diffusers/pr_11234/en/_app/immutable/nodes/145.b7ece0a9.js

import{s as Xe,n as Be,o as Ne}from"../chunks/scheduler.8c3d61f6.js";import{S as Ee,i as Pe,g as n,s as a,r,A as Qe,h as p,f as i,c as l,j as He,u as f,x as o,k as Se,y as ze,a as s,v as d,d as u,t as m,w as c}from"../chunks/index.da70eac4.js";import{C as E}from"../chunks/CodeBlock.a9c4becf.js";import{H as P,E as Ae}from"../chunks/index.5d4ab994.js";function Ye(ve){let g,Q,B,z,y,A,h,Ue='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',Y,x,$e='Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from <a href="https://github.com/CompVis" rel="nofollow">CompVis</a>, <a href="https://stability.ai/" rel="nofollow">Stability AI</a> and <a href="https://laion.ai/" rel="nofollow">LAION</a>. Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. This specific type of diffusion model was proposed in <a href="https://huggingface.co/papers/2112.10752" rel="nofollow">High-Resolution Image Synthesis with Latent Diffusion Models</a> by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.',q,w,Ge="Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs.",K,Z,Te='For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI <a href="https://stability.ai/blog/stable-diffusion-announcement" rel="nofollow">announcement</a> and our own <a href="https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work" rel="nofollow">blog post</a> for more technical details.',O,M,Je='You can find the original codebase for Stable Diffusion v1.0 at <a href="https://github.com/CompVis/stable-diffusion" rel="nofollow">CompVis/stable-diffusion</a> and Stable Diffusion v2.0 at <a href="https://github.com/Stability-AI/stablediffusion" rel="nofollow">Stability-AI/stablediffusion</a> as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the <a href="https://huggingface.co/CompVis" rel="nofollow">CompVis</a>, <a href="https://huggingface.co/runwayml" rel="nofollow">Runway</a>, and <a href="https://huggingface.co/stabilityai" rel="nofollow">Stability AI</a> Hub organizations. Explore these organizations to find the best checkpoint for your use-case!',ee,S,Ce="The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:",te,b,We='<div class="rounded-xl border border-gray-200"><table class="min-w-full divide-y-2 divide-gray-200 bg-white text-sm"><thead><tr><th class="px-4 py-2 font-medium text-gray-900 text-left">Pipeline</th> <th class="px-4 py-2 font-medium text-gray-900 text-left">Supported tasks</th> <th class="px-4 py-2 font-medium text-gray-900 text-left">🤗 Space</th></tr></thead> <tbody class="divide-y divide-gray-200"><tr><td class="px-4 py-2 text-gray-700"><a href="./text2img">StableDiffusion</a></td> <td class="px-4 py-2 text-gray-700">text-to-image</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/stabilityai/stable-diffusion"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./img2img">StableDiffusionImg2Img</a></td> <td class="px-4 py-2 text-gray-700">image-to-image</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/huggingface/diffuse-the-rest"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./inpaint">StableDiffusionInpaint</a></td> <td class="px-4 py-2 text-gray-700">inpainting</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./depth2img">StableDiffusionDepth2Img</a></td> <td class="px-4 py-2 text-gray-700">depth-to-image</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/radames/stable-diffusion-depth2img"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./image_variation">StableDiffusionImageVariation</a></td> <td class="px-4 py-2 text-gray-700">image variation</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/lambdalabs/stable-diffusion-image-variations"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./stable_diffusion_safe">StableDiffusionPipelineSafe</a></td> <td class="px-4 py-2 text-gray-700">filtered text-to-image</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./stable_diffusion_2">StableDiffusion2</a></td> <td class="px-4 py-2 text-gray-700">text-to-image, inpainting, depth-to-image, super-resolution</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/stabilityai/stable-diffusion"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./stable_diffusion_xl">StableDiffusionXL</a></td> <td class="px-4 py-2 text-gray-700">text-to-image, image-to-image</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/RamAnanth1/stable-diffusion-xl"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./latent_upscale">StableDiffusionLatentUpscale</a></td> <td class="px-4 py-2 text-gray-700">super-resolution</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/huggingface-projects/stable-diffusion-latent-upscaler"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./upscale">StableDiffusionUpscale</a></td> <td class="px-4 py-2 text-gray-700">super-resolution</td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./ldm3d_diffusion">StableDiffusionLDM3D</a></td> <td class="px-4 py-2 text-gray-700">text-to-rgb, text-to-depth, text-to-pano</td> <td class="px-4 py-2"><a href="https://huggingface.co/spaces/r23/ldm3d-space"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a></td></tr> <tr><td class="px-4 py-2 text-gray-700"><a href="./ldm3d_diffusion">StableDiffusionUpscaleLDM3D</a></td> <td class="px-4 py-2 text-gray-700">ldm3d super-resolution</td></tr></tbody></table></div>',ie,v,se,U,Ve="To help you get the most out of the Stable Diffusion pipelines, here are a few tips for improving performance and usability. These tips are applicable to all Stable Diffusion pipelines.",ae,$,le,G,ke='<a href="/docs/diffusers/pr_11234/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline">StableDiffusionPipeline</a> uses the <a href="/docs/diffusers/pr_11234/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a> by default, but 🤗 Diffusers provides many other schedulers (some of which are faster or output better quality) that are compatible. For example, if you want to use the <a href="/docs/diffusers/pr_11234/en/api/schedulers/euler#diffusers.EulerDiscreteScheduler">EulerDiscreteScheduler</a> instead of the default:',ne,T,pe,J,oe,C,je="To save memory and use the same components across multiple pipelines, use the <code>.components</code> method to avoid loading weights into RAM more than once.",re,W,fe,V,de,k,De='The Stable Diffusion pipelines are automatically supported in <a href="https://github.com/gradio-app/gradio/" rel="nofollow">Gradio</a>, a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed:',ue,j,me,D,Fe='Then, create a web demo around any Stable Diffusion-based pipeline. For example, you can create an image generation pipeline in a single line of code with Gradio’s <a href="https://www.gradio.app/docs/interface#interface-from-pipeline" rel="nofollow"><code>Interface.from_pipeline</code></a> function:',ce,F,ge,_,_e="which opens an intuitive drag-and-drop interface in your browser:",he,I,Ie='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gradio-panda.png"/>',be,R,Re="Similarly, you could create a demo for an image-to-image pipeline with:",ye,L,xe,H,Le=`By default, the web demo runs on a local server. If you’d like to share it with others, you can generate a temporary public
link by setting <code>share=True</code> in <code>launch()</code>. Or, you can host your demo on <a href="https://huggingface.co/spaces" rel="nofollow">Hugging Face Spaces</a><a href="https://huggingface.co/spaces" rel="nofollow">https://huggingface.co/spaces</a> for a permanent link.`,we,X,Ze,N,Me;return y=new P({props:{title:"Stable Diffusion pipelines",local:"stable-diffusion-pipelines",headingTag:"h1"}}),v=new P({props:{title:"Tips",local:"tips",headingTag:"h2"}}),$=new P({props:{title:"Explore tradeoff between speed and quality",local:"explore-tradeoff-between-speed-and-quality",headingTag:"h3"}}),T=new E({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTJDJTIwRXVsZXJEaXNjcmV0ZVNjaGVkdWxlciUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMkNvbXBWaXMlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTQlMjIpJTBBcGlwZWxpbmUuc2NoZWR1bGVyJTIwJTNEJTIwRXVsZXJEaXNjcmV0ZVNjaGVkdWxlci5mcm9tX2NvbmZpZyhwaXBlbGluZS5zY2hlZHVsZXIuY29uZmlnKSUwQSUwQSUyMyUyMG9yJTBBZXVsZXJfc2NoZWR1bGVyJTIwJTNEJTIwRXVsZXJEaXNjcmV0ZVNjaGVkdWxlci5mcm9tX3ByZXRyYWluZWQoJTIyQ29tcFZpcyUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNCUyMiUyQyUyMHN1YmZvbGRlciUzRCUyMnNjaGVkdWxlciUyMiklMEFwaXBlbGluZSUyMCUzRCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJDb21wVmlzJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS00JTIyJTJDJTIwc2NoZWR1bGVyJTNEZXVsZXJfc2NoZWR1bGVyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, EulerDiscreteScheduler

pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>)
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

<span class="hljs-comment"># or</span>
euler_scheduler = EulerDiscreteScheduler.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, scheduler=euler_scheduler)`,wrap:!1}}),J=new P({props:{title:"Reuse pipeline components to save memory",local:"reuse-pipeline-components-to-save-memory",headingTag:"h3"}}),W=new E({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> (
    StableDiffusionPipeline,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionInpaintPipeline,
)

text2img = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>)
img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
inpaint = StableDiffusionInpaintPipeline(**text2img.components)

<span class="hljs-comment"># now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline</span>`,wrap:!1}}),V=new P({props:{title:"Create web demos using gradio",local:"create-web-demos-using-gradio",headingTag:"h3"}}),j=new E({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1VJTIwZ3JhZGlv",highlighted:"pip install -U gradio",wrap:!1}}),F=new E({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwZ3JhZGlvJTIwYXMlMjBnciUwQSUwQXBpcGUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyQ29tcFZpcyUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNCUyMiklMEElMEFnci5JbnRlcmZhY2UuZnJvbV9waXBlbGluZShwaXBlKS5sYXVuY2goKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr

pipe = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>)

gr.Interface.from_pipeline(pipe).launch()`,wrap:!1}}),L=new E({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvbkltZzJJbWdQaXBlbGluZSUwQWltcG9ydCUyMGdyYWRpbyUyMGFzJTIwZ3IlMEElMEElMEFwaXBlJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uSW1nMkltZ1BpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJzdGFibGUtZGlmZnVzaW9uLXYxLTUlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIpJTBBJTBBZ3IuSW50ZXJmYWNlLmZyb21fcGlwZWxpbmUocGlwZSkubGF1bmNoKCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionImg2ImgPipeline
<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr


pipe = StableDiffusionImg2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>)

gr.Interface.from_pipeline(pipe).launch()`,wrap:!1}}),X=new 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