import{s as J,n as K,o as Q}from"../chunks/scheduler.8c3d61f6.js";import{S as Y,i as Z,g,s as r,r as b,A as ee,h as _,f as t,c as s,j as A,u as q,x as G,k as O,y as D,a as n,v as x,d as v,t as $,w}from"../chunks/index.da70eac4.js";import{D as B}from"../chunks/Docstring.36ab7a66.js";import{H as z,E as oe}from"../chunks/getInferenceSnippets.33773fa8.js";function te(W){let i,E,I,F,a,P,p,R,d,l,k,h,M="Hugging Face Hybrid Inference that allow running VAE decode remotely.",C,u,L,c,f,U,y,X="Hugging Face Hybrid Inference that allow running VAE encode remotely.",S,m,N,T,V;return a=new z({props:{title:"Hybrid Inference API Reference",local:"hybrid-inference-api-reference",headingTag:"h1"}}),p=new z({props:{title:"Remote Decode",local:"diffusers.utils.remote_decode",headingTag:"h2"}}),l=new B({props:{name:"diffusers.utils.remote_decode",anchor:"diffusers.utils.remote_decode",parameters:[{name:"endpoint",val:": str"},{name:"tensor",val:": torch.Tensor"},{name:"processor",val:": typing.Union[ForwardRef('VaeImageProcessor'), ForwardRef('VideoProcessor'), NoneType] = None"},{name:"do_scaling",val:": bool = True"},{name:"scaling_factor",val:": typing.Optional[float] = None"},{name:"shift_factor",val:": typing.Optional[float] = None"},{name:"output_type",val:": typing.Literal['mp4', 'pil', 'pt'] = 'pil'"},{name:"return_type",val:": typing.Literal['mp4', 'pil', 'pt'] = 'pil'"},{name:"image_format",val:": typing.Literal['png', 'jpg'] = 'jpg'"},{name:"partial_postprocess",val:": bool = False"},{name:"input_tensor_type",val:": typing.Literal['binary'] = 'binary'"},{name:"output_tensor_type",val:": typing.Literal['binary'] = 'binary'"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"diffusers.utils.remote_decode.endpoint",description:`<strong>endpoint</strong> (<code>str</code>) &#x2014;
Endpoint for Remote Decode.`,name:"endpoint"},{anchor:"diffusers.utils.remote_decode.tensor",description:`<strong>tensor</strong> (<code>torch.Tensor</code>) &#x2014;
Tensor to be decoded.`,name:"tensor"},{anchor:"diffusers.utils.remote_decode.processor",description:`<strong>processor</strong> (<code>VaeImageProcessor</code> or <code>VideoProcessor</code>, <em>optional</em>) &#x2014;
Used with <code>return_type=&quot;pt&quot;</code>, and <code>return_type=&quot;pil&quot;</code> for Video models.`,name:"processor"},{anchor:"diffusers.utils.remote_decode.do_scaling",description:`<strong>do_scaling</strong> (<code>bool</code>, default <code>True</code>, <em>optional</em>) &#x2014;
<strong>DEPRECATED</strong>. <strong>pass <code>scaling_factor</code>/<code>shift_factor</code> instead.</strong> <strong>still set
do_scaling=None/do_scaling=False for no scaling until option is removed</strong> When <code>True</code> scaling e.g. <code>latents / self.vae.config.scaling_factor</code> is applied remotely. If <code>False</code>, input must be passed with scaling
applied.`,name:"do_scaling"},{anchor:"diffusers.utils.remote_decode.scaling_factor",description:`<strong>scaling_factor</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Scaling is applied when passed e.g. <a href="https://github.com/huggingface/diffusers/blob/7007febae5cff000d4df9059d9cf35133e8b2ca9/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L1083C37-L1083C77" rel="nofollow"><code>latents / self.vae.config.scaling_factor</code></a>.</p>
<ul>
<li>SD v1: 0.18215</li>
<li>SD XL: 0.13025</li>
<li>Flux: 0.3611
If <code>None</code>, input must be passed with scaling applied.</li>
</ul>`,name:"scaling_factor"},{anchor:"diffusers.utils.remote_decode.shift_factor",description:`<strong>shift_factor</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Shift is applied when passed e.g. <code>latents + self.vae.config.shift_factor</code>.</p>
<ul>
<li>Flux: 0.1159
If <code>None</code>, input must be passed with scaling applied.</li>
</ul>`,name:"shift_factor"},{anchor:"diffusers.utils.remote_decode.output_type",description:`<strong>output_type</strong> (<code>&quot;mp4&quot;</code> or <code>&quot;pil&quot;</code> or <code>&quot;pt&quot;, default </code>&#x201C;pil&#x201D;) &#x2014;
<strong>Endpoint</strong> output type. Subject to change. Report feedback on preferred type.</p>
<p><code>&quot;mp4&quot;: Supported by video models. Endpoint returns </code>bytes<code>of video.</code>&#x201C;pil&#x201D;<code>: Supported by image and video models. Image models: Endpoint returns </code>bytes<code>of an image in</code>image_format<code>. Video models: Endpoint returns </code>torch.Tensor<code>with partial</code>postprocessing<code>applied. Requires</code>processor<code>as a flag (any</code>None<code>value will work).</code>&#x201C;pt&#x201D;<code>: Support by image and video models. Endpoint returns </code>torch.Tensor<code>. With </code>partial_postprocess=True<code>the tensor is postprocessed</code>uint8\` image tensor.</p>
<p>Recommendations:
<code>&quot;pt&quot;</code> with <code>partial_postprocess=True</code> is the smallest transfer for full quality. <code>&quot;pt&quot;</code> with
<code>partial_postprocess=False</code> is the most compatible with third party code. <code>&quot;pil&quot;</code> with
<code>image_format=&quot;jpg&quot;</code> is the smallest transfer overall.`,name:"output_type"},{anchor:"diffusers.utils.remote_decode.return_type",description:`<strong>return_type</strong> (<code>&quot;mp4&quot;</code> or <code>&quot;pil&quot;</code> or <code>&quot;pt&quot;, default </code>&#x201C;pil&#x201D;) &#x2014;
<strong>Function</strong> return type.</p>
<p><code>&quot;mp4&quot;: Function returns </code>bytes<code>of video.</code>&#x201C;pil&#x201D;<code>: Function returns </code>PIL.Image.Image<code>. With </code>output_type=&#x201C;pil&#x201D; no further processing is applied. With <code>output_type=&quot;pt&quot; a </code>PIL.Image.Image<code>is created.</code>partial_postprocess=False<code> </code>processor<code>is required.</code>partial_postprocess=True<code> </code>processor<code>is **not** required.</code>&#x201C;pt&#x201D;<code>: Function returns </code>torch.Tensor<code>. </code>processor<code>is **not** required.</code>partial_postprocess=False<code>tensor is</code>float16<code>or</code>bfloat16<code>, without denormalization. </code>partial_postprocess=True<code>tensor is</code>uint8\`, denormalized.`,name:"return_type"},{anchor:"diffusers.utils.remote_decode.image_format",description:`<strong>image_format</strong> (<code>&quot;png&quot;</code> or <code>&quot;jpg&quot;</code>, default <code>jpg</code>) &#x2014;
Used with <code>output_type=&quot;pil&quot;</code>. Endpoint returns <code>jpg</code> or <code>png</code>.`,name:"image_format"},{anchor:"diffusers.utils.remote_decode.partial_postprocess",description:`<strong>partial_postprocess</strong> (<code>bool</code>, default <code>False</code>) &#x2014;
Used with <code>output_type=&quot;pt&quot;</code>. <code>partial_postprocess=False</code> tensor is <code>float16</code> or <code>bfloat16</code>, without
denormalization. <code>partial_postprocess=True</code> tensor is <code>uint8</code>, denormalized.`,name:"partial_postprocess"},{anchor:"diffusers.utils.remote_decode.input_tensor_type",description:`<strong>input_tensor_type</strong> (<code>&quot;binary&quot;</code>, default <code>&quot;binary&quot;</code>) &#x2014;
Tensor transfer type.`,name:"input_tensor_type"},{anchor:"diffusers.utils.remote_decode.output_tensor_type",description:`<strong>output_tensor_type</strong> (<code>&quot;binary&quot;</code>, default <code>&quot;binary&quot;</code>) &#x2014;
Tensor transfer type.`,name:"output_tensor_type"},{anchor:"diffusers.utils.remote_decode.height",description:`<strong>height</strong> (<code>int</code>, <strong>optional</strong>) &#x2014;
Required for <code>&quot;packed&quot;</code> latents.`,name:"height"},{anchor:"diffusers.utils.remote_decode.width",description:`<strong>width</strong> (<code>int</code>, <strong>optional</strong>) &#x2014;
Required for <code>&quot;packed&quot;</code> latents.`,name:"width"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/utils/remote_utils.py#L188",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>output (<code>Image.Image</code> or <code>List[Image.Image]</code> or <code>bytes</code> or <code>torch.Tensor</code>).</p>
`}}),u=new z({props:{title:"Remote Encode",local:"diffusers.utils.remote_utils.remote_encode",headingTag:"h2"}}),f=new B({props:{name:"diffusers.utils.remote_utils.remote_encode",anchor:"diffusers.utils.remote_utils.remote_encode",parameters:[{name:"endpoint",val:": str"},{name:"image",val:": typing.Union[ForwardRef('torch.Tensor'), PIL.Image.Image]"},{name:"scaling_factor",val:": typing.Optional[float] = None"},{name:"shift_factor",val:": typing.Optional[float] = None"}],parametersDescription:[{anchor:"diffusers.utils.remote_utils.remote_encode.endpoint",description:`<strong>endpoint</strong> (<code>str</code>) &#x2014;
Endpoint for Remote Decode.`,name:"endpoint"},{anchor:"diffusers.utils.remote_utils.remote_encode.image",description:`<strong>image</strong> (<code>torch.Tensor</code> or <code>PIL.Image.Image</code>) &#x2014;
Image to be encoded.`,name:"image"},{anchor:"diffusers.utils.remote_utils.remote_encode.scaling_factor",description:`<strong>scaling_factor</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Scaling is applied when passed e.g. <code>latents * self.vae.config.scaling_factor</code>.<ul>
<li>SD v1: 0.18215</li>
<li>SD XL: 0.13025</li>
<li>Flux: 0.3611
If <code>None</code>, input must be passed with scaling applied.</li>
</ul>`,name:"scaling_factor"},{anchor:"diffusers.utils.remote_utils.remote_encode.shift_factor",description:`<strong>shift_factor</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Shift is applied when passed e.g. <code>latents - self.vae.config.shift_factor</code>.<ul>
<li>Flux: 0.1159
If <code>None</code>, input must be passed with scaling applied.</li>
</ul>`,name:"shift_factor"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/utils/remote_utils.py#L380",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>output (<code>torch.Tensor</code>).</p>
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