import{s as Yn,o as Fn,n as qn}from"../chunks/scheduler.53228c21.js";import{S as Hn,i as zn,e as o,s,c,h as On,a as r,d as t,b as a,f as C,g as m,j as p,k as V,l,m as i,n as u,t as M,o as y,p as f}from"../chunks/index.100fac89.js";import{C as et}from"../chunks/CopyLLMTxtMenu.fb2ec027.js";import{D as ce}from"../chunks/Docstring.86a664ae.js";import{C as Ce}from"../chunks/CodeBlock.d30a6509.js";import{E as nt}from"../chunks/ExampleCodeBlock.15d6107c.js";import{H as G,E as tt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.09d4dba9.js";function st(Ae){let _,I="Examples:",b,j,T;return j=new Ce({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> Kandinsky5T2VPipeline
<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><span class="hljs-comment"># Available models:</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>model_id = <span class="hljs-string">&quot;ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A cat and a dog baking a cake together in a kitchen.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>negative_prompt = <span class="hljs-string">&quot;Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>output = pipe(
<span class="hljs-meta">... </span>    prompt=prompt,
<span class="hljs-meta">... </span>    negative_prompt=negative_prompt,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    num_frames=<span class="hljs-number">121</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>    guidance_scale=<span class="hljs-number">5.0</span>,
<span class="hljs-meta">... </span>).frames[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>export_to_video(output, <span class="hljs-string">&quot;output.mp4&quot;</span>, fps=<span class="hljs-number">24</span>, quality=<span class="hljs-number">9</span>)`,wrap:!1}}),{c(){_=o("p"),_.textContent=I,b=s(),c(j.$$.fragment)},l(g){_=r(g,"P",{"data-svelte-h":!0}),p(_)!=="svelte-kvfsh7"&&(_.textContent=I),b=a(g),m(j.$$.fragment,g)},m(g,v){i(g,_,v),i(g,b,v),u(j,g,v),T=!0},p:qn,i(g){T||(M(j.$$.fragment,g),T=!0)},o(g){y(j.$$.fragment,g),T=!1},d(g){g&&(t(_),t(b)),f(j,g)}}}function at(Ae){let _,I,b,j,T,g,v,Ze,W,wn="Kandinsky 5.0 Video is created by the Kandinsky team: Alexey Letunovskiy, Maria Kovaleva, Ivan Kirillov, Lev Novitskiy, Denis Koposov, Dmitrii Mikhailov, Anna Averchenkova, Andrey Shutkin, Julia Agafonova, Olga Kim, Anastasiia Kargapoltseva, Nikita Kiselev, Anna Dmitrienko,  Anastasia Maltseva, Kirill Chernyshev, Ilia Vasiliev, Viacheslav Vasilev, Vladimir Polovnikov, Yury Kolabushin, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina, Tatiana Nikulina, Polina Gavrilova, Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko, Denis Dimitrov",Be,$,Un="Kandinsky 5.0 is a family of diffusion models for Video & Image generation. Kandinsky 5.0 T2V Lite is a lightweight video generation model (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.",Ge,Q,Jn="The model introduces several key innovations:",Ie,E,xn="<li><strong>Latent diffusion pipeline</strong> with <strong>Flow Matching</strong> for improved training stability</li> <li><strong>Diffusion Transformer (DiT)</strong> as the main generative backbone with cross-attention to text embeddings</li> <li>Dual text encoding using <strong>Qwen2.5-VL</strong> and <strong>CLIP</strong> for comprehensive text understanding</li> <li><strong>HunyuanVideo 3D VAE</strong> for efficient video encoding and decoding</li> <li><strong>Sparse attention mechanisms</strong> (NABLA) for efficient long-sequence processing</li>",We,K,Vn='The original codebase can be found at <a href="https://github.com/ai-forever/Kandinsky-5" rel="nofollow">ai-forever/Kandinsky-5</a>.',$e,A,Cn='<p>Check out the <a href="https://huggingface.co/ai-forever" rel="nofollow">AI Forever</a> organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.</p>',Qe,P,Ee,L,An="Kandinsky 5.0 T2V Lite comes in several variants optimized for different use cases:",Ke,N,Zn="<thead><tr><th>model_id</th> <th>Description</th> <th>Use Cases</th></tr></thead> <tbody><tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers</strong></td> <td>5 second Supervised Fine-Tuned model</td> <td>Highest generation quality</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers</strong></td> <td>10 second Supervised Fine-Tuned model</td> <td>Highest generation quality</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers</strong></td> <td>5 second Classifier-Free Guidance distilled</td> <td>2× faster inference</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers</strong></td> <td>10 second Classifier-Free Guidance distilled</td> <td>2× faster inference</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers</strong></td> <td>5 second Diffusion distilled to 16 steps</td> <td>6× faster inference, minimal quality loss</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers</strong></td> <td>10 second Diffusion distilled to 16 steps</td> <td>6× faster inference, minimal quality loss</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers</strong></td> <td>5 second Base pretrained model</td> <td>Research and fine-tuning</td></tr> <tr><td><strong>ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers</strong></td> <td>10 second Base pretrained model</td> <td>Research and fine-tuning</td></tr></tbody>",Pe,X,Bn="All models are available in 5-second and 10-second video generation versions.",Le,S,Ne,d,D,ln,me,Gn="Pipeline for text-to-video generation using Kandinsky 5.0.",on,ue,In=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,rn,k,R,pn,Me,Wn="The call function to the pipeline for generation.",dn,Z,cn,B,Y,mn,ye,$n="Validate input parameters for the pipeline.",un,w,F,Mn,fe,Qn="Encodes a single prompt (positive or negative) into text encoder hidden states.",yn,he,En=`This method combines embeddings from both Qwen2.5-VL and CLIP text encoders to create comprehensive text
representations for video generation.`,fn,U,q,hn,ge,Kn="Create a sparse temporal attention (STA) mask for efficient video generation.",gn,_e,Pn=`This method generates a mask that limits attention to nearby frames and spatial positions, reducing
computational complexity for video generation.`,_n,J,H,jn,je,Ln="Generate sparse attention parameters for the transformer based on sample dimensions.",Tn,Te,Nn=`This method computes the sparse attention configuration needed for efficient video processing in the
transformer model.`,vn,x,z,bn,ve,Xn="Prepare initial latent variables for video generation.",kn,be,Sn="This method creates random noise latents or uses provided latents as starting point for the denoising process.",Xe,O,Se,ee,De,ne,Re,te,Ye,se,Dn="<strong>⚠️ Warning!</strong> all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:",Fe,ae,qe,ie,He,le,Rn="<strong>⚠️ Warning!</strong> all nocfg and diffusion distilled models should be infered wothout CFG (<code>guidance_scale=1.0</code>):",ze,oe,Oe,re,en,pe,nn,de,tn,Ve,sn;return T=new et({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),v=new G({props:{title:"Kandinsky 5.0 Video",local:"kandinsky-50-video",headingTag:"h1"}}),P=new G({props:{title:"Available Models",local:"available-models",headingTag:"h2"}}),S=new G({props:{title:"Kandinsky5T2VPipeline",local:"diffusers.Kandinsky5T2VPipeline",headingTag:"h2"}}),D=new ce({props:{name:"class diffusers.Kandinsky5T2VPipeline",anchor:"diffusers.Kandinsky5T2VPipeline",parameters:[{name:"transformer",val:": Kandinsky5Transformer3DModel"},{name:"vae",val:": AutoencoderKLHunyuanVideo"},{name:"text_encoder",val:": Qwen2_5_VLForConditionalGeneration"},{name:"tokenizer",val:": Qwen2VLProcessor"},{name:"text_encoder_2",val:": CLIPTextModel"},{name:"tokenizer_2",val:": CLIPTokenizer"},{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.transformer",description:`<strong>transformer</strong> (<code>Kandinsky5Transformer3DModel</code>) &#x2014;
Conditional Transformer to denoise the encoded video latents.`,name:"transformer"},{anchor:"diffusers.Kandinsky5T2VPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoder_kl_hunyuan_video#diffusers.AutoencoderKLHunyuanVideo">AutoencoderKLHunyuanVideo</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.`,name:"vae"},{anchor:"diffusers.Kandinsky5T2VPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>Qwen2_5_VLForConditionalGeneration</code>) &#x2014;
Frozen text-encoder (Qwen2.5-VL).`,name:"text_encoder"},{anchor:"diffusers.Kandinsky5T2VPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>AutoProcessor</code>) &#x2014;
Tokenizer for Qwen2.5-VL.`,name:"tokenizer"},{anchor:"diffusers.Kandinsky5T2VPipeline.text_encoder_2",description:`<strong>text_encoder_2</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen CLIP text encoder.`,name:"text_encoder_2"},{anchor:"diffusers.Kandinsky5T2VPipeline.tokenizer_2",description:`<strong>tokenizer_2</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer for CLIP.`,name:"tokenizer_2"},{anchor:"diffusers.Kandinsky5T2VPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>transformer</code> to denoise the encoded video latents.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L107"}}),R=new ce({props:{name:"__call__",anchor:"diffusers.Kandinsky5T2VPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 768"},{name:"num_frames",val:": int = 121"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 5.0"},{name:"num_videos_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds_qwen",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds_clip",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds_qwen",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds_clip",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_cu_seqlens",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_cu_seqlens",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 512"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.__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, pass <code>prompt_embeds</code> instead.`,name:"prompt"},{anchor:"diffusers.Kandinsky5T2VPipeline.__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 avoid during video generation. If not defined, pass <code>negative_prompt_embeds</code>
instead. Ignored when not using guidance (<code>guidance_scale</code> &lt; <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, defaults to <code>512</code>) &#x2014;
The height in pixels of the generated video.`,name:"height"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, defaults to <code>768</code>) &#x2014;
The width in pixels of the generated video.`,name:"width"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, defaults to <code>25</code>) &#x2014;
The number of frames in the generated video.`,name:"num_frames"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, defaults to <code>50</code>) &#x2014;
The number of denoising steps.`,name:"num_inference_steps"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, defaults to <code>5.0</code>) &#x2014;
Guidance scale as defined in classifier-free guidance.`,name:"guidance_scale"},{anchor:"diffusers.Kandinsky5T2VPipeline.__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.Kandinsky5T2VPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
A torch generator to make generation deterministic.`,name:"generator"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents.`,name:"latents"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings.`,name:"prompt_embeds"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings.`,name:"negative_prompt_embeds"},{anchor:"diffusers.Kandinsky5T2VPipeline.__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 generated video.`,name:"output_type"},{anchor:"diffusers.Kandinsky5T2VPipeline.__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>KandinskyPipelineOutput</code>.`,name:"return_dict"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <code>PipelineCallback</code>, <code>MultiPipelineCallbacks</code>, <em>optional</em>) &#x2014;
A function that is called at the end of each denoising step.`,name:"callback_on_step_end"},{anchor:"diffusers.Kandinsky5T2VPipeline.__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.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, defaults to <code>512</code>) &#x2014;
The maximum sequence length for text encoding.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L615",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <code>KandinskyPipelineOutput</code> is returned, otherwise a <code>tuple</code> is returned
where the first element is a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~KandinskyPipelineOutput</code> or <code>tuple</code></p>
`}}),Z=new nt({props:{anchor:"diffusers.Kandinsky5T2VPipeline.__call__.example",$$slots:{default:[st]},$$scope:{ctx:Ae}}}),Y=new ce({props:{name:"check_inputs",anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs",parameters:[{name:"prompt",val:""},{name:"negative_prompt",val:""},{name:"height",val:""},{name:"width",val:""},{name:"prompt_embeds_qwen",val:" = None"},{name:"prompt_embeds_clip",val:" = None"},{name:"negative_prompt_embeds_qwen",val:" = None"},{name:"negative_prompt_embeds_clip",val:" = None"},{name:"prompt_cu_seqlens",val:" = None"},{name:"negative_prompt_cu_seqlens",val:" = None"},{name:"callback_on_step_end_tensor_inputs",val:" = None"}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.prompt",description:"<strong>prompt</strong> &#x2014; Input prompt",name:"prompt"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.negative_prompt",description:"<strong>negative_prompt</strong> &#x2014; Negative prompt for guidance",name:"negative_prompt"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.height",description:"<strong>height</strong> &#x2014; Video height",name:"height"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.width",description:"<strong>width</strong> &#x2014; Video width",name:"width"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.prompt_embeds_qwen",description:"<strong>prompt_embeds_qwen</strong> &#x2014; Pre-computed Qwen prompt embeddings",name:"prompt_embeds_qwen"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.prompt_embeds_clip",description:"<strong>prompt_embeds_clip</strong> &#x2014; Pre-computed CLIP prompt embeddings",name:"prompt_embeds_clip"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.negative_prompt_embeds_qwen",description:"<strong>negative_prompt_embeds_qwen</strong> &#x2014; Pre-computed Qwen negative prompt embeddings",name:"negative_prompt_embeds_qwen"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.negative_prompt_embeds_clip",description:"<strong>negative_prompt_embeds_clip</strong> &#x2014; Pre-computed CLIP negative prompt embeddings",name:"negative_prompt_embeds_clip"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.prompt_cu_seqlens",description:"<strong>prompt_cu_seqlens</strong> &#x2014; Pre-computed cumulative sequence lengths for Qwen positive prompt",name:"prompt_cu_seqlens"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.negative_prompt_cu_seqlens",description:"<strong>negative_prompt_cu_seqlens</strong> &#x2014; Pre-computed cumulative sequence lengths for Qwen negative prompt",name:"negative_prompt_cu_seqlens"},{anchor:"diffusers.Kandinsky5T2VPipeline.check_inputs.callback_on_step_end_tensor_inputs",description:"<strong>callback_on_step_end_tensor_inputs</strong> &#x2014; Callback tensor inputs",name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L446",raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> — If inputs are invalid</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),F=new ce({props:{name:"encode_prompt",anchor:"diffusers.Kandinsky5T2VPipeline.encode_prompt",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"num_videos_per_prompt",val:": int = 1"},{name:"max_sequence_length",val:": int = 512"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"dtype",val:": typing.Optional[torch.dtype] = None"}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
Prompt to be encoded.`,name:"prompt"},{anchor:"diffusers.Kandinsky5T2VPipeline.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 to generate per prompt.`,name:"num_videos_per_prompt"},{anchor:"diffusers.Kandinsky5T2VPipeline.encode_prompt.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Maximum sequence length for text encoding.`,name:"max_sequence_length"},{anchor:"diffusers.Kandinsky5T2VPipeline.encode_prompt.device",description:`<strong>device</strong> (<code>torch.device</code>, <em>optional</em>) &#x2014;
Torch device.`,name:"device"},{anchor:"diffusers.Kandinsky5T2VPipeline.encode_prompt.dtype",description:`<strong>dtype</strong> (<code>torch.dtype</code>, <em>optional</em>) &#x2014;
Torch dtype.`,name:"dtype"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L353",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li>Qwen text embeddings of shape (batch_size * num_videos_per_prompt, sequence_length, embedding_dim)</li>
<li>CLIP pooled embeddings of shape (batch_size * num_videos_per_prompt, clip_embedding_dim)</li>
<li>Cumulative sequence lengths (<code>cu_seqlens</code>) for Qwen embeddings of shape (batch_size *
num_videos_per_prompt + 1,)</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple[torch.Tensor, torch.Tensor, torch.Tensor]</p>
`}}),q=new ce({props:{name:"fast_sta_nabla",anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla",parameters:[{name:"T",val:": int"},{name:"H",val:": int"},{name:"W",val:": int"},{name:"wT",val:": int = 3"},{name:"wH",val:": int = 3"},{name:"wW",val:": int = 3"},{name:"device",val:" = 'cuda'"}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.T",description:"<strong>T</strong> (int) &#x2014; Number of temporal frames",name:"T"},{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.H",description:"<strong>H</strong> (int) &#x2014; Height in latent space",name:"H"},{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.W",description:"<strong>W</strong> (int) &#x2014; Width in latent space",name:"W"},{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.wT",description:"<strong>wT</strong> (int) &#x2014; Temporal attention window size",name:"wT"},{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.wH",description:"<strong>wH</strong> (int) &#x2014; Height attention window size",name:"wH"},{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.wW",description:"<strong>wW</strong> (int) &#x2014; Width attention window size",name:"wW"},{anchor:"diffusers.Kandinsky5T2VPipeline.fast_sta_nabla.device",description:"<strong>device</strong> (str) &#x2014; Device to create tensor on",name:"device"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L182",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Sparse attention mask of shape (T<em>H</em>W, T<em>H</em>W)</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>torch.Tensor</p>
`}}),H=new ce({props:{name:"get_sparse_params",anchor:"diffusers.Kandinsky5T2VPipeline.get_sparse_params",parameters:[{name:"sample",val:""},{name:"device",val:""}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.get_sparse_params.sample",description:"<strong>sample</strong> (torch.Tensor) &#x2014; Input sample tensor",name:"sample"},{anchor:"diffusers.Kandinsky5T2VPipeline.get_sparse_params.device",description:"<strong>device</strong> (torch.device) &#x2014; Device to place tensors on",name:"device"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L217",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Dictionary containing sparse attention parameters</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Dict</p>
`}}),z=new ce({props:{name:"prepare_latents",anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents",parameters:[{name:"batch_size",val:": int"},{name:"num_channels_latents",val:": int = 16"},{name:"height",val:": int = 480"},{name:"width",val:": int = 832"},{name:"num_frames",val:": int = 81"},{name:"dtype",val:": typing.Optional[torch.dtype] = None"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.batch_size",description:"<strong>batch_size</strong> (int) &#x2014; Number of videos to generate",name:"batch_size"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.num_channels_latents",description:"<strong>num_channels_latents</strong> (int) &#x2014; Number of channels in latent space",name:"num_channels_latents"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.height",description:"<strong>height</strong> (int) &#x2014; Height of generated video",name:"height"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.width",description:"<strong>width</strong> (int) &#x2014; Width of generated video",name:"width"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.num_frames",description:"<strong>num_frames</strong> (int) &#x2014; Number of frames in video",name:"num_frames"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.dtype",description:"<strong>dtype</strong> (torch.dtype) &#x2014; Data type for latents",name:"dtype"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.device",description:"<strong>device</strong> (torch.device) &#x2014; Device to create latents on",name:"device"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.generator",description:"<strong>generator</strong> (torch.Generator) &#x2014; Random number generator",name:"generator"},{anchor:"diffusers.Kandinsky5T2VPipeline.prepare_latents.latents",description:"<strong>latents</strong> (torch.Tensor) &#x2014; Pre-existing latents to use",name:"latents"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py#L527",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Prepared latent tensor</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>torch.Tensor</p>
`}}),O=new G({props:{title:"Usage Examples",local:"usage-examples",headingTag:"h2"}}),ee=new G({props:{title:"Basic Text-to-Video Generation",local:"basic-text-to-video-generation",headingTag:"h3"}}),ne=new Ce({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> Kandinsky5T2VPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video

<span class="hljs-comment"># Load the pipeline</span>
model_id = <span class="hljs-string">&quot;ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers&quot;</span>
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># Generate video</span>
prompt = <span class="hljs-string">&quot;A cat and a dog baking a cake together in a kitchen.&quot;</span>
negative_prompt = <span class="hljs-string">&quot;Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards&quot;</span>

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=<span class="hljs-number">512</span>,
    width=<span class="hljs-number">768</span>,
    num_frames=<span class="hljs-number">121</span>,  <span class="hljs-comment"># ~5 seconds at 24fps</span>
    num_inference_steps=<span class="hljs-number">50</span>,
    guidance_scale=<span class="hljs-number">5.0</span>,
).frames[<span class="hljs-number">0</span>]

export_to_video(output, <span class="hljs-string">&quot;output.mp4&quot;</span>, fps=<span class="hljs-number">24</span>, quality=<span class="hljs-number">9</span>)`,wrap:!1}}),te=new G({props:{title:"10 second Models",local:"10-second-models",headingTag:"h3"}}),ae=new Ce({props:{code:"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",highlighted:`pipe = Kandinsky5T2VPipeline.from_pretrained(
    <span class="hljs-string">&quot;ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers&quot;</span>, 
    torch_dtype=torch.bfloat16
)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.transformer.set_attention_backend(
    <span class="hljs-string">&quot;flex&quot;</span>
)                                       <span class="hljs-comment"># &lt;--- Sett attention bakend to Flex</span>
pipe.transformer.<span class="hljs-built_in">compile</span>(
    mode=<span class="hljs-string">&quot;max-autotune-no-cudagraphs&quot;</span>, 
    dynamic=<span class="hljs-literal">True</span>
)                                       <span class="hljs-comment"># &lt;--- Compile with max-autotune-no-cudagraphs</span>

prompt = <span class="hljs-string">&quot;A cat and a dog baking a cake together in a kitchen.&quot;</span>
negative_prompt = <span class="hljs-string">&quot;Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards&quot;</span>

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=<span class="hljs-number">512</span>,
    width=<span class="hljs-number">768</span>,
    num_frames=<span class="hljs-number">241</span>,
    num_inference_steps=<span class="hljs-number">50</span>,
    guidance_scale=<span class="hljs-number">5.0</span>,
).frames[<span class="hljs-number">0</span>]

export_to_video(output, <span class="hljs-string">&quot;output.mp4&quot;</span>, fps=<span class="hljs-number">24</span>, quality=<span class="hljs-number">9</span>)`,wrap:!1}}),ie=new G({props:{title:"Diffusion Distilled model",local:"diffusion-distilled-model",headingTag:"h3"}}),oe=new Ce({props:{code:"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",highlighted:`model_id = <span class="hljs-string">&quot;ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers&quot;</span>
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

output = pipe(
    prompt=<span class="hljs-string">&quot;A beautiful sunset over mountains&quot;</span>,
    num_inference_steps=<span class="hljs-number">16</span>,  <span class="hljs-comment"># &lt;--- Model is distilled in 16 steps</span>
    guidance_scale=<span class="hljs-number">1.0</span>,      <span class="hljs-comment"># &lt;--- no CFG</span>
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    author = {Alexey Letunovskiy <span class="hljs-keyword">and</span> Maria Kovaleva <span class="hljs-keyword">and</span> Ivan Kirillov <span class="hljs-keyword">and</span> Lev Novitskiy <span class="hljs-keyword">and</span> Denis Koposov <span class="hljs-keyword">and</span>
              Dmitrii Mikhailov <span class="hljs-keyword">and</span> Anna Averchenkova <span class="hljs-keyword">and</span> Andrey Shutkin <span class="hljs-keyword">and</span> Julia Agafonova <span class="hljs-keyword">and</span> Olga Kim <span class="hljs-keyword">and</span>
              Anastasiia Kargapoltseva <span class="hljs-keyword">and</span> Nikita Kiselev <span class="hljs-keyword">and</span> Vladimir Arkhipkin <span class="hljs-keyword">and</span> Vladimir Korviakov <span class="hljs-keyword">and</span>
              Nikolai Gerasimenko <span class="hljs-keyword">and</span> Denis Parkhomenko <span class="hljs-keyword">and</span> Anna Dmitrienko <span class="hljs-keyword">and</span> Anastasia Maltseva <span class="hljs-keyword">and</span>
              Kirill Chernyshev <span class="hljs-keyword">and</span> Ilia Vasiliev <span class="hljs-keyword">and</span> Viacheslav Vasilev <span class="hljs-keyword">and</span> Vladimir Polovnikov <span class="hljs-keyword">and</span>
              Yury Kolabushin <span class="hljs-keyword">and</span> Alexander Belykh <span class="hljs-keyword">and</span> Mikhail Mamaev <span class="hljs-keyword">and</span> Anastasia Aliaskina <span class="hljs-keyword">and</span>
              Tatiana Nikulina <span class="hljs-keyword">and</span> Polina Gavrilova <span class="hljs-keyword">and</span> Denis Dimitrov},
    title = {Kandinsky <span class="hljs-number">5</span>.<span class="hljs-number">0</span>: A family of diffusion models for Video &amp; Image generation},
    howpublished = {\\url{https:<span class="hljs-comment">//github.com/ai-forever/Kandinsky-5}},</span>
    year = <span class="hljs-number">2025</span>
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