import{S as ir,i as lr,s as rr,e as a,k as p,w as y,t as l,M as dr,c as o,d as n,m as c,a as s,x as v,h as r,b as u,G as e,g as _,y as I,q as w,o as P,B as $,v as pr,L as Qt}from"../../../chunks/vendor-hf-doc-builder.js";import{T as ta}from"../../../chunks/Tip-hf-doc-builder.js";import{D as E}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as oe}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as kt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Yt}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function cr(J){let d,b,f,m,U,i,h,S;return{c(){d=a("p"),b=l("For text-to-image we use "),f=a("code"),m=l("stabilityai/stable-diffusion-2-1-unclip-small"),U=l(" as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. "),i=a("a"),h=l("stabilityai/stable-diffusion-2-1-unclip"),S=l(" was trained on OpenCLIP ViT-H, so we don\u2019t recommend its use."),this.h()},l(Y){d=o(Y,"P",{});var W=s(d);b=r(W,"For text-to-image we use "),f=o(W,"CODE",{});var F=s(f);m=r(F,"stabilityai/stable-diffusion-2-1-unclip-small"),F.forEach(n),U=r(W," as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. "),i=o(W,"A",{href:!0,rel:!0});var Gt=s(i);h=r(Gt,"stabilityai/stable-diffusion-2-1-unclip"),Gt.forEach(n),S=r(W," was trained on OpenCLIP ViT-H, so we don\u2019t recommend its use."),W.forEach(n),this.h()},h(){u(i,"href","https://hf.co/stabilityai/stable-diffusion-2-1-unclip"),u(i,"rel","nofollow")},m(Y,W){_(Y,d,W),e(d,b),e(d,f),e(f,m),e(d,U),e(d,i),e(i,h),e(d,S)},d(Y){Y&&n(d)}}}function mr(J){let d,b,f,m,U;return m=new oe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableUnCLIPPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableUnCLIPPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;fusing/stable-unclip-2-1-l&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)  <span class="hljs-comment"># TODO update model path</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;astronaut_horse.png&quot;</span>)`}}),{c(){d=a("p"),b=l("Examples:"),f=p(),y(m.$$.fragment)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"Examples:"),h.forEach(n),f=c(i),v(m.$$.fragment,i)},m(i,h){_(i,d,h),e(d,b),_(i,f,h),I(m,i,h),U=!0},p:Qt,i(i){U||(w(m.$$.fragment,i),U=!0)},o(i){P(m.$$.fragment,i),U=!1},d(i){i&&n(d),i&&n(f),$(m,i)}}}function fr(J){let d,b,f,m,U;return{c(){d=a("p"),b=l("\u26A0\uFE0F Don\u2019t enable attention slicing if you\u2019re already using "),f=a("code"),m=l("scaled_dot_product_attention"),U=l(` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won\u2019t need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!`)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"\u26A0\uFE0F Don\u2019t enable attention slicing if you\u2019re already using "),f=o(h,"CODE",{});var S=s(f);m=r(S,"scaled_dot_product_attention"),S.forEach(n),U=r(h,` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won\u2019t need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!`),h.forEach(n)},m(i,h){_(i,d,h),e(d,b),e(d,f),e(f,m),e(d,U)},d(i){i&&n(d)}}}function gr(J){let d,b,f,m,U;return m=new oe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>,
<span class="hljs-meta">... </span>    torch_dtype=torch.float16,
<span class="hljs-meta">... </span>    use_safetensors=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_attention_slicing()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt).images[<span class="hljs-number">0</span>]`}}),{c(){d=a("p"),b=l("Examples:"),f=p(),y(m.$$.fragment)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"Examples:"),h.forEach(n),f=c(i),v(m.$$.fragment,i)},m(i,h){_(i,d,h),e(d,b),_(i,f,h),I(m,i,h),U=!0},p:Qt,i(i){U||(w(m.$$.fragment,i),U=!0)},o(i){P(m.$$.fragment,i),U=!1},d(i){i&&n(d),i&&n(f),$(m,i)}}}function ur(J){let d,b;return{c(){d=a("p"),b=l(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(f){d=o(f,"P",{});var m=s(d);b=r(m,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),m.forEach(n)},m(f,m){_(f,d,m),e(d,b)},d(f){f&&n(d)}}}function hr(J){let d,b,f,m,U;return m=new oe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){d=a("p"),b=l("Examples:"),f=p(),y(m.$$.fragment)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"Examples:"),h.forEach(n),f=c(i),v(m.$$.fragment,i)},m(i,h){_(i,d,h),e(d,b),_(i,f,h),I(m,i,h),U=!0},p:Qt,i(i){U||(w(m.$$.fragment,i),U=!0)},o(i){P(m.$$.fragment,i),U=!1},d(i){i&&n(d),i&&n(f),$(m,i)}}}function _r(J){let d,b,f,m,U;return m=new oe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableUnCLIPImg2ImgPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;fusing/stable-unclip-2-1-l-img2img&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)  <span class="hljs-comment"># TODO update model path</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>response = requests.get(url)
<span class="hljs-meta">&gt;&gt;&gt; </span>init_image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>init_image = init_image.resize((<span class="hljs-number">768</span>, <span class="hljs-number">512</span>))

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A fantasy landscape, trending on artstation&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt, init_image).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;fantasy_landscape.png&quot;</span>)`}}),{c(){d=a("p"),b=l("Examples:"),f=p(),y(m.$$.fragment)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"Examples:"),h.forEach(n),f=c(i),v(m.$$.fragment,i)},m(i,h){_(i,d,h),e(d,b),_(i,f,h),I(m,i,h),U=!0},p:Qt,i(i){U||(w(m.$$.fragment,i),U=!0)},o(i){P(m.$$.fragment,i),U=!1},d(i){i&&n(d),i&&n(f),$(m,i)}}}function br(J){let d,b,f,m,U;return{c(){d=a("p"),b=l("\u26A0\uFE0F Don\u2019t enable attention slicing if you\u2019re already using "),f=a("code"),m=l("scaled_dot_product_attention"),U=l(` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won\u2019t need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!`)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"\u26A0\uFE0F Don\u2019t enable attention slicing if you\u2019re already using "),f=o(h,"CODE",{});var S=s(f);m=r(S,"scaled_dot_product_attention"),S.forEach(n),U=r(h,` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won\u2019t need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!`),h.forEach(n)},m(i,h){_(i,d,h),e(d,b),e(d,f),e(f,m),e(d,U)},d(i){i&&n(d)}}}function yr(J){let d,b,f,m,U;return m=new oe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>,
<span class="hljs-meta">... </span>    torch_dtype=torch.float16,
<span class="hljs-meta">... </span>    use_safetensors=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_attention_slicing()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt).images[<span class="hljs-number">0</span>]`}}),{c(){d=a("p"),b=l("Examples:"),f=p(),y(m.$$.fragment)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"Examples:"),h.forEach(n),f=c(i),v(m.$$.fragment,i)},m(i,h){_(i,d,h),e(d,b),_(i,f,h),I(m,i,h),U=!0},p:Qt,i(i){U||(w(m.$$.fragment,i),U=!0)},o(i){P(m.$$.fragment,i),U=!1},d(i){i&&n(d),i&&n(f),$(m,i)}}}function vr(J){let d,b;return{c(){d=a("p"),b=l(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(f){d=o(f,"P",{});var m=s(d);b=r(m,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),m.forEach(n)},m(f,m){_(f,d,m),e(d,b)},d(f){f&&n(d)}}}function Ir(J){let d,b,f,m,U;return m=new oe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){d=a("p"),b=l("Examples:"),f=p(),y(m.$$.fragment)},l(i){d=o(i,"P",{});var h=s(d);b=r(h,"Examples:"),h.forEach(n),f=c(i),v(m.$$.fragment,i)},m(i,h){_(i,d,h),e(d,b),_(i,f,h),I(m,i,h),U=!0},p:Qt,i(i){U||(w(m.$$.fragment,i),U=!0)},o(i){P(m.$$.fragment,i),U=!1},d(i){i&&n(d),i&&n(f),$(m,i)}}}function wr(J){let d,b,f,m,U,i,h,S,Y,W,F,Gt,Vt,Oa,Ha,na,Bt,Ka,aa,Xt,qt,eo,oa,Q,se,Ot,Be,to,Ht,no,sa,L,ao,Kt,oo,so,en,io,lo,tn,ro,po,ia,q,ie,nn,Xe,co,an,mo,la,Ne,ra,le,da,O,re,on,Fe,fo,sn,go,pa,ze,ca,de,uo,ln,ho,_o,ma,De,fa,H,pe,rn,Re,bo,dn,yo,ga,T,Ae,vo,pn,Io,wo,Ye,Po,Nt,$o,Uo,To,R,Qe,Mo,cn,Jo,Co,ce,xo,k,qe,Eo,mn,jo,Zo,me,So,fe,Wo,ge,Oe,Lo,He,ko,fn,Go,Vo,Bo,ue,Ke,Xo,gn,No,Fo,he,et,zo,tt,Do,un,Ro,Ao,Yo,G,nt,Qo,at,qo,ot,Oo,Ho,Ko,_e,es,be,ts,ye,st,ns,it,as,lt,os,ss,is,ve,rt,ls,z,rs,hn,ds,ps,_n,cs,ms,bn,fs,gs,us,j,dt,hs,K,_s,yn,bs,ys,vn,vs,Is,ws,In,Ps,$s,pt,wn,Us,Ts,Pn,Ms,Js,ct,Cs,$n,xs,Es,js,Un,Zs,ua,ee,Ie,Tn,mt,Ss,Mn,Ws,ha,M,ft,Ls,Jn,ks,Gs,gt,Vs,Ft,Bs,Xs,Ns,A,ut,Fs,Cn,zs,Ds,we,Rs,V,ht,As,xn,Ys,Qs,Pe,qs,$e,Os,Ue,_t,Hs,bt,Ks,En,ei,ti,ni,Te,yt,ai,jn,oi,si,Me,vt,ii,It,li,Zn,ri,di,pi,B,wt,ci,Pt,mi,$t,fi,gi,ui,Je,hi,Ce,_i,xe,Ut,bi,Tt,yi,Mt,vi,Ii,wi,Ee,Jt,Pi,D,$i,Sn,Ui,Ti,Wn,Mi,Ji,Ln,Ci,xi,Ei,Z,Ct,ji,te,Zi,kn,Si,Wi,Gn,Li,ki,Gi,Vn,Vi,Bi,xt,Bn,Xi,Ni,Xn,Fi,zi,Et,Di,Nn,Ri,Ai,Yi,Fn,Qi,_a,ne,je,zn,jt,qi,Dn,Oi,ba,ae,Zt,Hi,Rn,Ki,ya;return i=new kt({}),Be=new kt({}),Xe=new kt({}),Ne=new oe({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> UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline
<span class="hljs-keyword">from</span> diffusers.models <span class="hljs-keyword">import</span> PriorTransformer
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPTokenizer, CLIPTextModelWithProjection

prior_model_id = <span class="hljs-string">&quot;kakaobrain/karlo-v1-alpha&quot;</span>
data_type = torch.float16
prior = PriorTransformer.from_pretrained(prior_model_id, subfolder=<span class="hljs-string">&quot;prior&quot;</span>, torch_dtype=data_type)

prior_text_model_id = <span class="hljs-string">&quot;openai/clip-vit-large-patch14&quot;</span>
prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder=<span class="hljs-string">&quot;prior_scheduler&quot;</span>)
prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)

stable_unclip_model_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1-unclip-small&quot;</span>

pipe = StableUnCLIPPipeline.from_pretrained(
    stable_unclip_model_id,
    torch_dtype=data_type,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
    prior_tokenizer=prior_tokenizer,
    prior_text_encoder=prior_text_model,
    prior=prior,
    prior_scheduler=prior_scheduler,
)

pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
wave_prompt = <span class="hljs-string">&quot;dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular&quot;</span>

images = pipe(prompt=wave_prompt).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;waves.png&quot;</span>)`}}),le=new ta({props:{warning:!0,$$slots:{default:[cr]},$$scope:{ctx:J}}}),Fe=new kt({}),ze=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableUnCLIPImg2ImgPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">import</span> torch

pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1-unclip&quot;</span>, torch_dtype=torch.float16, variation=<span class="hljs-string">&quot;fp16&quot;</span>
)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png&quot;</span>
init_image = load_image(url)

images = pipe(init_image).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;variation_image.png&quot;</span>)`}}),De=new oe({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyQSUyMGZhbnRhc3klMjBsYW5kc2NhcGUlMkMlMjB0cmVuZGluZyUyMG9uJTIwYXJ0c3RhdGlvbiUyMiUwQSUwQWltYWdlcyUyMCUzRCUyMHBpcGUoaW5pdF9pbWFnZSUyQyUyMHByb21wdCUzRHByb21wdCkuaW1hZ2VzJTBBaW1hZ2VzJTVCMCU1RC5zYXZlKCUyMnZhcmlhdGlvbl9pbWFnZV90d28ucG5nJTIyKQ==",highlighted:`prompt = <span class="hljs-string">&quot;A fantasy landscape, trending on artstation&quot;</span>

images = pipe(init_image, prompt=prompt).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;variation_image_two.png&quot;</span>)`}}),Re=new kt({}),Ae=new E({props:{name:"class diffusers.StableUnCLIPPipeline",anchor:"diffusers.StableUnCLIPPipeline",parameters:[{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior",val:": PriorTransformer"},{name:"prior_scheduler",val:": KarrasDiffusionSchedulers"},{name:"image_normalizer",val:": StableUnCLIPImageNormalizer"},{name:"image_noising_scheduler",val:": KarrasDiffusionSchedulers"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"vae",val:": AutoencoderKL"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPPipeline.prior_tokenizer",description:`<strong>prior_tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
A <code>CLIPTokenizer</code>.`,name:"prior_tokenizer"},{anchor:"diffusers.StableUnCLIPPipeline.prior_text_encoder",description:`<strong>prior_text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen <code>CLIPTextModelWithProjection</code> text-encoder.`,name:"prior_text_encoder"},{anchor:"diffusers.StableUnCLIPPipeline.prior",description:`<strong>prior</strong> (<a href="/docs/diffusers/main/en/api/models/prior_transformer#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonincal unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior"},{anchor:"diffusers.StableUnCLIPPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>KarrasDiffusionSchedulers</code>) &#x2014;
Scheduler used in the prior denoising process.`,name:"prior_scheduler"},{anchor:"diffusers.StableUnCLIPPipeline.image_normalizer",description:`<strong>image_normalizer</strong> (<code>StableUnCLIPImageNormalizer</code>) &#x2014;
Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
embeddings after the noise has been applied.`,name:"image_normalizer"},{anchor:"diffusers.StableUnCLIPPipeline.image_noising_scheduler",description:`<strong>image_noising_scheduler</strong> (<code>KarrasDiffusionSchedulers</code>) &#x2014;
Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
by the <code>noise_level</code>.`,name:"image_noising_scheduler"},{anchor:"diffusers.StableUnCLIPPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
A <code>CLIPTokenizer</code>.`,name:"tokenizer"},{anchor:"diffusers.StableUnCLIPPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen <code>CLIPTextModel</code> text-encoder.`,name:"text_encoder"},{anchor:"diffusers.StableUnCLIPPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.StableUnCLIPPipeline.scheduler",description:`<strong>scheduler</strong> (<code>KarrasDiffusionSchedulers</code>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents.`,name:"scheduler"},{anchor:"diffusers.StableUnCLIPPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py#L53"}}),Qe=new E({props:{name:"__call__",anchor:"diffusers.StableUnCLIPPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 20"},{name:"guidance_scale",val:": float = 10.0"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"noise_level",val:": int = 0"},{name:"prior_num_inference_steps",val:": int = 25"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"prior_latents",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide image generation. If not defined, you need to pass <code>prompt_embeds</code>.`,name:"prompt"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014;
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Whether or not to return a <a href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
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The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
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A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined in
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Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
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`,returnType:`
<p><a
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<code>Image</code> or tensor representing an image batch. The image is encoded to its CLIP embedding which the
<code>unet</code> is conditioned on. The image is <em>not</em> encoded by the <code>vae</code> and then used as the latents in the
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A higher guidance scale value encourages the model to generate images closely linked to the text
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to
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Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
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A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
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Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
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The output format of the generated image. Choose between <code>PIL.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
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The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
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A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined in
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The amount of noise to add to the image embeddings. A higher <code>noise_level</code> increases the variance in
the final un-noised images. See <a href="/docs/diffusers/main/en/api/pipelines/stable_unclip#diffusers.StableUnCLIPPipeline.noise_image_embeddings">StableUnCLIPPipeline.noise_image_embeddings()</a> for more details.`,name:"noise_level"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated CLIP embeddings to condition the <code>unet</code> on. These latents are not used in the denoising
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`,returnType:`
<p><a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
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Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha)

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Memory savings are lower than using `),_n=o(Le,"CODE",{});var Ul=s(_n);cs=r(Ul,"enable_sequential_cpu_offload"),Ul.forEach(n),ms=r(Le,`, but performance is much better due to the
iterative execution of the `),bn=o(Le,"CODE",{});var Tl=s(bn);fs=r(Tl,"unet"),Tl.forEach(n),gs=r(Le,"."),Le.forEach(n),Za.forEach(n),us=c(C),j=o(C,"DIV",{class:!0});var X=s(j);v(dt.$$.fragment,X),hs=c(X),K=o(X,"P",{});var Dt=s(K);_s=r(Dt,"Add noise to the image embeddings. The amount of noise is controlled by a "),yn=o(Dt,"CODE",{});var Ml=s(yn);bs=r(Ml,"noise_level"),Ml.forEach(n),ys=r(Dt,` input. A higher
`),vn=o(Dt,"CODE",{});var Jl=s(vn);vs=r(Jl,"noise_level"),Jl.forEach(n),Is=r(Dt," increases the variance in the final un-noised images."),Dt.forEach(n),ws=c(X),In=o(X,"P",{});var Cl=s(In);Ps=r(Cl,"The noise is applied in two ways:"),Cl.forEach(n),$s=c(X),pt=o(X,"OL",{});var Sa=s(pt);wn=o(Sa,"LI",{});var xl=s(wn);Us=r(xl,"A noise schedule is applied directly to the embeddings."),xl.forEach(n),Ts=c(Sa),Pn=o(Sa,"LI",{});var El=s(Pn);Ms=r(El,"A vector of sinusoidal time embeddings are appended to the output."),El.forEach(n),Sa.forEach(n),Js=c(X),ct=o(X,"P",{});var Wa=s(ct);Cs=r(Wa,"In both cases, the amount of noise is controlled by the same "),$n=o(Wa,"CODE",{});var jl=s($n);xs=r(jl,"noise_level"),jl.forEach(n),Es=r(Wa,"."),Wa.forEach(n),js=c(X),Un=o(X,"P",{});var Zl=s(Un);Zs=r(Zl,"The embeddings are normalized before the noise is applied and un-normalized after the noise is applied."),Zl.forEach(n),X.forEach(n),C.forEach(n),ua=c(t),ee=o(t,"H2",{class:!0});var La=s(ee);Ie=o(La,"A",{id:!0,class:!0,href:!0});var Sl=s(Ie);Tn=o(Sl,"SPAN",{});var Wl=s(Tn);v(mt.$$.fragment,Wl),Wl.forEach(n),Sl.forEach(n),Ss=c(La),Mn=o(La,"SPAN",{});var Ll=s(Mn);Ws=r(Ll,"StableUnCLIPImg2ImgPipeline"),Ll.forEach(n),La.forEach(n),ha=c(t),M=o(t,"DIV",{class:!0});var x=s(M);v(ft.$$.fragment,x),Ls=c(x),Jn=o(x,"P",{});var kl=s(Jn);ks=r(kl,"Pipeline for text-guided image-to-image generation using stable unCLIP."),kl.forEach(n),Gs=c(x),gt=o(x,"P",{});var ka=s(gt);Vs=r(ka,"This model inherits from "),Ft=o(ka,"A",{href:!0});var Gl=s(Ft);Bs=r(Gl,"DiffusionPipeline"),Gl.forEach(n),Xs=r(ka,`. Check the superclass documentation for the generic methods
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`),Gn=o(At,"CODE",{});var ql=s(Gn);Li=r(ql,"noise_level"),ql.forEach(n),ki=r(At," increases the variance in the final un-noised images."),At.forEach(n),Gi=c(N),Vn=o(N,"P",{});var Ol=s(Vn);Vi=r(Ol,"The noise is applied in two ways:"),Ol.forEach(n),Bi=c(N),xt=o(N,"OL",{});var Aa=s(xt);Bn=o(Aa,"LI",{});var Hl=s(Bn);Xi=r(Hl,"A noise schedule is applied directly to the embeddings."),Hl.forEach(n),Ni=c(Aa),Xn=o(Aa,"LI",{});var Kl=s(Xn);Fi=r(Kl,"A vector of sinusoidal time embeddings are appended to the output."),Kl.forEach(n),Aa.forEach(n),zi=c(N),Et=o(N,"P",{});var Ya=s(Et);Di=r(Ya,"In both cases, the amount of noise is controlled by the same "),Nn=o(Ya,"CODE",{});var er=s(Nn);Ri=r(er,"noise_level"),er.forEach(n),Ai=r(Ya,"."),Ya.forEach(n),Yi=c(N),Fn=o(N,"P",{});var tr=s(Fn);Qi=r(tr,"The embeddings are normalized before the noise is applied and un-normalized after the noise is applied."),tr.forEach(n),N.forEach(n),x.forEach(n),_a=c(t),ne=o(t,"H2",{class:!0});var 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