import{s as Ge,n as Se,o as Re}from"../chunks/scheduler.182ea377.js";import{S as Ne,i as Ye,g as i,s as n,r as p,A as Le,h as o,f as l,c as a,j as Qe,u as r,x as M,k as Ue,y as De,a as s,v as c,d as u,t as d,w as m}from"../chunks/index.abf12888.js";import{C as y}from"../chunks/CodeBlock.57fe6e13.js";import{D as He}from"../chunks/DocNotebookDropdown.d9060979.js";import{H as ge}from"../chunks/Heading.16916d63.js";function ze(Je){let f,z,D,A,h,F,w,V,U,je='The <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline">StableDiffusionPipeline</a> supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the <a href="https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer" rel="nofollow">Stable Diffusion Conceptualizer</a>.',q,g,Te='This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you’re interested in teaching a model new concepts with textual inversion, take a look at the <a href="./training/text_inversion">Textual Inversion</a> training guide.',E,J,_e="Login to your Hugging Face account:",P,j,K,T,Ce="Import the necessary libraries:",O,_,ee,C,te,Z,Ze='Pick a Stable Diffusion checkpoint and a pre-learned concept from the <a href="https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer" rel="nofollow">Stable Diffusion Conceptualizer</a>:',le,v,se,x,ve="Now you can load a pipeline, and pass the pre-learned concept to it:",ne,k,ae,W,xe="Create a prompt with the pre-learned concept by using the special placeholder token <code>&lt;cat-toy&gt;</code>, and choose the number of samples and rows of images you’d like to generate:",ie,$,oe,X,ke="Then run the pipeline (feel free to adjust the parameters like <code>num_inference_steps</code> and <code>guidance_scale</code> to see how they affect image quality), save the generated images and visualize them with the helper function you created at the beginning:",pe,I,re,b,We='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/textual_inversion_inference.png"/>',Me,B,ce,Q,$e="Stable Diffusion XL (SDXL) can also use textual inversion vectors for inference. In contrast to Stable Diffusion 1 and 2, SDXL has two text encoders so you’ll need two textual inversion embeddings - one for each text encoder model.",ue,G,Xe="Let’s download the SDXL textual inversion embeddings and have a closer look at it’s structure:",de,S,me,R,fe,N,Ie=`There are two tensors, <code>&quot;clip-g&quot;</code> and <code>&quot;clip-l&quot;</code>.
<code>&quot;clip-g&quot;</code> corresponds to the bigger text encoder in SDXL and refers to
<code>pipe.text_encoder_2</code> and <code>&quot;clip-l&quot;</code> refers to <code>pipe.text_encoder</code>.`,ye,Y,Be=`Now you can load each tensor separately by passing them along with the correct text encoder and tokenizer
to <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img#diffusers.StableDiffusionImg2ImgPipeline.load_textual_inversion">load_textual_inversion()</a>:`,be,L,he,H,we;return h=new ge({props:{title:"Textual inversion",local:"textual-inversion",headingTag:"h1"}}),w=new He({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/textual_inversion_inference.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/textual_inversion_inference.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/textual_inversion_inference.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/textual_inversion_inference.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/textual_inversion_inference.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/textual_inversion_inference.ipynb"}]}}),j=new y({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()`,wrap:!1}}),_=new y({props:{code:"aW1wb3J0JTIwb3MlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQWltcG9ydCUyMFBJTCUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQSUwQWZyb20lMjBkaWZmdXNlcnMlMjBpbXBvcnQlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZSUwQWZyb20lMjBkaWZmdXNlcnMudXRpbHMlMjBpbXBvcnQlMjBtYWtlX2ltYWdlX2dyaWQlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQ0xJUEZlYXR1cmVFeHRyYWN0b3IlMkMlMjBDTElQVGV4dE1vZGVsJTJDJTIwQ0xJUFRva2VuaXplcg==",highlighted:`<span class="hljs-keyword">import</span> os
<span class="hljs-keyword">import</span> torch

<span class="hljs-keyword">import</span> PIL
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer`,wrap:!1}}),C=new ge({props:{title:"Stable Diffusion 1 and 2",local:"stable-diffusion-1-and-2",headingTag:"h2"}}),v=new y({props:{code:"cHJldHJhaW5lZF9tb2RlbF9uYW1lX29yX3BhdGglMjAlM0QlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUwQXJlcG9faWRfZW1iZWRzJTIwJTNEJTIwJTIyc2QtY29uY2VwdHMtbGlicmFyeSUyRmNhdC10b3klMjI=",highlighted:`pretrained_model_name_or_path = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
repo_id_embeds = <span class="hljs-string">&quot;sd-concepts-library/cat-toy&quot;</span>`,wrap:!1}}),k=new y({props:{code:"cGlwZWxpbmUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwcHJldHJhaW5lZF9tb2RlbF9uYW1lX29yX3BhdGglMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlJTBBKS50byglMjJjdWRhJTIyKSUwQSUwQXBpcGVsaW5lLmxvYWRfdGV4dHVhbF9pbnZlcnNpb24ocmVwb19pZF9lbWJlZHMp",highlighted:`pipeline = StableDiffusionPipeline.from_pretrained(
    pretrained_model_name_or_path, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipeline.load_textual_inversion(repo_id_embeds)`,wrap:!1}}),$=new y({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyYSUyMGdyYWZpdHRpJTIwaW4lMjBhJTIwZmF2ZWxhJTIwd2FsbCUyMHdpdGglMjBhJTIwJTNDY2F0LXRveSUzRSUyMG9uJTIwaXQlMjIlMEElMEFudW1fc2FtcGxlcyUyMCUzRCUyMDIlMEFudW1fcm93cyUyMCUzRCUyMDI=",highlighted:`prompt = <span class="hljs-string">&quot;a grafitti in a favela wall with a &lt;cat-toy&gt; on it&quot;</span>

num_samples = <span class="hljs-number">2</span>
num_rows = <span class="hljs-number">2</span>`,wrap:!1}}),I=new y({props:{code:"YWxsX2ltYWdlcyUyMCUzRCUyMCU1QiU1RCUwQWZvciUyMF8lMjBpbiUyMHJhbmdlKG51bV9yb3dzKSUzQSUwQSUyMCUyMCUyMCUyMGltYWdlcyUyMCUzRCUyMHBpcGUocHJvbXB0JTJDJTIwbnVtX2ltYWdlc19wZXJfcHJvbXB0JTNEbnVtX3NhbXBsZXMlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNENTAlMkMlMjBndWlkYW5jZV9zY2FsZSUzRDcuNSkuaW1hZ2VzJTBBJTIwJTIwJTIwJTIwYWxsX2ltYWdlcy5leHRlbmQoaW1hZ2VzKSUwQSUwQWdyaWQlMjAlM0QlMjBtYWtlX2ltYWdlX2dyaWQoYWxsX2ltYWdlcyUyQyUyMG51bV9zYW1wbGVzJTJDJTIwbnVtX3Jvd3MpJTBBZ3JpZA==",highlighted:`all_images = []
<span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_rows):
    images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">7.5</span>).images
    all_images.extend(images)

grid = make_image_grid(all_images, num_samples, num_rows)
grid`,wrap:!1}}),B=new ge({props:{title:"Stable Diffusion XL",local:"stable-diffusion-xl",headingTag:"h2"}}),S=new y({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQWZyb20lMjBzYWZldGVuc29ycy50b3JjaCUyMGltcG9ydCUyMGxvYWRfZmlsZSUwQSUwQWZpbGUlMjAlM0QlMjBoZl9odWJfZG93bmxvYWQoJTIyZG4xMTglMkZ1bmFlc3RoZXRpY1hMJTIyJTJDJTIwZmlsZW5hbWUlM0QlMjJ1bmFlc3RoZXRpY1hMdjMxLnNhZmV0ZW5zb3JzJTIyKSUwQXN0YXRlX2RpY3QlMjAlM0QlMjBsb2FkX2ZpbGUoZmlsZSklMEFzdGF0ZV9kaWN0",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download
<span class="hljs-keyword">from</span> safetensors.torch <span class="hljs-keyword">import</span> load_file

file = hf_hub_download(<span class="hljs-string">&quot;dn118/unaestheticXL&quot;</span>, filename=<span class="hljs-string">&quot;unaestheticXLv31.safetensors&quot;</span>)
state_dict = load_file(file)
state_dict`,wrap:!1}}),R=new y({props:{code:"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",highlighted:`{<span class="hljs-string">&#x27;clip_g&#x27;</span>: tensor(<span class="hljs-string">[[ 0.0077, -0.0112,  0.0065,  ...,  0.0195,  0.0159,  0.0275],
         ...,
         [-0.0170,  0.0213,  0.0143,  ..., -0.0302, -0.0240, -0.0362]]</span>,
 <span class="hljs-string">&#x27;clip_l&#x27;</span>: tensor(<span class="hljs-string">[[ 0.0023,  0.0192,  0.0213,  ..., -0.0385,  0.0048, -0.0011],
         ...,
         [ 0.0475, -0.0508, -0.0145,  ...,  0.0070, -0.0089, -0.0163]]</span>,`,wrap:!1}}),L=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipe = AutoPipelineForText2Image.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, variant=<span class="hljs-string">&quot;fp16&quot;</span>, torch_dtype=torch.float16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(state_dict[<span class="hljs-string">&quot;clip_g&quot;</span>], token=<span class="hljs-string">&quot;unaestheticXLv31&quot;</span>, text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
pipe.load_textual_inversion(state_dict[<span class="hljs-string">&quot;clip_l&quot;</span>], token=<span class="hljs-string">&quot;unaestheticXLv31&quot;</span>, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)

<span class="hljs-comment"># the embedding should be used as a negative embedding, so we pass it as a negative prompt</span>
generator = torch.Generator().manual_seed(<span class="hljs-number">33</span>)
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