import{s as Ut,o as kt,n as L}from"../chunks/scheduler.182ea377.js";import{S as Wt,i as Zt,g as d,s as r,r as h,A as Pt,h as m,f as a,c as l,j as x,u as _,x as I,k as $,y as i,a as g,v as b,d as y,t as v,w}from"../chunks/index.abf12888.js";import{T as ft}from"../chunks/Tip.230e2334.js";import{D as Z}from"../chunks/Docstring.93f6f462.js";import{C as We}from"../chunks/CodeBlock.57fe6e13.js";import{E as ke}from"../chunks/ExampleCodeBlock.658f5cd6.js";import{H as jt}from"../chunks/Heading.16916d63.js";function At(T){let n,u="Examples:",s,o,c;return o=new We({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> AmusedPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AmusedPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;amused/amused-512&quot;</span>, variant=<span class="hljs-string">&quot;fp16&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>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>image = pipe(prompt).images[<span class="hljs-number">0</span>]`,wrap:!1}}),{c(){n=d("p"),n.textContent=u,s=r(),h(o.$$.fragment)},l(t){n=m(t,"P",{"data-svelte-h":!0}),I(n)!=="svelte-kvfsh7"&&(n.textContent=u),s=l(t),_(o.$$.fragment,t)},m(t,f){g(t,n,f),g(t,s,f),b(o,t,f),c=!0},p:L,i(t){c||(y(o.$$.fragment,t),c=!0)},o(t){v(o.$$.fragment,t),c=!1},d(t){t&&(a(n),a(s)),w(o,t)}}}function Gt(T){let n,u=`⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`;return{c(){n=d("p"),n.textContent=u},l(s){n=m(s,"P",{"data-svelte-h":!0}),I(n)!=="svelte-17p1lpg"&&(n.textContent=u)},m(s,o){g(s,n,o)},p:L,d(s){s&&a(n)}}}function Ct(T){let n,u="Examples:",s,o,c;return o=new We({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>)`,wrap:!1}}),{c(){n=d("p"),n.textContent=u,s=r(),h(o.$$.fragment)},l(t){n=m(t,"P",{"data-svelte-h":!0}),I(n)!=="svelte-kvfsh7"&&(n.textContent=u),s=l(t),_(o.$$.fragment,t)},m(t,f){g(t,n,f),g(t,s,f),b(o,t,f),c=!0},p:L,i(t){c||(y(o.$$.fragment,t),c=!0)},o(t){v(o.$$.fragment,t),c=!1},d(t){t&&(a(n),a(s)),w(o,t)}}}function Ft(T){let n,u="Examples:",s,o,c;return o=new We({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> AmusedImg2ImgPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AmusedImg2ImgPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;amused/amused-512&quot;</span>, variant=<span class="hljs-string">&quot;fp16&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>prompt = <span class="hljs-string">&quot;winter mountains&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_image = (
<span class="hljs-meta">... </span>    load_image(
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg&quot;</span>
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>    .resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
<span class="hljs-meta">... </span>    .convert(<span class="hljs-string">&quot;RGB&quot;</span>)
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt, input_image).images[<span class="hljs-number">0</span>]`,wrap:!1}}),{c(){n=d("p"),n.textContent=u,s=r(),h(o.$$.fragment)},l(t){n=m(t,"P",{"data-svelte-h":!0}),I(n)!=="svelte-kvfsh7"&&(n.textContent=u),s=l(t),_(o.$$.fragment,t)},m(t,f){g(t,n,f),g(t,s,f),b(o,t,f),c=!0},p:L,i(t){c||(y(o.$$.fragment,t),c=!0)},o(t){v(o.$$.fragment,t),c=!1},d(t){t&&(a(n),a(s)),w(o,t)}}}function Bt(T){let n,u=`⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`;return{c(){n=d("p"),n.textContent=u},l(s){n=m(s,"P",{"data-svelte-h":!0}),I(n)!=="svelte-17p1lpg"&&(n.textContent=u)},m(s,o){g(s,n,o)},p:L,d(s){s&&a(n)}}}function Nt(T){let n,u="Examples:",s,o,c;return o=new We({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>)`,wrap:!1}}),{c(){n=d("p"),n.textContent=u,s=r(),h(o.$$.fragment)},l(t){n=m(t,"P",{"data-svelte-h":!0}),I(n)!=="svelte-kvfsh7"&&(n.textContent=u),s=l(t),_(o.$$.fragment,t)},m(t,f){g(t,n,f),g(t,s,f),b(o,t,f),c=!0},p:L,i(t){c||(y(o.$$.fragment,t),c=!0)},o(t){v(o.$$.fragment,t),c=!1},d(t){t&&(a(n),a(s)),w(o,t)}}}function Et(T){let n,u="Examples:",s,o,c;return o=new We({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQW11c2VkSW5wYWludFBpcGVsaW5lJTBBZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGxvYWRfaW1hZ2UlMEElMEFwaXBlJTIwJTNEJTIwQW11c2VkSW5wYWludFBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJhbXVzZWQlMkZhbXVzZWQtNTEyJTIyJTJDJTIwdmFyaWFudCUzRCUyMmZwMTYlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMEEpJTBBcGlwZSUyMCUzRCUyMHBpcGUudG8oJTIyY3VkYSUyMiklMEElMEFwcm9tcHQlMjAlM0QlMjAlMjJmYWxsJTIwbW91bnRhaW5zJTIyJTBBaW5wdXRfaW1hZ2UlMjAlM0QlMjAoJTBBJTIwJTIwJTIwJTIwbG9hZF9pbWFnZSglMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZkaWZmdXNlcnMlMkZkb2NzLWltYWdlcyUyRnJlc29sdmUlMkZtYWluJTJGb3Blbl9tdXNlJTJGbW91bnRhaW5zXzEuanBnJTIyJTBBJTIwJTIwJTIwJTIwKSUwQSUyMCUyMCUyMCUyMC5yZXNpemUoKDUxMiUyQyUyMDUxMikpJTBBJTIwJTIwJTIwJTIwLmNvbnZlcnQoJTIyUkdCJTIyKSUwQSklMEFtYXNrJTIwJTNEJTIwKCUwQSUyMCUyMCUyMCUyMGxvYWRfaW1hZ2UoJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGZGlmZnVzZXJzJTJGZG9jcy1pbWFnZXMlMkZyZXNvbHZlJTJGbWFpbiUyRm9wZW5fbXVzZSUyRm1vdW50YWluc18xX21hc2sucG5nJTIyJTBBJTIwJTIwJTIwJTIwKSUwQSUyMCUyMCUyMCUyMC5yZXNpemUoKDUxMiUyQyUyMDUxMikpJTBBJTIwJTIwJTIwJTIwLmNvbnZlcnQoJTIyTCUyMiklMEEpJTBBcGlwZShwcm9tcHQlMkMlMjBpbnB1dF9pbWFnZSUyQyUyMG1hc2spLmltYWdlcyU1QjAlNUQuc2F2ZSglMjJvdXQucG5nJTIyKQ==",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> AmusedInpaintPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AmusedInpaintPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;amused/amused-512&quot;</span>, variant=<span class="hljs-string">&quot;fp16&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>prompt = <span class="hljs-string">&quot;fall mountains&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_image = (
<span class="hljs-meta">... </span>    load_image(
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg&quot;</span>
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>    .resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
<span class="hljs-meta">... </span>    .convert(<span class="hljs-string">&quot;RGB&quot;</span>)
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>mask = (
<span class="hljs-meta">... </span>    load_image(
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png&quot;</span>
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>    .resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
<span class="hljs-meta">... </span>    .convert(<span class="hljs-string">&quot;L&quot;</span>)
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe(prompt, input_image, mask).images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;out.png&quot;</span>)`,wrap:!1}}),{c(){n=d("p"),n.textContent=u,s=r(),h(o.$$.fragment)},l(t){n=m(t,"P",{"data-svelte-h":!0}),I(n)!=="svelte-kvfsh7"&&(n.textContent=u),s=l(t),_(o.$$.fragment,t)},m(t,f){g(t,n,f),g(t,s,f),b(o,t,f),c=!0},p:L,i(t){c||(y(o.$$.fragment,t),c=!0)},o(t){v(o.$$.fragment,t),c=!1},d(t){t&&(a(n),a(s)),w(o,t)}}}function Xt(T){let n,u=`⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`;return{c(){n=d("p"),n.textContent=u},l(s){n=m(s,"P",{"data-svelte-h":!0}),I(n)!=="svelte-17p1lpg"&&(n.textContent=u)},m(s,o){g(s,n,o)},p:L,d(s){s&&a(n)}}}function Vt(T){let n,u="Examples:",s,o,c;return o=new We({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>)`,wrap:!1}}),{c(){n=d("p"),n.textContent=u,s=r(),h(o.$$.fragment)},l(t){n=m(t,"P",{"data-svelte-h":!0}),I(n)!=="svelte-kvfsh7"&&(n.textContent=u),s=l(t),_(o.$$.fragment,t)},m(t,f){g(t,n,f),g(t,s,f),b(o,t,f),c=!0},p:L,i(t){c||(y(o.$$.fragment,t),c=!0)},o(t){v(o.$$.fragment,t),c=!1},d(t){t&&(a(n),a(s)),w(o,t)}}}function Lt(T){let n,u,s,o,c,t,f,ht='Amused is a lightweight text to image model based off of the <a href="https://arxiv.org/pdf/2301.00704.pdf" rel="nofollow">muse</a> architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.',Pe,oe,_t="Amused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes.",Ae,se,bt='<thead><tr><th>Model</th> <th>Params</th></tr></thead> <tbody><tr><td><a href="https://huggingface.co/amused/amused-256" rel="nofollow">amused-256</a></td> <td>603M</td></tr> <tr><td><a href="https://huggingface.co/amused/amused-512" rel="nofollow">amused-512</a></td> <td>608M</td></tr></tbody>',Ge,ae,Ce,M,ie,Ve,B,re,Le,ve,yt="The call function to the pipeline for generation.",Re,R,Qe,U,le,Se,we,vt=`Enable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>. When this
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
up during training is not guaranteed.`,qe,Q,ze,S,Ye,q,pe,He,Ie,wt='Disable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>.',Fe,J,ce,De,N,de,Oe,Te,It="The call function to the pipeline for generation.",Ke,z,et,k,me,tt,xe,Tt=`Enable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>. When this
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
up during training is not guaranteed.`,nt,Y,ot,H,st,D,ge,at,$e,xt='Disable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>.',Be,j,ue,it,E,fe,rt,Me,$t="The call function to the pipeline for generation.",lt,O,pt,W,he,ct,Je,Mt=`Enable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>. When this
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
up during training is not guaranteed.`,dt,K,mt,ee,gt,te,_e,ut,je,Jt='Disable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>.',Ne,Ze,Ee;return c=new jt({props:{title:"aMUSEd",local:"amused",headingTag:"h1"}}),ae=new jt({props:{title:"AmusedPipeline",local:"diffusers.AmusedPipeline",headingTag:"h2"}}),ie=new Z({props:{name:"class diffusers.AmusedPipeline",anchor:"diffusers.AmusedPipeline",parameters:[{name:"vqvae",val:": VQModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"transformer",val:": UVit2DModel"},{name:"scheduler",val:": AmusedScheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/amused/pipeline_amused.py#L44"}}),re=new Z({props:{name:"__call__",anchor:"diffusers.AmusedPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 12"},{name:"guidance_scale",val:": float = 10.0"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"latents",val:": typing.Optional[torch.IntTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:" = '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:"micro_conditioning_aesthetic_score",val:": int = 6"},{name:"micro_conditioning_crop_coord",val:": typing.Tuple[int, int] = (0, 0)"},{name:"temperature",val:": typing.Union[int, typing.Tuple[int, int], typing.List[int]] = (2, 0)"}],parametersDescription:[{anchor:"diffusers.AmusedPipeline.__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.AmusedPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.transformer.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.AmusedPipeline.__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.AmusedPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#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.AmusedPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 10.0) &#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.AmusedPipeline.__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.AmusedPipeline.__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.AmusedPipeline.__call__.generator",description:`<strong>generator</strong> (<code>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.AmusedPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.IntTensor</code>, <em>optional</em>) &#x2014;
Pre-generated tokens representing latent vectors in <code>self.vqvae</code>, to be used as inputs for image
gneration. If not provided, the starting latents will be completely masked.`,name:"latents"},{anchor:"diffusers.AmusedPipeline.__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 (prompt weighting). If not
provided, text embeddings are generated from the <code>prompt</code> input argument. A single vector from the
pooled and projected final hidden states.`,name:"prompt_embeds"},{anchor:"diffusers.AmusedPipeline.__call__.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.`,name:"encoder_hidden_states"},{anchor:"diffusers.AmusedPipeline.__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 (prompt weighting). If
not provided, <code>negative_prompt_embeds</code> are generated from the <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AmusedPipeline.__call__.negative_encoder_hidden_states",description:`<strong>negative_encoder_hidden_states</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Analogous to <code>encoder_hidden_states</code> for the positive prompt.`,name:"negative_encoder_hidden_states"},{anchor:"diffusers.AmusedPipeline.__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.AmusedPipeline.__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.AmusedPipeline.__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.AmusedPipeline.__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.AmusedPipeline.__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 in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow"><code>self.processor</code></a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.AmusedPipeline.__call__.micro_conditioning_aesthetic_score",description:`<strong>micro_conditioning_aesthetic_score</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
The targeted aesthetic score according to the laion aesthetic classifier. See <a href="https://laion.ai/blog/laion-aesthetics/" rel="nofollow">https://laion.ai/blog/laion-aesthetics/</a>
and the micro-conditioning section of <a href="https://arxiv.org/abs/2307.01952" rel="nofollow">https://arxiv.org/abs/2307.01952</a>.`,name:"micro_conditioning_aesthetic_score"},{anchor:"diffusers.AmusedPipeline.__call__.micro_conditioning_crop_coord",description:`<strong>micro_conditioning_crop_coord</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to (0, 0)) &#x2014;
The targeted height, width crop coordinates. See the micro-conditioning section of <a href="https://arxiv.org/abs/2307.01952" rel="nofollow">https://arxiv.org/abs/2307.01952</a>.`,name:"micro_conditioning_crop_coord"},{anchor:"diffusers.AmusedPipeline.__call__.temperature",description:`<strong>temperature</strong> (<code>Union[int, Tuple[int, int], List[int]]</code>, <em>optional</em>, defaults to (2, 0)) &#x2014;
Configures the temperature scheduler on <code>self.scheduler</code> see <code>AmusedScheduler#set_timesteps</code>.`,name:"temperature"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/amused/pipeline_amused.py#L74",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> is returned, otherwise a
<code>tuple</code> is returned where the first element is a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),R=new ke({props:{anchor:"diffusers.AmusedPipeline.__call__.example",$$slots:{default:[At]},$$scope:{ctx:T}}}),le=new Z({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.AmusedPipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.AmusedPipeline.enable_xformers_memory_efficient_attention.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
function of xFormers.`,name:"attention_op"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1987"}}),Q=new ft({props:{warning:!0,$$slots:{default:[Gt]},$$scope:{ctx:T}}}),S=new ke({props:{anchor:"diffusers.AmusedPipeline.enable_xformers_memory_efficient_attention.example",$$slots:{default:[Ct]},$$scope:{ctx:T}}}),pe=new Z({props:{name:"disable_xformers_memory_efficient_attention",anchor:"diffusers.AmusedPipeline.disable_xformers_memory_efficient_attention",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2022"}}),ce=new Z({props:{name:"class diffusers.AmusedImg2ImgPipeline",anchor:"diffusers.AmusedImg2ImgPipeline",parameters:[{name:"vqvae",val:": VQModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"transformer",val:": UVit2DModel"},{name:"scheduler",val:": AmusedScheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/amused/pipeline_amused_img2img.py#L52"}}),de=new Z({props:{name:"__call__",anchor:"diffusers.AmusedImg2ImgPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = None"},{name:"strength",val:": float = 0.5"},{name:"num_inference_steps",val:": int = 12"},{name:"guidance_scale",val:": float = 10.0"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:" = '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:"micro_conditioning_aesthetic_score",val:": int = 6"},{name:"micro_conditioning_crop_coord",val:": typing.Tuple[int, int] = (0, 0)"},{name:"temperature",val:": typing.Union[int, typing.Tuple[int, int], typing.List[int]] = (2, 0)"}],parametersDescription:[{anchor:"diffusers.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__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>, numpy array or tensor representing an image batch to be used as the starting point. For both
numpy array and pytorch tensor, the expected value range is between <code>[0, 1]</code> If it&#x2019;s a tensor or a list
or tensors, the expected shape should be <code>(B, C, H, W)</code> or <code>(C, H, W)</code>. If it is a numpy array or a
list of arrays, the expected shape should be <code>(B, H, W, C)</code> or <code>(H, W, C)</code> It can also accept image
latents as <code>image</code>, but if passing latents directly it is not encoded again.`,name:"image"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) &#x2014;
Indicates extent to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code> is used as a
starting point and more noise is added the higher 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 is maximum and the denoising
process runs for the full number of iterations specified in <code>num_inference_steps</code>. A value of 1
essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#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.AmusedImg2ImgPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 10.0) &#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.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__call__.generator",description:`<strong>generator</strong> (<code>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.AmusedImg2ImgPipeline.__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 (prompt weighting). If not
provided, text embeddings are generated from the <code>prompt</code> input argument. A single vector from the
pooled and projected final hidden states.`,name:"prompt_embeds"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.`,name:"encoder_hidden_states"},{anchor:"diffusers.AmusedImg2ImgPipeline.__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 (prompt weighting). If
not provided, <code>negative_prompt_embeds</code> are generated from the <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.negative_encoder_hidden_states",description:`<strong>negative_encoder_hidden_states</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Analogous to <code>encoder_hidden_states</code> for the positive prompt.`,name:"negative_encoder_hidden_states"},{anchor:"diffusers.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__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.AmusedImg2ImgPipeline.__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 in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow"><code>self.processor</code></a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.micro_conditioning_aesthetic_score",description:`<strong>micro_conditioning_aesthetic_score</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
The targeted aesthetic score according to the laion aesthetic classifier. See <a href="https://laion.ai/blog/laion-aesthetics/" rel="nofollow">https://laion.ai/blog/laion-aesthetics/</a>
and the micro-conditioning section of <a href="https://arxiv.org/abs/2307.01952" rel="nofollow">https://arxiv.org/abs/2307.01952</a>.`,name:"micro_conditioning_aesthetic_score"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.micro_conditioning_crop_coord",description:`<strong>micro_conditioning_crop_coord</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to (0, 0)) &#x2014;
The targeted height, width crop coordinates. See the micro-conditioning section of <a href="https://arxiv.org/abs/2307.01952" rel="nofollow">https://arxiv.org/abs/2307.01952</a>.`,name:"micro_conditioning_crop_coord"},{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.temperature",description:`<strong>temperature</strong> (<code>Union[int, Tuple[int, int], List[int]]</code>, <em>optional</em>, defaults to (2, 0)) &#x2014;
Configures the temperature scheduler on <code>self.scheduler</code> see <code>AmusedScheduler#set_timesteps</code>.`,name:"temperature"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/amused/pipeline_amused_img2img.py#L87",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> is returned, otherwise a
<code>tuple</code> is returned where the first element is a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),z=new ke({props:{anchor:"diffusers.AmusedImg2ImgPipeline.__call__.example",$$slots:{default:[Ft]},$$scope:{ctx:T}}}),me=new Z({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.AmusedImg2ImgPipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.AmusedImg2ImgPipeline.enable_xformers_memory_efficient_attention.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
function of xFormers.`,name:"attention_op"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1987"}}),Y=new ft({props:{warning:!0,$$slots:{default:[Bt]},$$scope:{ctx:T}}}),H=new ke({props:{anchor:"diffusers.AmusedImg2ImgPipeline.enable_xformers_memory_efficient_attention.example",$$slots:{default:[Nt]},$$scope:{ctx:T}}}),ge=new Z({props:{name:"disable_xformers_memory_efficient_attention",anchor:"diffusers.AmusedImg2ImgPipeline.disable_xformers_memory_efficient_attention",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2022"}}),ue=new Z({props:{name:"class diffusers.AmusedInpaintPipeline",anchor:"diffusers.AmusedInpaintPipeline",parameters:[{name:"vqvae",val:": VQModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"transformer",val:": UVit2DModel"},{name:"scheduler",val:": AmusedScheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py#L60"}}),fe=new Z({props:{name:"__call__",anchor:"diffusers.AmusedInpaintPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = None"},{name:"mask_image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = None"},{name:"strength",val:": float = 1.0"},{name:"num_inference_steps",val:": int = 12"},{name:"guidance_scale",val:": float = 10.0"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:" = '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:"micro_conditioning_aesthetic_score",val:": int = 6"},{name:"micro_conditioning_crop_coord",val:": typing.Tuple[int, int] = (0, 0)"},{name:"temperature",val:": typing.Union[int, typing.Tuple[int, int], typing.List[int]] = (2, 0)"}],parametersDescription:[{anchor:"diffusers.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__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>, numpy array or tensor representing an image batch to be used as the starting point. For both
numpy array and pytorch tensor, the expected value range is between <code>[0, 1]</code> If it&#x2019;s a tensor or a list
or tensors, the expected shape should be <code>(B, C, H, W)</code> or <code>(C, H, W)</code>. If it is a numpy array or a
list of arrays, the expected shape should be <code>(B, H, W, C)</code> or <code>(H, W, C)</code> It can also accept image
latents as <code>image</code>, but if passing latents directly it is not encoded again.`,name:"image"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.mask_image",description:`<strong>mask_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>, numpy array or tensor representing an image batch to mask <code>image</code>. White pixels in the mask
are repainted while black pixels are preserved. If <code>mask_image</code> is a PIL image, it is converted to a
single channel (luminance) before use. If it&#x2019;s a numpy array or pytorch tensor, it should contain one
color channel (L) instead of 3, so the expected shape for pytorch tensor would be <code>(B, 1, H, W)</code>, <code>(B, H, W)</code>, <code>(1, H, W)</code>, <code>(H, W)</code>. And for numpy array would be for <code>(B, H, W, 1)</code>, <code>(B, H, W)</code>, <code>(H, W, 1)</code>, or <code>(H, W)</code>.`,name:"mask_image"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Indicates extent to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code> is used as a
starting point and more noise is added the higher 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 is maximum and the denoising
process runs for the full number of iterations specified in <code>num_inference_steps</code>. A value of 1
essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#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.AmusedInpaintPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 10.0) &#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.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__call__.generator",description:`<strong>generator</strong> (<code>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.AmusedInpaintPipeline.__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 (prompt weighting). If not
provided, text embeddings are generated from the <code>prompt</code> input argument. A single vector from the
pooled and projected final hidden states.`,name:"prompt_embeds"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.`,name:"encoder_hidden_states"},{anchor:"diffusers.AmusedInpaintPipeline.__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 (prompt weighting). If
not provided, <code>negative_prompt_embeds</code> are generated from the <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.negative_encoder_hidden_states",description:`<strong>negative_encoder_hidden_states</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Analogous to <code>encoder_hidden_states</code> for the positive prompt.`,name:"negative_encoder_hidden_states"},{anchor:"diffusers.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__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.AmusedInpaintPipeline.__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 in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow"><code>self.processor</code></a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.micro_conditioning_aesthetic_score",description:`<strong>micro_conditioning_aesthetic_score</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
The targeted aesthetic score according to the laion aesthetic classifier. See <a href="https://laion.ai/blog/laion-aesthetics/" rel="nofollow">https://laion.ai/blog/laion-aesthetics/</a>
and the micro-conditioning section of <a href="https://arxiv.org/abs/2307.01952" rel="nofollow">https://arxiv.org/abs/2307.01952</a>.`,name:"micro_conditioning_aesthetic_score"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.micro_conditioning_crop_coord",description:`<strong>micro_conditioning_crop_coord</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to (0, 0)) &#x2014;
The targeted height, width crop coordinates. See the micro-conditioning section of <a href="https://arxiv.org/abs/2307.01952" rel="nofollow">https://arxiv.org/abs/2307.01952</a>.`,name:"micro_conditioning_crop_coord"},{anchor:"diffusers.AmusedInpaintPipeline.__call__.temperature",description:`<strong>temperature</strong> (<code>Union[int, Tuple[int, int], List[int]]</code>, <em>optional</em>, defaults to (2, 0)) &#x2014;
Configures the temperature scheduler on <code>self.scheduler</code> see <code>AmusedScheduler#set_timesteps</code>.`,name:"temperature"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py#L103",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> is returned, otherwise a
<code>tuple</code> is returned where the first element is a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
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Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
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