import{S as hr,i as cr,s as dr,e as i,k as u,w as _,t as r,M as mr,c as a,d as s,m as h,a as l,x as b,h as o,b as n,G as e,g as f,y as v,L as gr,q as w,o as y,B as S,v as _r}from"../../../chunks/vendor-hf-doc-builder.js";import{I as se}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{C as mt}from"../../../chunks/CodeBlock-hf-doc-builder.js";function br(Ua){let $,Qt,P,V,gt,ie,xs,_t,As,Wt,m,Ms,bt,Ls,Ns,ae,Ts,Os,le,Cs,Us,re,Bs,Hs,Kt,Me,j,Rs,oe,Fs,Gs,ne,zs,Vs,Jt,Y,Ys,pe,Qs,Ws,Xt,k,Q,vt,fe,Ks,wt,Js,Zt,q,W,yt,ue,Xs,St,Zs,es,E,ei,Le,ti,si,Ne,ii,ai,ts,g,x,Et,li,ri,he,oi,ni,Te,pi,fi,A,Dt,ui,hi,ce,ci,di,Oe,mi,gi,M,It,_i,bi,de,vi,wi,Ce,yi,Si,L,$t,Ei,Di,me,Ii,$i,Ue,Pi,ji,N,Pt,ki,qi,ge,xi,Ai,jt,Mi,ss,K,Li,Be,Ni,Ti,is,T,J,kt,_e,Oi,qt,Ci,as,He,O,xt,Ui,Bi,be,Hi,Ri,Re,Fi,ls,ve,rs,Fe,C,At,Gi,zi,we,Vi,Yi,Ge,Qi,os,ye,ns,U,X,Mt,Se,Wi,Lt,Ki,ps,ze,B,Nt,Ji,Xi,Ee,Zi,ea,Ve,ta,fs,De,us,H,Z,Tt,Ie,sa,Ot,ia,hs,Ye,R,Ct,aa,la,$e,ra,oa,Qe,na,cs,Pe,ds,F,ee,Ut,je,pa,Bt,fa,ms,We,G,Ht,ua,ha,ke,ca,da,Rt,ma,gs,qe,_s,z,te,Ft,xe,ga,Gt,_a,bs,c,ba,Ke,va,wa,zt,ya,Sa,Je,Ea,Da,Xe,Ia,$a,Ze,Pa,ja,et,ka,qa,tt,xa,Aa,Vt,Ma,La,Yt,Na,Ta,st,Oa,Ca,vs,Ae,ws;return ie=new se({}),fe=new se({}),ue=new se({}),_e=new se({}),ve=new mt({props:{code:`from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch

repo_id = "stabilityai/stable-diffusion-2-base"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

prompt = "High quality photo of an astronaut riding a horse in space"
image = pipe(prompt, num_inference_steps=25).images[0]
image.save("astronaut.png")`,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.save(<span class="hljs-string">&quot;astronaut.png&quot;</span>)`}}),ye=new mt({props:{code:`from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch

repo_id = "stabilityai/stable-diffusion-2"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

prompt = "High quality photo of an astronaut riding a horse in space"
image = pipe(prompt, guidance_scale=9, num_inference_steps=25).images[0]
image.save("astronaut.png")`,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&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, guidance_scale=<span class="hljs-number">9</span>, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;astronaut.png&quot;</span>)`}}),Se=new se({}),De=new mt({props:{code:`import PIL
import requests
import torch
from io import BytesIO

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler


def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")


img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))

repo_id = "stabilityai/stable-diffusion-2-inpainting"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]

image.save("yellow_cat.png")`,highlighted:`<span class="hljs-keyword">import</span> PIL
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline, DPMSolverMultistepScheduler


<span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url</span>):
    response = requests.get(url)
    <span class="hljs-keyword">return</span> PIL.Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)


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 = download_image(img_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
mask_image = download_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>]

image.save(<span class="hljs-string">&quot;yellow_cat.png&quot;</span>)`}}),Ie=new se({}),Pe=new mt({props:{code:`import requests
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionUpscalePipeline
import torch

# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")

# let's download an  image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))
prompt = "a white cat"
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
upscaled_image.save("upsampled_cat.png")`,highlighted:`<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionUpscalePipeline
<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>
response = requests.get(url)
low_res_img = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
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>]
upscaled_image.save(<span class="hljs-string">&quot;upsampled_cat.png&quot;</span>)`}}),je=new se({}),qe=new mt({props:{code:`import torch
import requests
from PIL import Image

from diffusers import StableDiffusionDepth2ImgPipeline

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-depth",
    torch_dtype=torch.float16,
).to("cuda")


url = "http://images.cocodataset.org/val2017/000000039769.jpg"
init_image = Image.open(requests.get(url, stream=True).raw)
prompt = "two tigers"
n_propmt = "bad, deformed, ugly, bad anotomy"
image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> requests
<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> StableDiffusionDepth2ImgPipeline

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 = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
prompt = <span class="hljs-string">&quot;two tigers&quot;</span>
n_propmt = <span class="hljs-string">&quot;bad, deformed, ugly, bad anotomy&quot;</span>
image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=<span class="hljs-number">0.7</span>).images[<span class="hljs-number">0</span>]`}}),xe=new se({}),Ae=new mt({props:{code:`from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

# or
euler_scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=euler_scheduler)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, EulerDiscreteScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># or</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>euler_scheduler = EulerDiscreteScheduler.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2&quot;</span>, scheduler=euler_scheduler)`}}),{c(){$=i("meta"),Qt=u(),P=i("h1"),V=i("a"),gt=i("span"),_(ie.$$.fragment),xs=u(),_t=i("span"),As=r("Stable diffusion 2"),Wt=u(),m=i("p"),Ms=r("Stable Diffusion 2 is a text-to-image "),bt=i("em"),Ls=r("latent diffusion"),Ns=r(" model built upon the work of "),ae=i("a"),Ts=r("Stable Diffusion 1"),Os=r(`.
The project to train Stable Diffusion 2 was led by Robin Rombach and Katherine Crowson from `),le=i("a"),Cs=r("Stability AI"),Us=r(" and "),re=i("a"),Bs=r("LAION"),Hs=r("."),Kt=u(),Me=i("p"),j=i("em"),Rs=r(`The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels.
These models are trained on an aesthetic subset of the `),oe=i("a"),Fs=r("LAION-5B dataset"),Gs=r(" created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using "),ne=i("a"),zs=r("LAION\u2019s NSFW filter"),Vs=r("."),Jt=u(),Y=i("p"),Ys=r("For more details about how Stable Diffusion 2 works and how it differs from Stable Diffusion 1, please refer to the official "),pe=i("a"),Qs=r("launch announcement post"),Ws=r("."),Xt=u(),k=i("h2"),Q=i("a"),vt=i("span"),_(fe.$$.fragment),Ks=u(),wt=i("span"),Js=r("Tips"),Zt=u(),q=i("h3"),W=i("a"),yt=i("span"),_(ue.$$.fragment),Xs=u(),St=i("span"),Zs=r("Available checkpoints:"),es=u(),E=i("p"),ei=r("Note that the architecture is more or less identical to "),Le=i("a"),ti=r("Stable Diffusion 1"),si=r(" so please refer to "),Ne=i("a"),ii=r("this page"),ai=r(" for API documentation."),ts=u(),g=i("ul"),x=i("li"),Et=i("em"),li=r("Text-to-Image (512x512 resolution)"),ri=r(": "),he=i("a"),oi=r("stabilityai/stable-diffusion-2-base"),ni=r(" with "),Te=i("a"),pi=r("StableDiffusionPipeline"),fi=u(),A=i("li"),Dt=i("em"),ui=r("Text-to-Image (768x768 resolution)"),hi=r(": "),ce=i("a"),ci=r("stabilityai/stable-diffusion-2"),di=r(" with "),Oe=i("a"),mi=r("StableDiffusionPipeline"),gi=u(),M=i("li"),It=i("em"),_i=r("Image Inpainting (512x512 resolution)"),bi=r(": "),de=i("a"),vi=r("stabilityai/stable-diffusion-2-inpainting"),wi=r(" with "),Ce=i("a"),yi=r("StableDiffusionInpaintPipeline"),Si=u(),L=i("li"),$t=i("em"),Ei=r("Super-Resolution (x4 resolution resolution)"),Di=r(": "),me=i("a"),Ii=r("stable-diffusion-x4-upscaler"),$i=u(),Ue=i("a"),Pi=r("StableDiffusionUpscalePipeline"),ji=u(),N=i("li"),Pt=i("em"),ki=r("Depth-to-Image (512x512 resolution)"),qi=r(": "),ge=i("a"),xi=r("stabilityai/stable-diffusion-2-depth"),Ai=r(" with "),jt=i("code"),Mi=r("StableDiffusionDepth2ImagePipeline"),ss=u(),K=i("p"),Li=r("We recommend using the "),Be=i("a"),Ni=r("DPMSolverMultistepScheduler"),Ti=r(" as it\u2019s currently the fastest scheduler there is."),is=u(),T=i("h3"),J=i("a"),kt=i("span"),_(_e.$$.fragment),Oi=u(),qt=i("span"),Ci=r("Text-to-Image"),as=u(),He=i("ul"),O=i("li"),xt=i("em"),Ui=r("Text-to-Image (512x512 resolution)"),Bi=r(": "),be=i("a"),Hi=r("stabilityai/stable-diffusion-2-base"),Ri=r(" with "),Re=i("a"),Fi=r("StableDiffusionPipeline"),ls=u(),_(ve.$$.fragment),rs=u(),Fe=i("ul"),C=i("li"),At=i("em"),Gi=r("Text-to-Image (768x768 resolution)"),zi=r(": "),we=i("a"),Vi=r("stabilityai/stable-diffusion-2"),Yi=r(" with "),Ge=i("a"),Qi=r("StableDiffusionPipeline"),os=u(),_(ye.$$.fragment),ns=u(),U=i("h3"),X=i("a"),Mt=i("span"),_(Se.$$.fragment),Wi=u(),Lt=i("span"),Ki=r("Image Inpainting"),ps=u(),ze=i("ul"),B=i("li"),Nt=i("em"),Ji=r("Image Inpainting (512x512 resolution)"),Xi=r(": "),Ee=i("a"),Zi=r("stabilityai/stable-diffusion-2-inpainting"),ea=r(" with "),Ve=i("a"),ta=r("StableDiffusionInpaintPipeline"),fs=u(),_(De.$$.fragment),us=u(),H=i("h3"),Z=i("a"),Tt=i("span"),_(Ie.$$.fragment),sa=u(),Ot=i("span"),ia=r("Super-Resolution"),hs=u(),Ye=i("ul"),R=i("li"),Ct=i("em"),aa=r("Image Upscaling (x4 resolution resolution)"),la=r(": "),$e=i("a"),ra=r("stable-diffusion-x4-upscaler"),oa=r(" with "),Qe=i("a"),na=r("StableDiffusionUpscalePipeline"),cs=u(),_(Pe.$$.fragment),ds=u(),F=i("h3"),ee=i("a"),Ut=i("span"),_(je.$$.fragment),pa=u(),Bt=i("span"),fa=r("Depth-to-Image"),ms=u(),We=i("ul"),G=i("li"),Ht=i("em"),ua=r("Depth-Guided Text-to-Image"),ha=r(": "),ke=i("a"),ca=r("stabilityai/stable-diffusion-2-depth"),da=u(),Rt=i("code"),ma=r("StableDiffusionDepth2ImagePipeline"),gs=u(),_(qe.$$.fragment),_s=u(),z=i("h3"),te=i("a"),Ft=i("span"),_(xe.$$.fragment),ga=u(),Gt=i("span"),_a=r("How to load and use different schedulers."),bs=u(),c=i("p"),ba=r("The stable diffusion pipeline uses "),Ke=i("a"),va=r("DDIMScheduler"),wa=r(" scheduler by default. But "),zt=i("code"),ya=r("diffusers"),Sa=r(" provides many other schedulers that can be used with the stable diffusion pipeline such as "),Je=i("a"),Ea=r("PNDMScheduler"),Da=r(", "),Xe=i("a"),Ia=r("LMSDiscreteScheduler"),$a=r(", "),Ze=i("a"),Pa=r("EulerDiscreteScheduler"),ja=r(", "),et=i("a"),ka=r("EulerAncestralDiscreteScheduler"),qa=r(` etc.
To use a different scheduler, you can either change it via the `),tt=i("a"),xa=r("ConfigMixin.from_config()"),Aa=r(" method or pass the "),Vt=i("code"),Ma=r("scheduler"),La=r(" argument to the "),Yt=i("code"),Na=r("from_pretrained"),Ta=r(" method of the pipeline. For example, to use the "),st=i("a"),Oa=r("EulerDiscreteScheduler"),Ca=r(", you can do the following:"),vs=u(),_(Ae.$$.fragment),this.h()},l(t){const p=mr('[data-svelte="svelte-1phssyn"]',document.head);$=a(p,"META",{name:!0,content:!0}),p.forEach(s),Qt=h(t),P=a(t,"H1",{class:!0});var ys=l(P);V=a(ys,"A",{id:!0,class:!0,href:!0});var Ba=l(V);gt=a(Ba,"SPAN",{});var Ha=l(gt);b(ie.$$.fragment,Ha),Ha.forEach(s),Ba.forEach(s),xs=h(ys),_t=a(ys,"SPAN",{});var Ra=l(_t);As=o(Ra,"Stable diffusion 2"),Ra.forEach(s),ys.forEach(s),Wt=h(t),m=a(t,"P",{});var D=l(m);Ms=o(D,"Stable Diffusion 2 is a text-to-image "),bt=a(D,"EM",{});var Fa=l(bt);Ls=o(Fa,"latent diffusion"),Fa.forEach(s),Ns=o(D," model built upon the work of "),ae=a(D,"A",{href:!0,rel:!0});var Ga=l(ae);Ts=o(Ga,"Stable Diffusion 1"),Ga.forEach(s),Os=o(D,`.
The project to train Stable Diffusion 2 was led by Robin Rombach and Katherine Crowson from `),le=a(D,"A",{href:!0,rel:!0});var za=l(le);Cs=o(za,"Stability AI"),za.forEach(s),Us=o(D," and "),re=a(D,"A",{href:!0,rel:!0});var Va=l(re);Bs=o(Va,"LAION"),Va.forEach(s),Hs=o(D,"."),D.forEach(s),Kt=h(t),Me=a(t,"P",{});var Ya=l(Me);j=a(Ya,"EM",{});var it=l(j);Rs=o(it,`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 `),oe=a(it,"A",{href:!0,rel:!0});var Qa=l(oe);Fs=o(Qa,"LAION-5B dataset"),Qa.forEach(s),Gs=o(it," created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using "),ne=a(it,"A",{href:!0,rel:!0});var Wa=l(ne);zs=o(Wa,"LAION\u2019s NSFW filter"),Wa.forEach(s),Vs=o(it,"."),it.forEach(s),Ya.forEach(s),Jt=h(t),Y=a(t,"P",{});var Ss=l(Y);Ys=o(Ss,"For more details about how Stable Diffusion 2 works and how it differs from Stable Diffusion 1, please refer to the official "),pe=a(Ss,"A",{href:!0,rel:!0});var Ka=l(pe);Qs=o(Ka,"launch announcement post"),Ka.forEach(s),Ws=o(Ss,"."),Ss.forEach(s),Xt=h(t),k=a(t,"H2",{class:!0});var Es=l(k);Q=a(Es,"A",{id:!0,class:!0,href:!0});var Ja=l(Q);vt=a(Ja,"SPAN",{});var Xa=l(vt);b(fe.$$.fragment,Xa),Xa.forEach(s),Ja.forEach(s),Ks=h(Es),wt=a(Es,"SPAN",{});var Za=l(wt);Js=o(Za,"Tips"),Za.forEach(s),Es.forEach(s),Zt=h(t),q=a(t,"H3",{class:!0});var 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But "),zt=a(d,"CODE",{});var ir=l(zt);ya=o(ir,"diffusers"),ir.forEach(s),Sa=o(d," provides many other schedulers that can be used with the stable diffusion pipeline such as "),Je=a(d,"A",{href:!0});var ar=l(Je);Ea=o(ar,"PNDMScheduler"),ar.forEach(s),Da=o(d,", "),Xe=a(d,"A",{href:!0});var lr=l(Xe);Ia=o(lr,"LMSDiscreteScheduler"),lr.forEach(s),$a=o(d,", "),Ze=a(d,"A",{href:!0});var rr=l(Ze);Pa=o(rr,"EulerDiscreteScheduler"),rr.forEach(s),ja=o(d,", "),et=a(d,"A",{href:!0});var or=l(et);ka=o(or,"EulerAncestralDiscreteScheduler"),or.forEach(s),qa=o(d,` etc.
To use a different scheduler, you can either change it via the `),tt=a(d,"A",{href:!0});var nr=l(tt);xa=o(nr,"ConfigMixin.from_config()"),nr.forEach(s),Aa=o(d," method or pass the "),Vt=a(d,"CODE",{});var pr=l(Vt);Ma=o(pr,"scheduler"),pr.forEach(s),La=o(d," argument to the "),Yt=a(d,"CODE",{});var fr=l(Yt);Na=o(fr,"from_pretrained"),fr.forEach(s),Ta=o(d," method of the pipeline. 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