diffusers/pr_12262/en/_app/immutable/nodes/314.13f1fa21.js

import{s as Ke,o as Oe,n as Pe}from"../chunks/scheduler.8c3d61f6.js";import{S as es,i as ss,g as b,s as o,r,A as ls,h as T,f as l,c as i,j as qe,u,x as J,k as Le,y as ts,a as t,v as h,d as c,t as M,w as f}from"../chunks/index.da70eac4.js";import{C as _}from"../chunks/CodeBlock.a9c4becf.js";import{D as ns}from"../chunks/DocNotebookDropdown.48852948.js";import{H as se,E as as}from"../chunks/getInferenceSnippets.676f6ee5.js";import{H as os,a as Fe}from"../chunks/HfOption.6c3b4e77.js";function is(j){let n,m;return n=new _({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()`,wrap:!1}}),{c(){r(n.$$.fragment)},l(a){u(n.$$.fragment,a)},m(a,d){h(n,a,d),m=!0},p:Pe,i(a){m||(c(n.$$.fragment,a),m=!0)},o(a){M(n.$$.fragment,a),m=!1},d(a){f(n,a)}}}function ps(j){let n,m;return n=new _({props:{code:"aGYlMjBhdXRoJTIwbG9naW4=",highlighted:"hf auth login",wrap:!1}}),{c(){r(n.$$.fragment)},l(a){u(n.$$.fragment,a)},m(a,d){h(n,a,d),m=!0},p:Pe,i(a){m||(c(n.$$.fragment,a),m=!0)},o(a){M(n.$$.fragment,a),m=!1},d(a){f(n,a)}}}function rs(j){let n,m,a,d;return n=new Fe({props:{id:"login",option:"notebook",$$slots:{default:[is]},$$scope:{ctx:j}}}),a=new Fe({props:{id:"login",option:"hf CLI",$$slots:{default:[ps]},$$scope:{ctx:j}}}),{c(){r(n.$$.fragment),m=o(),r(a.$$.fragment)},l(p){u(n.$$.fragment,p),m=i(p),u(a.$$.fragment,p)},m(p,w){h(n,p,w),t(p,m,w),h(a,p,w),d=!0},p(p,w){const y={};w&2&&(y.$$scope={dirty:w,ctx:p}),n.$set(y);const $={};w&2&&($.$$scope={dirty:w,ctx:p}),a.$set($)},i(p){d||(c(n.$$.fragment,p),c(a.$$.fragment,p),d=!0)},o(p){M(n.$$.fragment,p),M(a.$$.fragment,p),d=!1},d(p){p&&l(m),f(n,p),f(a,p)}}}function us(j){let n,m,a,d,p,w,y,$,U,Ge='Share your pipeline or models and schedulers on the Hub with the <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin">PushToHubMixin</a> class. This class:',le,g,Ce="<li>creates a repository on the Hub</li> <li>saves your model, scheduler, or pipeline files so they can be reloaded later</li> <li>uploads folder containing these files to the Hub</li>",te,v,Ve='This guide will show you how to upload your files to the Hub with the <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin">PushToHubMixin</a> class.',ne,k,Ee='Log in to your Hugging Face account with your access <a href="https://huggingface.co/settings/tokens" rel="nofollow">token</a>.',ae,I,oe,B,ie,Z,De='To push a model to the Hub, call <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> and specify the repository id of the model.',pe,G,re,C,Xe='The <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method saves the model’s <code>config.json</code> file and the weights are automatically saved as safetensors files.',ue,V,Re='Load the model again with <a href="/docs/diffusers/pr_12262/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a>.',he,E,ce,D,Me,X,Ne='To push a scheduler to the Hub, call <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> and specify the repository id of the scheduler.',fe,R,me,N,We='The <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> function saves the scheduler’s <code>scheduler_config.json</code> file to the specified repository.',be,W,xe='Load the scheduler again with <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.SchedulerMixin.from_pretrained">from_pretrained()</a>.',Te,x,Je,H,de,Y,He="To push a pipeline to the Hub, initialize the pipeline components with your desired parameters.",we,z,ye,Q,Ye='Pass all components to the pipeline and call <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a>.',_e,S,je,A,ze='The <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method saves each component to a subfolder in the repository. Load the pipeline again with <a href="/docs/diffusers/pr_12262/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a>.',Ie,q,$e,L,Ue,F,Qe='Set <code>private=True</code> in <a href="/docs/diffusers/pr_12262/en/api/schedulers/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> to keep a model, scheduler, or pipeline files private.',ge,P,ve,K,Se='Private repositories are only visible to you. Other users won’t be able to clone the repository and it won’t appear in search results. Even if a user has the URL to your private repository, they’ll receive a <code>404 - Sorry, we can&#39;t find the page you are looking for</code>. You must be <a href="https://huggingface.co/docs/huggingface_hub/quick-start#login" rel="nofollow">logged in</a> to load a model from a private repository.',ke,O,Be,ee,Ze;return p=new ns({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/push_to_hub.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/push_to_hub.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/push_to_hub.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/push_to_hub.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/push_to_hub.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/push_to_hub.ipynb"}]}}),y=new se({props:{title:"Sharing pipelines and models",local:"sharing-pipelines-and-models",headingTag:"h1"}}),I=new os({props:{id:"login",options:["notebook","hf CLI"],$$slots:{default:[rs]},$$scope:{ctx:j}}}),B=new se({props:{title:"Models",local:"models",headingTag:"h2"}}),G=new _({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ControlNetModel

controlnet = ControlNetModel(
    block_out_channels=(<span class="hljs-number">32</span>, <span class="hljs-number">64</span>),
    layers_per_block=<span class="hljs-number">2</span>,
    in_channels=<span class="hljs-number">4</span>,
    down_block_types=(<span class="hljs-string">&quot;DownBlock2D&quot;</span>, <span class="hljs-string">&quot;CrossAttnDownBlock2D&quot;</span>),
    cross_attention_dim=<span class="hljs-number">32</span>,
    conditioning_embedding_out_channels=(<span class="hljs-number">16</span>, <span class="hljs-number">32</span>),
)
controlnet.push_to_hub(<span class="hljs-string">&quot;my-controlnet-model&quot;</span>)`,wrap:!1}}),E=new _({props:{code:"bW9kZWwlMjAlM0QlMjBDb250cm9sTmV0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMnlvdXItbmFtZXNwYWNlJTJGbXktY29udHJvbG5ldC1tb2RlbCUyMik=",highlighted:'model = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;your-namespace/my-controlnet-model&quot;</span>)',wrap:!1}}),D=new se({props:{title:"Scheduler",local:"scheduler",headingTag:"h2"}}),R=new _({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERESU1TY2hlZHVsZXIlMEElMEFzY2hlZHVsZXIlMjAlM0QlMjBERElNU2NoZWR1bGVyKCUwQSUyMCUyMCUyMCUyMGJldGFfc3RhcnQlM0QwLjAwMDg1JTJDJTBBJTIwJTIwJTIwJTIwYmV0YV9lbmQlM0QwLjAxMiUyQyUwQSUyMCUyMCUyMCUyMGJldGFfc2NoZWR1bGUlM0QlMjJzY2FsZWRfbGluZWFyJTIyJTJDJTBBJTIwJTIwJTIwJTIwY2xpcF9zYW1wbGUlM0RGYWxzZSUyQyUwQSUyMCUyMCUyMCUyMHNldF9hbHBoYV90b19vbmUlM0RGYWxzZSUyQyUwQSklMEFzY2hlZHVsZXIucHVzaF90b19odWIoJTIybXktY29udHJvbG5ldC1zY2hlZHVsZXIlMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMScheduler

scheduler = DDIMScheduler(
    beta_start=<span class="hljs-number">0.00085</span>,
    beta_end=<span class="hljs-number">0.012</span>,
    beta_schedule=<span class="hljs-string">&quot;scaled_linear&quot;</span>,
    clip_sample=<span class="hljs-literal">False</span>,
    set_alpha_to_one=<span class="hljs-literal">False</span>,
)
scheduler.push_to_hub(<span class="hljs-string">&quot;my-controlnet-scheduler&quot;</span>)`,wrap:!1}}),x=new _({props:{code:"c2NoZWR1bGVyJTIwJTNEJTIwRERJTVNjaGVkdWxlci5mcm9tX3ByZXRyYWluZWQoJTIyeW91ci1uYW1lcHNhY2UlMkZteS1jb250cm9sbmV0LXNjaGVkdWxlciUyMik=",highlighted:'scheduler = DDIMScheduler.from_pretrained(<span class="hljs-string">&quot;your-namepsace/my-controlnet-scheduler&quot;</span>)',wrap:!1}}),H=new se({props:{title:"Pipeline",local:"pipeline",headingTag:"h2"}}),z=new _({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> (
    UNet2DConditionModel,
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionPipeline,
)
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPTextModel, CLIPTextConfig, CLIPTokenizer

unet = UNet2DConditionModel(
    block_out_channels=(<span class="hljs-number">32</span>, <span class="hljs-number">64</span>),
    layers_per_block=<span class="hljs-number">2</span>,
    sample_size=<span class="hljs-number">32</span>,
    in_channels=<span class="hljs-number">4</span>,
    out_channels=<span class="hljs-number">4</span>,
    down_block_types=(<span class="hljs-string">&quot;DownBlock2D&quot;</span>, <span class="hljs-string">&quot;CrossAttnDownBlock2D&quot;</span>),
    up_block_types=(<span class="hljs-string">&quot;CrossAttnUpBlock2D&quot;</span>, <span class="hljs-string">&quot;UpBlock2D&quot;</span>),
    cross_attention_dim=<span class="hljs-number">32</span>,
)

scheduler = DDIMScheduler(
    beta_start=<span class="hljs-number">0.00085</span>,
    beta_end=<span class="hljs-number">0.012</span>,
    beta_schedule=<span class="hljs-string">&quot;scaled_linear&quot;</span>,
    clip_sample=<span class="hljs-literal">False</span>,
    set_alpha_to_one=<span class="hljs-literal">False</span>,
)

vae = AutoencoderKL(
    block_out_channels=[<span class="hljs-number">32</span>, <span class="hljs-number">64</span>],
    in_channels=<span class="hljs-number">3</span>,
    out_channels=<span class="hljs-number">3</span>,
    down_block_types=[<span class="hljs-string">&quot;DownEncoderBlock2D&quot;</span>, <span class="hljs-string">&quot;DownEncoderBlock2D&quot;</span>],
    up_block_types=[<span class="hljs-string">&quot;UpDecoderBlock2D&quot;</span>, <span class="hljs-string">&quot;UpDecoderBlock2D&quot;</span>],
    latent_channels=<span class="hljs-number">4</span>,
)

text_encoder_config = CLIPTextConfig(
    bos_token_id=<span class="hljs-number">0</span>,
    eos_token_id=<span class="hljs-number">2</span>,
    hidden_size=<span class="hljs-number">32</span>,
    intermediate_size=<span class="hljs-number">37</span>,
    layer_norm_eps=<span class="hljs-number">1e-05</span>,
    num_attention_heads=<span class="hljs-number">4</span>,
    num_hidden_layers=<span class="hljs-number">5</span>,
    pad_token_id=<span class="hljs-number">1</span>,
    vocab_size=<span class="hljs-number">1000</span>,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-clip&quot;</span>)`,wrap:!1}}),S=new _({props:{code:"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",highlighted:`components = {
    <span class="hljs-string">&quot;unet&quot;</span>: unet,
    <span class="hljs-string">&quot;scheduler&quot;</span>: scheduler,
    <span class="hljs-string">&quot;vae&quot;</span>: vae,
    <span class="hljs-string">&quot;text_encoder&quot;</span>: text_encoder,
    <span class="hljs-string">&quot;tokenizer&quot;</span>: tokenizer,
    <span class="hljs-string">&quot;safety_checker&quot;</span>: <span class="hljs-literal">None</span>,
    <span class="hljs-string">&quot;feature_extractor&quot;</span>: <span class="hljs-literal">None</span>,
}

pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(<span class="hljs-string">&quot;my-pipeline&quot;</span>)`,wrap:!1}}),q=new _({props:{code:"cGlwZWxpbmUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyeW91ci1uYW1lc3BhY2UlMkZteS1waXBlbGluZSUyMik=",highlighted:'pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;your-namespace/my-pipeline&quot;</span>)',wrap:!1}}),L=new se({props:{title:"Privacy",local:"privacy",headingTag:"h2"}}),P=new _({props:{code:"Y29udHJvbG5ldC5wdXNoX3RvX2h1YiglMjJteS1jb250cm9sbmV0LW1vZGVsLXByaXZhdGUlMjIlMkMlMjBwcml2YXRlJTNEVHJ1ZSk=",highlighted:'controlnet.push_to_hub(<span class="hljs-string">&quot;my-controlnet-model-private&quot;</span>, private=<span class="hljs-literal">True</span>)',wrap:!1}}),O=new 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hs='{"title":"Sharing pipelines and models","local":"sharing-pipelines-and-models","sections":[{"title":"Models","local":"models","sections":[],"depth":2},{"title":"Scheduler","local":"scheduler","sections":[],"depth":2},{"title":"Pipeline","local":"pipeline","sections":[],"depth":2},{"title":"Privacy","local":"privacy","sections":[],"depth":2}],"depth":1}';function cs(j){return Oe(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ds extends es{constructor(n){super(),ss(this,n,cs,us,Ke,{})}}export{ds as component};