import{s as Me,n as Ae,o as Fe}from"../chunks/scheduler.8c3d61f6.js";import{S as Oe,i as ze,g as n,s,r as o,A as Ge,h as l,f as i,c as a,j as y,u as m,x as K,k as x,y as u,a as r,v as f,d,t as p,w as g}from"../chunks/index.da70eac4.js";import{D as S}from"../chunks/Docstring.6b390b9a.js";import{H as I,E as Se}from"../chunks/EditOnGithub.1e64e623.js";function je(Te){let _,Y,Q,Z,L,ee,w,De="Utility and helper functions for working with 🤗 Diffusers.",te,P,ie,$,C,Ie,j,Ee="Convert a numpy image or a batch of images to a PIL image.",re,T,se,c,D,Le,W,Ue="Convert a torch image to a PIL image.",ae,E,ne,h,U,we,q,Ne="Loads <code>image</code> to a PIL Image.",oe,N,le,k,R,me,V,fe,H,M,de,A,pe,v,F,Pe,B,ke="Prepares a single grid of images. Useful for visualization purposes.",ge,O,ue,b,z,Ce,J,Re=`A helper function to create random tensors on the desired <code>device</code> with the desired <code>dtype</code>. When
passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor
is always created on the CPU.`,_e,G,$e,X,ce;return L=new I({props:{title:"Utilities",local:"utilities",headingTag:"h1"}}),P=new I({props:{title:"numpy_to_pil",local:"diffusers.utils.numpy_to_pil",headingTag:"h2"}}),C=new S({props:{name:"diffusers.utils.numpy_to_pil",anchor:"diffusers.utils.numpy_to_pil",parameters:[{name:"images",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_10130/src/diffusers/utils/pil_utils.py#L37"}}),T=new I({props:{title:"pt_to_pil",local:"diffusers.utils.pt_to_pil",headingTag:"h2"}}),D=new S({props:{name:"diffusers.utils.pt_to_pil",anchor:"diffusers.utils.pt_to_pil",parameters:[{name:"images",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_10130/src/diffusers/utils/pil_utils.py#L27"}}),E=new I({props:{title:"load_image",local:"diffusers.utils.load_image",headingTag:"h2"}}),U=new S({props:{name:"diffusers.utils.load_image",anchor:"diffusers.utils.load_image",parameters:[{name:"image",val:": typing.Union[str, PIL.Image.Image]"},{name:"convert_method",val:": typing.Optional[typing.Callable[[PIL.Image.Image], PIL.Image.Image]] = None"}],parametersDescription:[{anchor:"diffusers.utils.load_image.image",description:`<strong>image</strong> (<code>str</code> or <code>PIL.Image.Image</code>) &#x2014;
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&#x201C;RGB&#x201D;.`,name:"convert_method"}],source:"https://github.com/huggingface/diffusers/blob/vr_10130/src/diffusers/utils/loading_utils.py#L13",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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<p><code>PIL.Image.Image</code></p>
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typing.Optional[ForwardRef('torch.dtype')] = None"},{name:"layout",val:": typing.Optional[ForwardRef('torch.layout')] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_10130/src/diffusers/utils/torch_utils.py#L38"}}),G=new 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