import{S as Oi,i as Fi,s as Wi,e as n,k as c,w as x,t as s,M as zi,c as i,d as t,m as f,a,x as P,h as r,b as h,G as e,g as $,y as I,q as S,o as E,B as k,v as Vi,L as Yt}from"../../../../chunks/vendor-hf-doc-builder.js";import{T as Qt}from"../../../../chunks/Tip-hf-doc-builder.js";import{D as V}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as Zt}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ui}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Xt}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Gi(q){let l,y,p,m,w;return m=new Zt({props:{code:`import torch
import requests
from PIL import Image

from diffusers import StableDiffusionDepth2ImgPipeline

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-depth",
    torch_dtype=torch.float16,
)
pipe.to("cuda")


url = "http://images.cocodataset.org/val2017/000000039769.jpg"
init_image = Image.open(requests.get(url, stream=True).raw)
prompt = "two tigers"
n_propmt = "bad, deformed, ugly, bad anotomy"
image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]`,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">import</span> requests
<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> diffusers <span class="hljs-keyword">import</span> StableDiffusionDepth2ImgPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-depth&quot;</span>,
<span class="hljs-meta">... </span>    torch_dtype=torch.float16,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>init_image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;two tigers&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>n_propmt = <span class="hljs-string">&quot;bad, deformed, ugly, bad anotomy&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=<span class="hljs-number">0.7</span>).images[<span class="hljs-number">0</span>]`}}),{c(){l=n("p"),y=s("Examples:"),p=c(),x(m.$$.fragment)},l(d){l=i(d,"P",{});var g=a(l);y=r(g,"Examples:"),g.forEach(t),p=f(d),P(m.$$.fragment,d)},m(d,g){$(d,l,g),e(l,y),$(d,p,g),I(m,d,g),w=!0},p:Yt,i(d){w||(S(m.$$.fragment,d),w=!0)},o(d){E(m.$$.fragment,d),w=!1},d(d){d&&t(l),d&&t(p),k(m,d)}}}function Ri(q){let l,y,p,m,w;return m=new Zt({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(){l=n("p"),y=s("Examples:"),p=c(),x(m.$$.fragment)},l(d){l=i(d,"P",{});var g=a(l);y=r(g,"Examples:"),g.forEach(t),p=f(d),P(m.$$.fragment,d)},m(d,g){$(d,l,g),e(l,y),$(d,p,g),I(m,d,g),w=!0},p:Yt,i(d){w||(S(m.$$.fragment,d),w=!0)},o(d){E(m.$$.fragment,d),w=!1},d(d){d&&t(l),d&&t(p),k(m,d)}}}function Bi(q){let l,y;return{c(){l=n("p"),y=s("This function is experimental and might change in the future.")},l(p){l=i(p,"P",{});var m=a(l);y=r(m,"This function is experimental and might change in the future."),m.forEach(t)},m(p,m){$(p,l,m),e(l,y)},d(p){p&&t(l)}}}function Hi(q){let l,y,p,m,w,d,g,T;return{c(){l=n("p"),y=s("It is required to be logged in ("),p=n("code"),m=s("huggingface-cli login"),w=s(") when you want to use private or "),d=n("a"),g=s(`gated
models`),T=s("."),this.h()},l(u){l=i(u,"P",{});var _=a(l);y=r(_,"It is required to be logged in ("),p=i(_,"CODE",{});var A=a(p);m=r(A,"huggingface-cli login"),A.forEach(t),w=r(_,") when you want to use private or "),d=i(_,"A",{href:!0,rel:!0});var C=a(d);g=r(C,`gated
models`),C.forEach(t),T=r(_,"."),_.forEach(t),this.h()},h(){h(d,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),h(d,"rel","nofollow")},m(u,_){$(u,l,_),e(l,y),e(l,p),e(p,m),e(l,w),e(l,d),e(d,g),e(l,T)},d(u){u&&t(l)}}}function Ki(q){let l,y,p,m,w,d,g,T;return g=new Zt({props:{code:`from diffusers import StableDiffusionPipeline
import torch

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("sd-concepts-library/cat-toy")

prompt = "A <cat-toy> backpack"

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("cat-backpack.png")`,highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(<span class="hljs-string">&quot;sd-concepts-library/cat-toy&quot;</span>)

prompt = <span class="hljs-string">&quot;A &lt;cat-toy&gt; backpack&quot;</span>

image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;cat-backpack.png&quot;</span>)`}}),{c(){l=n("p"),y=s("To load a textual inversion embedding vector in "),p=n("code"),m=s("diffusers"),w=s(" format:"),d=c(),x(g.$$.fragment)},l(u){l=i(u,"P",{});var _=a(l);y=r(_,"To load a textual inversion embedding vector in "),p=i(_,"CODE",{});var A=a(p);m=r(A,"diffusers"),A.forEach(t),w=r(_," format:"),_.forEach(t),d=f(u),P(g.$$.fragment,u)},m(u,_){$(u,l,_),e(l,y),e(l,p),e(p,m),e(l,w),$(u,d,_),I(g,u,_),T=!0},p:Yt,i(u){T||(S(g.$$.fragment,u),T=!0)},o(u){E(g.$$.fragment,u),T=!1},d(u){u&&t(l),u&&t(d),k(g,u)}}}function Ji(q){let l,y,p,m,w,d,g,T;return g=new Zt({props:{code:`from diffusers import StableDiffusionPipeline
import torch

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("character.png")`,highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(<span class="hljs-string">&quot;./charturnerv2.pt&quot;</span>, token=<span class="hljs-string">&quot;charturnerv2&quot;</span>)

prompt = <span class="hljs-string">&quot;charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details.&quot;</span>

image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;character.png&quot;</span>)`}}),{c(){l=n("p"),y=s("e.g. from "),p=n("a"),m=s("civitAI"),w=s(" and then load the vector locally:"),d=c(),x(g.$$.fragment),this.h()},l(u){l=i(u,"P",{});var _=a(l);y=r(_,"e.g. from "),p=i(_,"A",{href:!0,rel:!0});var A=a(p);m=r(A,"civitAI"),A.forEach(t),w=r(_," and then load the vector locally:"),_.forEach(t),d=f(u),P(g.$$.fragment,u),this.h()},h(){h(p,"href","https://civitai.com/models/3036?modelVersionId=9857"),h(p,"rel","nofollow")},m(u,_){$(u,l,_),e(l,y),e(l,p),e(p,m),e(l,w),$(u,d,_),I(g,u,_),T=!0},p:Yt,i(u){T||(S(g.$$.fragment,u),T=!0)},o(u){E(g.$$.fragment,u),T=!1},d(u){u&&t(l),u&&t(d),k(g,u)}}}function Qi(q){let l,y;return{c(){l=n("p"),y=s("This function is experimental and might change in the future.")},l(p){l=i(p,"P",{});var m=a(l);y=r(m,"This function is experimental and might change in the future."),m.forEach(t)},m(p,m){$(p,l,m),e(l,y)},d(p){p&&t(l)}}}function Xi(q){let l,y,p,m,w,d,g,T;return{c(){l=n("p"),y=s("It is required to be logged in ("),p=n("code"),m=s("huggingface-cli login"),w=s(") when you want to use private or "),d=n("a"),g=s(`gated
models`),T=s("."),this.h()},l(u){l=i(u,"P",{});var _=a(l);y=r(_,"It is required to be logged in ("),p=i(_,"CODE",{});var A=a(p);m=r(A,"huggingface-cli login"),A.forEach(t),w=r(_,") when you want to use private or "),d=i(_,"A",{href:!0,rel:!0});var C=a(d);g=r(C,`gated
models`),C.forEach(t),T=r(_,"."),_.forEach(t),this.h()},h(){h(d,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),h(d,"rel","nofollow")},m(u,_){$(u,l,_),e(l,y),e(l,p),e(p,m),e(l,w),e(l,d),e(d,g),e(l,T)},d(u){u&&t(l)}}}function Yi(q){let l,y,p,m,w,d,g,T,u,_,A,C,Je,se,eo,Qe,to,kt,M,oo,re,no,io,le,ao,so,de,ro,lo,pe,po,co,Tt,G,Ce,fo,mo,Xe,uo,ho,At,Me,go,Lt,Ne,ce,Ye,_o,bo,fe,vo,qt,Ue,yo,jt,Oe,me,Ze,Do,wo,ue,$o,Ct,v,he,xo,et,Po,Io,ge,So,Fe,Eo,ko,To,tt,Ao,Lo,_e,be,ot,qo,jo,We,Co,Mo,ve,nt,No,Uo,ze,Oo,Fo,it,Wo,zo,at,ye,st,Vo,Go,Ve,Ro,Bo,O,De,Ho,rt,Ko,Jo,H,Qo,F,we,Xo,lt,Yo,Zo,dt,en,tn,K,$e,on,xe,nn,pt,an,sn,rn,N,Pe,ln,ct,dn,pn,ft,cn,fn,mt,mn,un,J,hn,Q,Ie,gn,ut,_n,bn,L,Se,vn,R,yn,ht,Dn,wn,gt,$n,xn,Pn,X,In,Y,Sn,_t,En,kn,Z,Tn,bt,An,Ln,ee,qn,U,Ee,jn,B,Cn,Ge,Mn,Nn,ke,vt,Un,On,Fn,te,Wn,oe,zn,ne,Te,Vn,yt,Gn,Rn,ie,Ae,Bn,Le,Hn,Dt,Kn,Jn,Mt;return d=new Ui({}),se=new Ui({}),he=new V({props:{name:"class diffusers.StableDiffusionDepth2ImgPipeline",anchor:"diffusers.StableDiffusionDepth2ImgPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"depth_estimator",val:": DPTForDepthEstimation"},{name:"feature_extractor",val:": DPTFeatureExtractor"}],parametersDescription:[{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.16.0/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"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder. Stable Diffusion uses the text portion of
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIP</a>, specifically
the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.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.StableDiffusionDepth2ImgPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.16.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/v0.16.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.16.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.16.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py#L58"}}),De=new V({props:{name:"__call__",anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"image",val:": typing.Union[torch.FloatTensor, PIL.Image.Image] = None"},{name:"depth_map",val:": typing.Optional[torch.FloatTensor] = None"},{name:"strength",val:": float = 0.8"},{name:"num_inference_steps",val:": typing.Optional[int] = 50"},{name:"guidance_scale",val:": typing.Optional[float] = 7.5"},{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:": typing.Optional[float] = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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"}],parametersDescription:[{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__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.StableDiffusionDepth2ImgPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.FloatTensor</code> or <code>PIL.Image.Image</code>) &#x2014;
<code>Image</code>, or tensor representing an image batch, that will be used as the starting point for the
process.`,name:"image"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.8) &#x2014;
Conceptually, indicates how much to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code>
will be used as a starting point, adding more noise to it the larger the <code>strength</code>. The number of
denoising steps depends on the amount of noise initially added. When <code>strength</code> is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
<code>num_inference_steps</code>. A value of 1, therefore, essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by <code>strength</code>.`,name:"num_inference_steps"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#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.StableDiffusionDepth2ImgPipeline.__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. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code>
is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__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.StableDiffusionDepth2ImgPipeline.__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/v0.16.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.generator",description:`<strong>generator</strong> (<code>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.StableDiffusionDepth2ImgPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <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.StableDiffusionDepth2ImgPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <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.StableDiffusionDepth2ImgPipeline.__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
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.16.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that will be called every <code>callback_steps</code> steps during inference. The function will be
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<p><a
  href="/docs/diffusers/v0.16.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
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`,returnType:`
<p><a
  href="/docs/diffusers/v0.16.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
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