diffusers/pr_14313/en/_app/immutable/nodes/334.B-f9ZG71.js

import"../chunks/DsnmJJEf.js";import{i as u,h,C as T,H as w,a,E as y,s as b}from"../chunks/CmJXCtRL.js";import{p as f,o as I,s as e,f as v,a as d,b as Z,d as c,n as B}from"../chunks/DK803DsY.js";const W='{"title":"Textual Inversion","local":"textual-inversion","sections":[],"depth":1}';var M=c('<meta name="hf:doc:metadata"/>'),U=c('<p></p> <!> <!> <p><a href="https://huggingface.co/papers/2208.01618" rel="nofollow">Textual Inversion</a> is a method for generating personalized images of a concept. It works by fine-tuning a models word embeddings on 3-5 images of the concept (for example, pixel art) that is associated with a unique token (<code>&lt;sks&gt;</code>). This allows you to use the <code>&lt;sks&gt;</code> token in your prompt to trigger the model to generate pixel art images.</p> <p>Textual Inversion weights are very lightweight and typically only a few KBs because they’re only word embeddings. However, this also means the word embeddings need to be loaded after loading a model with <a href="/docs/diffusers/pr_14313/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a>.</p> <!> <p>Load the word embeddings with <a href="/docs/diffusers/pr_14313/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion">load_textual_inversion()</a> and include the unique token in the prompt to activate its generation.</p> <!> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_txt_embed.png"/></div> <p>Textual Inversion can also be trained to learn <em>negative embeddings</em> to steer generation away from unwanted characteristics such as “blurry” or “ugly”. It is useful for improving image quality.</p> <p>EasyNegative is a widely used negative embedding that contains multiple learned negative concepts. Load the negative embeddings and specify the file name and token associated with the negative embeddings. Pass the token to <code>negative_prompt</code> in your pipeline to activate it.</p> <!> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png"/></div> <!> <p></p>',1);function G(J,m){f(m,!1),I(()=>{new URLSearchParams(window.location.search).get("fw")}),u();var s=U();h("1mqg1pd",r=>{var p=M();b(p,"content",W),d(r,p)});var t=e(v(s),2);T(t,{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"});var n=e(t,2);w(n,{title:"Textual Inversion",local:"textual-inversion",headingTag:"h1"});var i=e(n,6);a(i,{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQXV0b1BpcGVsaW5lRm9yVGV4dDJJbWFnZSUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwQXV0b1BpcGVsaW5lRm9yVGV4dDJJbWFnZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmxlLWRpZmZ1c2lvbi12MS01JTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTJDJTBBJTIwJTIwJTIwJTIwZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKQ==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>,
    dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1});var o=e(i,4);a(o,{code:"cGlwZWxpbmUubG9hZF90ZXh0dWFsX2ludmVyc2lvbiglMjJzZC1jb25jZXB0cy1saWJyYXJ5JTJGZ3RhNS1hcnR3b3JrJTIyKSUwQXByb21wdCUyMCUzRCUyMCUyMkElMjBjdXRlJTIwYnJvd24lMjBiZWFyJTIwZWF0aW5nJTIwYSUyMHNsaWNlJTIwb2YlMjBwaXp6YSUyQyUyMHN0dW5uaW5nJTIwY29sb3IlMjBzY2hlbWUlMkMlMjBtYXN0ZXJwaWVjZSUyQyUyMGlsbHVzdHJhdGlvbiUyQyUyMCUzQ2d0YTUtYXJ0d29yayUzRSUyMHN0eWxlJTIyJTBBcGlwZWxpbmUocHJvbXB0KS5pbWFnZXMlNUIwJTVE",highlighted:`pipeline.load_textual_inversion(<span class="hljs-string">&quot;sd-concepts-library/gta5-artwork&quot;</span>)
prompt = <span class="hljs-string">&quot;A cute brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration, &lt;gta5-artwork&gt; style&quot;</span>
pipeline(prompt).images[<span class="hljs-number">0</span>]`,lang:"py",wrap:!1});var l=e(o,8);a(l,{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>,
    dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_textual_inversion(
    <span class="hljs-string">&quot;EvilEngine/easynegative&quot;</span>,
    weight_name=<span class="hljs-string">&quot;easynegative.safetensors&quot;</span>,
    token=<span class="hljs-string">&quot;easynegative&quot;</span>
)
prompt = <span class="hljs-string">&quot;A cute brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration&quot;</span>
negative_prompt = <span class="hljs-string">&quot;easynegative&quot;</span>
pipeline(prompt, negative_prompt).images[<span class="hljs-number">0</span>]`,lang:"py",wrap:!1});var g=e(l,4);y(g,{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/textual_inversion_inference.md"}),B(2),d(J,s),Z()}export{G as component};