import{S as pt,i as dt,s as ft,e as s,k as h,w as C,t as p,M as mt,c as i,d as t,m as g,a as o,x as P,h as d,b as c,G as n,g as u,y as A,q as x,o as k,B as $,v as ut,L as ht}from"../../../chunks/vendor-hf-doc-builder.js";import{T as gt}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Ye}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as _t}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Re}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as yt}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Mt(X){let l,w,f,m,_,a,y,v;return{c(){l=s("p"),w=p("Make sure to check out the Schedulers "),f=s("a"),m=p("guide"),_=p(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),a=s("a"),y=p("reuse components across pipelines"),v=p(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(U){l=i(U,"P",{});var M=o(l);w=d(M,"Make sure to check out the Schedulers "),f=i(M,"A",{href:!0});var S=o(f);m=d(S,"guide"),S.forEach(t),_=d(M," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),a=i(M,"A",{href:!0});var H=o(a);y=d(H,"reuse components across pipelines"),H.forEach(t),v=d(M," section to learn how to efficiently load the same components into multiple pipelines."),M.forEach(t),this.h()},h(){c(f,"href","/using-diffusers/schedulers"),c(a,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(U,M){u(U,l,M),n(l,w),n(l,f),n(f,m),n(l,_),n(l,a),n(a,y),n(l,v)},d(U){U&&t(l)}}}function bt(X){let l,w,f,m,_;return m=new _t({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> SemanticStableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = SemanticStableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </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>out = pipe(
<span class="hljs-meta">... </span>    prompt=<span class="hljs-string">&quot;a photo of the face of a woman&quot;</span>,
<span class="hljs-meta">... </span>    num_images_per_prompt=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span>    guidance_scale=<span class="hljs-number">7</span>,
<span class="hljs-meta">... </span>    editing_prompt=[
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;smiling, smile&quot;</span>,  <span class="hljs-comment"># Concepts to apply</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;glasses, wearing glasses&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;curls, wavy hair, curly hair&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;beard, full beard, mustache&quot;</span>,
<span class="hljs-meta">... </span>    ],
<span class="hljs-meta">... </span>    reverse_editing_direction=[
<span class="hljs-meta">... </span>        <span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>        <span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>        <span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>        <span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    ],  <span class="hljs-comment"># Direction of guidance i.e. increase all concepts</span>
<span class="hljs-meta">... </span>    edit_warmup_steps=[<span class="hljs-number">10</span>, <span class="hljs-number">10</span>, <span class="hljs-number">10</span>, <span class="hljs-number">10</span>],  <span class="hljs-comment"># Warmup period for each concept</span>
<span class="hljs-meta">... </span>    edit_guidance_scale=[<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">5</span>, <span class="hljs-number">5.4</span>],  <span class="hljs-comment"># Guidance scale for each concept</span>
<span class="hljs-meta">... </span>    edit_threshold=[
<span class="hljs-meta">... </span>        <span class="hljs-number">0.99</span>,
<span class="hljs-meta">... </span>        <span class="hljs-number">0.975</span>,
<span class="hljs-meta">... </span>        <span class="hljs-number">0.925</span>,
<span class="hljs-meta">... </span>        <span class="hljs-number">0.96</span>,
<span class="hljs-meta">... </span>    ],  <span class="hljs-comment"># Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions</span>
<span class="hljs-meta">... </span>    edit_momentum_scale=<span class="hljs-number">0.3</span>,  <span class="hljs-comment"># Momentum scale that will be added to the latent guidance</span>
<span class="hljs-meta">... </span>    edit_mom_beta=<span class="hljs-number">0.6</span>,  <span class="hljs-comment"># Momentum beta</span>
<span class="hljs-meta">... </span>    edit_weights=[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],  <span class="hljs-comment"># Weights of the individual concepts against each other</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = out.images[<span class="hljs-number">0</span>]`}}),{c(){l=s("p"),w=p("Examples:"),f=h(),C(m.$$.fragment)},l(a){l=i(a,"P",{});var y=o(l);w=d(y,"Examples:"),y.forEach(t),f=g(a),P(m.$$.fragment,a)},m(a,y){u(a,l,y),n(l,w),u(a,f,y),A(m,a,y),_=!0},p:ht,i(a){_||(x(m.$$.fragment,a),_=!0)},o(a){k(m.$$.fragment,a),_=!1},d(a){a&&t(l),a&&t(f),$(m,a)}}}function wt(X){let l,w,f,m,_,a,y,v,U,M,S,H,L,je,ve,de,O,Ue,fe,F,ne,Te,me,N,ue,T,E,se,W,Je,ie,Ie,he,b,Q,De,ae,Ce,Pe,J,Ae,q,xe,ke,K,$e,Ne,Ee,j,V,Ge,oe,Be,Ze,G,ge,I,B,le,Y,Le,re,We,_e,D,R,Qe,ce,Ve,ye;return a=new Re({}),N=new gt({props:{$$slots:{default:[Mt]},$$scope:{ctx:X}}}),W=new Re({}),Q=new Ye({props:{name:"class diffusers.SemanticStableDiffusionPipeline",anchor:"diffusers.SemanticStableDiffusionPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.SemanticStableDiffusionPipeline.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.SemanticStableDiffusionPipeline.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.SemanticStableDiffusionPipeline.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.SemanticStableDiffusionPipeline.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.SemanticStableDiffusionPipeline.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"},{anchor:"diffusers.SemanticStableDiffusionPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>Q16SafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow">model card</a> for more details
about a model&#x2019;s potential harms.`,name:"safety_checker"},{anchor:"diffusers.SemanticStableDiffusionPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor" rel="nofollow">CLIPImageProcessor</a>) &#x2014;
A <code>CLIPImageProcessor</code> to extract features from generated images; used as inputs to the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py#L21"}}),V=new Ye({props:{name:"__call__",anchor:"diffusers.SemanticStableDiffusionPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 7.5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",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:"editing_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"editing_prompt_embeddings",val:": typing.Optional[torch.Tensor] = None"},{name:"reverse_editing_direction",val:": typing.Union[bool, typing.List[bool], NoneType] = False"},{name:"edit_guidance_scale",val:": typing.Union[float, typing.List[float], NoneType] = 5"},{name:"edit_warmup_steps",val:": typing.Union[int, typing.List[int], NoneType] = 10"},{name:"edit_cooldown_steps",val:": typing.Union[int, typing.List[int], NoneType] = None"},{name:"edit_threshold",val:": typing.Union[float, typing.List[float], NoneType] = 0.9"},{name:"edit_momentum_scale",val:": typing.Optional[float] = 0.1"},{name:"edit_mom_beta",val:": typing.Optional[float] = 0.4"},{name:"edit_weights",val:": typing.Optional[typing.List[float]] = None"},{name:"sem_guidance",val:": typing.Optional[typing.List[torch.Tensor]] = None"}],parametersDescription:[{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide image generation.`,name:"prompt"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__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.`,name:"num_inference_steps"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#x2014;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"guidance_scale"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass <code>negative_prompt_embeds</code> instead. Ignored when not using guidance (<code>guidance_scale &lt; 1</code>).`,name:"negative_prompt"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__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.SemanticStableDiffusionPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__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 generated image. Choose between <code>PIL.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.editing_prompt",description:`<strong>editing_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting
<code>editing_prompt = None</code>. Guidance direction of prompt should be specified via
<code>reverse_editing_direction</code>.`,name:"editing_prompt"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.editing_prompt_embeddings",description:`<strong>editing_prompt_embeddings</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
specified via <code>reverse_editing_direction</code>.`,name:"editing_prompt_embeddings"},{anchor:"diffusers.SemanticStableDiffusionPipeline.__call__.reverse_editing_direction",description:`<strong>reverse_editing_direction</strong> (<code>bool</code> or <code>List[bool]</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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