import{s as de,o as he,n as Me}from"../chunks/scheduler.182ea377.js";import{S as be,i as ye,g as m,s as a,r as d,A as we,h as r,f as t,c as i,j as fe,u as h,x as J,k as ue,y as Je,a as s,v as M,d as b,t as y,w}from"../chunks/index.abf12888.js";import{T as ge}from"../chunks/Tip.230e2334.js";import{C as se}from"../chunks/CodeBlock.57fe6e13.js";import{H as S}from"../chunks/Heading.16916d63.js";function Ze(z){let n,Z='Make sure to check out the Stable Diffusion <a href="overview#tips">Tips</a> section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!',f,o,u='If you’re interested in using one of the official checkpoints for a task, explore 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!';return{c(){n=m("p"),n.innerHTML=Z,f=a(),o=m("p"),o.innerHTML=u},l(p){n=r(p,"P",{"data-svelte-h":!0}),J(n)!=="svelte-1j961ct"&&(n.innerHTML=Z),f=i(p),o=r(p,"P",{"data-svelte-h":!0}),J(o)!=="svelte-z4pn9c"&&(o.innerHTML=u)},m(p,c){s(p,n,c),s(p,f,c),s(p,o,c)},p:Me,d(p){p&&(t(n),t(f),t(o))}}}function Ue(z){let n,Z,f,o,u,p,c,ae='Stable Diffusion 2 is a text-to-image <em>latent diffusion</em> model built upon the work of the original <a href="https://stability.ai/blog/stable-diffusion-public-release" rel="nofollow">Stable Diffusion</a>, and it was led by Robin Rombach and Katherine Crowson from <a href="https://stability.ai/" rel="nofollow">Stability AI</a> and <a href="https://laion.ai/" rel="nofollow">LAION</a>.',x,U,ie=`<em>The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels.
These models are trained on an aesthetic subset of the <a href="https://laion.ai/blog/laion-5b/" rel="nofollow">LAION-5B dataset</a> created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using <a href="https://openreview.net/forum?id=M3Y74vmsMcY" rel="nofollow">LAION’s NSFW filter</a>.</em>`,N,T,ne='For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official <a href="https://stability.ai/blog/stable-diffusion-v2-release" rel="nofollow">announcement post</a>.',F,j,pe='The architecture of Stable Diffusion 2 is more or less identical to the original <a href="./text2img">Stable Diffusion model</a> so check out it’s API documentation for how to use Stable Diffusion 2. We recommend using the <a href="/docs/diffusers/v0.26.0/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler">DPMSolverMultistepScheduler</a> as it gives a reasonable speed/quality trade-off and can be run with as little as 20 steps.',Y,W,oe="Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image:",Q,G,me='<thead><tr><th>Task</th> <th>Repository</th></tr></thead> <tbody><tr><td>text-to-image (512x512)</td> <td><a href="https://huggingface.co/stabilityai/stable-diffusion-2-base" rel="nofollow">stabilityai/stable-diffusion-2-base</a></td></tr> <tr><td>text-to-image (768x768)</td> <td><a href="https://huggingface.co/stabilityai/stable-diffusion-2" rel="nofollow">stabilityai/stable-diffusion-2</a></td></tr> <tr><td>inpainting</td> <td><a href="https://huggingface.co/stabilityai/stable-diffusion-2-inpainting" rel="nofollow">stabilityai/stable-diffusion-2-inpainting</a></td></tr> <tr><td>super-resolution</td> <td><a href="https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler" rel="nofollow">stable-diffusion-x4-upscaler</a></td></tr> <tr><td>depth-to-image</td> <td><a href="https://huggingface.co/stabilityai/stable-diffusion-2-depth" rel="nofollow">stabilityai/stable-diffusion-2-depth</a></td></tr></tbody>',E,B,re="Here are some examples for how to use Stable Diffusion 2 for each task:",H,g,D,X,L,k,q,I,A,V,P,R,K,_,O,v,ee,C,le,$,te;return u=new S({props:{title:"Stable Diffusion 2",local:"stable-diffusion-2",headingTag:"h1"}}),g=new ge({props:{$$slots:{default:[Ze]},$$scope:{ctx:z}}}),X=new S({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),k=new se({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline, DPMSolverMultistepScheduler
<span class="hljs-keyword">import</span> torch

repo_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-base&quot;</span>
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision=<span class="hljs-string">&quot;fp16&quot;</span>)

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;High quality photo of an astronaut riding a horse in space&quot;</span>
image = pipe(prompt, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),I=new S({props:{title:"Inpainting",local:"inpainting",headingTag:"h2"}}),V=new se({props:{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> DiffusionPipeline, DPMSolverMultistepScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

img_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png&quot;</span>
mask_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png&quot;</span>

init_image = load_image(img_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
mask_image = load_image(mask_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

repo_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-inpainting&quot;</span>
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision=<span class="hljs-string">&quot;fp16&quot;</span>)

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;Face of a yellow cat, high resolution, sitting on a park bench&quot;</span>
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]
make_image_grid([init_image, mask_image, image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">3</span>)`,wrap:!1}}),R=new S({props:{title:"Super-resolution",local:"super-resolution",headingTag:"h2"}}),_=new se({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionUpscalePipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid
<span class="hljs-keyword">import</span> torch

<span class="hljs-comment"># load model and scheduler</span>
model_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-x4-upscaler&quot;</span>
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># let&#x27;s download an  image</span>
url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png&quot;</span>
low_res_img = load_image(url)
low_res_img = low_res_img.resize((<span class="hljs-number">128</span>, <span class="hljs-number">128</span>))
prompt = <span class="hljs-string">&quot;a white cat&quot;</span>
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[<span class="hljs-number">0</span>]
make_image_grid([low_res_img.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>)), upscaled_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,wrap:!1}}),v=new S({props:{title:"Depth-to-image",local:"depth-to-image",headingTag:"h2"}}),C=new se({props:{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> StableDiffusionDepth2ImgPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-depth&quot;</span>,
    torch_dtype=torch.float16,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)


url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
init_image = load_image(url)
prompt = <span class="hljs-string">&quot;two tigers&quot;</span>
negative_prompt = <span class="hljs-string">&quot;bad, deformed, ugly, bad anotomy&quot;</span>
image = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=<span class="hljs-number">0.7</span>).images[<span class="hljs-number">0</span>]
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