Buckets:

download
raw
6.28 kB
import{s as Y,n as Z,o as ee}from"../chunks/scheduler.53228c21.js";import{S as te,i as re,e as d,s as a,c as b,h as ne,a as l,d as r,b as s,f as U,g as v,j as W,k as V,l as y,m as n,n as E,t as x,o as I,p as M}from"../chunks/index.cac5d66a.js";import{C as ae}from"../chunks/CopyLLMTxtMenu.0ef49226.js";import{D as Q}from"../chunks/Docstring.9de32ff4.js";import{H as X,E as se}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.48d5cb47.js";function oe(B){let o,L,w,P,f,H,c,z,p,F='A Transformer model for image-like data from <a href="https://huggingface.co/baidu/ERNIE-Image" rel="nofollow">ERNIE-Image</a>.',C,u,J='A Transformer model for image-like data from <a href="https://huggingface.co/baidu/ERNIE-Image-Turbo" rel="nofollow">ERNIE-Image-Turbo</a>.',k,g,R,i,h,j,m,_,G,T,K='The <a href="/docs/diffusers/pr_13921/en/api/models/ernie_image_transformer2d#diffusers.ErnieImageTransformer2DModel">ErnieImageTransformer2DModel</a> forward method.',N,$,S,D,q;return f=new ae({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),c=new X({props:{title:"ErnieImageTransformer2DModel",local:"ernieimagetransformer2dmodel",headingTag:"h1"}}),g=new X({props:{title:"ErnieImageTransformer2DModel",local:"diffusers.ErnieImageTransformer2DModel",headingTag:"h2"}}),h=new Q({props:{name:"class diffusers.ErnieImageTransformer2DModel",anchor:"diffusers.ErnieImageTransformer2DModel",parameters:[{name:"hidden_size",val:": int = 3072"},{name:"num_attention_heads",val:": int = 24"},{name:"num_layers",val:": int = 24"},{name:"ffn_hidden_size",val:": int = 8192"},{name:"in_channels",val:": int = 128"},{name:"out_channels",val:": int = 128"},{name:"patch_size",val:": int = 1"},{name:"text_in_dim",val:": int = 2560"},{name:"rope_theta",val:": int = 256"},{name:"rope_axes_dim",val:": typing.Tuple[int, int, int] = (32, 48, 48)"},{name:"eps",val:": float = 1e-06"},{name:"qk_layernorm",val:": bool = True"}],source:"https://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/models/transformers/transformer_ernie_image.py#L292"}}),_=new Q({props:{name:"forward",anchor:"diffusers.ErnieImageTransformer2DModel.forward",parameters:[{name:"hidden_states",val:": Tensor"},{name:"timestep",val:": Tensor"},{name:"text_bth",val:": Tensor"},{name:"text_lens",val:": Tensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.ErnieImageTransformer2DModel.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, in_channels, height, width)</code>) &#x2014;
Input <code>hidden_states</code>.`,name:"hidden_states"},{anchor:"diffusers.ErnieImageTransformer2DModel.forward.timestep",description:`<strong>timestep</strong> (<code>torch.LongTensor</code>) &#x2014;
Used to indicate denoising step.`,name:"timestep"},{anchor:"diffusers.ErnieImageTransformer2DModel.forward.text_bth",description:`<strong>text_bth</strong> (<code>torch.Tensor</code>) &#x2014;
Conditional text embeddings (embeddings computed from the input conditions such as prompts) to use,
shaped <code>(batch_size, text_length, embed_dims)</code>.`,name:"text_bth"},{anchor:"diffusers.ErnieImageTransformer2DModel.forward.text_lens",description:`<strong>text_lens</strong> (<code>torch.Tensor</code>) &#x2014;
Per-sample text sequence lengths used to build the attention mask.`,name:"text_lens"},{anchor:"diffusers.ErnieImageTransformer2DModel.forward.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>~models.transformer_2d.Transformer2DModelOutput</code> instead of a plain
tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/models/transformers/transformer_ernie_image.py#L344"}}),$=new se({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/models/ernie_image_transformer2d.md"}}),{c(){o=d("meta"),L=a(),w=d("p"),P=a(),b(f.$$.fragment),H=a(),b(c.$$.fragment),z=a(),p=d("p"),p.innerHTML=F,C=a(),u=d("p"),u.innerHTML=J,k=a(),b(g.$$.fragment),R=a(),i=d("div"),b(h.$$.fragment),j=a(),m=d("div"),b(_.$$.fragment),G=a(),T=d("p"),T.innerHTML=K,N=a(),b($.$$.fragment),S=a(),D=d("p"),this.h()},l(e){const t=ne("svelte-u9bgzb",document.head);o=l(t,"META",{name:!0,content:!0}),t.forEach(r),L=s(e),w=l(e,"P",{}),U(w).forEach(r),P=s(e),v(f.$$.fragment,e),H=s(e),v(c.$$.fragment,e),z=s(e),p=l(e,"P",{"data-svelte-h":!0}),W(p)!=="svelte-19ccbtc"&&(p.innerHTML=F),C=s(e),u=l(e,"P",{"data-svelte-h":!0}),W(u)!=="svelte-9wlmvy"&&(u.innerHTML=J),k=s(e),v(g.$$.fragment,e),R=s(e),i=l(e,"DIV",{class:!0});var A=U(i);v(h.$$.fragment,A),j=s(A),m=l(A,"DIV",{class:!0});var O=U(m);v(_.$$.fragment,O),G=s(O),T=l(O,"P",{"data-svelte-h":!0}),W(T)!=="svelte-126eyse"&&(T.innerHTML=K),O.forEach(r),A.forEach(r),N=s(e),v($.$$.fragment,e),S=s(e),D=l(e,"P",{}),U(D).forEach(r),this.h()},h(){V(o,"name","hf:doc:metadata"),V(o,"content",ie),V(m,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),V(i,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){y(document.head,o),n(e,L,t),n(e,w,t),n(e,P,t),E(f,e,t),n(e,H,t),E(c,e,t),n(e,z,t),n(e,p,t),n(e,C,t),n(e,u,t),n(e,k,t),E(g,e,t),n(e,R,t),n(e,i,t),E(h,i,null),y(i,j),y(i,m),E(_,m,null),y(m,G),y(m,T),n(e,N,t),E($,e,t),n(e,S,t),n(e,D,t),q=!0},p:Z,i(e){q||(x(f.$$.fragment,e),x(c.$$.fragment,e),x(g.$$.fragment,e),x(h.$$.fragment,e),x(_.$$.fragment,e),x($.$$.fragment,e),q=!0)},o(e){I(f.$$.fragment,e),I(c.$$.fragment,e),I(g.$$.fragment,e),I(h.$$.fragment,e),I(_.$$.fragment,e),I($.$$.fragment,e),q=!1},d(e){e&&(r(L),r(w),r(P),r(H),r(z),r(p),r(C),r(u),r(k),r(R),r(i),r(N),r(S),r(D)),r(o),M(f,e),M(c,e),M(g,e),M(h),M(_),M($,e)}}}const ie='{"title":"ErnieImageTransformer2DModel","local":"ernieimagetransformer2dmodel","sections":[{"title":"ErnieImageTransformer2DModel","local":"diffusers.ErnieImageTransformer2DModel","sections":[],"depth":2}],"depth":1}';function me(B){return ee(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ue extends te{constructor(o){super(),re(this,o,me,oe,Y,{})}}export{ue as component};

Xet Storage Details

Size:
6.28 kB
·
Xet hash:
76d530ed5c224c0dfc95bb85ed930e4bf3b885431270fec383f47f1d7100ac4d

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.