import{S as Na,i as Xa,s as Fa,e as a,k as l,w as v,t as d,M as La,c as s,d as n,m as r,a as i,x as y,h as c,b as o,G as e,g as f,y as x,q as w,o as E,B as M,v as Va,L as za}from"../../../chunks/vendor-hf-doc-builder.js";import{D as ke}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Ra}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as it}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ha}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Qa(ot){let m,Z,b,g,U;return g=new Ra({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwU3RhYmxlRGlmZnVzaW9uQXR0ZW5kQW5kRXhjaXRlUGlwZWxpbmUlMEElMEFwaXBlJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uQXR0ZW5kQW5kRXhjaXRlUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMkNvbXBWaXMlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTQlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMEEpLnRvKCUyMmN1ZGElMjIpJTBBJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyYSUyMGNhdCUyMGFuZCUyMGElMjBmcm9nJTIyJTBBJTBBJTIzJTIwdXNlJTIwZ2V0X2luZGljZXMlMjBmdW5jdGlvbiUyMHRvJTIwZmluZCUyMG91dCUyMGluZGljZXMlMjBvZiUyMHRoZSUyMHRva2VucyUyMHlvdSUyMHdhbnQlMjB0byUyMGFsdGVyJTBBcGlwZS5nZXRfaW5kaWNlcyhwcm9tcHQpJTBBJTBBdG9rZW5faW5kaWNlcyUyMCUzRCUyMCU1QjIlMkMlMjA1JTVEJTBBc2VlZCUyMCUzRCUyMDYxNDElMEFnZW5lcmF0b3IlMjAlM0QlMjB0b3JjaC5HZW5lcmF0b3IoJTIyY3VkYSUyMikubWFudWFsX3NlZWQoc2VlZCklMEElMEFpbWFnZXMlMjAlM0QlMjBwaXBlKCUwQSUyMCUyMCUyMCUyMHByb21wdCUzRHByb21wdCUyQyUwQSUyMCUyMCUyMCUyMHRva2VuX2luZGljZXMlM0R0b2tlbl9pbmRpY2VzJTJDJTBBJTIwJTIwJTIwJTIwZ3VpZGFuY2Vfc2NhbGUlM0Q3LjUlMkMlMEElMjAlMjAlMjAlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IlMkMlMEElMjAlMjAlMjAlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNENTAlMkMlMEElMjAlMjAlMjAlMjBtYXhfaXRlcl90b19hbHRlciUzRDI1JTJDJTBBKS5pbWFnZXMlMEElMEFpbWFnZSUyMCUzRCUyMGltYWdlcyU1QjAlNUQlMEFpbWFnZS5zYXZlKGYlMjIuLiUyRmltYWdlcyUyRiU3QnByb21wdCU3RF8lN0JzZWVkJTdELnBuZyUyMik=",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> StableDiffusionAttendAndExcitePipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a cat and a frog&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># use get_indices function to find out indices of the tokens you want to alter</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.get_indices(prompt)
{<span class="hljs-number">0</span>: <span class="hljs-string">&#x27;&lt;|startoftext|&gt;&#x27;</span>, <span class="hljs-number">1</span>: <span class="hljs-string">&#x27;a&lt;/w&gt;&#x27;</span>, <span class="hljs-number">2</span>: <span class="hljs-string">&#x27;cat&lt;/w&gt;&#x27;</span>, <span class="hljs-number">3</span>: <span class="hljs-string">&#x27;and&lt;/w&gt;&#x27;</span>, <span class="hljs-number">4</span>: <span class="hljs-string">&#x27;a&lt;/w&gt;&#x27;</span>, <span class="hljs-number">5</span>: <span class="hljs-string">&#x27;frog&lt;/w&gt;&#x27;</span>, <span class="hljs-number">6</span>: <span class="hljs-string">&#x27;&lt;|endoftext|&gt;&#x27;</span>}

<span class="hljs-meta">&gt;&gt;&gt; </span>token_indices = [<span class="hljs-number">2</span>, <span class="hljs-number">5</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>seed = <span class="hljs-number">6141</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(seed)

<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(
<span class="hljs-meta">... </span>    prompt=prompt,
<span class="hljs-meta">... </span>    token_indices=token_indices,
<span class="hljs-meta">... </span>    guidance_scale=<span class="hljs-number">7.5</span>,
<span class="hljs-meta">... </span>    generator=generator,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>    max_iter_to_alter=<span class="hljs-number">25</span>,
<span class="hljs-meta">... </span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span>image = images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">f&quot;../images/<span class="hljs-subst">{prompt}</span>_<span class="hljs-subst">{seed}</span>.png&quot;</span>)`}}),{c(){m=a("p"),Z=d("Examples:"),b=l(),v(g.$$.fragment)},l(u){m=s(u,"P",{});var k=i(m);Z=c(k,"Examples:"),k.forEach(n),b=r(u),y(g.$$.fragment,u)},m(u,k){f(u,m,k),e(m,Z),f(u,b,k),x(g,u,k),U=!0},p:za,i(u){U||(w(g.$$.fragment,u),U=!0)},o(u){E(g.$$.fragment,u),U=!1},d(u){u&&n(m),u&&n(b),M(g,u)}}}function qa(ot){let m,Z,b,g,U,u,k,De,Pt,lt,S,B,Pe,Y,St,Se,$t,rt,G,It,O,Ct,Jt,dt,be,Zt,ct,ve,$e,Bt,pt,ye,Gt,ft,A,Ie,K,Wt,Rt,Ce,ee,Nt,Xt,Je,te,Ft,Lt,Ze,ne,Vt,ut,$,W,Be,ae,zt,Ge,Ht,ht,R,We,T,Re,Qt,qt,Ne,Yt,Ot,xe,Kt,en,we,tn,nn,Xe,j,Fe,se,an,sn,Le,Ve,on,ln,Ee,rn,dn,Me,ie,cn,mt,I,N,ze,oe,pn,He,fn,gt,le,_t,C,X,Qe,re,un,qe,hn,bt,h,de,mn,Ye,gn,_n,ce,bn,Ae,vn,yn,xn,D,pe,wn,Oe,En,Mn,F,An,L,fe,Tn,ue,jn,Ke,Un,kn,Dn,V,he,Pn,J,Sn,et,$n,In,tt,Cn,Jn,Zn,P,me,Bn,nt,Gn,Wn,at,Rn,Nn,z,ge,Xn,st,Fn,vt;return u=new it({}),Y=new it({}),ae=new it({}),oe=new it({}),le=new Ra({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> StableDiffusionAttendAndExcitePipeline

model_id = <span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;a cat and a frog&quot;</span>

<span class="hljs-comment"># use get_indices function to find out indices of the tokens you want to alter</span>
pipe.get_indices(prompt)

token_indices = [<span class="hljs-number">2</span>, <span class="hljs-number">5</span>]
seed = <span class="hljs-number">6141</span>
generator = torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(seed)

images = pipe(
    prompt=prompt,
    token_indices=token_indices,
    guidance_scale=<span class="hljs-number">7.5</span>,
    generator=generator,
    num_inference_steps=<span class="hljs-number">50</span>,
    max_iter_to_alter=<span class="hljs-number">25</span>,
).images

image = images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">f&quot;../images/<span class="hljs-subst">{prompt}</span>_<span class="hljs-subst">{seed}</span>.png&quot;</span>)`}}),re=new it({}),de=new ke({props:{name:"class diffusers.StableDiffusionAttendAndExcitePipeline",anchor:"diffusers.StableDiffusionAttendAndExcitePipeline",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.StableDiffusionAttendAndExcitePipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.17.0/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.StableDiffusionAttendAndExcitePipeline.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.StableDiffusionAttendAndExcitePipeline.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.StableDiffusionAttendAndExcitePipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.17.0/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.17.0/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/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.17.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.17.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionSafetyChecker</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 details.`,name:"safety_checker"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L165"}}),pe=new ke({props:{name:"__call__",anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"token_indices",val:": typing.Union[typing.List[int], typing.List[typing.List[int]]]"},{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:"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:"max_iter_to_alter",val:": int = 25"},{name:"thresholds",val:": dict = {0: 0.05, 10: 0.5, 20: 0.8}"},{name:"scale_factor",val:": int = 20"},{name:"attn_res",val:": typing.Optional[typing.Tuple[int]] = (16, 16)"}],parametersDescription:[{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__call__.token_indices",description:`<strong>token_indices</strong> (<code>List[int]</code>) &#x2014;
The token indices to alter with attend-and-excite.`,name:"token_indices"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[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.StableDiffusionAttendAndExcitePipeline.__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 will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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.StableDiffusionAttendAndExcitePipeline.__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/v0.17.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__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
called with the following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__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 will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"},{anchor:"diffusers.StableDiffusionAttendAndExcitePipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
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