import{S as Hr,i as Kr,s as el,e as o,k as l,w as _,t as s,M as tl,c as a,d as n,m as d,a as i,x as b,h as r,b as c,G as e,g as h,y as v,q as y,o as I,B as w,v as nl,L as Fo}from"../../../chunks/vendor-hf-doc-builder.js";import{T as Ns}from"../../../chunks/Tip-hf-doc-builder.js";import{D as J}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Jt}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as xt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as No}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ol(E){let m,U,u,g,$,p,P,R;return{c(){m=o("p"),U=s("For text-to-image we use "),u=o("code"),g=s("stabilityai/stable-diffusion-2-1-unclip-small"),$=s(" as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. "),p=o("a"),P=s("stabilityai/stable-diffusion-2-1-unclip"),R=s(" was trained on OpenCLIP ViT-H, so we don\u2019t recommend its use."),this.h()},l(A){m=a(A,"P",{});var S=i(m);U=r(S,"For text-to-image we use "),u=a(S,"CODE",{});var V=i(u);g=r(V,"stabilityai/stable-diffusion-2-1-unclip-small"),V.forEach(n),$=r(S," as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. "),p=a(S,"A",{href:!0,rel:!0});var Et=i(p);P=r(Et,"stabilityai/stable-diffusion-2-1-unclip"),Et.forEach(n),R=r(S," was trained on OpenCLIP ViT-H, so we don\u2019t recommend its use."),S.forEach(n),this.h()},h(){c(p,"href","https://hf.co/stabilityai/stable-diffusion-2-1-unclip"),c(p,"rel","nofollow")},m(A,S){h(A,m,S),e(m,U),e(m,u),e(u,g),e(m,$),e(m,p),e(p,P),e(m,R)},d(A){A&&n(m)}}}function al(E){let m,U,u,g,$;return g=new Jt({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(){m=o("p"),U=s("Examples:"),u=l(),_(g.$$.fragment)},l(p){m=a(p,"P",{});var P=i(m);U=r(P,"Examples:"),P.forEach(n),u=d(p),b(g.$$.fragment,p)},m(p,P){h(p,m,P),e(m,U),h(p,u,P),v(g,p,P),$=!0},p:Fo,i(p){$||(y(g.$$.fragment,p),$=!0)},o(p){I(g.$$.fragment,p),$=!1},d(p){p&&n(m),p&&n(u),w(g,p)}}}function il(E){let m,U;return{c(){m=o("p"),U=s(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(u){m=a(u,"P",{});var g=i(m);U=r(g,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),g.forEach(n)},m(u,g){h(u,m,g),e(m,U)},d(u){u&&n(m)}}}function sl(E){let m,U,u,g,$;return g=new Jt({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(){m=o("p"),U=s("Examples:"),u=l(),_(g.$$.fragment)},l(p){m=a(p,"P",{});var P=i(m);U=r(P,"Examples:"),P.forEach(n),u=d(p),b(g.$$.fragment,p)},m(p,P){h(p,m,P),e(m,U),h(p,u,P),v(g,p,P),$=!0},p:Fo,i(p){$||(y(g.$$.fragment,p),$=!0)},o(p){I(g.$$.fragment,p),$=!1},d(p){p&&n(m),p&&n(u),w(g,p)}}}function rl(E){let m,U,u,g,$;return g=new Jt({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(){m=o("p"),U=s("Examples:"),u=l(),_(g.$$.fragment)},l(p){m=a(p,"P",{});var P=i(m);U=r(P,"Examples:"),P.forEach(n),u=d(p),b(g.$$.fragment,p)},m(p,P){h(p,m,P),e(m,U),h(p,u,P),v(g,p,P),$=!0},p:Fo,i(p){$||(y(g.$$.fragment,p),$=!0)},o(p){I(g.$$.fragment,p),$=!1},d(p){p&&n(m),p&&n(u),w(g,p)}}}function ll(E){let m,U;return{c(){m=o("p"),U=s(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(u){m=a(u,"P",{});var g=i(m);U=r(g,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),g.forEach(n)},m(u,g){h(u,m,g),e(m,U)},d(u){u&&n(m)}}}function dl(E){let m,U,u,g,$;return g=new Jt({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(){m=o("p"),U=s("Examples:"),u=l(),_(g.$$.fragment)},l(p){m=a(p,"P",{});var P=i(m);U=r(P,"Examples:"),P.forEach(n),u=d(p),b(g.$$.fragment,p)},m(p,P){h(p,m,P),e(m,U),h(p,u,P),v(g,p,P),$=!0},p:Fo,i(p){$||(y(g.$$.fragment,p),$=!0)},o(p){I(g.$$.fragment,p),$=!1},d(p){p&&n(m),p&&n(u),w(g,p)}}}function pl(E){let m,U,u,g,$,p,P,R,A,S,V,Et,Lt,zo,Ro,Fn,Zt,Ao,zn,St,Xt,Do,Rn,D,ne,Nt,Ze,Yo,Ft,Qo,An,W,qo,zt,Oo,Ho,Rt,Ko,ea,At,ta,na,Dn,Y,oe,Dt,Se,oa,Yt,aa,Yn,We,Qn,ae,qn,Q,ie,Qt,ke,ia,qt,sa,On,je,Hn,se,ra,Ot,la,da,Kn,Ge,eo,q,re,Ht,Be,pa,Kt,ca,to,M,Ve,ma,en,fa,ga,Xe,ua,Wt,ha,_a,ba,F,Ne,va,tn,ya,Ia,le,wa,de,Fe,Pa,nn,Ua,$a,pe,ze,Ma,Re,Ta,on,Ca,xa,Ja,ce,Ae,Ea,an,La,Za,me,De,Sa,Ye,Wa,sn,ka,ja,Ga,k,Qe,Ba,qe,Va,Oe,Xa,Na,Fa,fe,za,ge,Ra,ue,He,Aa,Ke,Da,et,Ya,Qa,qa,he,tt,Oa,X,Ha,rn,Ka,ei,ln,ti,ni,dn,oi,ai,ii,L,nt,si,O,ri,pn,li,di,cn,pi,ci,mi,mn,fi,gi,ot,fn,ui,hi,gn,_i,bi,at,vi,un,yi,Ii,wi,hn,Pi,no,H,_e,_n,it,Ui,bn,$i,oo,T,st,Mi,vn,Ti,Ci,rt,xi,kt,Ji,Ei,Li,z,lt,Zi,yn,Si,Wi,be,ki,ve,dt,ji,In,Gi,Bi,ye,pt,Vi,ct,Xi,wn,Ni,Fi,zi,Ie,mt,Ri,Pn,Ai,Di,we,ft,Yi,gt,Qi,Un,qi,Oi,Hi,j,ut,Ki,ht,es,_t,ts,ns,os,Pe,as,Ue,is,$e,bt,ss,vt,rs,yt,ls,ds,ps,Me,It,cs,N,ms,$n,fs,gs,Mn,us,hs,Tn,_s,bs,vs,Z,wt,ys,K,Is,Cn,ws,Ps,xn,Us,$s,Ms,Jn,Ts,Cs,Pt,En,xs,Js,Ln,Es,Ls,Ut,Zs,Zn,Ss,Ws,ks,Sn,js,ao,ee,Te,Wn,$t,Gs,kn,Bs,io,te,Mt,Vs,jn,Xs,so;return p=new xt({}),Ze=new xt({}),Se=new xt({}),We=new Jt({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>)`}}),ae=new Ns({props:{warning:!0,$$slots:{default:[ol]},$$scope:{ctx:E}}}),ke=new xt({}),je=new Jt({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>)`}}),Ge=new Jt({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>)`}}),Be=new xt({}),Ve=new J({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/v0.19.2/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/v0.19.2/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <a href="/docs/diffusers/v0.19.2/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/v0.19.2/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/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py#L53"}}),Ne=new J({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;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 10.0) &#x2014;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"guidance_scale"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass <code>negative_prompt_embeds</code> instead. Ignored when not using guidance (<code>guidance_scale &lt; 1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/v0.19.2/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
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
tensor is generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, <code>negative_prompt_embeds</code> are generated from the <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generated image. Choose between <code>PIL.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.StableUnCLIPPipeline.__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/v0.19.2/en/api/pipelines/latent_diffusion#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;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py" rel="nofollow"><code>self.processor</code></a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.noise_level",description:`<strong>noise_level</strong> (<code>int</code>, <em>optional</em>, defaults to <code>0</code>) &#x2014;
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/v0.19.2/en/api/pipelines/stable_unclip#diffusers.StableUnCLIPPipeline.noise_image_embeddings">StableUnCLIPPipeline.noise_image_embeddings()</a> for more details.`,name:"noise_level"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.prior_num_inference_steps",description:`<strong>prior_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 25) &#x2014;
The number of denoising steps in the prior denoising process. More denoising steps usually lead to a
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A higher guidance scale value encourages the model to generate images closely linked to the text
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Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
embedding generation in the prior denoising process. Can be used to tweak the same generation with
different prompts. If not provided, a latents tensor is generated by sampling using the supplied random
<code>generator</code>.`,name:"prior_latents"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py#L610",returnDescription:`
<p><code>~ pipeline_utils.ImagePipelineOutput</code> if <code>return_dict</code> is True, otherwise a <code>tuple</code>. When returning
a tuple, the first element is a list with the generated images.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.19.2/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),le=new No({props:{anchor:"diffusers.StableUnCLIPPipeline.__call__.example",$$slots:{default:[al]},$$scope:{ctx:E}}}),Fe=new J({props:{name:"enable_attention_slicing",anchor:"diffusers.StableUnCLIPPipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[str, int, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPPipeline.enable_attention_slicing.slice_size",description:`<strong>slice_size</strong> (<code>str</code> or <code>int</code>, <em>optional</em>, defaults to <code>&quot;auto&quot;</code>) &#x2014;
When <code>&quot;auto&quot;</code>, halves the input to the attention heads, so attention will be computed in two steps. If
<code>&quot;max&quot;</code>, maximum amount of memory will be saved by running only one slice at a time. If a number is
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Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
function of xFormers.`,name:"attention_op"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/pipeline_utils.py#L1608"}}),fe=new Ns({props:{warning:!0,$$slots:{default:[il]},$$scope:{ctx:E}}}),ge=new No({props:{anchor:"diffusers.StableUnCLIPPipeline.enable_xformers_memory_efficient_attention.example",$$slots:{default:[sl]},$$scope:{ctx:E}}}),He=new J({props:{name:"disable_xformers_memory_efficient_attention",anchor:"diffusers.StableUnCLIPPipeline.disable_xformers_memory_efficient_attention",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/pipeline_utils.py#L1643"}}),tt=new J({props:{name:"enable_model_cpu_offload",anchor:"diffusers.StableUnCLIPPipeline.enable_model_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py#L160"}}),nt=new J({props:{name:"noise_image_embeddings",anchor:"diffusers.StableUnCLIPPipeline.noise_image_embeddings",parameters:[{name:"image_embeds",val:": Tensor"},{name:"noise_level",val:": int"},{name:"noise",val:": typing.Optional[torch.FloatTensor] = None"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py#L564"}}),it=new xt({}),st=new J({props:{name:"class diffusers.StableUnCLIPImg2ImgPipeline",anchor:"diffusers.StableUnCLIPImg2ImgPipeline",parameters:[{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"image_encoder",val:": CLIPVisionModelWithProjection"},{name:"image_normalizer",val:": StableUnCLIPImageNormalizer"},{name:"image_noising_scheduler",val:": KarrasDiffusionSchedulers"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModel"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"vae",val:": AutoencoderKL"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
Feature extractor for image pre-processing before being encoded.`,name:"feature_extractor"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.image_encoder",description:`<strong>image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
CLIP vision model for encoding images.`,name:"image_encoder"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.image_normalizer",description:`<strong>image_normalizer</strong> (<code>StableUnCLIPImageNormalizer</code>) &#x2014;
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embeddings after the noise has been applied.`,name:"image_normalizer"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.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
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A [<code>~transformers.CLIPTokenizer</code>)].`,name:"tokenizer"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIPTextModel</a>) &#x2014;
Frozen <a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIPTextModel</a> text-encoder.`,name:"text_encoder"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/v0.19.2/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <a href="/docs/diffusers/v0.19.2/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.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.StableUnCLIPImg2ImgPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.19.2/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/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py#L66"}}),lt=new J({props:{name:"__call__",anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__",parameters:[{name:"image",val:": typing.Union[torch.FloatTensor, PIL.Image.Image] = None"},{name:"prompt",val:": typing.Union[str, typing.List[str]] = 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"},{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:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, either <code>prompt_embeds</code> will be
used or prompt is initialized to <code>&quot;&quot;</code>.`,name:"prompt"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.FloatTensor</code> or <code>PIL.Image.Image</code>) &#x2014;
<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
denoising process like it is in the standard Stable Diffusion text-guided image variation process.`,name:"image"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__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.StableUnCLIPImg2ImgPipeline.__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.StableUnCLIPImg2ImgPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 10.0) &#x2014;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"guidance_scale"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass <code>negative_prompt_embeds</code> instead. Ignored when not using guidance (<code>guidance_scale &lt; 1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/v0.19.2/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
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
tensor is generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, <code>negative_prompt_embeds</code> are generated from the <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
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/v0.19.2/en/api/pipelines/latent_diffusion#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
following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py" rel="nofollow"><code>self.processor</code></a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.__call__.noise_level",description:`<strong>noise_level</strong> (<code>int</code>, <em>optional</em>, defaults to <code>0</code>) &#x2014;
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/v0.19.2/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
process. If you want to provide pre-generated latents, pass them to <code>__call__</code> as <code>latents</code>.`,name:"image_embeds"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py#L578",returnDescription:`
<p><code>~ pipeline_utils.ImagePipelineOutput</code> if <code>return_dict</code> is True, otherwise a <code>tuple</code>. When returning
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`,returnType:`
<p><a
  href="/docs/diffusers/v0.19.2/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
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When <code>&quot;auto&quot;</code>, halves the input to the attention heads, so attention will be computed in two steps. If
<code>&quot;max&quot;</code>, maximum amount of memory will be saved by running only one slice at a time. If a number is
provided, uses as many slices as <code>attention_head_dim // slice_size</code>. In this case, <code>attention_head_dim</code>
must be a multiple of <code>slice_size</code>.`,name:"slice_size"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/pipeline_utils.py#L1669"}}),pt=new J({props:{name:"disable_attention_slicing",anchor:"diffusers.StableUnCLIPImg2ImgPipeline.disable_attention_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/pipeline_utils.py#L1684"}}),mt=new J({props:{name:"enable_vae_slicing",anchor:"diffusers.StableUnCLIPImg2ImgPipeline.enable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py#L148"}}),ft=new J({props:{name:"disable_vae_slicing",anchor:"diffusers.StableUnCLIPImg2ImgPipeline.disable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py#L156"}}),ut=new J({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.StableUnCLIPImg2ImgPipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPImg2ImgPipeline.enable_xformers_memory_efficient_attention.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
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computed in one step.`),Jo.forEach(n),xo.forEach(n),zi=d(x),Ie=a(x,"DIV",{class:!0});var Eo=i(Ie);b(mt.$$.fragment,Eo),Ri=d(Eo),Pn=a(Eo,"P",{});var Sr=i(Pn);Ai=r(Sr,`Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.`),Sr.forEach(n),Eo.forEach(n),Di=d(x),we=a(x,"DIV",{class:!0});var Lo=i(we);b(ft.$$.fragment,Lo),Yi=d(Lo),gt=a(Lo,"P",{});var Zo=i(gt);Qi=r(Zo,"Disable sliced VAE decoding. If "),Un=a(Zo,"CODE",{});var Wr=i(Un);qi=r(Wr,"enable_vae_slicing"),Wr.forEach(n),Oi=r(Zo,` was previously enabled, this method will go back to
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up during training is not guaranteed.`),So.forEach(n),os=d(Ee),b(Pe.$$.fragment,Ee),as=d(Ee),b(Ue.$$.fragment,Ee),Ee.forEach(n),is=d(x),$e=a(x,"DIV",{class:!0});var Wo=i($e);b(bt.$$.fragment,Wo),ss=d(Wo),vt=a(Wo,"P",{});var ko=i(vt);rs=r(ko,"Disable memory efficient attention from "),yt=a(ko,"A",{href:!0,rel:!0});var jr=i(yt);ls=r(jr,"xFormers"),jr.forEach(n),ds=r(ko,"."),ko.forEach(n),Wo.forEach(n),ps=d(x),Me=a(x,"DIV",{class:!0});var jo=i(Me);b(It.$$.fragment,jo),cs=d(jo),N=a(jo,"P",{});var Le=i(N);ms=r(Le,`Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
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`),xn=a(Vt,"CODE",{});var Nr=i(xn);Us=r(Nr,"noise_level"),Nr.forEach(n),$s=r(Vt," increases the variance in the final un-noised images."),Vt.forEach(n),Ms=d(B),Jn=a(B,"P",{});var Fr=i(Jn);Ts=r(Fr,"The noise is applied in two ways:"),Fr.forEach(n),Cs=d(B),Pt=a(B,"OL",{});var Go=i(Pt);En=a(Go,"LI",{});var zr=i(En);xs=r(zr,"A noise schedule is applied directly to the embeddings."),zr.forEach(n),Js=d(Go),Ln=a(Go,"LI",{});var Rr=i(Ln);Es=r(Rr,"A vector of sinusoidal time embeddings are appended to the output."),Rr.forEach(n),Go.forEach(n),Ls=d(B),Ut=a(B,"P",{});var Bo=i(Ut);Zs=r(Bo,"In both cases, the amount of noise is controlled by the same "),Zn=a(Bo,"CODE",{});var Ar=i(Zn);Ss=r(Ar,"noise_level"),Ar.forEach(n),Ws=r(Bo,"."),Bo.forEach(n),ks=d(B),Sn=a(B,"P",{});var Dr=i(Sn);js=r(Dr,"The embeddings are normalized before the noise is applied and un-normalized after the noise is applied."),Dr.forEach(n),B.forEach(n),x.forEach(n),ao=d(t),ee=a(t,"H2",{class:!0});var 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