<span class="hljs-built_in">len</span>(chunk_ids)}</span>&quot;</span>)
    ch_start = chunk_ids[i]
    ch_end = video_length <span class="hljs-keyword">if</span> i == <span class="hljs-built_in">len</span>(chunk_ids) - <span class="hljs-number">1</span> <span class="hljs-keyword">else</span> chunk_ids[i + <span class="hljs-number">1</span>]
    <span class="hljs-comment"># Attach the first frame for Cross Frame Attention</span>
    frame_ids = [<span class="hljs-number">0</span>] + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(ch_start, ch_end))
    <span class="hljs-comment"># Fix the seed for the temporal consistency</span>
    generator.manual_seed(seed)
    output = pipe(prompt=prompt, video_length=<span class="hljs-built_in">len</span>(frame_ids), generator=generator, frame_ids=frame_ids)
    result.append(output.images[<span class="hljs-number">1</span>:])

<span class="hljs-comment"># Concatenate chunks and save</span>
result = np.concatenate(result)
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>)`}}),je=new F({}),Ie=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQSUwQWZpbGVuYW1lJTIwJTNEJTIwJTIyX19hc3NldHNfXyUyRnBvc2VzX3NrZWxldG9uX2dpZnMlMkZkYW5jZTFfY29yci5tcDQlMjIlMEFyZXBvX2lkJTIwJTNEJTIwJTIyUEFJUiUyRlRleHQyVmlkZW8tWmVybyUyMiUwQXZpZGVvX3BhdGglMjAlM0QlMjBoZl9odWJfZG93bmxvYWQocmVwb190eXBlJTNEJTIyc3BhY2UlMjIlMkMlMjByZXBvX2lkJTNEcmVwb19pZCUyQyUyMGZpbGVuYW1lJTNEZmlsZW5hbWUp",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)`}}),ke=new U({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwaW1hZ2VpbyUwQSUwQXJlYWRlciUyMCUzRCUyMGltYWdlaW8uZ2V0X3JlYWRlcih2aWRlb19wYXRoJTJDJTIwJTIyZmZtcGVnJTIyKSUwQWZyYW1lX2NvdW50JTIwJTNEJTIwOCUwQXBvc2VfaW1hZ2VzJTIwJTNEJTIwJTVCSW1hZ2UuZnJvbWFycmF5KHJlYWRlci5nZXRfZGF0YShpKSklMjBmb3IlMjBpJTIwaW4lMjByYW5nZShmcmFtZV9jb3VudCklNUQ=",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)]`}}),Be=new U({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> 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>)`}}),Ne=new F({}),Ce=new F({}),Xe=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQSUwQWZpbGVuYW1lJTIwJTNEJTIwJTIyX19hc3NldHNfXyUyRnBpeDJwaXglMjB2aWRlbyUyRmNhbWVsLm1wNCUyMiUwQXJlcG9faWQlMjAlM0QlMjAlMjJQQUlSJTJGVGV4dDJWaWRlby1aZXJvJTIyJTBBdmlkZW9fcGF0aCUyMCUzRCUyMGhmX2h1Yl9kb3dubG9hZChyZXBvX3R5cGUlM0QlMjJzcGFjZSUyMiUyQyUyMHJlcG9faWQlM0RyZXBvX2lkJTJDJTIwZmlsZW5hbWUlM0RmaWxlbmFtZSk=",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)`}}),$e=new U({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwaW1hZ2VpbyUwQSUwQXJlYWRlciUyMCUzRCUyMGltYWdlaW8uZ2V0X3JlYWRlcih2aWRlb19wYXRoJTJDJTIwJTIyZmZtcGVnJTIyKSUwQWZyYW1lX2NvdW50JTIwJTNEJTIwOCUwQXZpZGVvJTIwJTNEJTIwJTVCSW1hZ2UuZnJvbWFycmF5KHJlYWRlci5nZXRfZGF0YShpKSklMjBmb3IlMjBpJTIwaW4lMjByYW5nZShmcmFtZV9jb3VudCklNUQ=",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)]`}}),ze=new U({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> 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>)`}}),De=new F({}),He=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQSUwQWZpbGVuYW1lJTIwJTNEJTIwJTIyX19hc3NldHNfXyUyRmNhbm55X3ZpZGVvc19tcDQlMkZnaXJsX3R1cm5pbmcubXA0JTIyJTBBcmVwb19pZCUyMCUzRCUyMCUyMlBBSVIlMkZUZXh0MlZpZGVvLVplcm8lMjIlMEF2aWRlb19wYXRoJTIwJTNEJTIwaGZfaHViX2Rvd25sb2FkKHJlcG9fdHlwZSUzRCUyMnNwYWNlJTIyJTJDJTIwcmVwb19pZCUzRHJlcG9faWQlMkMlMjBmaWxlbmFtZSUzRGZpbGVuYW1lKQ==",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)`}}),qe=new U({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwaW1hZ2VpbyUwQSUwQXJlYWRlciUyMCUzRCUyMGltYWdlaW8uZ2V0X3JlYWRlcih2aWRlb19wYXRoJTJDJTIwJTIyZmZtcGVnJTIyKSUwQWZyYW1lX2NvdW50JTIwJTNEJTIwOCUwQWNhbm55X2VkZ2VzJTIwJTNEJTIwJTVCSW1hZ2UuZnJvbWFycmF5KHJlYWRlci5nZXRfZGF0YShpKSklMjBmb3IlMjBpJTIwaW4lMjByYW5nZShmcmFtZV9jb3VudCklNUQ=",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>
canny_edges = [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)]`}}),et=new U({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> 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>(canny_edges), <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>(canny_edges), image=canny_edges, latents=latents).images
imageio.mimsave(<span class="hljs-string">&quot;video.mp4&quot;</span>, result, fps=<span class="hljs-number">4</span>)`}}),ot=new F({}),st=new Bo({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/v0.21.0/en/api/models/autoencoderkl#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 (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"text_encoder"},{anchor:"diffusers.TextToVideoZeroPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
A <code>CLIPTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.TextToVideoZeroPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/v0.21.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <a href="/docs/diffusers/v0.21.0/en/api/models/unet3d-cond#diffusers.UNet3DConditionModel">UNet3DConditionModel</a> to denoise the encoded video latents.`,name:"unet"},{anchor:"diffusers.TextToVideoZeroPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.21.0/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/v0.21.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.21.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.21.0/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 more details
about a model&#x2019;s potential harms.`,name:"safety_checker"},{anchor:"diffusers.TextToVideoZeroPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
A <code>CLIPImageProcessor</code> to extract features from generated images; used as inputs to the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/v0.21.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L274"}}),at=new Bo({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"},{name:"frame_ids",val:": typing.Optional[typing.List[int]] = None"}],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 image generation. If not defined, you need to pass <code>prompt_embeds</code>.`,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 <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#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 <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#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;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,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 to guide what to not include in video generation. If not defined, you need to
pass <code>negative_prompt_embeds</code> instead. Ignored when not using guidance (<code>guidance_scale &lt; 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;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/v0.21.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,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;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.TextToVideoZeroPipeline.__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 is generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;numpy&quot;</code>) &#x2014;
The output format of the generated video. Choose between <code>&quot;latent&quot;</code> and <code>&quot;numpy&quot;</code>.`,name:"output_type"},{anchor:"diffusers.TextToVideoZeroPipeline.__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
<a href="/docs/diffusers/v0.21.0/en/api/pipelines/text_to_video_zero#diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput">TextToVideoPipelineOutput</a> instead of
a plain tuple.`,name:"return_dict"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.TextToVideoZeroPipeline.__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 is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.motion_field_strength_x",description:`<strong>motion_field_strength_x</strong> (<code>float</code>, <em>optional</em>, defaults to 12) &#x2014;
Strength of motion in generated video along x-axis. See the <a href="https://arxiv.org/abs/2303.13439" rel="nofollow">paper</a>,
Sect. 3.3.1.`,name:"motion_field_strength_x"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.motion_field_strength_y",description:`<strong>motion_field_strength_y</strong> (<code>float</code>, <em>optional</em>, defaults to 12) &#x2014;
Strength of motion in generated video along y-axis. See the <a href="https://arxiv.org/abs/2303.13439" rel="nofollow">paper</a>,
Sect. 3.3.1.`,name:"motion_field_strength_y"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.t0",description:`<strong>t0</strong> (<code>int</code>, <em>optional</em>, defaults to 44) &#x2014;
Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the
<a href="https://arxiv.org/abs/2303.13439" rel="nofollow">paper</a>, Sect. 3.3.1.`,name:"t0"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.t1",description:`<strong>t1</strong> (<code>int</code>, <em>optional</em>, defaults to 47) &#x2014;
Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the
<a href="https://arxiv.org/abs/2303.13439" rel="nofollow">paper</a>, Sect. 3.3.1.`,name:"t1"},{anchor:"diffusers.TextToVideoZeroPipeline.__call__.frame_ids",description:`<strong>frame_ids</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Indexes of the frames that are being generated. This is used when generating longer videos
chunk-by-chunk.`,name:"frame_ids"}],source:"https://github.com/huggingface/diffusers/blob/v0.21.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L420",returnDescription:`
<p>The output contains a <code>ndarray</code> of the generated video, when <code>output_type</code> != <code>"latent"</code>, otherwise a
latent code of generated videos and a list of <code>bool</code>s indicating whether the corresponding generated
video contains \u201Cnot-safe-for-work\u201D (nsfw) content..</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.21.0/en/api/pipelines/text_to_video_zero#diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput"
>TextToVideoPipelineOutput</a></p>
`}}),lt=new Bo({props:{name:"backward_loop",anchor:"diffusers.TextToVideoZeroPipeline.backward_loop",parameters:[{name:"latents",val:""},{name:"timesteps",val:""},{name:"prompt_embeds",val:""},{name:"guidance_scale",val:""},{name:"callback",val:""},{name:"callback_steps",val:""},{name:"num_warmup_steps",val:""},{name:"extra_step_kwargs",val:""},{name:"cross_attention_kwargs",val:" = None"}],parametersDescription:[{anchor:"diffusers.TextToVideoZeroPipeline.backward_loop.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
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To generate a video from prompt with additional pose control
`),wt=n(e,"OL",{});var Ur=a(wt);Ue=n(Ur,"LI",{});var bs=a(Ue);ao=n(bs,"P",{});var Ir=a(ao);sa=r(Ir,"Download a demo video"),Ir.forEach(o),na=c(bs),f(Ie.$$.fragment,bs),bs.forEach(o),Ur.forEach(o),es=c(e),X=n(e,"OL",{start:!0});var ws=a(X);P=n(ws,"LI",{});var Ut=a(P);lo=n(Ut,"P",{});var kr=a(lo);aa=r(kr,"Read video containing extracted pose images"),kr.forEach(o),la=c(Ut),f(ke.$$.fragment,Ut),ra=c(Ut),We=n(Ut,"P",{});var Ts=a(We);ia=r(Ts,"To extract pose from actual video, read "),Tt=n(Ts,"A",{href:!0});var Wr=a(Tt);pa=r(Wr,"ControlNet documentation"),Wr.forEach(o),da=r(Ts,"."),Ts.forEach(o),Ut.forEach(o),ca=c(ws),Ve=n(ws,"LI",{});var Js=a(Ve);Ee=n(Js,"P",{});var Ms=a(Ee);ha=r(Ms,"Run "),ro=n(Ms,"CODE",{});var Vr=a(ro);ma=r(Vr,"StableDiffusionControlNetPipeline"),Vr.forEach(o),fa=r(Ms," with our custom attention processor"),Ms.forEach(o),ua=c(Js),f(Be.$$.fragment,Js),Js.forEach(o),ws.forEach(o),ts=c(e),$=n(e,"H3",{class:!0});var Zs=a($);K=n(Zs,"A",{id:!0,class:!0,href:!0});var Er=a(K);io=n(Er,"SPAN",{});var Br=a(io);f(Ne.$$.fragment,Br),Br.forEach(o),Er.forEach(o),_a=c(Zs),po=n(Zs,"SPAN",{});var Nr=a(po);ya=r(Nr,"Text-To-Video with Edge Control"),Nr.forEach(o),Zs.forEach(o),os=c(e),ee=n(e,"P",{});var xs=a(ee);ga=r(xs,`To generate a video from prompt with additional pose control,
follow the steps described above for pose-guided generation using `),Ge=n(xs,"A",{href:!0,rel:!0});var Gr=a(Ge);va=r(Gr,"Canny edge ControlNet model"),Gr.forEach(o),ba=r(xs,"."),xs.forEach(o),ss=c(e),S=n(e,"H3",{class:!0});var js=a(S);te=n(js,"A",{id:!0,class:!0,href:!0});var Cr=a(te);co=n(Cr,"SPAN",{});var Rr=a(co);f(Ce.$$.fragment,Rr),Rr.forEach(o),Cr.forEach(o),wa=c(js),ho=n(js,"SPAN",{});var Xr=a(ho);Ta=r(Xr,"Video Instruct-Pix2Pix"),Xr.forEach(o),js.forEach(o),ns=c(e),oe=n(e,"P",{});var Us=a(oe);Ja=r(Us,"To perform text-guided video editing (with "),Jt=n(Us,"A",{href:!0});var Pr=a(Jt);Ma=r(Pr,"InstructPix2Pix"),Pr.forEach(o),Za=r(Us,"):"),Us.forEach(o),as=c(e),W=n(e,"OL",{});var It=a(W);Re=n(It,"LI",{});var Is=a(Re);mo=n(Is,"P",{});var $r=a(mo);xa=r($r,"Download a demo video"),$r.forEach(o),ja=c(Is),f(Xe.$$.fragment,Is),Is.forEach(o),Ua=c(It),Pe=n(It,"LI",{});var ks=a(Pe);fo=n(ks,"P",{});var Sr=a(fo);Ia=r(Sr,"Read video from path"),Sr.forEach(o),ka=c(ks),f($e.$$.fragment,ks),ks.forEach(o),Wa=c(It),Se=n(It,"LI",{});var Ws=a(Se);Qe=n(Ws,"P",{});var Vs=a(Qe);Va=r(Vs,"Run "),uo=n(Vs,"CODE",{});var Qr=a(uo);Ea=r(Qr,"StableDiffusionInstructPix2PixPipeline"),Qr.forEach(o),Ba=r(Vs," with our custom attention processor"),Vs.forEach(o),Na=c(Ws),f(ze.$$.fragment,Ws),Ws.forEach(o),It.forEach(o),ls=c(e),Q=n(e,"H3",{class:!0});var Es=a(Q);se=n(Es,"A",{id:!0,class:!0,href:!0});var zr=a(se);_o=n(zr,"SPAN",{});var Dr=a(_o);f(De.$$.fragment,Dr),Dr.forEach(o),zr.forEach(o),Ga=c(Es),yo=n(Es,"SPAN",{});var Yr=a(yo);Ca=r(Yr,"DreamBooth specialization"),Yr.forEach(o),Es.forEach(o),rs=c(e),v=n(e,"P",{});var w=a(v);Ra=r(w,"Methods "),go=n(w,"STRONG",{});var Fr=a(go);Xa=r(Fr,"Text-To-Video"),Fr.forEach(o),Pa=r(w,", "),vo=n(w,"STRONG",{});var Ar=a(vo);$a=r(Ar,"Text-To-Video with Pose Control"),Ar.forEach(o),Sa=r(w," and "),bo=n(w,"STRONG",{});var Hr=a(bo);Qa=r(Hr,"Text-To-Video with Edge Control"),Hr.forEach(o),za=r(w,`
can run with custom `),Mt=n(w,"A",{href:!0});var Lr=a(Mt);Da=r(Lr,"DreamBooth"),Lr.forEach(o),Ya=r(w,` models, as shown below for
`),Ye=n(w,"A",{href:!0,rel:!0});var qr=a(Ye);Fa=r(qr,"Canny edge ControlNet model"),qr.forEach(o),Aa=r(w,` and
`),Fe=n(w,"A",{href:!0,rel:!0});var Or=a(Fe);Ha=r(Or,"Avatar style DreamBooth"),Or.forEach(o),La=r(w," model"),w.forEach(o),is=c(e),V=n(e,"OL",{});var kt=a(V);Ae=n(kt,"LI",{});var Bs=a(Ae);wo=n(Bs,"P",{});var Kr=a(wo);qa=r(Kr,"Download a demo video"),Kr.forEach(o),Oa=c(Bs),f(He.$$.fragment,Bs),Bs.forEach(o),Ka=c(kt),Le=n(kt,"LI",{});var Ns=a(Le);To=n(Ns,"P",{});var ei=a(To);el=r(ei,"Read video from path"),ei.forEach(o),tl=c(Ns),f(qe.$$.fragment,Ns),Ns.forEach(o),ol=c(kt),Oe=n(kt,"LI",{});var Gs=a(Oe);Ke=n(Gs,"P",{});var Cs=a(Ke);sl=r(Cs,"Run "),Jo=n(Cs,"CODE",{});var ti=a(Jo);nl=r(ti,"StableDiffusionControlNetPipeline"),ti.forEach(o),al=r(Cs," with custom trained DreamBooth model"),Cs.forEach(o),ll=c(Gs),f(et.$$.fragment,Gs),Gs.forEach(o),kt.forEach(o),ps=c(e),ne=n(e,"P",{});var Rs=a(ne);rl=r(Rs,"You can filter out some available DreamBooth-trained models with "),tt=n(Rs,"A",{href:!0,rel:!0});var oi=a(tt);il=r(oi,"this link"),oi.forEach(o),pl=r(Rs,"."),Rs.forEach(o),ds=c(e),z=n(e,"H2",{class:!0});var Xs=a(z);ae=n(Xs,"A",{id:!0,class:!0,href:!0});var si=a(ae);Mo=n(si,"SPAN",{});var ni=a(Mo);f(ot.$$.fragment,ni),ni.forEach(o),si.forEach(o),dl=c(Xs),Zo=n(Xs,"SPAN",{});var ai=a(Zo);cl=r(ai,"TextToVideoZeroPipeline"),ai.forEach(o),Xs.forEach(o),cs=c(e),b=n(e,"DIV",{class:!0});var M=a(b);f(st.$$.fragment,M),hl=c(M),xo=n(M,"P",{});var li=a(xo);ml=r(li,"Pipeline for zero-shot text-to-video generation using Stable Diffusion."),li.forEach(o),fl=c(M),nt=n(M,"P",{});var Ps=a(nt);ul=r(Ps,"This model inherits from "),Zt=n(Ps,"A",{href:!0});var ri=a(Zt);_l=r(ri,"DiffusionPipeline"),ri.forEach(o),yl=r(Ps,`. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`),Ps.forEach(o),gl=c(M),le=n(M,"DIV",{class:!0});var $s=a(le);f(at.$$.fragment,$s),vl=c($s),jo=n($s,"P",{});var ii=a(jo);bl=r(ii,"The call function to the pipeline for generation."),ii.forEach(o),$s.forEach(o),wl=c(M),re=n(M,"DIV",{class:!0});var Ss=a(re);f(lt.$$.fragment,Ss),Tl=c(Ss),Uo=n(Ss,"P",{});var pi=a(Uo);Jl=r(pi,"Perform backward process given list of time steps."),pi.forEach(o),Ss.forEach(o),Ml=c(M),ie=n(M,"DIV",{class:!0});var Qs=a(ie);f(rt.$$.fragment,Qs),Zl=c(Qs),Io=n(Qs,"P",{});var di=a(Io);xl=r(di,"Perform DDPM forward process from time t0 to t1. 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