diffusers/pr_13481/en/_app/immutable/nodes/161.3bb5bf5b.js

import{s as Ae,o as Pe,n as De}from"../chunks/scheduler.53228c21.js";import{S as Ie,i as We,e as d,s as a,c as g,h as Re,a as c,d as t,b as s,f as O,g as _,j as v,k as z,l as h,m as i,n as y,t as b,o as T,p as w}from"../chunks/index.100fac89.js";import{C as Xe}from"../chunks/CopyLLMTxtMenu.ed6919ca.js";import{D as Me}from"../chunks/Docstring.1c305f7c.js";import{C as ve}from"../chunks/CodeBlock.d30a6509.js";import{E as Ge}from"../chunks/ExampleCodeBlock.6eee431e.js";import{H as ue,E as Ve}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.75de4b50.js";function Ne(Q){let l,C="If you get the error message below, you need to finetune the weights for your downstream task:",u,r,p;return r=new ve({props:{code:"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",highlighted:`Some weights of UNet2DConditionModel were not initialized from the model checkpoint <span class="hljs-built_in">at</span> stable-<span class="hljs-keyword">diffusion-v1-5/stable-diffusion-v1-5 </span><span class="hljs-keyword">and </span>are newly initialized <span class="hljs-keyword">because </span>the <span class="hljs-keyword">shapes </span><span class="hljs-keyword">did </span>not match:
- conv_in.weight: found <span class="hljs-keyword">shape </span>torch.Size([<span class="hljs-number">320</span>, <span class="hljs-number">4</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>]) in the checkpoint <span class="hljs-keyword">and </span>torch.Size([<span class="hljs-number">320</span>, <span class="hljs-number">9</span>, <span class="hljs-number">3</span>, <span class="hljs-number">3</span>]) in the model <span class="hljs-keyword">instantiated
</span>You <span class="hljs-keyword">should </span>probably TRAIN this model on a down-stream task to <span class="hljs-keyword">be </span>able to use it for predictions <span class="hljs-keyword">and </span>inference.`,wrap:!1}}),{c(){l=d("p"),l.textContent=C,u=a(),g(r.$$.fragment)},l(n){l=c(n,"P",{"data-svelte-h":!0}),v(l)!=="svelte-xueb0m"&&(l.textContent=C),u=s(n),_(r.$$.fragment,n)},m(n,f){i(n,l,f),i(n,u,f),y(r,n,f),p=!0},p:De,i(n){p||(b(r.$$.fragment,n),p=!0)},o(n){T(r.$$.fragment,n),p=!1},d(n){n&&(t(l),t(u)),w(r,n)}}}function Se(Q){let l,C="Examples:",u,r,p;return r=new ve({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download pipeline from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/ldm-text2im-large-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download pipeline that requires an authorization token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># For more information on access tokens, please refer to this section</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># of the documentation](https://huggingface.co/docs/hub/security-tokens)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Use a different scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LMSDiscreteScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.scheduler = scheduler`,wrap:!1}}),{c(){l=d("p"),l.textContent=C,u=a(),g(r.$$.fragment)},l(n){l=c(n,"P",{"data-svelte-h":!0}),v(l)!=="svelte-kvfsh7"&&(l.textContent=C),u=s(n),_(r.$$.fragment,n)},m(n,f){i(n,l,f),i(n,u,f),y(r,n,f),p=!0},p:De,i(n){p||(b(r.$$.fragment,n),p=!0)},o(n){T(r.$$.fragment,n),p=!1},d(n){n&&(t(l),t(u)),w(r,n)}}}function He(Q){let l,C,u,r,p,n,f,K,J,xe='LongCat-AudioDiT is a text-to-audio diffusion model from Meituan LongCat. The diffusers integration exposes a standard <a href="/docs/diffusers/pr_13481/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a> interface for text-conditioned audio generation.',ee,Z,Ce="This pipeline supports loading the original flat LongCat checkpoint layout from either a local directory or a Hugging Face Hub repository containing:",oe,L,je="<li><code>config.json</code></li> <li><code>model.safetensors</code></li>",te,B,$e="The loader builds the text encoder, transformer, and VAE from <code>config.json</code>, restores component weights from <code>model.safetensors</code>, and ties the shared UMT5 embedding when needed.",ne,G,Ue='This pipeline was adapted from the LongCat-AudioDiT reference implementation: <a href="https://github.com/meituan-longcat/LongCat-AudioDiT" rel="nofollow">https://github.com/meituan-longcat/LongCat-AudioDiT</a>',ie,D,ae,A,se,P,le,I,ke="<li><code>audio_end_in_s</code> is the most direct way to control output duration.</li> <li><code>output_type=&quot;pt&quot;</code> returns a PyTorch tensor shaped <code>(batch, channels, samples)</code>.</li>",re,W,de,x,R,fe,$,X,he,Y,Je="Function invoked when calling the pipeline for generation.",ge,m,V,_e,F,Ze="Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.",ye,E,Le="The pipeline is set in evaluation mode (<code>model.eval()</code>) by default.",be,U,Te,N,Be=`<p>&gt; To use private or <a href="https://huggingface.co/docs/hub/models-gated#gated-models" rel="nofollow">gated</a> models, log-in
with <code>hf &gt; auth login</code>.</p>`,we,k,ce,S,pe,q,me;return p=new Xe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new ue({props:{title:"LongCat-AudioDiT",local:"longcat-audiodit",headingTag:"h1"}}),D=new ue({props:{title:"Usage",local:"usage",headingTag:"h2"}}),A=new ve({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> soundfile <span class="hljs-keyword">as</span> sf
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LongCatAudioDiTPipeline

pipeline = LongCatAudioDiTPipeline.from_pretrained(
    <span class="hljs-string">&quot;meituan-longcat/LongCat-AudioDiT-1B&quot;</span>,
    torch_dtype=torch.float16,
)
pipeline = pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)

audio = pipeline(
    prompt=<span class="hljs-string">&quot;A calm ocean wave ambience with soft wind in the background.&quot;</span>,
    audio_end_in_s=<span class="hljs-number">5.0</span>,
    num_inference_steps=<span class="hljs-number">16</span>,
    guidance_scale=<span class="hljs-number">4.0</span>,
    output_type=<span class="hljs-string">&quot;pt&quot;</span>,
).audios

output = audio[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>].<span class="hljs-built_in">float</span>().cpu().numpy()
sf.write(<span class="hljs-string">&quot;longcat.wav&quot;</span>, output, pipeline.sample_rate)`,wrap:!1}}),P=new ue({props:{title:"Tips",local:"tips",headingTag:"h2"}}),W=new ue({props:{title:"LongCatAudioDiTPipeline",local:"diffusers.LongCatAudioDiTPipeline",headingTag:"h2"}}),R=new Me({props:{name:"class diffusers.LongCatAudioDiTPipeline",anchor:"diffusers.LongCatAudioDiTPipeline",parameters:[{name:"vae",val:": LongCatAudioDiTVae"},{name:"text_encoder",val:": UMT5EncoderModel"},{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"transformer",val:": LongCatAudioDiTTransformer"},{name:"scheduler",val:": diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_13481/src/diffusers/pipelines/longcat_audio_dit/pipeline_longcat_audio_dit.py#L76"}}),X=new Me({props:{name:"__call__",anchor:"diffusers.LongCatAudioDiTPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"audio_duration_s",val:": float | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"num_inference_steps",val:": int = 16"},{name:"guidance_scale",val:": float = 4.0"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"output_type",val:": str = 'np'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"}],parametersDescription:[{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.prompt",description:"<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014; Prompt or prompts that guide audio generation.",name:"prompt"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.negative_prompt",description:"<strong>negative_prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014; Negative prompt(s) for classifier-free guidance.",name:"negative_prompt"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.audio_duration_s",description:`<strong>audio_duration_s</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Target audio duration in seconds. Ignored when <code>latents</code> is provided.`,name:"audio_duration_s"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents of shape <code>(batch_size, duration, latent_dim)</code>.`,name:"latents"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.num_inference_steps",description:"<strong>num_inference_steps</strong> (<code>int</code>, defaults to 16) &#x2014; Number of denoising steps.",name:"num_inference_steps"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.guidance_scale",description:"<strong>guidance_scale</strong> (<code>float</code>, defaults to 4.0) &#x2014; Guidance scale for classifier-free guidance.",name:"guidance_scale"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.generator",description:"<strong>generator</strong> (<code>torch.Generator</code> or <code>list[torch.Generator]</code>, <em>optional</em>) &#x2014; Random generator(s).",name:"generator"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.output_type",description:"<strong>output_type</strong> (<code>str</code>, defaults to <code>&quot;np&quot;</code>) &#x2014; Output format: <code>&quot;np&quot;</code>, <code>&quot;pt&quot;</code>, or <code>&quot;latent&quot;</code>.",name:"output_type"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to return <code>AudioPipelineOutput</code>.",name:"return_dict"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function called at the end of each denoising step with the pipeline, step index, timestep, and tensor
inputs specified by <code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.LongCatAudioDiTPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>list</code>, defaults to <code>[&quot;latents&quot;]</code>) &#x2014;
Tensor inputs passed to <code>callback_on_step_end</code>.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/vr_13481/src/diffusers/pipelines/longcat_audio_dit/pipeline_longcat_audio_dit.py#L196"}}),V=new Me({props:{name:"from_pretrained",anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": str | os.PathLike"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>repo id</em> (for example <code>CompVis/ldm-text2im-large-256</code>) of a pretrained pipeline
hosted on the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_pipeline_directory/</code>) containing pipeline weights
saved using
<a href="/docs/diffusers/pr_13481/en/api/pipelines/overview#diffusers.DiffusionPipeline.save_pretrained">save_pretrained()</a>.</li>
<li>A path to a <em>directory</em> (for example <code>./my_pipeline_directory/</code>) containing a dduf file</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.torch_dtype",description:`<strong>torch_dtype</strong> (<code>torch.dtype</code> or <code>dict[str, Union[str, torch.dtype]]</code>, <em>optional</em>) &#x2014;
Override the default <code>torch.dtype</code> and load the model with another dtype. To load submodels with
different dtype pass a <code>dict</code> (for example <code>{&apos;transformer&apos;: torch.bfloat16, &apos;vae&apos;: torch.float16}</code>).
Set the default dtype for unspecified components with <code>default</code> (for example <code>{&apos;transformer&apos;: torch.bfloat16, &apos;default&apos;: torch.float16}</code>). If a component is not specified and no default is set,
<code>torch.float32</code> is used.`,name:"torch_dtype"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.custom_pipeline",description:`<strong>custom_pipeline</strong> (<code>str</code>, <em>optional</em>) &#x2014;</p>
<blockquote class="warning">
<p>&gt; &#x1F9EA; This is an experimental feature and may change in the future.</p>
</blockquote>
<p>Can be either:</p>
<ul>
<li>A string, the <em>repo id</em> (for example <code>hf-internal-testing/diffusers-dummy-pipeline</code>) of a custom
pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines
the custom pipeline.</li>
<li>A string, the <em>file name</em> of a community pipeline hosted on GitHub under
<a href="https://github.com/huggingface/diffusers/tree/main/examples/community" rel="nofollow">Community</a>. Valid file
names must match the file name and not the pipeline script (<code>clip_guided_stable_diffusion</code>
instead of <code>clip_guided_stable_diffusion.py</code>). Community pipelines are always loaded from the
current main branch of GitHub.</li>
<li>A path to a directory (<code>./my_pipeline_directory/</code>) containing a custom pipeline. The directory
must contain a file called <code>pipeline.py</code> that defines the custom pipeline.</li>
</ul>
<p>For more information on how to load and create custom pipelines, please have a look at <a href="https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview" rel="nofollow">Loading and
Adding Custom
Pipelines</a>`,name:"custom_pipeline"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) &#x2014;
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.`,name:"cache_dir"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
<code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.`,name:"revision"},{anchor:"diffusers.LongCatAudioDiTPipeline.from_pretrained.custom_revision",description:`<strong>custom_revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
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Speed up model loading only loading the pretrained weights and not initializing the weights. This also
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