import{S as xr,i as Cr,s as Lr,e as i,k as l,w as u,t as s,M as Er,c as o,d as n,m as p,a,x as h,h as r,b as c,G as e,g as I,y as _,q as b,o as v,B as P,v as kr,L as _i}from"../../../chunks/vendor-hf-doc-builder.js";import{D as S}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as It}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as lt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as hi}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Sr(z){let g,k,$,m,C;return m=new It({props:{code:`import torch
from diffusers import StableUnCLIPPipeline

pipe = StableUnCLIPPipeline.from_pretrained(
    "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
)  # TODO update model path
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
images = pipe(prompt).images
images[0].save("astronaut_horse.png")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableUnCLIPPipeline

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

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;astronaut_horse.png&quot;</span>)`}}),{c(){g=i("p"),k=s("Examples:"),$=l(),u(m.$$.fragment)},l(d){g=o(d,"P",{});var y=a(g);k=r(y,"Examples:"),y.forEach(n),$=p(d),h(m.$$.fragment,d)},m(d,y){I(d,g,y),e(g,k),I(d,$,y),_(m,d,y),C=!0},p:_i,i(d){C||(b(m.$$.fragment,d),C=!0)},o(d){v(m.$$.fragment,d),C=!1},d(d){d&&n(g),d&&n($),P(m,d)}}}function Ur(z){let g,k,$,m,C;return m=new It({props:{code:`import torch
from diffusers import DiffusionPipeline
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
# Workaround for not accepting attention shape using VAE for Flash Attention
pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

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

from diffusers import StableUnCLIPImg2ImgPipeline

pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
    "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16
)  # TODO update model path
pipe = pipe.to("cuda")

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((768, 512))

prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt, init_image).images
images[0].save("fantasy_landscape.png")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt, init_image).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;fantasy_landscape.png&quot;</span>)`}}),{c(){g=i("p"),k=s("Examples:"),$=l(),u(m.$$.fragment)},l(d){g=o(d,"P",{});var y=a(g);k=r(y,"Examples:"),y.forEach(n),$=p(d),h(m.$$.fragment,d)},m(d,y){I(d,g,y),e(g,k),I(d,$,y),_(m,d,y),C=!0},p:_i,i(d){C||(b(m.$$.fragment,d),C=!0)},o(d){v(m.$$.fragment,d),C=!1},d(d){d&&n(g),d&&n($),P(m,d)}}}function jr(z){let g,k,$,m,C;return m=new It({props:{code:`import torch
from diffusers import DiffusionPipeline
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
# Workaround for not accepting attention shape using VAE for Flash Attention
pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){g=i("p"),k=s("Examples:"),$=l(),u(m.$$.fragment)},l(d){g=o(d,"P",{});var y=a(g);k=r(y,"Examples:"),y.forEach(n),$=p(d),h(m.$$.fragment,d)},m(d,y){I(d,g,y),e(g,k),I(d,$,y),_(m,d,y),C=!0},p:_i,i(d){C||(b(m.$$.fragment,d),C=!0)},o(d){v(m.$$.fragment,d),C=!1},d(d){d&&n(g),d&&n($),P(m,d)}}}function Dr(z){let g,k,$,m,C,d,y,yt,bi,kn,ne,vi,pt,Pi,Ii,Sn,R,ie,$t,Ie,yi,wt,$i,Un,j,wi,xt,xi,Ci,Ct,Li,Ei,Lt,ki,Si,Et,Ui,Ti,Tn,J,oe,kt,ye,ji,St,Di,jn,dt,Ai,Dn,Q,ae,Ut,$e,Oi,Tt,qi,An,we,On,X,se,jt,xe,zi,Dt,Ni,qn,Ce,zn,Y,re,At,Le,Fi,Ot,Vi,Nn,w,Ee,Mi,qt,Wi,Gi,ke,Bi,ct,Ki,Hi,Ri,N,Se,Ji,zt,Qi,Xi,le,Yi,F,Ue,Zi,Nt,eo,to,Ft,no,io,pe,Te,oo,je,ao,Vt,so,ro,lo,V,De,po,Mt,co,mo,Wt,fo,go,de,Ae,uo,Oe,ho,Gt,_o,bo,vo,D,qe,Po,Bt,Io,yo,Kt,$o,wo,Ht,xo,Co,ce,Lo,me,ze,Eo,Rt,ko,So,fe,Ne,Uo,Fe,To,Jt,jo,Do,Ao,U,Ve,Oo,Z,qo,Qt,zo,No,Xt,Fo,Vo,Mo,Yt,Wo,Go,Me,Zt,Bo,Ko,en,Ho,Ro,We,Jo,tn,Qo,Xo,Yo,nn,Zo,Fn,ee,ge,on,Ge,ea,an,ta,Vn,x,Be,na,sn,ia,oa,Ke,aa,mt,sa,ra,la,M,He,pa,rn,da,ca,ue,ma,W,Re,fa,ln,ga,ua,pn,ha,_a,he,Je,ba,Qe,va,dn,Pa,Ia,ya,G,Xe,$a,cn,wa,xa,mn,Ca,La,_e,Ye,Ea,Ze,ka,fn,Sa,Ua,Ta,A,et,ja,gn,Da,Aa,un,Oa,qa,hn,za,Na,be,Fa,ve,tt,Va,_n,Ma,Wa,Pe,nt,Ga,it,Ba,bn,Ka,Ha,Ra,T,ot,Ja,te,Qa,vn,Xa,Ya,Pn,Za,es,ts,In,ns,is,at,yn,os,as,$n,ss,rs,st,ls,wn,ps,ds,cs,xn,ms,Mn;return d=new lt({}),Ie=new lt({}),ye=new lt({}),$e=new lt({}),we=new It({props:{code:`import torch
from diffusers import StableUnCLIPPipeline

pipe = StableUnCLIPPipeline.from_pretrained(
    "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
)  # TODO update model path
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
images = pipe(prompt).images
images[0].save("astronaut_horse.png")`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableUnCLIPPipeline

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

prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
images = pipe(prompt).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;astronaut_horse.png&quot;</span>)`}}),xe=new lt({}),Ce=new It({props:{code:`import requests
import torch
from PIL import Image
from io import BytesIO

from diffusers import StableUnCLIPImg2ImgPipeline

pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
    "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16
)  # TODO update model path
pipe = pipe.to("cuda")

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((768, 512))

prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt, init_image).images
images[0].save("fantasy_landscape.png")`,highlighted:`<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableUnCLIPImg2ImgPipeline

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

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

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

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

images = pipe(prompt, init_image).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;fantasy_landscape.png&quot;</span>)`}}),Le=new lt({}),Ee=new S({props:{name:"class diffusers.StableUnCLIPPipeline",anchor:"diffusers.StableUnCLIPPipeline",parameters:[{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior",val:": PriorTransformer"},{name:"prior_scheduler",val:": KarrasDiffusionSchedulers"},{name:"image_normalizer",val:": StableUnCLIPImageNormalizer"},{name:"image_noising_scheduler",val:": KarrasDiffusionSchedulers"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"vae",val:": AutoencoderKL"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPPipeline.prior_tokenizer",description:`<strong>prior_tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"prior_tokenizer"},{anchor:"diffusers.StableUnCLIPPipeline.prior_text_encoder",description:`<strong>prior_text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"prior_text_encoder"},{anchor:"diffusers.StableUnCLIPPipeline.prior",description:`<strong>prior</strong> (<a href="/docs/diffusers/main/en/api/models#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonincal unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior"},{anchor:"diffusers.StableUnCLIPPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>KarrasDiffusionSchedulers</code>) &#x2014;
Scheduler used in the prior denoising process.`,name:"prior_scheduler"},{anchor:"diffusers.StableUnCLIPPipeline.image_normalizer",description:`<strong>image_normalizer</strong> (<code>StableUnCLIPImageNormalizer</code>) &#x2014;
Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
embeddings after the noise has been applied.`,name:"image_normalizer"},{anchor:"diffusers.StableUnCLIPPipeline.image_noising_scheduler",description:`<strong>image_noising_scheduler</strong> (<code>KarrasDiffusionSchedulers</code>) &#x2014;
Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
by <code>noise_level</code> in <code>StableUnCLIPPipeline.__call__</code>.`,name:"image_noising_scheduler"},{anchor:"diffusers.StableUnCLIPPipeline.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.StableUnCLIPPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.StableUnCLIPPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.StableUnCLIPPipeline.scheduler",description:`<strong>scheduler</strong> (<code>KarrasDiffusionSchedulers</code>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents.`,name:"scheduler"},{anchor:"diffusers.StableUnCLIPPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py#L50"}}),Se=new S({props:{name:"__call__",anchor:"diffusers.StableUnCLIPPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 20"},{name:"guidance_scale",val:": float = 10.0"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"noise_level",val:": int = 0"},{name:"prior_num_inference_steps",val:": int = 25"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"prior_latents",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"diffusers.StableUnCLIPPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 10.0) &#x2014;
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:"guidance_scale"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. If not defined, one has to pass
<code>negative_prompt_embeds</code>. instead. If not defined, one has to pass <code>negative_prompt_embeds</code>. instead.
Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.StableUnCLIPPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
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The output format of the generate image. Choose between
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