import{s as Ta,o as va,n as $t}from"../chunks/scheduler.8c3d61f6.js";import{S as Ja,i as Ua,g as l,s as o,r as m,A as Pa,h as r,f as a,c as s,j as S,u,x as b,k as $,y as c,a as n,v as g,d as f,t as _,w as h}from"../chunks/index.589a98e8.js";import{T as xa}from"../chunks/Tip.42aa8582.js";import{D as V}from"../chunks/Docstring.27406313.js";import{C as Xe}from"../chunks/CodeBlock.36627b28.js";import{E as Ot}from"../chunks/ExampleCodeBlock.3dc467a7.js";import{H as U,E as ja}from"../chunks/EditOnGithub.e5a8d9cb.js";function Sa(I){let d,v="There are some restrictions on data types that can be used with the Stable Cascade models. The official checkpoints for the  <code>StableCascadePriorPipeline</code> do not support the <code>torch.float16</code> data type. Please use <code>torch.bfloat16</code> instead.",y,p,C="In order to use the <code>torch.bfloat16</code> data type with the <code>StableCascadeDecoderPipeline</code> you need to have PyTorch 2.2.0 or higher installed. This also means that using the <code>StableCascadeCombinedPipeline</code> with <code>torch.bfloat16</code> requires PyTorch 2.2.0 or higher, since it calls the <code>StableCascadeDecoderPipeline</code> internally.",i,w,Le="If it is not possible to install PyTorch 2.2.0 or higher in your environment, the <code>StableCascadeDecoderPipeline</code> can be used on its own with the <code>torch.float16</code> data type. You can download the full precision or <code>bf16</code> variant weights for the pipeline and cast the weights to <code>torch.float16</code>.";return{c(){d=l("p"),d.innerHTML=v,y=o(),p=l("p"),p.innerHTML=C,i=o(),w=l("p"),w.innerHTML=Le},l(M){d=r(M,"P",{"data-svelte-h":!0}),b(d)!=="svelte-1tw7sew"&&(d.innerHTML=v),y=s(M),p=r(M,"P",{"data-svelte-h":!0}),b(p)!=="svelte-szepwi"&&(p.innerHTML=C),i=s(M),w=r(M,"P",{"data-svelte-h":!0}),b(w)!=="svelte-jwlk0w"&&(w.innerHTML=Le)},m(M,P){n(M,d,P),n(M,y,P),n(M,p,P),n(M,i,P),n(M,w,P)},p:$t,d(M){M&&(a(d),a(y),a(p),a(i),a(w))}}}function $a(I){let d,v="Examples:",y,p,C;return p=new Xe({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> StableCascadeCombinedPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableCascadeCombinedPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stabilityai/stable-cascade&quot;</span>, variant=<span class="hljs-string">&quot;bf16&quot;</span>, torch_dtype=torch.bfloat16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_model_cpu_offload()
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;an image of a shiba inu, donning a spacesuit and helmet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt=prompt)`,wrap:!1}}),{c(){d=l("p"),d.textContent=v,y=o(),m(p.$$.fragment)},l(i){d=r(i,"P",{"data-svelte-h":!0}),b(d)!=="svelte-kvfsh7"&&(d.textContent=v),y=s(i),u(p.$$.fragment,i)},m(i,w){n(i,d,w),n(i,y,w),g(p,i,w),C=!0},p:$t,i(i){C||(f(p.$$.fragment,i),C=!0)},o(i){_(p.$$.fragment,i),C=!1},d(i){i&&(a(d),a(y)),h(p,i)}}}function Ia(I){let d,v="Examples:",y,p,C;return p=new Xe({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwU3RhYmxlQ2FzY2FkZVByaW9yUGlwZWxpbmUlMEElMEFwcmlvcl9waXBlJTIwJTNEJTIwU3RhYmxlQ2FzY2FkZVByaW9yUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMnN0YWJpbGl0eWFpJTJGc3RhYmxlLWNhc2NhZGUtcHJpb3IlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmJmbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKSUwQSUwQXByb21wdCUyMCUzRCUyMCUyMmFuJTIwaW1hZ2UlMjBvZiUyMGElMjBzaGliYSUyMGludSUyQyUyMGRvbm5pbmclMjBhJTIwc3BhY2VzdWl0JTIwYW5kJTIwaGVsbWV0JTIyJTBBcHJpb3Jfb3V0cHV0JTIwJTNEJTIwcGlwZShwcm9tcHQp",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> StableCascadePriorPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>prior_pipe = StableCascadePriorPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stabilityai/stable-cascade-prior&quot;</span>, torch_dtype=torch.bfloat16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;an image of a shiba inu, donning a spacesuit and helmet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prior_output = pipe(prompt)`,wrap:!1}}),{c(){d=l("p"),d.textContent=v,y=o(),m(p.$$.fragment)},l(i){d=r(i,"P",{"data-svelte-h":!0}),b(d)!=="svelte-kvfsh7"&&(d.textContent=v),y=s(i),u(p.$$.fragment,i)},m(i,w){n(i,d,w),n(i,y,w),g(p,i,w),C=!0},p:$t,i(i){C||(f(p.$$.fragment,i),C=!0)},o(i){_(p.$$.fragment,i),C=!1},d(i){i&&(a(d),a(y)),h(p,i)}}}function Za(I){let d,v="Examples:",y,p,C;return p=new Xe({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> StableCascadePriorPipeline, StableCascadeDecoderPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>prior_pipe = StableCascadePriorPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stabilityai/stable-cascade-prior&quot;</span>, torch_dtype=torch.bfloat16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen_pipe = StableCascadeDecoderPipeline.from_pretrain(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stabilityai/stable-cascade&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;an image of a shiba inu, donning a spacesuit and helmet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prior_output = pipe(prompt)
<span class="hljs-meta">&gt;&gt;&gt; </span>images = gen_pipe(prior_output.image_embeddings, prompt=prompt)`,wrap:!1}}),{c(){d=l("p"),d.textContent=v,y=o(),m(p.$$.fragment)},l(i){d=r(i,"P",{"data-svelte-h":!0}),b(d)!=="svelte-kvfsh7"&&(d.textContent=v),y=s(i),u(p.$$.fragment,i)},m(i,w){n(i,d,w),n(i,y,w),g(p,i,w),C=!0},p:$t,i(i){C||(f(p.$$.fragment,i),C=!0)},o(i){_(p.$$.fragment,i),C=!1},d(i){i&&(a(d),a(y)),h(p,i)}}}function ka(I){let d,v,y,p,C,i,w,Le=`This model is built upon the <a href="https://openreview.net/forum?id=gU58d5QeGv" rel="nofollow">Würstchen</a> architecture and its main
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this
important? The smaller the latent space, the <strong>faster</strong> you can run inference and the <strong>cheaper</strong> the training becomes.
How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being
encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a
1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the
highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable
Diffusion 1.5.`,M,P,Kt=`Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.`,ze,L,ea='The original codebase can be found at <a href="https://github.com/Stability-AI/StableCascade" rel="nofollow">Stability-AI/StableCascade</a>.',qe,F,Ae,z,ta=`Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name “Stable Cascade”.`,Oe,q,aa=`Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.
However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a
spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves
a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the
image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible
for generating the small 24 x 24 latents given a text prompt.`,Ke,A,na="The Stage C model operates on the small 24 x 24 latents and denoises the latents conditioned on text prompts. The model is also the largest component in the Cascade pipeline and is meant to be used with the <code>StableCascadePriorPipeline</code>",et,O,oa="The Stage B and Stage A models are used with the <code>StableCascadeDecoderPipeline</code> and are responsible for generating the final image given the small 24 x 24 latents.",tt,N,at,K,nt,ee,ot,te,st,ae,it,ne,lt,oe,sa="Loading the original format checkpoints is supported via <code>from_single_file</code> method in the StableCascadeUNet.",rt,se,dt,ie,ct,le,pt,re,ia="The model is intended for research purposes for now. Possible research areas and tasks include",mt,de,la="<li>Research on generative models.</li> <li>Safe deployment of models which have the potential to generate harmful content.</li> <li>Probing and understanding the limitations and biases of generative models.</li> <li>Generation of artworks and use in design and other artistic processes.</li> <li>Applications in educational or creative tools.</li>",ut,ce,ra="Excluded uses are described below.",gt,pe,ft,me,da=`The model was not trained to be factual or true representations of people or events,
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI’s <a href="https://stability.ai/use-policy" rel="nofollow">Acceptable Use Policy</a>.`,_t,ue,ht,ge,bt,fe,ca="<li>Faces and people in general may not be generated properly.</li> <li>The autoencoding part of the model is lossy.</li>",wt,_e,yt,T,he,It,Ie,pa="Combined Pipeline for text-to-image generation using Stable Cascade.",Zt,Ze,ma=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,kt,Z,be,Wt,ke,ua="Function invoked when calling the pipeline for generation.",Gt,Y,Bt,R,we,Vt,We,ga=`Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to <code>enable_sequential_cpu_offload</code>, this method moves one whole model at a time to the GPU when its <code>forward</code>
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
<code>enable_sequential_cpu_offload</code>, but performance is much better due to the iterative execution of the <code>unet</code>.`,Nt,H,ye,Yt,Ge,fa=`Offloads all models (<code>unet</code>, <code>text_encoder</code>, <code>vae</code>, and <code>safety checker</code> state dicts) to CPU using 🤗
Accelerate, significantly reducing memory usage. Models are moved to a <code>torch.device(&#39;meta&#39;)</code> and loaded on a
GPU only when their specific submodule’s <code>forward</code> method is called. Offloading happens on a submodule basis.
Memory savings are higher than using <code>enable_model_cpu_offload</code>, but performance is lower.`,Ct,Ce,Mt,x,Me,Rt,Be,_a="Pipeline for generating image prior for Stable Cascade.",Ht,Ve,ha=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,Et,k,Te,Dt,Ne,ba="Function invoked when calling the pipeline for generation.",Qt,E,Tt,ve,vt,G,Je,Xt,Ye,wa="Output class for WuerstchenPriorPipeline.",Jt,Ue,Ut,j,Pe,Lt,Re,ya="Pipeline for generating images from the Stable Cascade model.",Ft,He,Ca=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,zt,W,xe,qt,Ee,Ma="Function invoked when calling the pipeline for generation.",At,D,Pt,je,xt,Fe,jt;return C=new U({props:{title:"Stable Cascade",local:"stable-cascade",headingTag:"h1"}}),F=new U({props:{title:"Model Overview",local:"model-overview",headingTag:"h2"}}),N=new xa({props:{warning:!0,$$slots:{default:[Sa]},$$scope:{ctx:I}}}),K=new U({props:{title:"Usage example",local:"usage-example",headingTag:"h2"}}),ee=new Xe({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> StableCascadeDecoderPipeline, StableCascadePriorPipeline

prompt = <span class="hljs-string">&quot;an image of a shiba inu, donning a spacesuit and helmet&quot;</span>
negative_prompt = <span class="hljs-string">&quot;&quot;</span>

prior = StableCascadePriorPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade-prior&quot;</span>, variant=<span class="hljs-string">&quot;bf16&quot;</span>, torch_dtype=torch.bfloat16)
decoder = StableCascadeDecoderPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade&quot;</span>, variant=<span class="hljs-string">&quot;bf16&quot;</span>, torch_dtype=torch.float16)

prior.enable_model_cpu_offload()
prior_output = prior(
    prompt=prompt,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    negative_prompt=negative_prompt,
    guidance_scale=<span class="hljs-number">4.0</span>,
    num_images_per_prompt=<span class="hljs-number">1</span>,
    num_inference_steps=<span class="hljs-number">20</span>
)

decoder.enable_model_cpu_offload()
decoder_output = decoder(
    image_embeddings=prior_output.image_embeddings.to(torch.float16),
    prompt=prompt,
    negative_prompt=negative_prompt,
    guidance_scale=<span class="hljs-number">0.0</span>,
    output_type=<span class="hljs-string">&quot;pil&quot;</span>,
    num_inference_steps=<span class="hljs-number">10</span>
).images[<span class="hljs-number">0</span>]
decoder_output.save(<span class="hljs-string">&quot;cascade.png&quot;</span>)`,wrap:!1}}),te=new U({props:{title:"Using the Lite Versions of the Stage B and Stage C models",local:"using-the-lite-versions-of-the-stage-b-and-stage-c-models",headingTag:"h2"}}),ae=new Xe({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> (
    StableCascadeDecoderPipeline,
    StableCascadePriorPipeline,
    StableCascadeUNet,
)

prompt = <span class="hljs-string">&quot;an image of a shiba inu, donning a spacesuit and helmet&quot;</span>
negative_prompt = <span class="hljs-string">&quot;&quot;</span>

prior_unet = StableCascadeUNet.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade-prior&quot;</span>, subfolder=<span class="hljs-string">&quot;prior_lite&quot;</span>)
decoder_unet = StableCascadeUNet.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade&quot;</span>, subfolder=<span class="hljs-string">&quot;decoder_lite&quot;</span>)

prior = StableCascadePriorPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade-prior&quot;</span>, prior=prior_unet)
decoder = StableCascadeDecoderPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade&quot;</span>, decoder=decoder_unet)

prior.enable_model_cpu_offload()
prior_output = prior(
    prompt=prompt,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    negative_prompt=negative_prompt,
    guidance_scale=<span class="hljs-number">4.0</span>,
    num_images_per_prompt=<span class="hljs-number">1</span>,
    num_inference_steps=<span class="hljs-number">20</span>
)

decoder.enable_model_cpu_offload()
decoder_output = decoder(
    image_embeddings=prior_output.image_embeddings,
    prompt=prompt,
    negative_prompt=negative_prompt,
    guidance_scale=<span class="hljs-number">0.0</span>,
    output_type=<span class="hljs-string">&quot;pil&quot;</span>,
    num_inference_steps=<span class="hljs-number">10</span>
).images[<span class="hljs-number">0</span>]
decoder_output.save(<span class="hljs-string">&quot;cascade.png&quot;</span>)`,wrap:!1}}),ne=new U({props:{title:"Loading original checkpoints with from_single_file",local:"loading-original-checkpoints-with-fromsinglefile",headingTag:"h2"}}),se=new Xe({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> (
    StableCascadeDecoderPipeline,
    StableCascadePriorPipeline,
    StableCascadeUNet,
)

prompt = <span class="hljs-string">&quot;an image of a shiba inu, donning a spacesuit and helmet&quot;</span>
negative_prompt = <span class="hljs-string">&quot;&quot;</span>

prior_unet = StableCascadeUNet.from_single_file(
    <span class="hljs-string">&quot;https://huggingface.co/stabilityai/stable-cascade/resolve/main/stage_c_bf16.safetensors&quot;</span>,
    torch_dtype=torch.bfloat16
)
decoder_unet = StableCascadeUNet.from_single_file(
    <span class="hljs-string">&quot;https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors&quot;</span>,
    torch_dtype=torch.bfloat16
)

prior = StableCascadePriorPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade-prior&quot;</span>, prior=prior_unet, torch_dtype=torch.bfloat16)
decoder = StableCascadeDecoderPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-cascade&quot;</span>, decoder=decoder_unet, torch_dtype=torch.bfloat16)

prior.enable_model_cpu_offload()
prior_output = prior(
    prompt=prompt,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    negative_prompt=negative_prompt,
    guidance_scale=<span class="hljs-number">4.0</span>,
    num_images_per_prompt=<span class="hljs-number">1</span>,
    num_inference_steps=<span class="hljs-number">20</span>
)

decoder.enable_model_cpu_offload()
decoder_output = decoder(
    image_embeddings=prior_output.image_embeddings,
    prompt=prompt,
    negative_prompt=negative_prompt,
    guidance_scale=<span class="hljs-number">0.0</span>,
    output_type=<span class="hljs-string">&quot;pil&quot;</span>,
    num_inference_steps=<span class="hljs-number">10</span>
).images[<span class="hljs-number">0</span>]
decoder_output.save(<span class="hljs-string">&quot;cascade-single-file.png&quot;</span>)`,wrap:!1}}),ie=new U({props:{title:"Uses",local:"uses",headingTag:"h2"}}),le=new U({props:{title:"Direct Use",local:"direct-use",headingTag:"h3"}}),pe=new U({props:{title:"Out-of-Scope Use",local:"out-of-scope-use",headingTag:"h3"}}),ue=new U({props:{title:"Limitations and Bias",local:"limitations-and-bias",headingTag:"h2"}}),ge=new U({props:{title:"Limitations",local:"limitations",headingTag:"h3"}}),_e=new U({props:{title:"StableCascadeCombinedPipeline",local:"diffusers.StableCascadeCombinedPipeline",headingTag:"h2"}}),he=new V({props:{name:"class diffusers.StableCascadeCombinedPipeline",anchor:"diffusers.StableCascadeCombinedPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModel"},{name:"decoder",val:": StableCascadeUNet"},{name:"scheduler",val:": DDPMWuerstchenScheduler"},{name:"vqgan",val:": PaellaVQModel"},{name:"prior_prior",val:": StableCascadeUNet"},{name:"prior_text_encoder",val:": CLIPTextModel"},{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_scheduler",val:": DDPMWuerstchenScheduler"},{name:"prior_feature_extractor",val:": Optional = None"},{name:"prior_image_encoder",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.StableCascadeCombinedPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
The decoder tokenizer to be used for text inputs.`,name:"tokenizer"},{anchor:"diffusers.StableCascadeCombinedPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The decoder text encoder to be used for text inputs.`,name:"text_encoder"},{anchor:"diffusers.StableCascadeCombinedPipeline.decoder",description:`<strong>decoder</strong> (<code>StableCascadeUNet</code>) &#x2014;
The decoder model to be used for decoder image generation pipeline.`,name:"decoder"},{anchor:"diffusers.StableCascadeCombinedPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) &#x2014;
The scheduler to be used for decoder image generation pipeline.`,name:"scheduler"},{anchor:"diffusers.StableCascadeCombinedPipeline.vqgan",description:`<strong>vqgan</strong> (<code>PaellaVQModel</code>) &#x2014;
The VQGAN model to be used for decoder image generation pipeline.`,name:"vqgan"},{anchor:"diffusers.StableCascadeCombinedPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor" rel="nofollow">CLIPImageProcessor</a>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>image_encoder</code>.`,name:"feature_extractor"},{anchor:"diffusers.StableCascadeCombinedPipeline.image_encoder",description:`<strong>image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen CLIP image-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"image_encoder"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_prior",description:`<strong>prior_prior</strong> (<code>StableCascadeUNet</code>) &#x2014;
The prior model to be used for prior pipeline.`,name:"prior_prior"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) &#x2014;
The scheduler to be used for prior pipeline.`,name:"prior_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L45"}}),be=new V({props:{name:"__call__",anchor:"diffusers.StableCascadeCombinedPipeline.__call__",parameters:[{name:"prompt",val:": Union = None"},{name:"images",val:": Union = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"prior_num_inference_steps",val:": int = 60"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"num_inference_steps",val:": int = 12"},{name:"decoder_guidance_scale",val:": float = 0.0"},{name:"negative_prompt",val:": Union = None"},{name:"prompt_embeds",val:": Optional = None"},{name:"prompt_embeds_pooled",val:": Optional = None"},{name:"negative_prompt_embeds",val:": Optional = None"},{name:"negative_prompt_embeds_pooled",val:": Optional = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": Union = None"},{name:"latents",val:": Optional = None"},{name:"output_type",val:": Optional = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"prior_callback_on_step_end",val:": Optional = None"},{name:"prior_callback_on_step_end_tensor_inputs",val:": List = ['latents']"},{name:"callback_on_step_end",val:": Optional = None"},{name:"callback_on_step_end_tensor_inputs",val:": List = ['latents']"}],parametersDescription:[{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation for the prior and decoder.`,name:"prompt"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.images",description:`<strong>images</strong> (<code>torch.Tensor</code>, <code>PIL.Image.Image</code>, <code>List[torch.Tensor]</code>, <code>List[PIL.Image.Image]</code>, <em>optional</em>) &#x2014;
The images to guide the image generation for the prior.`,name:"images"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.prompt_embeds_pooled",description:`<strong>prompt_embeds_pooled</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds_pooled"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, <em>e.g.</em>
prompt weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code>
input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.negative_prompt_embeds_pooled",description:`<strong>negative_prompt_embeds_pooled</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, <em>e.g.</em>
prompt weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code>
input argument.`,name:"negative_prompt_embeds_pooled"},{anchor:"diffusers.StableCascadeCombinedPipeline.__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.StableCascadeCombinedPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.prior_guidance_scale",description:`<strong>prior_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>prior_guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting
<code>prior_guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked
to the text <code>prompt</code>, usually at the expense of lower image quality.`,name:"prior_guidance_scale"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.prior_num_inference_steps",description:`<strong>prior_num_inference_steps</strong> (<code>Union[int, Dict[float, int]]</code>, <em>optional</em>, defaults to 60) &#x2014;
The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. For more specific timestep spacing, you can pass customized
<code>prior_timesteps</code>`,name:"prior_num_inference_steps"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. For more specific timestep spacing, you can pass customized
<code>timesteps</code>`,name:"num_inference_steps"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.decoder_guidance_scale",description:`<strong>decoder_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"decoder_guidance_scale"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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 will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableCascadeCombinedPipeline.__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 generate image. Choose between: <code>&quot;pil&quot;</code> (<code>PIL.Image.Image</code>), <code>&quot;np&quot;</code>
(<code>np.array</code>) or <code>&quot;pt&quot;</code> (<code>torch.Tensor</code>).`,name:"output_type"},{anchor:"diffusers.StableCascadeCombinedPipeline.__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.StableCascadeCombinedPipeline.__call__.prior_callback_on_step_end",description:`<strong>prior_callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>.`,name:"prior_callback_on_step_end"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.prior_callback_on_step_end_tensor_inputs",description:`<strong>prior_callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>prior_callback_on_step_end</code> function. The tensors specified in the
list will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in
the <code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"prior_callback_on_step_end_tensor_inputs"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L152",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code> <a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> 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>
`}}),Y=new Ot({props:{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.example",$$slots:{default:[$a]},$$scope:{ctx:I}}}),we=new V({props:{name:"enable_model_cpu_offload",anchor:"diffusers.StableCascadeCombinedPipeline.enable_model_cpu_offload",parameters:[{name:"gpu_id",val:": Optional = None"},{name:"device",val:": Union = 'cuda'"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L124"}}),ye=new V({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.StableCascadeCombinedPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:": Optional = None"},{name:"device",val:": Union = 'cuda'"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L134"}}),Ce=new U({props:{title:"StableCascadePriorPipeline",local:"diffusers.StableCascadePriorPipeline",headingTag:"h2"}}),Me=new V({props:{name:"class diffusers.StableCascadePriorPipeline",anchor:"diffusers.StableCascadePriorPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior",val:": StableCascadeUNet"},{name:"scheduler",val:": DDPMWuerstchenScheduler"},{name:"resolution_multiple",val:": float = 42.67"},{name:"feature_extractor",val:": Optional = None"},{name:"image_encoder",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.StableCascadePriorPipeline.prior",description:`<strong>prior</strong> (<code>StableCascadeUNet</code>) &#x2014;
The Stable Cascade prior to approximate the image embedding from the text and/or image embedding.`,name:"prior"},{anchor:"diffusers.StableCascadePriorPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder
(<a href="https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" rel="nofollow">laion/CLIP-ViT-bigG-14-laion2B-39B-b160k</a>).`,name:"text_encoder"},{anchor:"diffusers.StableCascadePriorPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor" rel="nofollow">CLIPImageProcessor</a>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>image_encoder</code>.`,name:"feature_extractor"},{anchor:"diffusers.StableCascadePriorPipeline.image_encoder",description:`<strong>image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen CLIP image-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"image_encoder"},{anchor:"diffusers.StableCascadePriorPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.StableCascadePriorPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>prior</code> to generate image embedding.`,name:"scheduler"},{anchor:"diffusers.StableCascadePriorPipeline.resolution_multiple",description:`<strong>resolution_multiple</strong> (&#x2018;float&#x2019;, <em>optional</em>, defaults to 42.67) &#x2014;
Default resolution for multiple images generated.`,name:"resolution_multiple"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py#L72"}}),Te=new V({props:{name:"__call__",anchor:"diffusers.StableCascadePriorPipeline.__call__",parameters:[{name:"prompt",val:": Union = None"},{name:"images",val:": Union = None"},{name:"height",val:": int = 1024"},{name:"width",val:": int = 1024"},{name:"num_inference_steps",val:": int = 20"},{name:"timesteps",val:": List = None"},{name:"guidance_scale",val:": float = 4.0"},{name:"negative_prompt",val:": Union = None"},{name:"prompt_embeds",val:": Optional = None"},{name:"prompt_embeds_pooled",val:": Optional = None"},{name:"negative_prompt_embeds",val:": Optional = None"},{name:"negative_prompt_embeds_pooled",val:": Optional = None"},{name:"image_embeds",val:": Optional = None"},{name:"num_images_per_prompt",val:": Optional = 1"},{name:"generator",val:": Union = None"},{name:"latents",val:": Optional = None"},{name:"output_type",val:": Optional = 'pt'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": Optional = None"},{name:"callback_on_step_end_tensor_inputs",val:": List = ['latents']"}],parametersDescription:[{anchor:"diffusers.StableCascadePriorPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 60) &#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.StableCascadePriorPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 8.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>decoder_guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting
<code>decoder_guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely
linked to the text <code>prompt</code>, usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>decoder_guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.prompt_embeds_pooled",description:`<strong>prompt_embeds_pooled</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting.
If not provided, pooled text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds_pooled"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.negative_prompt_embeds_pooled",description:`<strong>negative_prompt_embeds_pooled</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds_pooled will be generated from <code>negative_prompt</code>
input argument.`,name:"negative_prompt_embeds_pooled"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated image embeddings. Can be used to easily tweak image inputs, <em>e.g.</em> prompt weighting. If
not provided, image embeddings will be generated from <code>image</code> input argument if existing.`,name:"image_embeds"},{anchor:"diffusers.StableCascadePriorPipeline.__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.StableCascadePriorPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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 will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableCascadePriorPipeline.__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 generate image. Choose between: <code>&quot;pil&quot;</code> (<code>PIL.Image.Image</code>), <code>&quot;np&quot;</code>
(<code>np.array</code>) or <code>&quot;pt&quot;</code> (<code>torch.Tensor</code>).`,name:"output_type"},{anchor:"diffusers.StableCascadePriorPipeline.__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.StableCascadePriorPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.StableCascadePriorPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py#L365",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>StableCascadePriorPipelineOutput</code> or <code>tuple</code> <code>StableCascadePriorPipelineOutput</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 image
embeddings.</p>
`}}),E=new Ot({props:{anchor:"diffusers.StableCascadePriorPipeline.__call__.example",$$slots:{default:[Ia]},$$scope:{ctx:I}}}),ve=new U({props:{title:"StableCascadePriorPipelineOutput",local:"diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput",headingTag:"h2"}}),Je=new V({props:{name:"class diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput",anchor:"diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput",parameters:[{name:"image_embeddings",val:": Union"},{name:"prompt_embeds",val:": Union"},{name:"prompt_embeds_pooled",val:": Union"},{name:"negative_prompt_embeds",val:": Union"},{name:"negative_prompt_embeds_pooled",val:": Union"}],parametersDescription:[{anchor:"diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput.image_embeddings",description:`<strong>image_embeddings</strong> (<code>torch.Tensor</code> or <code>np.ndarray</code>) &#x2014;
Prior image embeddings for text prompt`,name:"image_embeddings"},{anchor:"diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>) &#x2014;
Text embeddings for the prompt.`,name:"prompt_embeds"},{anchor:"diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>) &#x2014;
Text embeddings for the negative prompt.`,name:"negative_prompt_embeds"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py#L51"}}),Ue=new U({props:{title:"StableCascadeDecoderPipeline",local:"diffusers.StableCascadeDecoderPipeline",headingTag:"h2"}}),Pe=new V({props:{name:"class diffusers.StableCascadeDecoderPipeline",anchor:"diffusers.StableCascadeDecoderPipeline",parameters:[{name:"decoder",val:": StableCascadeUNet"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModel"},{name:"scheduler",val:": DDPMWuerstchenScheduler"},{name:"vqgan",val:": PaellaVQModel"},{name:"latent_dim_scale",val:": float = 10.67"}],parametersDescription:[{anchor:"diffusers.StableCascadeDecoderPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
The CLIP tokenizer.`,name:"tokenizer"},{anchor:"diffusers.StableCascadeDecoderPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The CLIP text encoder.`,name:"text_encoder"},{anchor:"diffusers.StableCascadeDecoderPipeline.decoder",description:`<strong>decoder</strong> (<code>StableCascadeUNet</code>) &#x2014;
The Stable Cascade decoder unet.`,name:"decoder"},{anchor:"diffusers.StableCascadeDecoderPipeline.vqgan",description:`<strong>vqgan</strong> (<code>PaellaVQModel</code>) &#x2014;
The VQGAN model.`,name:"vqgan"},{anchor:"diffusers.StableCascadeDecoderPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>prior</code> to generate image embedding.`,name:"scheduler"},{anchor:"diffusers.StableCascadeDecoderPipeline.latent_dim_scale",description:`<strong>latent_dim_scale</strong> (float, <code>optional</code>, defaults to 10.67) &#x2014;
Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
height=24 and width=24, the VQ latent shape needs to be height=int(24<em>10.67)=256 and
width=int(24</em>10.67)=256 in order to match the training conditions.`,name:"latent_dim_scale"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py#L50"}}),xe=new V({props:{name:"__call__",anchor:"diffusers.StableCascadeDecoderPipeline.__call__",parameters:[{name:"image_embeddings",val:": Union"},{name:"prompt",val:": Union = None"},{name:"num_inference_steps",val:": int = 10"},{name:"guidance_scale",val:": float = 0.0"},{name:"negative_prompt",val:": Union = None"},{name:"prompt_embeds",val:": Optional = None"},{name:"prompt_embeds_pooled",val:": Optional = None"},{name:"negative_prompt_embeds",val:": Optional = None"},{name:"negative_prompt_embeds_pooled",val:": Optional = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": Union = None"},{name:"latents",val:": Optional = None"},{name:"output_type",val:": Optional = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": Optional = None"},{name:"callback_on_step_end_tensor_inputs",val:": List = ['latents']"}],parametersDescription:[{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.image_embedding",description:`<strong>image_embedding</strong> (<code>torch.Tensor</code> or <code>List[torch.Tensor]</code>) &#x2014;
Image Embeddings either extracted from an image or generated by a Prior Model.`,name:"image_embedding"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#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.StableCascadeDecoderPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>decoder_guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting
<code>decoder_guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely
linked to the text <code>prompt</code>, usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>decoder_guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.prompt_embeds_pooled",description:`<strong>prompt_embeds_pooled</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting.
If not provided, pooled text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds_pooled"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.negative_prompt_embeds_pooled",description:`<strong>negative_prompt_embeds_pooled</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds_pooled will be generated from <code>negative_prompt</code>
input argument.`,name:"negative_prompt_embeds_pooled"},{anchor:"diffusers.StableCascadeDecoderPipeline.__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.StableCascadeDecoderPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
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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
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableCascadeDecoderPipeline.__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 generate image. Choose between: <code>&quot;pil&quot;</code> (<code>PIL.Image.Image</code>), <code>&quot;np&quot;</code>
(<code>np.array</code>) or <code>&quot;pt&quot;</code> (<code>torch.Tensor</code>).`,name:"output_type"},{anchor:"diffusers.StableCascadeDecoderPipeline.__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.StableCascadeDecoderPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.StableCascadeDecoderPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py#L284",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code> <a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> 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 image
embeddings.</p>
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