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hf-doc-build/doc-dev / diffusers /pr_13235 /en /_app /immutable /nodes /187.267f10bc.js

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	import{s as Pn,o as xn,n as Kt}from"../chunks/scheduler.53228c21.js";import{S as In,i as jn,e as l,s as o,c,h as Sn,a as r,d as n,b as s,f as I,g as p,j as h,k as x,l as d,m as a,n as m,t as g,o as u,p as f}from"../chunks/index.100fac89.js";import{C as Zn}from"../chunks/CopyLLMTxtMenu.dc6bbc11.js";import{D as W}from"../chunks/Docstring.0e2c91bd.js";import{C as Qe}from"../chunks/CodeBlock.0adb3827.js";import{E as Ot}from"../chunks/ExampleCodeBlock.3a7b8f9f.js";import{H as v,E as $n}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.b1ca0dcf.js";function kn(N){let _,J="Examples:",y,b,w;return b=new Qe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableCascadeCombinedPipeline

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

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

	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"an image of a shiba inu, donning a spacesuit and helmet"</span>
	<span class="hljs-meta">>>> </span>prior_output = pipe(prompt)`,lang:"py",wrap:!1}}),{c(){_=l("p"),_.textContent=J,y=o(),c(b.$$.fragment)},l(i){_=r(i,"P",{"data-svelte-h":!0}),h(_)!=="svelte-kvfsh7"&&(_.textContent=J),y=s(i),p(b.$$.fragment,i)},m(i,C){a(i,_,C),a(i,y,C),m(b,i,C),w=!0},p:Kt,i(i){w\|\|(g(b.$$.fragment,i),w=!0)},o(i){u(b.$$.fragment,i),w=!1},d(i){i&&(n(_),n(y)),f(b,i)}}}function Nn(N){let _,J="Examples:",y,b,w;return b=new Qe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableCascadePriorPipeline, StableCascadeDecoderPipeline

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

	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"an image of a shiba inu, donning a spacesuit and helmet"</span>
	<span class="hljs-meta">>>> </span>prior_output = pipe(prompt)
	<span class="hljs-meta">>>> </span>images = gen_pipe(prior_output.image_embeddings, prompt=prompt)`,lang:"py",wrap:!1}}),{c(){_=l("p"),_.textContent=J,y=o(),c(b.$$.fragment)},l(i){_=r(i,"P",{"data-svelte-h":!0}),h(_)!=="svelte-kvfsh7"&&(_.textContent=J),y=s(i),p(b.$$.fragment,i)},m(i,C){a(i,_,C),a(i,y,C),m(b,i,C),w=!0},p:Kt,i(i){w\|\|(g(b.$$.fragment,i),w=!0)},o(i){u(b.$$.fragment,i),w=!1},d(i){i&&(n(_),n(y)),f(b,i)}}}function Gn(N){let _,J,y,b,w,i,C,ze,Q,en=`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.`,Fe,X,tn=`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.`,Le,z,nn='The original codebase can be found at <a href="https://github.com/Stability-AI/StableCascade" rel="nofollow">Stability-AI/StableCascade</a>.',qe,F,Ae,L,an=`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,on=`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,sn="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,ln="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,G,rn="<p>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.</p> <p>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.</p> <p>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>.</p>",nt,K,at,ee,ot,te,st,ne,it,ae,lt,oe,dn="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,cn="The model is intended for research purposes for now. Possible research areas and tasks include",mt,de,pn="<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>",gt,ce,mn="Excluded uses are described below.",ut,pe,ft,me,gn=`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,ge,ht,ue,bt,fe,un="<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,M,he,Zt,Se,fn="Combined Pipeline for text-to-image generation using Stable Cascade.",$t,Ze,_n=`This model inherits from <a href="/docs/diffusers/pr_13235/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
	library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,kt,j,be,Wt,$e,hn="Function invoked when calling the pipeline for generation.",Nt,B,Gt,V,we,Bt,ke,bn=`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>.`,Vt,Y,ye,Yt,We,wn=`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('meta')</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,U,Me,Rt,Ne,yn="Pipeline for generating image prior for Stable Cascade.",Et,Ge,Cn=`This model inherits from <a href="/docs/diffusers/pr_13235/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.)`,Ht,S,Te,Dt,Be,Mn="Function invoked when calling the pipeline for generation.",Qt,R,Tt,ve,vt,$,Je,Xt,Ve,Tn="Output class for WuerstchenPriorPipeline.",Jt,Ue,Ut,P,Pe,zt,Ye,vn="Pipeline for generating images from the Stable Cascade model.",Ft,Re,Jn=`This model inherits from <a href="/docs/diffusers/pr_13235/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.)`,Lt,Z,xe,qt,Ee,Un="Function invoked when calling the pipeline for generation.",At,E,Pt,Ie,xt,Xe,It;return w=new Zn({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),C=new v({props:{title:"Stable Cascade",local:"stable-cascade",headingTag:"h1"}}),F=new v({props:{title:"Model Overview",local:"model-overview",headingTag:"h2"}}),K=new v({props:{title:"Usage example",local:"usage-example",headingTag:"h2"}}),ee=new Qe({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">"an image of a shiba inu, donning a spacesuit and helmet"</span>
	negative_prompt = <span class="hljs-string">""</span>

	prior = StableCascadePriorPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-cascade-prior"</span>, variant=<span class="hljs-string">"bf16"</span>, torch_dtype=torch.bfloat16)
	decoder = StableCascadeDecoderPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-cascade"</span>, variant=<span class="hljs-string">"bf16"</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">"pil"</span>,
	num_inference_steps=<span class="hljs-number">10</span>
	).images[<span class="hljs-number">0</span>]
	decoder_output.save(<span class="hljs-string">"cascade.png"</span>)`,lang:"python",wrap:!1}}),te=new v({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"}}),ne=new Qe({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">"an image of a shiba inu, donning a spacesuit and helmet"</span>
	negative_prompt = <span class="hljs-string">""</span>

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

	prior = StableCascadePriorPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-cascade-prior"</span>, prior=prior_unet)
	decoder = StableCascadeDecoderPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-cascade"</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">"pil"</span>,
	num_inference_steps=<span class="hljs-number">10</span>
	).images[<span class="hljs-number">0</span>]
	decoder_output.save(<span class="hljs-string">"cascade.png"</span>)`,lang:"python",wrap:!1}}),ae=new v({props:{title:"Loading original checkpoints with from_single_file",local:"loading-original-checkpoints-with-fromsinglefile",headingTag:"h2"}}),se=new Qe({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">"an image of a shiba inu, donning a spacesuit and helmet"</span>
	negative_prompt = <span class="hljs-string">""</span>

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

	prior = StableCascadePriorPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-cascade-prior"</span>, prior=prior_unet, torch_dtype=torch.bfloat16)
	decoder = StableCascadeDecoderPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-cascade"</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">"pil"</span>,
	num_inference_steps=<span class="hljs-number">10</span>
	).images[<span class="hljs-number">0</span>]
	decoder_output.save(<span class="hljs-string">"cascade-single-file.png"</span>)`,lang:"python",wrap:!1}}),ie=new v({props:{title:"Uses",local:"uses",headingTag:"h2"}}),le=new v({props:{title:"Direct Use",local:"direct-use",headingTag:"h3"}}),pe=new v({props:{title:"Out-of-Scope Use",local:"out-of-scope-use",headingTag:"h3"}}),ge=new v({props:{title:"Limitations and Bias",local:"limitations-and-bias",headingTag:"h2"}}),ue=new v({props:{title:"Limitations",local:"limitations",headingTag:"h3"}}),_e=new v({props:{title:"StableCascadeCombinedPipeline",local:"diffusers.StableCascadeCombinedPipeline",headingTag:"h2"}}),he=new W({props:{name:"class diffusers.StableCascadeCombinedPipeline",anchor:"diffusers.StableCascadeCombinedPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"decoder",val:": StableCascadeUNet"},{name:"scheduler",val:": DDPMWuerstchenScheduler"},{name:"vqgan",val:": PaellaVQModel"},{name:"prior_prior",val:": StableCascadeUNet"},{name:"prior_text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_scheduler",val:": DDPMWuerstchenScheduler"},{name:"prior_feature_extractor",val:": transformers.models.clip.image_processing_clip.CLIPImageProcessor \| None = None"},{name:"prior_image_encoder",val:": transformers.models.clip.modeling_clip.CLIPVisionModelWithProjection \| None = None"}],parametersDescription:[{anchor:"diffusers.StableCascadeCombinedPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) —
	The decoder tokenizer to be used for text inputs.`,name:"tokenizer"},{anchor:"diffusers.StableCascadeCombinedPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) —
	The decoder text encoder to be used for text inputs.`,name:"text_encoder"},{anchor:"diffusers.StableCascadeCombinedPipeline.decoder",description:`<strong>decoder</strong> (<code>StableCascadeUNet</code>) —
	The decoder model to be used for decoder image generation pipeline.`,name:"decoder"},{anchor:"diffusers.StableCascadeCombinedPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) —
	The scheduler to be used for decoder image generation pipeline.`,name:"scheduler"},{anchor:"diffusers.StableCascadeCombinedPipeline.vqgan",description:`<strong>vqgan</strong> (<code>PaellaVQModel</code>) —
	The VQGAN model to be used for decoder image generation pipeline.`,name:"vqgan"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_prior",description:`<strong>prior_prior</strong> (<code>StableCascadeUNet</code>) —
	The prior model to be used for prior pipeline.`,name:"prior_prior"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_text_encoder",description:`<strong>prior_text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) —
	The prior text encoder to be used for text inputs.`,name:"prior_text_encoder"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_tokenizer",description:`<strong>prior_tokenizer</strong> (<code>CLIPTokenizer</code>) —
	The prior tokenizer to be used for text inputs.`,name:"prior_tokenizer"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) —
	The scheduler to be used for prior pipeline.`,name:"prior_scheduler"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_feature_extractor",description:`<strong>prior_feature_extractor</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor" rel="nofollow">CLIPImageProcessor</a>) —
	Model that extracts features from generated images to be used as inputs for the <code>image_encoder</code>.`,name:"prior_feature_extractor"},{anchor:"diffusers.StableCascadeCombinedPipeline.prior_image_encoder",description:`<strong>prior_image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) —
	Frozen CLIP image-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"prior_image_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L45"}}),be=new W({props:{name:"__call__",anchor:"diffusers.StableCascadeCombinedPipeline.__call__",parameters:[{name:"prompt",val:": str \| list[str] \| None = None"},{name:"images",val:": torch.Tensor \| PIL.Image.Image \| list[torch.Tensor] \| list[PIL.Image.Image] = 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:": str \| list[str] \| None = None"},{name:"prompt_embeds",val:": torch.Tensor \| None = None"},{name:"prompt_embeds_pooled",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds_pooled",val:": torch.Tensor \| None = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator \| list[torch._C.Generator] \| None = None"},{name:"latents",val:": torch.Tensor \| None = None"},{name:"output_type",val:": str \| None = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"prior_callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = None"},{name:"prior_callback_on_step_end_tensor_inputs",val:": list = ['latents']"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = 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>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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) —
	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) —
	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) —
	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) —
	Guidance scale as defined in <a href="https://huggingface.co/papers/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://huggingface.co/papers/2205.11487" rel="nofollow">Imagen Paper</a>. Guidance scale is enabled by
	setting <code>prior_guidance_scale > 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>int \| dict[float, int]</code>, <em>optional</em>, defaults to 60) —
	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) —
	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) —
	Guidance scale as defined in <a href="https://huggingface.co/papers/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://huggingface.co/papers/2205.11487" rel="nofollow">Imagen Paper</a>. Guidance scale is enabled by setting
	<code>guidance_scale > 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>) —
	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>) —
	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 be 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>"pil"</code>) —
	The output format of the generate image. Choose between: <code>"pil"</code> (<code>PIL.Image.Image</code>), <code>"np"</code>
	(<code>np.array</code>) or <code>"pt"</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>) —
	Whether or not to return a <a href="/docs/diffusers/pr_13235/en/api/pipelines/stable_unclip#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>) —
	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>) —
	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>) —
	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>) —
	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/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L158",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><a
	href="/docs/diffusers/pr_13235/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput"
	>ImagePipelineOutput</a> or <code>tuple</code> <a
	href="/docs/diffusers/pr_13235/en/api/pipelines/stable_unclip#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>
	`}}),B=new Ot({props:{anchor:"diffusers.StableCascadeCombinedPipeline.__call__.example",$$slots:{default:[kn]},$$scope:{ctx:N}}}),we=new W({props:{name:"enable_model_cpu_offload",anchor:"diffusers.StableCascadeCombinedPipeline.enable_model_cpu_offload",parameters:[{name:"gpu_id",val:": int \| None = None"},{name:"device",val:": torch.device \| str = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L130"}}),ye=new W({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.StableCascadeCombinedPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:": int \| None = None"},{name:"device",val:": torch.device \| str = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py#L140"}}),Ce=new v({props:{title:"StableCascadePriorPipeline",local:"diffusers.StableCascadePriorPipeline",headingTag:"h2"}}),Me=new W({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:": transformers.models.clip.image_processing_clip.CLIPImageProcessor \| None = None"},{name:"image_encoder",val:": transformers.models.clip.modeling_clip.CLIPVisionModelWithProjection \| None = None"}],parametersDescription:[{anchor:"diffusers.StableCascadePriorPipeline.prior",description:`<strong>prior</strong> (<code>StableCascadeUNet</code>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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> (‘float’, <em>optional</em>, defaults to 42.67) —
	Default resolution for multiple images generated.`,name:"resolution_multiple"}],source:"https://github.com/huggingface/diffusers/blob/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py#L80"}}),Te=new W({props:{name:"__call__",anchor:"diffusers.StableCascadePriorPipeline.__call__",parameters:[{name:"prompt",val:": str \| list[str] \| None = None"},{name:"images",val:": torch.Tensor \| PIL.Image.Image \| list[torch.Tensor] \| list[PIL.Image.Image] = 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:": str \| list[str] \| None = None"},{name:"prompt_embeds",val:": torch.Tensor \| None = None"},{name:"prompt_embeds_pooled",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds_pooled",val:": torch.Tensor \| None = None"},{name:"image_embeds",val:": torch.Tensor \| None = None"},{name:"num_images_per_prompt",val:": int \| None = 1"},{name:"generator",val:": torch._C.Generator \| list[torch._C.Generator] \| None = None"},{name:"latents",val:": torch.Tensor \| None = None"},{name:"output_type",val:": str \| None = 'pt'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = 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>) —
	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) —
	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) —
	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) —
	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) —
	Guidance scale as defined in <a href="https://huggingface.co/papers/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://huggingface.co/papers/2205.11487" rel="nofollow">Imagen Paper</a>. Guidance scale is enabled by
	setting <code>decoder_guidance_scale > 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>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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>) —
	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) —
	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>) —
	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>) —
	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 be 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>"pil"</code>) —
	The output format of the generate image. Choose between: <code>"pil"</code> (<code>PIL.Image.Image</code>), <code>"np"</code>
	(<code>np.array</code>) or <code>"pt"</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>) —
	Whether or not to return a <a href="/docs/diffusers/pr_13235/en/api/pipelines/stable_unclip#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>) —
	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>) —
	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/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py#L375",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>
	`}}),R=new Ot({props:{anchor:"diffusers.StableCascadePriorPipeline.__call__.example",$$slots:{default:[Wn]},$$scope:{ctx:N}}}),ve=new v({props:{title:"StableCascadePriorPipelineOutput",local:"diffusers.pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput",headingTag:"h2"}}),Je=new W({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:": torch.Tensor \| numpy.ndarray"},{name:"prompt_embeds",val:": torch.Tensor \| numpy.ndarray"},{name:"prompt_embeds_pooled",val:": torch.Tensor \| numpy.ndarray"},{name:"negative_prompt_embeds",val:": torch.Tensor \| numpy.ndarray"},{name:"negative_prompt_embeds_pooled",val:": torch.Tensor \| numpy.ndarray"}],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>) —
	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>) —
	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>) —
	Text embeddings for the negative prompt.`,name:"negative_prompt_embeds"}],source:"https://github.com/huggingface/diffusers/blob/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py#L60"}}),Ue=new v({props:{title:"StableCascadeDecoderPipeline",local:"diffusers.StableCascadeDecoderPipeline",headingTag:"h2"}}),Pe=new W({props:{name:"class diffusers.StableCascadeDecoderPipeline",anchor:"diffusers.StableCascadeDecoderPipeline",parameters:[{name:"decoder",val:": StableCascadeUNet"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{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>) —
	The CLIP tokenizer.`,name:"tokenizer"},{anchor:"diffusers.StableCascadeDecoderPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) —
	The CLIP text encoder.`,name:"text_encoder"},{anchor:"diffusers.StableCascadeDecoderPipeline.decoder",description:`<strong>decoder</strong> (<code>StableCascadeUNet</code>) —
	The Stable Cascade decoder unet.`,name:"decoder"},{anchor:"diffusers.StableCascadeDecoderPipeline.vqgan",description:`<strong>vqgan</strong> (<code>PaellaVQModel</code>) —
	The VQGAN model.`,name:"vqgan"},{anchor:"diffusers.StableCascadeDecoderPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDPMWuerstchenScheduler</code>) —
	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) —
	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/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py#L58"}}),xe=new W({props:{name:"__call__",anchor:"diffusers.StableCascadeDecoderPipeline.__call__",parameters:[{name:"image_embeddings",val:": torch.Tensor \| list[torch.Tensor]"},{name:"prompt",val:": str \| list[str] = None"},{name:"num_inference_steps",val:": int = 10"},{name:"guidance_scale",val:": float = 0.0"},{name:"negative_prompt",val:": str \| list[str] \| None = None"},{name:"prompt_embeds",val:": torch.Tensor \| None = None"},{name:"prompt_embeds_pooled",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor \| None = None"},{name:"negative_prompt_embeds_pooled",val:": torch.Tensor \| None = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator \| list[torch._C.Generator] \| None = None"},{name:"latents",val:": torch.Tensor \| None = None"},{name:"output_type",val:": str \| None = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = 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>) —
	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>) —
	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) —
	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) —
	Guidance scale as defined in <a href="https://huggingface.co/papers/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://huggingface.co/papers/2205.11487" rel="nofollow">Imagen Paper</a>. Guidance scale is enabled by
	setting <code>decoder_guidance_scale > 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>) —
	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>) —
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	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>) —
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	<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/vr_13235/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py#L304",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><a
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