import{S as ha,i as ga,s as _a,e as n,k as c,w as D,t as i,M as ba,c as a,d as t,m as f,a as l,x,h as s,b as m,G as e,g as w,y as $,q as I,o as M,B as S,v as ya,L as Bt}from"../../../../chunks/vendor-hf-doc-builder.js";import{T as ua}from"../../../../chunks/Tip-hf-doc-builder.js";import{D as Y}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as Xt}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as jn}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Rt}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function va(k){let d,y,h,p,b,r,u,q,G,oe,U,C,A,ge,F,Q,_e;return{c(){d=n("p"),y=i("Make sure to check out the Stable Diffusion "),h=n("a"),p=i("Tips"),b=i(" section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),r=c(),u=n("p"),q=i("If you\u2019re interested in using one of the official checkpoints for a task, explore the "),G=n("a"),oe=i("CompVis"),U=i(", "),C=n("a"),A=i("Runway"),ge=i(", and "),F=n("a"),Q=i("Stability AI"),_e=i(" Hub organizations!"),this.h()},l(E){d=a(E,"P",{});var J=l(d);y=s(J,"Make sure to check out the Stable Diffusion "),h=a(J,"A",{href:!0});var R=l(h);p=s(R,"Tips"),R.forEach(t),b=s(J," section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),J.forEach(t),r=f(E),u=a(E,"P",{});var Z=l(u);q=s(Z,"If you\u2019re interested in using one of the official checkpoints for a task, explore the "),G=a(Z,"A",{href:!0,rel:!0});var j=l(G);oe=s(j,"CompVis"),j.forEach(t),U=s(Z,", "),C=a(Z,"A",{href:!0,rel:!0});var B=l(C);A=s(B,"Runway"),B.forEach(t),ge=s(Z,", and "),F=a(Z,"A",{href:!0,rel:!0});var ne=l(F);Q=s(ne,"Stability AI"),ne.forEach(t),_e=s(Z," Hub organizations!"),Z.forEach(t),this.h()},h(){m(h,"href","overview#tips"),m(G,"href","https://huggingface.co/CompVis"),m(G,"rel","nofollow"),m(C,"href","https://huggingface.co/runwayml"),m(C,"rel","nofollow"),m(F,"href","https://huggingface.co/stabilityai"),m(F,"rel","nofollow")},m(E,J){w(E,d,J),e(d,y),e(d,h),e(h,p),e(d,b),w(E,r,J),w(E,u,J),e(u,q),e(u,G),e(G,oe),e(u,U),e(u,C),e(C,A),e(u,ge),e(u,F),e(F,Q),e(u,_e)},d(E){E&&t(d),E&&t(r),E&&t(u)}}}function wa(k){let d,y,h,p,b;return p=new Xt({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjByZXF1ZXN0cyUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQSUwQWZyb20lMjBkaWZmdXNlcnMlMjBpbXBvcnQlMjBTdGFibGVEaWZmdXNpb25EZXB0aDJJbWdQaXBlbGluZSUwQSUwQXBpcGUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25EZXB0aDJJbWdQaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLTItZGVwdGglMjIlMkMlMEElMjAlMjAlMjAlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMkMlMEEpJTBBcGlwZS50byglMjJjdWRhJTIyKSUwQSUwQSUwQXVybCUyMCUzRCUyMCUyMmh0dHAlM0ElMkYlMkZpbWFnZXMuY29jb2RhdGFzZXQub3JnJTJGdmFsMjAxNyUyRjAwMDAwMDAzOTc2OS5qcGclMjIlMEFpbml0X2ltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQXByb21wdCUyMCUzRCUyMCUyMnR3byUyMHRpZ2VycyUyMiUwQW5fcHJvcG10JTIwJTNEJTIwJTIyYmFkJTJDJTIwZGVmb3JtZWQlMkMlMjB1Z2x5JTJDJTIwYmFkJTIwYW5vdG9teSUyMiUwQWltYWdlJTIwJTNEJTIwcGlwZShwcm9tcHQlM0Rwcm9tcHQlMkMlMjBpbWFnZSUzRGluaXRfaW1hZ2UlMkMlMjBuZWdhdGl2ZV9wcm9tcHQlM0RuX3Byb3BtdCUyQyUyMHN0cmVuZ3RoJTNEMC43KS5pbWFnZXMlNUIwJTVE",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(){d=n("p"),y=i("Examples:"),h=c(),D(p.$$.fragment)},l(r){d=a(r,"P",{});var u=l(d);y=s(u,"Examples:"),u.forEach(t),h=f(r),x(p.$$.fragment,r)},m(r,u){w(r,d,u),e(d,y),w(r,h,u),$(p,r,u),b=!0},p:Bt,i(r){b||(I(p.$$.fragment,r),b=!0)},o(r){M(p.$$.fragment,r),b=!1},d(r){r&&t(d),r&&t(h),S(p,r)}}}function Da(k){let d,y;return{c(){d=n("p"),y=i(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(h){d=a(h,"P",{});var p=l(d);y=s(p,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),p.forEach(t)},m(h,p){w(h,d,p),e(d,y)},d(h){h&&t(d)}}}function xa(k){let d,y,h,p,b;return p=new Xt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){d=n("p"),y=i("Examples:"),h=c(),D(p.$$.fragment)},l(r){d=a(r,"P",{});var u=l(d);y=s(u,"Examples:"),u.forEach(t),h=f(r),x(p.$$.fragment,r)},m(r,u){w(r,d,u),e(d,y),w(r,h,u),$(p,r,u),b=!0},p:Bt,i(r){b||(I(p.$$.fragment,r),b=!0)},o(r){M(p.$$.fragment,r),b=!1},d(r){r&&t(d),r&&t(h),S(p,r)}}}function $a(k){let d,y,h,p,b;return p=new Xt({props:{code:"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",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(){d=n("p"),y=i("To load a textual inversion embedding vector in \u{1F917} Diffusers format:"),h=c(),D(p.$$.fragment)},l(r){d=a(r,"P",{});var u=l(d);y=s(u,"To load a textual inversion embedding vector in \u{1F917} Diffusers format:"),u.forEach(t),h=f(r),x(p.$$.fragment,r)},m(r,u){w(r,d,u),e(d,y),w(r,h,u),$(p,r,u),b=!0},p:Bt,i(r){b||(I(p.$$.fragment,r),b=!0)},o(r){M(p.$$.fragment,r),b=!1},d(r){r&&t(d),r&&t(h),S(p,r)}}}function Ia(k){let d,y,h,p,b;return p=new Xt({props:{code:"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",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(){d=n("p"),y=i("locally:"),h=c(),D(p.$$.fragment)},l(r){d=a(r,"P",{});var u=l(d);y=s(u,"locally:"),u.forEach(t),h=f(r),x(p.$$.fragment,r)},m(r,u){w(r,d,u),e(d,y),w(r,h,u),$(p,r,u),b=!0},p:Bt,i(r){b||(I(p.$$.fragment,r),b=!0)},o(r){M(p.$$.fragment,r),b=!1},d(r){r&&t(d),r&&t(h),S(p,r)}}}function Ma(k){let d,y,h,p,b,r,u,q,G,oe,U,C,A,ge,F,Q,_e,E,J,R,Z,j,B,ne,be,Vt,nt,Yt,$t,g,ye,qt,at,Qt,zt,ve,Ht,Fe,Ot,Kt,eo,it,to,oo,z,Re,Be,no,ao,io,Xe,Ve,so,ro,lo,Ye,qe,po,co,fo,V,we,mo,st,uo,ho,ae,go,ie,De,_o,rt,bo,yo,se,xe,vo,$e,wo,lt,Do,xo,$o,W,Ie,Io,Me,Mo,Se,So,To,Po,re,Eo,le,ko,de,Te,Uo,Pe,Jo,Ee,Zo,jo,Wo,T,ke,Lo,Ue,No,Qe,Go,Co,Ao,dt,Fo,Ro,pe,Bo,Je,Xo,Ze,Vo,Yo,qo,ce,Qo,P,je,zo,X,Ho,pt,Oo,Ko,ct,en,tn,ft,on,nn,an,We,sn,mt,rn,ln,dn,Le,pn,ze,cn,fn,mn,H,un,He,hn,gn,ut,_n,bn,yn,O,vn,Oe,wn,Dn,ht,xn,$n,In,fe,Ne,Mn,gt,Sn,It,K,me,_t,Ge,Tn,bt,Pn,Mt,ee,Ce,En,yt,kn,St;return r=new jn({}),R=new ua({props:{$$slots:{default:[va]},$$scope:{ctx:k}}}),be=new jn({}),ye=new Y({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/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIPTextModel</a>) &#x2014;
Frozen text-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>) &#x2014;
A <code>CLIPTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/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/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/main/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py#L65"}}),we=new Y({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, numpy.ndarray, typing.List[torch.FloatTensor], typing.List[PIL.Image.Image], typing.List[numpy.ndarray]] = 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[typing.List[str], 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"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"}],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 image generation. If not defined, you need to pass <code>prompt_embeds</code>.`,name:"prompt"},{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.FloatTensor</code>, <code>PIL.Image.Image</code>, <code>np.ndarray</code>, <code>List[torch.FloatTensor]</code>, <code>List[PIL.Image.Image]</code>, or <code>List[np.ndarray]</code>) &#x2014;
<code>Image</code> or tensor representing an image batch to be used as the starting point. Can accept image
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Depth prediction to be used as additional conditioning for the image generation process. If not
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starting point and more noise is added the higher the <code>strength</code>. The number of denoising steps depends
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`,returnType:`
<p><a
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`}}),ae=new Rt({props:{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.__call__.example",$$slots:{default:[wa]},$$scope:{ctx:k}}}),De=new Y({props:{name:"enable_attention_slicing",anchor:"diffusers.StableDiffusionDepth2ImgPipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[str, int, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.StableDiffusionDepth2ImgPipeline.enable_attention_slicing.slice_size",description:`<strong>slice_size</strong> (<code>str</code> or <code>int</code>, <em>optional</em>, defaults to <code>&quot;auto&quot;</code>) &#x2014;
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<li>A path to a <em>directory</em> (for example <code>./my_text_inversion_directory/</code>) containing the textual
inversion weights.</li>
<li>A path to a <em>file</em> (for example <code>./my_text_inversions.pt</code>) containing textual inversion weights.</li>
<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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