diffusers/pr_13830/en/_app/immutable/nodes/164.2d34daaa.js

import{s as Va,o as Sa,n as Y}from"../chunks/scheduler.53228c21.js";import{S as Xa,i as Qa,e as f,s as o,c as y,h as Ya,a as u,d as n,b as r,f as N,g as b,j as M,k as Z,l as g,m as l,n as _,t as w,o as J,p as T}from"../chunks/index.cac5d66a.js";import{C as Fa}from"../chunks/CopyLLMTxtMenu.bb9f4239.js";import{D as B}from"../chunks/Docstring.e1b6e370.js";import{C as V}from"../chunks/CodeBlock.606cbaf4.js";import{E as Ra}from"../chunks/ExampleCodeBlock.a5120892.js";import{H as Q,E as Aa}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.74b2027e.js";import{H as Kt,a as P}from"../chunks/HfOption.6b51ddef.js";function Ha(W){let s,U='To use LCMs, you need to load the LCM checkpoint for your supported model into <a href="/docs/diffusers/pr_13830/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.',c,d,t="A couple of notes to keep in mind when using LCMs are:",a,h,j="<li>Typically, batch size is doubled inside the pipeline for classifier-free guidance. But LCM applies guidance with guidance embeddings and doesn’t need to double the batch size, which leads to faster inference. The downside is that negative prompts don’t work with LCM because they don’t have any effect on the denoising process.</li> <li>The ideal range for <code>guidance_scale</code> is [3., 13.] because that is what the UNet was trained with. However, disabling <code>guidance_scale</code> with a value of 1.0 is also effective in most cases.</li>",i,v,L,p,I='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdxl_t2i.png"/>',R;return v=new V({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
<span class="hljs-keyword">import</span> torch

unet = UNet2DConditionModel.from_pretrained(
    <span class="hljs-string">&quot;latent-consistency/lcm-sdxl&quot;</span>,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
)
pipe = StableDiffusionXLPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, unet=unet, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

prompt = <span class="hljs-string">&quot;Self-portrait oil painting, a beautiful cyborg with golden hair, 8k&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt=prompt, num_inference_steps=<span class="hljs-number">4</span>, generator=generator, guidance_scale=<span class="hljs-number">8.0</span>
).images[<span class="hljs-number">0</span>]
image`,lang:"python",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),d=f("p"),d.textContent=t,a=o(),h=f("ul"),h.innerHTML=j,i=o(),y(v.$$.fragment),L=o(),p=f("div"),p.innerHTML=I,this.h()},l(C){s=u(C,"P",{"data-svelte-h":!0}),M(s)!=="svelte-10yqada"&&(s.innerHTML=U),c=r(C),d=u(C,"P",{"data-svelte-h":!0}),M(d)!=="svelte-185mgq9"&&(d.textContent=t),a=r(C),h=u(C,"UL",{"data-svelte-h":!0}),M(h)!=="svelte-1mvqiqn"&&(h.innerHTML=j),i=r(C),b(v.$$.fragment,C),L=r(C),p=u(C,"DIV",{class:!0,"data-svelte-h":!0}),M(p)!=="svelte-r54e1a"&&(p.innerHTML=I),this.h()},h(){Z(p,"class","flex justify-center")},m(C,E){l(C,s,E),l(C,c,E),l(C,d,E),l(C,a,E),l(C,h,E),l(C,i,E),_(v,C,E),l(C,L,E),l(C,p,E),R=!0},p:Y,i(C){R||(w(v.$$.fragment,C),R=!0)},o(C){J(v.$$.fragment,C),R=!1},d(C){C&&(n(s),n(c),n(d),n(a),n(h),n(i),n(L),n(p)),T(v,C)}}}function Pa(W){let s,U='To use LCM-LoRAs, you need to replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a> and load the LCM-LoRA weights with the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.',c,d,t="A couple of notes to keep in mind when using LCM-LoRAs are:",a,h,j='<li>Typically, batch size is doubled inside the pipeline for classifier-free guidance. But LCM applies guidance with guidance embeddings and doesn’t need to double the batch size, which leads to faster inference. The downside is that negative prompts don’t work with LCM because they don’t have any effect on the denoising process.</li> <li>You could use guidance with LCM-LoRAs, but it is very sensitive to high <code>guidance_scale</code> values and can lead to artifacts in the generated image. The best values we’ve found are between [1.0, 2.0].</li> <li>Replace <a href="https://hf.co/stabilityai/stable-diffusion-xl-base-1.0" rel="nofollow">stabilityai/stable-diffusion-xl-base-1.0</a> with any finetuned model. For example, try using the <a href="https://huggingface.co/Linaqruf/animagine-xl" rel="nofollow">animagine-xl</a> checkpoint to generate anime images with SDXL.</li>',i,v,L,p,I='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_t2i.png"/>',R;return v=new V({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> DiffusionPipeline, LCMScheduler

pipe = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
    torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdxl&quot;</span>)

prompt = <span class="hljs-string">&quot;Self-portrait oil painting, a beautiful cyborg with golden hair, 8k&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">42</span>)
image = pipe(
    prompt=prompt, num_inference_steps=<span class="hljs-number">4</span>, generator=generator, guidance_scale=<span class="hljs-number">1.0</span>
).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),d=f("p"),d.textContent=t,a=o(),h=f("ul"),h.innerHTML=j,i=o(),y(v.$$.fragment),L=o(),p=f("div"),p.innerHTML=I,this.h()},l(C){s=u(C,"P",{"data-svelte-h":!0}),M(s)!=="svelte-dfsxje"&&(s.innerHTML=U),c=r(C),d=u(C,"P",{"data-svelte-h":!0}),M(d)!=="svelte-1l0tu8o"&&(d.textContent=t),a=r(C),h=u(C,"UL",{"data-svelte-h":!0}),M(h)!=="svelte-yd1arq"&&(h.innerHTML=j),i=r(C),b(v.$$.fragment,C),L=r(C),p=u(C,"DIV",{class:!0,"data-svelte-h":!0}),M(p)!=="svelte-1qjqi5a"&&(p.innerHTML=I),this.h()},h(){Z(p,"class","flex justify-center")},m(C,E){l(C,s,E),l(C,c,E),l(C,d,E),l(C,a,E),l(C,h,E),l(C,i,E),_(v,C,E),l(C,L,E),l(C,p,E),R=!0},p:Y,i(C){R||(w(v.$$.fragment,C),R=!0)},o(C){J(v.$$.fragment,C),R=!1},d(C){C&&(n(s),n(c),n(d),n(a),n(h),n(i),n(L),n(p)),T(v,C)}}}function Da(W){let s,U,c,d;return s=new P({props:{id:"lcm-text2img",option:"LCM",$$slots:{default:[Ha]},$$scope:{ctx:W}}}),c=new P({props:{id:"lcm-text2img",option:"LCM-LoRA",$$slots:{default:[Pa]},$$scope:{ctx:W}}}),{c(){y(s.$$.fragment),U=o(),y(c.$$.fragment)},l(t){b(s.$$.fragment,t),U=r(t),b(c.$$.fragment,t)},m(t,a){_(s,t,a),l(t,U,a),_(c,t,a),d=!0},p(t,a){const h={};a&2&&(h.$$scope={dirty:a,ctx:t}),s.$set(h);const j={};a&2&&(j.$$scope={dirty:a,ctx:t}),c.$set(j)},i(t){d||(w(s.$$.fragment,t),w(c.$$.fragment,t),d=!0)},o(t){J(s.$$.fragment,t),J(c.$$.fragment,t),d=!1},d(t){t&&n(U),T(s,t),T(c,t)}}}function za(W){let s,U='To use LCMs for image-to-image, you need to load the LCM checkpoint for your supported model into <a href="/docs/diffusers/pr_13830/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.',c,d,t="<p>Experiment with different values for <code>num_inference_steps</code>, <code>strength</code>, and <code>guidance_scale</code> to get the best results.</p>",a,h,j,i,v='<div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-img2img.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption></div>',L;return h=new V({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> AutoPipelineForImage2Image, UNet2DConditionModel, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

unet = UNet2DConditionModel.from_pretrained(
    <span class="hljs-string">&quot;SimianLuo/LCM_Dreamshaper_v7&quot;</span>,
    subfolder=<span class="hljs-string">&quot;unet&quot;</span>,
    torch_dtype=torch.float16,
)

pipe = AutoPipelineForImage2Image.from_pretrained(
    <span class="hljs-string">&quot;Lykon/dreamshaper-7&quot;</span>,
    unet=unet,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png&quot;</span>)
prompt = <span class="hljs-string">&quot;Astronauts in a jungle, cold color palette, muted colors, detailed, 8k&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt,
    image=init_image,
    num_inference_steps=<span class="hljs-number">4</span>,
    guidance_scale=<span class="hljs-number">7.5</span>,
    strength=<span class="hljs-number">0.5</span>,
    generator=generator
).images[<span class="hljs-number">0</span>]
image`,lang:"python",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),d=f("blockquote"),d.innerHTML=t,a=o(),y(h.$$.fragment),j=o(),i=f("div"),i.innerHTML=v,this.h()},l(p){s=u(p,"P",{"data-svelte-h":!0}),M(s)!=="svelte-1fh5nns"&&(s.innerHTML=U),c=r(p),d=u(p,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),M(d)!=="svelte-no4hyc"&&(d.innerHTML=t),a=r(p),b(h.$$.fragment,p),j=r(p),i=u(p,"DIV",{class:!0,"data-svelte-h":!0}),M(i)!=="svelte-f0uu5w"&&(i.innerHTML=v),this.h()},h(){Z(d,"class","tip"),Z(i,"class","flex gap-4")},m(p,I){l(p,s,I),l(p,c,I),l(p,d,I),l(p,a,I),_(h,p,I),l(p,j,I),l(p,i,I),L=!0},p:Y,i(p){L||(w(h.$$.fragment,p),L=!0)},o(p){J(h.$$.fragment,p),L=!1},d(p){p&&(n(s),n(c),n(d),n(a),n(j),n(i)),T(h,p)}}}function qa(W){let s,U='To use LCM-LoRAs for image-to-image, you need to replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a> and load the LCM-LoRA weights with the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.',c,d,t="<p>Experiment with different values for <code>num_inference_steps</code>, <code>strength</code>, and <code>guidance_scale</code> to get the best results.</p>",a,h,j,i,v='<div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-img2img.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption></div>',L;return h=new V({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> AutoPipelineForImage2Image, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid, load_image

pipe = AutoPipelineForImage2Image.from_pretrained(
    <span class="hljs-string">&quot;Lykon/dreamshaper-7&quot;</span>,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdv1-5&quot;</span>)

init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png&quot;</span>)
prompt = <span class="hljs-string">&quot;Astronauts in a jungle, cold color palette, muted colors, detailed, 8k&quot;</span>

generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt,
    image=init_image,
    num_inference_steps=<span class="hljs-number">4</span>,
    guidance_scale=<span class="hljs-number">1</span>,
    strength=<span class="hljs-number">0.6</span>,
    generator=generator
).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),d=f("blockquote"),d.innerHTML=t,a=o(),y(h.$$.fragment),j=o(),i=f("div"),i.innerHTML=v,this.h()},l(p){s=u(p,"P",{"data-svelte-h":!0}),M(s)!=="svelte-1ylnahy"&&(s.innerHTML=U),c=r(p),d=u(p,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),M(d)!=="svelte-no4hyc"&&(d.innerHTML=t),a=r(p),b(h.$$.fragment,p),j=r(p),i=u(p,"DIV",{class:!0,"data-svelte-h":!0}),M(i)!=="svelte-1n6qllf"&&(i.innerHTML=v),this.h()},h(){Z(d,"class","tip"),Z(i,"class","flex gap-4")},m(p,I){l(p,s,I),l(p,c,I),l(p,d,I),l(p,a,I),_(h,p,I),l(p,j,I),l(p,i,I),L=!0},p:Y,i(p){L||(w(h.$$.fragment,p),L=!0)},o(p){J(h.$$.fragment,p),L=!1},d(p){p&&(n(s),n(c),n(d),n(a),n(j),n(i)),T(h,p)}}}function Ka(W){let s,U,c,d;return s=new P({props:{id:"lcm-img2img",option:"LCM",$$slots:{default:[za]},$$scope:{ctx:W}}}),c=new P({props:{id:"lcm-img2img",option:"LCM-LoRA",$$slots:{default:[qa]},$$scope:{ctx:W}}}),{c(){y(s.$$.fragment),U=o(),y(c.$$.fragment)},l(t){b(s.$$.fragment,t),U=r(t),b(c.$$.fragment,t)},m(t,a){_(s,t,a),l(t,U,a),_(c,t,a),d=!0},p(t,a){const h={};a&2&&(h.$$scope={dirty:a,ctx:t}),s.$set(h);const j={};a&2&&(j.$$scope={dirty:a,ctx:t}),c.$set(j)},i(t){d||(w(s.$$.fragment,t),w(c.$$.fragment,t),d=!0)},o(t){J(s.$$.fragment,t),J(c.$$.fragment,t),d=!1},d(t){t&&n(U),T(s,t),T(c,t)}}}function Oa(W){let s,U='Load the LCM checkpoint for your supported model into <a href="/docs/diffusers/pr_13830/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Then you can use the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method to load the LoRA weights into the LCM and generate a styled image in a few steps.',c,d,t,a,h='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdx_lora_mix.png"/>',j;return d=new V({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
<span class="hljs-keyword">import</span> torch

unet = UNet2DConditionModel.from_pretrained(
    <span class="hljs-string">&quot;latent-consistency/lcm-sdxl&quot;</span>,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
)
pipe = StableDiffusionXLPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, unet=unet, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights(<span class="hljs-string">&quot;TheLastBen/Papercut_SDXL&quot;</span>, weight_name=<span class="hljs-string">&quot;papercut.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;papercut&quot;</span>)

prompt = <span class="hljs-string">&quot;papercut, a cute fox&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt=prompt, num_inference_steps=<span class="hljs-number">4</span>, generator=generator, guidance_scale=<span class="hljs-number">8.0</span>
).images[<span class="hljs-number">0</span>]
image`,lang:"python",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),y(d.$$.fragment),t=o(),a=f("div"),a.innerHTML=h,this.h()},l(i){s=u(i,"P",{"data-svelte-h":!0}),M(s)!=="svelte-7il5fu"&&(s.innerHTML=U),c=r(i),b(d.$$.fragment,i),t=r(i),a=u(i,"DIV",{class:!0,"data-svelte-h":!0}),M(a)!=="svelte-1205m44"&&(a.innerHTML=h),this.h()},h(){Z(a,"class","flex justify-center")},m(i,v){l(i,s,v),l(i,c,v),_(d,i,v),l(i,t,v),l(i,a,v),j=!0},p:Y,i(i){j||(w(d.$$.fragment,i),j=!0)},o(i){J(d.$$.fragment,i),j=!1},d(i){i&&(n(s),n(c),n(t),n(a)),T(d,i)}}}function el(W){let s,U='Replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Then you can use the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method to load the LCM-LoRA weights and the style LoRA you want to use. Combine both LoRA adapters with the <code>~loaders.UNet2DConditionLoadersMixin.set_adapters</code> method and generate a styled image in a few steps.',c,d,t,a,h='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdx_lora_mix.png"/>',j;return d=new V({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> DiffusionPipeline, LCMScheduler

pipe = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
    torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdxl&quot;</span>, adapter_name=<span class="hljs-string">&quot;lcm&quot;</span>)
pipe.load_lora_weights(<span class="hljs-string">&quot;TheLastBen/Papercut_SDXL&quot;</span>, weight_name=<span class="hljs-string">&quot;papercut.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;papercut&quot;</span>)

pipe.set_adapters([<span class="hljs-string">&quot;lcm&quot;</span>, <span class="hljs-string">&quot;papercut&quot;</span>], adapter_weights=[<span class="hljs-number">1.0</span>, <span class="hljs-number">0.8</span>])

prompt = <span class="hljs-string">&quot;papercut, a cute fox&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(prompt, num_inference_steps=<span class="hljs-number">4</span>, guidance_scale=<span class="hljs-number">1</span>, generator=generator).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),y(d.$$.fragment),t=o(),a=f("div"),a.innerHTML=h,this.h()},l(i){s=u(i,"P",{"data-svelte-h":!0}),M(s)!=="svelte-ebohij"&&(s.innerHTML=U),c=r(i),b(d.$$.fragment,i),t=r(i),a=u(i,"DIV",{class:!0,"data-svelte-h":!0}),M(a)!=="svelte-1onami8"&&(a.innerHTML=h),this.h()},h(){Z(a,"class","flex justify-center")},m(i,v){l(i,s,v),l(i,c,v),_(d,i,v),l(i,t,v),l(i,a,v),j=!0},p:Y,i(i){j||(w(d.$$.fragment,i),j=!0)},o(i){J(d.$$.fragment,i),j=!1},d(i){i&&(n(s),n(c),n(t),n(a)),T(d,i)}}}function tl(W){let s,U,c,d;return s=new P({props:{id:"lcm-lora",option:"LCM",$$slots:{default:[Oa]},$$scope:{ctx:W}}}),c=new P({props:{id:"lcm-lora",option:"LCM-LoRA",$$slots:{default:[el]},$$scope:{ctx:W}}}),{c(){y(s.$$.fragment),U=o(),y(c.$$.fragment)},l(t){b(s.$$.fragment,t),U=r(t),b(c.$$.fragment,t)},m(t,a){_(s,t,a),l(t,U,a),_(c,t,a),d=!0},p(t,a){const h={};a&2&&(h.$$scope={dirty:a,ctx:t}),s.$set(h);const j={};a&2&&(j.$$scope={dirty:a,ctx:t}),c.$set(j)},i(t){d||(w(s.$$.fragment,t),w(c.$$.fragment,t),d=!0)},o(t){J(s.$$.fragment,t),J(c.$$.fragment,t),d=!1},d(t){t&&n(U),T(s,t),T(c,t)}}}function nl(W){let s,U='Load a ControlNet model trained on canny images and pass it to the <a href="/docs/diffusers/pr_13830/en/api/models/controlnet#diffusers.ControlNetModel">ControlNetModel</a>. Then you can load a LCM model into <a href="/docs/diffusers/pr_13830/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline">StableDiffusionControlNetPipeline</a> and replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Now pass the canny image to the pipeline and generate an image.',c,d,t="<p>Experiment with different values for <code>num_inference_steps</code>, <code>controlnet_conditioning_scale</code>, <code>cross_attention_kwargs</code>, and <code>guidance_scale</code> to get the best results.</p>",a,h,j,i,v='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdv1-5_controlnet.png"/>',L;return h=new V({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> cv2
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

image = load_image(
    <span class="hljs-string">&quot;https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png&quot;</span>
).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

image = np.array(image)

low_threshold = <span class="hljs-number">100</span>
high_threshold = <span class="hljs-number">200</span>

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, <span class="hljs-literal">None</span>]
image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
canny_image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;lllyasviel/sd-controlnet-canny&quot;</span>, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    <span class="hljs-string">&quot;SimianLuo/LCM_Dreamshaper_v7&quot;</span>,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    safety_checker=<span class="hljs-literal">None</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    <span class="hljs-string">&quot;the mona lisa&quot;</span>,
    image=canny_image,
    num_inference_steps=<span class="hljs-number">4</span>,
    generator=generator,
).images[<span class="hljs-number">0</span>]
make_image_grid([canny_image, image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"python",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),d=f("blockquote"),d.innerHTML=t,a=o(),y(h.$$.fragment),j=o(),i=f("div"),i.innerHTML=v,this.h()},l(p){s=u(p,"P",{"data-svelte-h":!0}),M(s)!=="svelte-11gxp3w"&&(s.innerHTML=U),c=r(p),d=u(p,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),M(d)!=="svelte-1os48ku"&&(d.innerHTML=t),a=r(p),b(h.$$.fragment,p),j=r(p),i=u(p,"DIV",{class:!0,"data-svelte-h":!0}),M(i)!=="svelte-1f4gba0"&&(i.innerHTML=v),this.h()},h(){Z(d,"class","tip"),Z(i,"class","flex justify-center")},m(p,I){l(p,s,I),l(p,c,I),l(p,d,I),l(p,a,I),_(h,p,I),l(p,j,I),l(p,i,I),L=!0},p:Y,i(p){L||(w(h.$$.fragment,p),L=!0)},o(p){J(h.$$.fragment,p),L=!1},d(p){p&&(n(s),n(c),n(d),n(a),n(j),n(i)),T(h,p)}}}function sl(W){let s,U='Load a ControlNet model trained on canny images and pass it to the <a href="/docs/diffusers/pr_13830/en/api/models/controlnet#diffusers.ControlNetModel">ControlNetModel</a>. Then you can load a Stable Diffusion v1.5 model into <a href="/docs/diffusers/pr_13830/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline">StableDiffusionControlNetPipeline</a> and replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Use the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method to load the LCM-LoRA weights, and pass the canny image to the pipeline and generate an image.',c,d,t="<p>Experiment with different values for <code>num_inference_steps</code>, <code>controlnet_conditioning_scale</code>, <code>cross_attention_kwargs</code>, and <code>guidance_scale</code> to get the best results.</p>",a,h,j,i,v='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_controlnet.png"/>',L;return h=new V({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> cv2
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

image = load_image(
    <span class="hljs-string">&quot;https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png&quot;</span>
).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

image = np.array(image)

low_threshold = <span class="hljs-number">100</span>
high_threshold = <span class="hljs-number">200</span>

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, <span class="hljs-literal">None</span>]
image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
canny_image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;lllyasviel/sd-controlnet-canny&quot;</span>, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    safety_checker=<span class="hljs-literal">None</span>,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdv1-5&quot;</span>)

generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    <span class="hljs-string">&quot;the mona lisa&quot;</span>,
    image=canny_image,
    num_inference_steps=<span class="hljs-number">4</span>,
    guidance_scale=<span class="hljs-number">1.5</span>,
    controlnet_conditioning_scale=<span class="hljs-number">0.8</span>,
    cross_attention_kwargs={<span class="hljs-string">&quot;scale&quot;</span>: <span class="hljs-number">1</span>},
    generator=generator,
).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),d=f("blockquote"),d.innerHTML=t,a=o(),y(h.$$.fragment),j=o(),i=f("div"),i.innerHTML=v,this.h()},l(p){s=u(p,"P",{"data-svelte-h":!0}),M(s)!=="svelte-1py26v8"&&(s.innerHTML=U),c=r(p),d=u(p,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),M(d)!=="svelte-1os48ku"&&(d.innerHTML=t),a=r(p),b(h.$$.fragment,p),j=r(p),i=u(p,"DIV",{class:!0,"data-svelte-h":!0}),M(i)!=="svelte-1s0658k"&&(i.innerHTML=v),this.h()},h(){Z(d,"class","tip"),Z(i,"class","flex justify-center")},m(p,I){l(p,s,I),l(p,c,I),l(p,d,I),l(p,a,I),_(h,p,I),l(p,j,I),l(p,i,I),L=!0},p:Y,i(p){L||(w(h.$$.fragment,p),L=!0)},o(p){J(h.$$.fragment,p),L=!1},d(p){p&&(n(s),n(c),n(d),n(a),n(j),n(i)),T(h,p)}}}function al(W){let s,U,c,d;return s=new P({props:{id:"lcm-controlnet",option:"LCM",$$slots:{default:[nl]},$$scope:{ctx:W}}}),c=new P({props:{id:"lcm-controlnet",option:"LCM-LoRA",$$slots:{default:[sl]},$$scope:{ctx:W}}}),{c(){y(s.$$.fragment),U=o(),y(c.$$.fragment)},l(t){b(s.$$.fragment,t),U=r(t),b(c.$$.fragment,t)},m(t,a){_(s,t,a),l(t,U,a),_(c,t,a),d=!0},p(t,a){const h={};a&2&&(h.$$scope={dirty:a,ctx:t}),s.$set(h);const j={};a&2&&(j.$$scope={dirty:a,ctx:t}),c.$set(j)},i(t){d||(w(s.$$.fragment,t),w(c.$$.fragment,t),d=!0)},o(t){J(s.$$.fragment,t),J(c.$$.fragment,t),d=!1},d(t){t&&n(U),T(s,t),T(c,t)}}}function ll(W){let s,U='Load a T2IAdapter trained on canny images and pass it to the <a href="/docs/diffusers/pr_13830/en/api/pipelines/stable_diffusion/adapter#diffusers.StableDiffusionXLAdapterPipeline">StableDiffusionXLAdapterPipeline</a>. Then load a LCM checkpoint into <a href="/docs/diffusers/pr_13830/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>. Now pass the canny image to the pipeline and generate an image.',c,d,t,a,h='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-t2i.png"/>',j;return d=new V({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> cv2
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

<span class="hljs-comment"># detect the canny map in low resolution to avoid high-frequency details</span>
image = load_image(
    <span class="hljs-string">&quot;https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png&quot;</span>
).resize((<span class="hljs-number">384</span>, <span class="hljs-number">384</span>))

image = np.array(image)

low_threshold = <span class="hljs-number">100</span>
high_threshold = <span class="hljs-number">200</span>

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, <span class="hljs-literal">None</span>]
image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
canny_image = Image.fromarray(image).resize((<span class="hljs-number">1024</span>, <span class="hljs-number">1216</span>))

adapter = T2IAdapter.from_pretrained(<span class="hljs-string">&quot;TencentARC/t2i-adapter-canny-sdxl-1.0&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

unet = UNet2DConditionModel.from_pretrained(
    <span class="hljs-string">&quot;latent-consistency/lcm-sdxl&quot;</span>,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
    unet=unet,
    adapter=adapter,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

prompt = <span class="hljs-string">&quot;the mona lisa, 4k picture, high quality&quot;</span>
negative_prompt = <span class="hljs-string">&quot;extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured&quot;</span>

generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=canny_image,
    num_inference_steps=<span class="hljs-number">4</span>,
    guidance_scale=<span class="hljs-number">5</span>,
    adapter_conditioning_scale=<span class="hljs-number">0.8</span>,
    adapter_conditioning_factor=<span class="hljs-number">1</span>,
    generator=generator,
).images[<span class="hljs-number">0</span>]`,lang:"python",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),y(d.$$.fragment),t=o(),a=f("div"),a.innerHTML=h,this.h()},l(i){s=u(i,"P",{"data-svelte-h":!0}),M(s)!=="svelte-19ic0av"&&(s.innerHTML=U),c=r(i),b(d.$$.fragment,i),t=r(i),a=u(i,"DIV",{class:!0,"data-svelte-h":!0}),M(a)!=="svelte-1m6j6i7"&&(a.innerHTML=h),this.h()},h(){Z(a,"class","flex justify-center")},m(i,v){l(i,s,v),l(i,c,v),_(d,i,v),l(i,t,v),l(i,a,v),j=!0},p:Y,i(i){j||(w(d.$$.fragment,i),j=!0)},o(i){J(d.$$.fragment,i),j=!1},d(i){i&&(n(s),n(c),n(t),n(a)),T(d,i)}}}function il(W){let s,U='Load a T2IAdapter trained on canny images and pass it to the <a href="/docs/diffusers/pr_13830/en/api/pipelines/stable_diffusion/adapter#diffusers.StableDiffusionXLAdapterPipeline">StableDiffusionXLAdapterPipeline</a>. Replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>, and use the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method to load the LCM-LoRA weights. Pass the canny image to the pipeline and generate an image.',c,d,t,a,h='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-t2i.png"/>',j;return d=new V({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjBjdjIlMEFpbXBvcnQlMjBudW1weSUyMGFzJTIwbnAlMEFmcm9tJTIwUElMJTIwaW1wb3J0JTIwSW1hZ2UlMEElMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwU3RhYmxlRGlmZnVzaW9uWExBZGFwdGVyUGlwZWxpbmUlMkMlMjBVTmV0MkRDb25kaXRpb25Nb2RlbCUyQyUyMFQySUFkYXB0ZXIlMkMlMjBMQ01TY2hlZHVsZXIlMEFmcm9tJTIwZGlmZnVzZXJzLnV0aWxzJTIwaW1wb3J0JTIwbG9hZF9pbWFnZSUyQyUyMG1ha2VfaW1hZ2VfZ3JpZCUwQSUwQSUyMyUyMGRldGVjdCUyMHRoZSUyMGNhbm55JTIwbWFwJTIwaW4lMjBsb3clMjByZXNvbHV0aW9uJTIwdG8lMjBhdm9pZCUyMGhpZ2gtZnJlcXVlbmN5JTIwZGV0YWlscyUwQWltYWdlJTIwJTNEJTIwbG9hZF9pbWFnZSglMEElMjAlMjAlMjAlMjAlMjJodHRwcyUzQSUyRiUyRmhmLmNvJTJGZGF0YXNldHMlMkZodWdnaW5nZmFjZSUyRmRvY3VtZW50YXRpb24taW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZkaWZmdXNlcnMlMkZpbnB1dF9pbWFnZV92ZXJtZWVyLnBuZyUyMiUwQSkucmVzaXplKCgzODQlMkMlMjAzODQpKSUwQSUwQWltYWdlJTIwJTNEJTIwbnAuYXJyYXkoaW1hZ2UpJTBBJTBBbG93X3RocmVzaG9sZCUyMCUzRCUyMDEwMCUwQWhpZ2hfdGhyZXNob2xkJTIwJTNEJTIwMjAwJTBBJTBBaW1hZ2UlMjAlM0QlMjBjdjIuQ2FubnkoaW1hZ2UlMkMlMjBsb3dfdGhyZXNob2xkJTJDJTIwaGlnaF90aHJlc2hvbGQpJTBBaW1hZ2UlMjAlM0QlMjBpbWFnZSU1QiUzQSUyQyUyMCUzQSUyQyUyME5vbmUlNUQlMEFpbWFnZSUyMCUzRCUyMG5wLmNvbmNhdGVuYXRlKCU1QmltYWdlJTJDJTIwaW1hZ2UlMkMlMjBpbWFnZSU1RCUyQyUyMGF4aXMlM0QyKSUwQWNhbm55X2ltYWdlJTIwJTNEJTIwSW1hZ2UuZnJvbWFycmF5KGltYWdlKS5yZXNpemUoKDEwMjQlMkMlMjAxMDI0KSklMEElMEFhZGFwdGVyJTIwJTNEJTIwVDJJQWRhcHRlci5mcm9tX3ByZXRyYWluZWQoJTIyVGVuY2VudEFSQyUyRnQyaS1hZGFwdGVyLWNhbm55LXNkeGwtMS4wJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTJDJTIwdmFyaWFudCUzRCUyMmZwMTYlMjIpLnRvKCUyMmN1ZGElMjIpJTBBJTBBcGlwZSUyMCUzRCUyMFN0YWJsZURpZmZ1c2lvblhMQWRhcHRlclBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJzdGFiaWxpdHlhaSUyRnN0YWJsZS1kaWZmdXNpb24teGwtYmFzZS0xLjAlMjIlMkMlMEElMjAlMjAlMjAlMjBhZGFwdGVyJTNEYWRhcHRlciUyQyUwQSUyMCUyMCUyMCUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiUyQyUwQSUyMCUyMCUyMCUyMHZhcmlhbnQlM0QlMjJmcDE2JTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQSUwQXBpcGUuc2NoZWR1bGVyJTIwJTNEJTIwTENNU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGUuc2NoZWR1bGVyLmNvbmZpZyklMEElMEFwaXBlLmxvYWRfbG9yYV93ZWlnaHRzKCUyMmxhdGVudC1jb25zaXN0ZW5jeSUyRmxjbS1sb3JhLXNkeGwlMjIpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIydGhlJTIwbW9uYSUyMGxpc2ElMkMlMjA0ayUyMHBpY3R1cmUlMkMlMjBoaWdoJTIwcXVhbGl0eSUyMiUwQW5lZ2F0aXZlX3Byb21wdCUyMCUzRCUyMCUyMmV4dHJhJTIwZGlnaXQlMkMlMjBmZXdlciUyMGRpZ2l0cyUyQyUyMGNyb3BwZWQlMkMlMjB3b3JzdCUyMHF1YWxpdHklMkMlMjBsb3clMjBxdWFsaXR5JTJDJTIwZ2xpdGNoJTJDJTIwZGVmb3JtZWQlMkMlMjBtdXRhdGVkJTJDJTIwdWdseSUyQyUyMGRpc2ZpZ3VyZWQlMjIlMEElMEFnZW5lcmF0b3IlMjAlM0QlMjB0b3JjaC5tYW51YWxfc2VlZCgwKSUwQWltYWdlJTIwJTNEJTIwcGlwZSglMEElMjAlMjAlMjAlMjBwcm9tcHQlM0Rwcm9tcHQlMkMlMEElMjAlMjAlMjAlMjBuZWdhdGl2ZV9wcm9tcHQlM0RuZWdhdGl2ZV9wcm9tcHQlMkMlMEElMjAlMjAlMjAlMjBpbWFnZSUzRGNhbm55X2ltYWdlJTJDJTBBJTIwJTIwJTIwJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDQlMkMlMEElMjAlMjAlMjAlMjBndWlkYW5jZV9zY2FsZSUzRDEuNSUyQyUwQSUyMCUyMCUyMCUyMGFkYXB0ZXJfY29uZGl0aW9uaW5nX3NjYWxlJTNEMC44JTJDJTBBJTIwJTIwJTIwJTIwYWRhcHRlcl9jb25kaXRpb25pbmdfZmFjdG9yJTNEMSUyQyUwQSUyMCUyMCUyMCUyMGdlbmVyYXRvciUzRGdlbmVyYXRvciUyQyUwQSkuaW1hZ2VzJTVCMCU1RA==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> cv2
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

<span class="hljs-comment"># detect the canny map in low resolution to avoid high-frequency details</span>
image = load_image(
    <span class="hljs-string">&quot;https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png&quot;</span>
).resize((<span class="hljs-number">384</span>, <span class="hljs-number">384</span>))

image = np.array(image)

low_threshold = <span class="hljs-number">100</span>
high_threshold = <span class="hljs-number">200</span>

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, <span class="hljs-literal">None</span>]
image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
canny_image = Image.fromarray(image).resize((<span class="hljs-number">1024</span>, <span class="hljs-number">1024</span>))

adapter = T2IAdapter.from_pretrained(<span class="hljs-string">&quot;TencentARC/t2i-adapter-canny-sdxl-1.0&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
    adapter=adapter,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdxl&quot;</span>)

prompt = <span class="hljs-string">&quot;the mona lisa, 4k picture, high quality&quot;</span>
negative_prompt = <span class="hljs-string">&quot;extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured&quot;</span>

generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=canny_image,
    num_inference_steps=<span class="hljs-number">4</span>,
    guidance_scale=<span class="hljs-number">1.5</span>,
    adapter_conditioning_scale=<span class="hljs-number">0.8</span>,
    adapter_conditioning_factor=<span class="hljs-number">1</span>,
    generator=generator,
).images[<span class="hljs-number">0</span>]`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.innerHTML=U,c=o(),y(d.$$.fragment),t=o(),a=f("div"),a.innerHTML=h,this.h()},l(i){s=u(i,"P",{"data-svelte-h":!0}),M(s)!=="svelte-duehn2"&&(s.innerHTML=U),c=r(i),b(d.$$.fragment,i),t=r(i),a=u(i,"DIV",{class:!0,"data-svelte-h":!0}),M(a)!=="svelte-djnrvk"&&(a.innerHTML=h),this.h()},h(){Z(a,"class","flex justify-center")},m(i,v){l(i,s,v),l(i,c,v),_(d,i,v),l(i,t,v),l(i,a,v),j=!0},p:Y,i(i){j||(w(d.$$.fragment,i),j=!0)},o(i){J(d.$$.fragment,i),j=!1},d(i){i&&(n(s),n(c),n(t),n(a)),T(d,i)}}}function ol(W){let s,U,c,d;return s=new P({props:{id:"lcm-t2i",option:"LCM",$$slots:{default:[ll]},$$scope:{ctx:W}}}),c=new P({props:{id:"lcm-t2i",option:"LCM-LoRA",$$slots:{default:[il]},$$scope:{ctx:W}}}),{c(){y(s.$$.fragment),U=o(),y(c.$$.fragment)},l(t){b(s.$$.fragment,t),U=r(t),b(c.$$.fragment,t)},m(t,a){_(s,t,a),l(t,U,a),_(c,t,a),d=!0},p(t,a){const h={};a&2&&(h.$$scope={dirty:a,ctx:t}),s.$set(h);const j={};a&2&&(j.$$scope={dirty:a,ctx:t}),c.$set(j)},i(t){d||(w(s.$$.fragment,t),w(c.$$.fragment,t),d=!0)},o(t){J(s.$$.fragment,t),J(c.$$.fragment,t),d=!1},d(t){t&&n(U),T(s,t),T(c,t)}}}function rl(W){let s,U="Examples:",c,d,t;return d=new V({props:{code:"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",highlighted:`<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">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;SimianLuo/LCM_Dreamshaper_v7&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To save GPU memory, torch.float16 can be used, but it may compromise image quality.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(torch_device=<span class="hljs-string">&quot;cuda&quot;</span>, torch_dtype=torch.float32)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;Self-portrait oil painting, a beautiful cyborg with golden hair, 8k&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Can be set to 1~50 steps. LCM support fast inference even &lt;= 4 steps. Recommend: 1~8 steps.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_inference_steps = <span class="hljs-number">4</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=<span class="hljs-number">8.0</span>).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;image.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.textContent=U,c=o(),y(d.$$.fragment)},l(a){s=u(a,"P",{"data-svelte-h":!0}),M(s)!=="svelte-kvfsh7"&&(s.textContent=U),c=r(a),b(d.$$.fragment,a)},m(a,h){l(a,s,h),l(a,c,h),_(d,a,h),t=!0},p:Y,i(a){t||(w(d.$$.fragment,a),t=!0)},o(a){J(d.$$.fragment,a),t=!1},d(a){a&&(n(s),n(c)),T(d,a)}}}function dl(W){let s,U="Examples:",c,d,t;return d=new V({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForImage2Image
<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> PIL

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AutoPipelineForImage2Image.from_pretrained(<span class="hljs-string">&quot;SimianLuo/LCM_Dreamshaper_v7&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To save GPU memory, torch.float16 can be used, but it may compromise image quality.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(torch_device=<span class="hljs-string">&quot;cuda&quot;</span>, torch_dtype=torch.float32)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;High altitude snowy mountains&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = PIL.Image.<span class="hljs-built_in">open</span>(<span class="hljs-string">&quot;./snowy_mountains.png&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Can be set to 1~50 steps. LCM support fast inference even &lt;= 4 steps. Recommend: 1~8 steps.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_inference_steps = <span class="hljs-number">4</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(
<span class="hljs-meta">... </span>    prompt=prompt, image=image, num_inference_steps=num_inference_steps, guidance_scale=<span class="hljs-number">8.0</span>
<span class="hljs-meta">... </span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span>images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;image.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){s=f("p"),s.textContent=U,c=o(),y(d.$$.fragment)},l(a){s=u(a,"P",{"data-svelte-h":!0}),M(s)!=="svelte-kvfsh7"&&(s.textContent=U),c=r(a),b(d.$$.fragment,a)},m(a,h){l(a,s,h),l(a,c,h),_(d,a,h),t=!0},p:Y,i(a){t||(w(d.$$.fragment,a),t=!0)},o(a){J(d.$$.fragment,a),t=!1},d(a){a&&(n(s),n(c)),T(d,a)}}}function pl(W){let s,U,c,d,t,a,h,j,i,v='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',L,p,I='Latent Consistency Models (LCMs) were proposed in <a href="https://huggingface.co/papers/2310.04378" rel="nofollow">Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference</a> by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.',R,C,E="The abstract of the paper is as follows:",Ot,Je,Hs='<em>Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: <a href="https://latent-consistency-models.github.io/" rel="nofollow">this https URL</a>.</em>',en,Te,Ps='A demo for the <a href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7" rel="nofollow">SimianLuo/LCM_Dreamshaper_v7</a> checkpoint can be found <a href="https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model" rel="nofollow">here</a>.',tn,Ue,Ds='The pipelines were contributed by <a href="https://luosiallen.github.io/" rel="nofollow">luosiallen</a>, <a href="https://github.com/nagolinc" rel="nofollow">nagolinc</a>, and <a href="https://github.com/dg845" rel="nofollow">dg845</a>.',nn,q,zs='<p>LCMs and LCM-LoRAs are available for Stable Diffusion v1.5, Stable Diffusion XL, and the SSD-1B model. You can find their checkpoints on the <a href="https://hf.co/collections/latent-consistency/latent-consistency-models-weights-654ce61a95edd6dffccef6a8" rel="nofollow">Latent Consistency</a> Collections.</p>',sn,je,an,K,ln,Ce,on,O,rn,ve,dn,Ze,qs='To use LCM-LoRAs for inpainting, you need to replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a> and load the LCM-LoRA weights with the <a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> method. Then you can use the pipeline as usual, and pass a text prompt, initial image, and mask image to generate an image in just 4 steps.',pn,Ie,cn,ee,Ks='<div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-inpaint.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption></div>',mn,We,fn,Le,Os="LCMs are compatible with adapters like LoRA, ControlNet, T2I-Adapter, and AnimateDiff. You can bring the speed of LCMs to these adapters to generate images in a certain style or condition the model on another input like a canny image.",un,$e,gn,xe,ea='<a href="../../tutorials/using_peft_for_inference">LoRA</a> adapters can be rapidly finetuned to learn a new style from just a few images and plugged into a pretrained model to generate images in that style.',hn,te,Mn,Ge,yn,ke,ta='<a href="./controlnet">ControlNet</a> are adapters that can be trained on a variety of inputs like canny edge, pose estimation, or depth. The ControlNet can be inserted into the pipeline to provide additional conditioning and control to the model for more accurate generation.',bn,Ne,na='You can find additional ControlNet models trained on other inputs in <a href="https://hf.co/lllyasviel" rel="nofollow">lllyasviel’s</a> repository.',_n,ne,wn,Ee,Jn,Be,sa='<a href="../../using-diffusers/t2i_adapter">T2I-Adapter</a> is an even more lightweight adapter than ControlNet, that provides an additional input to condition a pretrained model with. It is faster than ControlNet but the results may be slightly worse.',Tn,Re,aa='You can find additional T2I-Adapter checkpoints trained on other inputs in <a href="https://hf.co/TencentARC" rel="nofollow">TencentArc’s</a> repository.',Un,se,jn,Ve,Cn,Se,la='<a href="./animatediff">AnimateDiff</a> is an adapter that adds motion to an image. It can be used with most Stable Diffusion models, effectively turning them into “video generation” models. Generating good results with a video model usually requires generating multiple frames (16-24), which can be very slow with a regular Stable Diffusion model. LCM-LoRA can speed up this process by only taking 4-8 steps for each frame.',vn,Xe,ia='Load a <a href="/docs/diffusers/pr_13830/en/api/pipelines/animatediff#diffusers.AnimateDiffPipeline">AnimateDiffPipeline</a> and pass a <code>MotionAdapter</code> to it. Then replace the scheduler with the <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>, and combine both LoRA adapters with the <code>~loaders.UNet2DConditionLoadersMixin.set_adapters</code> method. Now you can pass a prompt to the pipeline and generate an animated image.',Zn,Qe,In,ae,oa='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-animatediff.gif"/>',Wn,Ye,Ln,$,Fe,zn,bt,ra="Pipeline for text-to-image generation using a latent consistency model.",qn,_t,da=`This model inherits from <a href="/docs/diffusers/pr_13830/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,Kn,wt,pa="The pipeline also inherits the following loading methods:",On,Jt,ca='<li><a href="/docs/diffusers/pr_13830/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion">load_textual_inversion()</a> for loading textual inversion embeddings</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for loading LoRA weights</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for saving LoRA weights</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file">from_single_file()</a> for loading <code>.ckpt</code> files</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter">load_ip_adapter()</a> for loading IP Adapters</li>',es,A,Ae,ts,Tt,ma="The call function to the pipeline for generation.",ns,le,ss,S,He,as,Ut,fa='Enables the FreeU mechanism as in <a href="https://huggingface.co/papers/2309.11497" rel="nofollow">https://huggingface.co/papers/2309.11497</a>.',ls,jt,ua="The suffixes after the scaling factors represent the stages where they are being applied.",is,Ct,ga=`Please refer to the <a href="https://github.com/ChenyangSi/FreeU" rel="nofollow">official repository</a> for combinations of the values
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.`,os,ie,Pe,rs,vt,ha="Disables the FreeU mechanism if enabled.",ds,oe,De,ps,Zt,Ma=`Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.`,cs,re,ze,ms,It,ya=`Disable sliced VAE decoding. If <code>enable_vae_slicing</code> was previously enabled, this method will go back to
computing decoding in one step.`,fs,de,qe,us,Wt,ba=`Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.`,gs,pe,Ke,hs,Lt,_a=`Disable tiled VAE decoding. If <code>enable_vae_tiling</code> was previously enabled, this method will go back to
computing decoding in one step.`,Ms,ce,Oe,ys,$t,wa="Encodes the prompt into text encoder hidden states.",bs,me,et,_s,xt,Ja='See <a href="https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298" rel="nofollow">https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298</a>',$n,tt,xn,x,nt,ws,Gt,Ta="Pipeline for image-to-image generation using a latent consistency model.",Js,kt,Ua=`This model inherits from <a href="/docs/diffusers/pr_13830/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,Ts,Nt,ja="The pipeline also inherits the following loading methods:",Us,Et,Ca='<li><a href="/docs/diffusers/pr_13830/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion">load_textual_inversion()</a> for loading textual inversion embeddings</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for loading LoRA weights</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for saving LoRA weights</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file">from_single_file()</a> for loading <code>.ckpt</code> files</li> <li><a href="/docs/diffusers/pr_13830/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter">load_ip_adapter()</a> for loading IP Adapters</li>',js,H,st,Cs,Bt,va="The call function to the pipeline for generation.",vs,fe,Zs,X,at,Is,Rt,Za='Enables the FreeU mechanism as in <a href="https://huggingface.co/papers/2309.11497" rel="nofollow">https://huggingface.co/papers/2309.11497</a>.',Ws,Vt,Ia="The suffixes after the scaling factors represent the stages where they are being applied.",Ls,St,Wa=`Please refer to the <a href="https://github.com/ChenyangSi/FreeU" rel="nofollow">official repository</a> for combinations of the values
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.`,$s,ue,lt,xs,Xt,La="Disables the FreeU mechanism if enabled.",Gs,ge,it,ks,Qt,$a=`Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.`,Ns,he,ot,Es,Yt,xa=`Disable sliced VAE decoding. If <code>enable_vae_slicing</code> was previously enabled, this method will go back to
computing decoding in one step.`,Bs,Me,rt,Rs,Ft,Ga=`Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.`,Vs,ye,dt,Ss,At,ka=`Disable tiled VAE decoding. If <code>enable_vae_tiling</code> was previously enabled, this method will go back to
computing decoding in one step.`,Xs,be,pt,Qs,Ht,Na="Encodes the prompt into text encoder hidden states.",Ys,_e,ct,Fs,Pt,Ea='See <a href="https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298" rel="nofollow">https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298</a>',Gn,mt,kn,D,ft,As,Dt,Ba="Output class for Stable Diffusion pipelines.",Nn,ut,En,qt,Bn;return t=new Fa({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),h=new Q({props:{title:"Latent Consistency Models",local:"latent-consistency-models",headingTag:"h1"}}),je=new Q({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),K=new Kt({props:{id:"lcm-text2img",options:["LCM","LCM-LoRA"],$$slots:{default:[Da]},$$scope:{ctx:W}}}),Ce=new Q({props:{title:"Image-to-image",local:"image-to-image",headingTag:"h2"}}),O=new Kt({props:{id:"lcm-img2img",options:["LCM","LCM-LoRA"],$$slots:{default:[Ka]},$$scope:{ctx:W}}}),ve=new Q({props:{title:"Inpainting",local:"inpainting",headingTag:"h2"}}),Ie=new V({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> AutoPipelineForInpainting, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

pipe = AutoPipelineForInpainting.from_pretrained(
    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-inpainting&quot;</span>,
    torch_dtype=torch.float16,
    variant=<span class="hljs-string">&quot;fp16&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdv1-5&quot;</span>)

init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png&quot;</span>)
mask_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png&quot;</span>)

prompt = <span class="hljs-string">&quot;concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)
image = pipe(
    prompt=prompt,
    image=init_image,
    mask_image=mask_image,
    generator=generator,
    num_inference_steps=<span class="hljs-number">4</span>,
    guidance_scale=<span class="hljs-number">4</span>,
).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),We=new Q({props:{title:"Adapters",local:"adapters",headingTag:"h2"}}),$e=new Q({props:{title:"LoRA",local:"lora",headingTag:"h3"}}),te=new Kt({props:{id:"lcm-lora",options:["LCM","LCM-LoRA"],$$slots:{default:[tl]},$$scope:{ctx:W}}}),Ge=new Q({props:{title:"ControlNet",local:"controlnet",headingTag:"h3"}}),ne=new Kt({props:{id:"lcm-controlnet",options:["LCM","LCM-LoRA"],$$slots:{default:[al]},$$scope:{ctx:W}}}),Ee=new Q({props:{title:"T2I-Adapter",local:"t2i-adapter",headingTag:"h3"}}),se=new Kt({props:{id:"lcm-t2i",options:["LCM","LCM-LoRA"],$$slots:{default:[ol]},$$scope:{ctx:W}}}),Ve=new Q({props:{title:"AnimateDiff",local:"animatediff",headingTag:"h3"}}),Qe=new V({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> MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_gif

adapter = MotionAdapter.from_pretrained(<span class="hljs-string">&quot;guoyww/animatediff-motion-adapter-v1-5&quot;</span>)
pipe = AnimateDiffPipeline.from_pretrained(
    <span class="hljs-string">&quot;frankjoshua/toonyou_beta6&quot;</span>,
    motion_adapter=adapter,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># set scheduler</span>
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

<span class="hljs-comment"># load LCM-LoRA</span>
pipe.load_lora_weights(<span class="hljs-string">&quot;latent-consistency/lcm-lora-sdv1-5&quot;</span>, adapter_name=<span class="hljs-string">&quot;lcm&quot;</span>)
pipe.load_lora_weights(<span class="hljs-string">&quot;guoyww/animatediff-motion-lora-zoom-in&quot;</span>, weight_name=<span class="hljs-string">&quot;diffusion_pytorch_model.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;motion-lora&quot;</span>)

pipe.set_adapters([<span class="hljs-string">&quot;lcm&quot;</span>, <span class="hljs-string">&quot;motion-lora&quot;</span>], adapter_weights=[<span class="hljs-number">0.55</span>, <span class="hljs-number">1.2</span>])

prompt = <span class="hljs-string">&quot;best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress&quot;</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)
frames = pipe(
    prompt=prompt,
    num_inference_steps=<span class="hljs-number">5</span>,
    guidance_scale=<span class="hljs-number">1.25</span>,
    cross_attention_kwargs={<span class="hljs-string">&quot;scale&quot;</span>: <span class="hljs-number">1</span>},
    num_frames=<span class="hljs-number">24</span>,
    generator=generator
).frames[<span class="hljs-number">0</span>]
export_to_gif(frames, <span class="hljs-string">&quot;animation.gif&quot;</span>)`,lang:"py",wrap:!1}}),Ye=new Q({props:{title:"LatentConsistencyModelPipeline",local:"diffusers.LatentConsistencyModelPipeline",headingTag:"h2"}}),Fe=new B({props:{name:"class diffusers.LatentConsistencyModelPipeline",anchor:"diffusers.LatentConsistencyModelPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": LCMScheduler"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"image_encoder",val:": transformers.models.clip.modeling_clip.CLIPVisionModelWithProjection | None = None"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/pr_13830/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.LatentConsistencyModelPipeline.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.LatentConsistencyModelPipeline.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.LatentConsistencyModelPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_13830/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.LatentConsistencyModelPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_13830/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. Currently only
supports <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.LatentConsistencyModelPipeline.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/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">model card</a> for
more details about a model&#x2019;s potential harms.`,name:"safety_checker"},{anchor:"diffusers.LatentConsistencyModelPipeline.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"},{anchor:"diffusers.LatentConsistencyModelPipeline.requires_safety_checker",description:`<strong>requires_safety_checker</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether the pipeline requires a safety checker component.`,name:"requires_safety_checker"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py#L133"}}),Ae=new B({props:{name:"__call__",anchor:"diffusers.LatentConsistencyModelPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str] = None"},{name:"height",val:": int | None = None"},{name:"width",val:": int | None = None"},{name:"num_inference_steps",val:": int = 4"},{name:"original_inference_steps",val:": int = None"},{name:"timesteps",val:": list = None"},{name:"guidance_scale",val:": float = 8.5"},{name:"num_images_per_prompt",val:": int | None = 1"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"prompt_embeds",val:": torch.Tensor | None = None"},{name:"ip_adapter_image",val:": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"},{name:"ip_adapter_image_embeds",val:": list[torch.Tensor] | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"cross_attention_kwargs",val:": dict[str, typing.Any] | None = None"},{name:"clip_skip",val:": int | None = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__call__.original_inference_steps",description:`<strong>original_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The original number of inference steps use to generate a linearly-spaced timestep schedule, from which
we will draw <code>num_inference_steps</code> evenly spaced timesteps from as our final timestep schedule,
following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
scheduler&#x2019;s <code>original_inference_steps</code> attribute.`,name:"original_inference_steps"},{anchor:"diffusers.LatentConsistencyModelPipeline.__call__.timesteps",description:`<strong>timesteps</strong> (<code>list[int]</code>, <em>optional</em>) &#x2014;
Custom timesteps to use for the denoising process. If not defined, equal spaced <code>num_inference_steps</code>
timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending
order.`,name:"timesteps"},{anchor:"diffusers.LatentConsistencyModelPipeline.__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>.
Note that the original latent consistency models paper uses a different CFG formulation where the
guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when <code>guidance_scale &gt; 0</code>).`,name:"guidance_scale"},{anchor:"diffusers.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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.LatentConsistencyModelPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</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.`,name:"prompt_embeds"},{anchor:"diffusers.LatentConsistencyModelPipeline.__call__.ip_adapter_image",description:`<strong>ip_adapter_image</strong> &#x2014; (<code>PipelineImageInput</code>, <em>optional</em>):
Optional image input to work with IP Adapters.`,name:"ip_adapter_image"},{anchor:"diffusers.LatentConsistencyModelPipeline.__call__.ip_adapter_image_embeds",description:`<strong>ip_adapter_image_embeds</strong> (<code>list[torch.Tensor]</code>, <em>optional</em>) &#x2014;
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape <code>(batch_size, num_images, emb_dim)</code>. It should
contain the negative image embedding if <code>do_classifier_free_guidance</code> is set to <code>True</code>. If not
provided, embeddings are computed from the <code>ip_adapter_image</code> input argument.`,name:"ip_adapter_image_embeds"},{anchor:"diffusers.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__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/pr_13830/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.LatentConsistencyModelPipeline.__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.LatentConsistencyModelPipeline.__call__.clip_skip",description:`<strong>clip_skip</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.`,name:"clip_skip"},{anchor:"diffusers.LatentConsistencyModelPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.LatentConsistencyModelPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>list</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py#L640",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/pr_13830/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> is returned,
otherwise a <code>tuple</code> is returned where the first element is a list with the generated images and the
second element is a list of <code>bool</code>s indicating whether the corresponding generated image contains
“not-safe-for-work” (nsfw) content.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_13830/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),le=new Ra({props:{anchor:"diffusers.LatentConsistencyModelPipeline.__call__.example",$$slots:{default:[rl]},$$scope:{ctx:W}}}),He=new B({props:{name:"enable_freeu",anchor:"diffusers.LatentConsistencyModelPipeline.enable_freeu",parameters:[{name:"s1",val:": float"},{name:"s2",val:": float"},{name:"b1",val:": float"},{name:"b2",val:": float"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelPipeline.enable_freeu.s1",description:`<strong>s1</strong> (<code>float</code>) &#x2014;
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate &#x201C;oversmoothing effect&#x201D; in the enhanced denoising process.`,name:"s1"},{anchor:"diffusers.LatentConsistencyModelPipeline.enable_freeu.s2",description:`<strong>s2</strong> (<code>float</code>) &#x2014;
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate &#x201C;oversmoothing effect&#x201D; in the enhanced denoising process.`,name:"s2"},{anchor:"diffusers.LatentConsistencyModelPipeline.enable_freeu.b1",description:"<strong>b1</strong> (<code>float</code>) &#x2014; Scaling factor for stage 1 to amplify the contributions of backbone features.",name:"b1"},{anchor:"diffusers.LatentConsistencyModelPipeline.enable_freeu.b2",description:"<strong>b2</strong> (<code>float</code>) &#x2014; Scaling factor for stage 2 to amplify the contributions of backbone features.",name:"b2"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/pipeline_utils.py#L2314"}}),Pe=new B({props:{name:"disable_freeu",anchor:"diffusers.LatentConsistencyModelPipeline.disable_freeu",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/pipeline_utils.py#L2336"}}),De=new B({props:{name:"enable_vae_slicing",anchor:"diffusers.LatentConsistencyModelPipeline.enable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/pipeline_utils.py#L2261"}}),ze=new B({props:{name:"disable_vae_slicing",anchor:"diffusers.LatentConsistencyModelPipeline.disable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/pipeline_utils.py#L2274"}}),qe=new B({props:{name:"enable_vae_tiling",anchor:"diffusers.LatentConsistencyModelPipeline.enable_vae_tiling",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/pipeline_utils.py#L2287"}}),Ke=new B({props:{name:"disable_vae_tiling",anchor:"diffusers.LatentConsistencyModelPipeline.disable_vae_tiling",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/pipeline_utils.py#L2301"}}),Oe=new B({props:{name:"encode_prompt",anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt",parameters:[{name:"prompt",val:""},{name:"device",val:""},{name:"num_images_per_prompt",val:""},{name:"do_classifier_free_guidance",val:""},{name:"negative_prompt",val:" = None"},{name:"prompt_embeds",val:": torch.Tensor | None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor | None = None"},{name:"lora_scale",val:": float | None = None"},{name:"clip_skip",val:": int | None = None"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
prompt to be encoded`,name:"prompt"},{anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>):
torch device`,name:"device"},{anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>) &#x2014;
number of images that should be generated per prompt`,name:"num_images_per_prompt"},{anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>) &#x2014;
whether to use classifier free guidance or not`,name:"do_classifier_free_guidance"},{anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt.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.LatentConsistencyModelPipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</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.LatentConsistencyModelPipeline.encode_prompt.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</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.LatentConsistencyModelPipeline.encode_prompt.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.`,name:"lora_scale"},{anchor:"diffusers.LatentConsistencyModelPipeline.encode_prompt.clip_skip",description:`<strong>clip_skip</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.`,name:"clip_skip"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py#L226"}}),et=new B({props:{name:"get_guidance_scale_embedding",anchor:"diffusers.LatentConsistencyModelPipeline.get_guidance_scale_embedding",parameters:[{name:"w",val:": Tensor"},{name:"embedding_dim",val:": int = 512"},{name:"dtype",val:": dtype = torch.float32"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelPipeline.get_guidance_scale_embedding.w",description:`<strong>w</strong> (<code>torch.Tensor</code>) &#x2014;
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.`,name:"w"},{anchor:"diffusers.LatentConsistencyModelPipeline.get_guidance_scale_embedding.embedding_dim",description:`<strong>embedding_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimension of the embeddings to generate.`,name:"embedding_dim"},{anchor:"diffusers.LatentConsistencyModelPipeline.get_guidance_scale_embedding.dtype",description:`<strong>dtype</strong> (<code>torch.dtype</code>, <em>optional</em>, defaults to <code>torch.float32</code>) &#x2014;
Data type of the generated embeddings.`,name:"dtype"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py#L517",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Embedding vectors with shape <code>(len(w), embedding_dim)</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),tt=new Q({props:{title:"LatentConsistencyModelImg2ImgPipeline",local:"diffusers.LatentConsistencyModelImg2ImgPipeline",headingTag:"h2"}}),nt=new B({props:{name:"class diffusers.LatentConsistencyModelImg2ImgPipeline",anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": LCMScheduler"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"image_encoder",val:": transformers.models.clip.modeling_clip.CLIPVisionModelWithProjection | None = None"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/pr_13830/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.LatentConsistencyModelImg2ImgPipeline.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.LatentConsistencyModelImg2ImgPipeline.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.LatentConsistencyModelImg2ImgPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_13830/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_13830/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. Currently only
supports <a href="/docs/diffusers/pr_13830/en/api/schedulers/lcm#diffusers.LCMScheduler">LCMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.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/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">model card</a> for
more details about a model&#x2019;s potential harms.`,name:"safety_checker"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.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"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.requires_safety_checker",description:`<strong>requires_safety_checker</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether the pipeline requires a safety checker component.`,name:"requires_safety_checker"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py#L154"}}),st=new B({props:{name:"__call__",anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str] = None"},{name:"image",val:": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"},{name:"num_inference_steps",val:": int = 4"},{name:"strength",val:": float = 0.8"},{name:"original_inference_steps",val:": int = None"},{name:"timesteps",val:": list = None"},{name:"guidance_scale",val:": float = 8.5"},{name:"num_images_per_prompt",val:": int | None = 1"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"prompt_embeds",val:": torch.Tensor | None = None"},{name:"ip_adapter_image",val:": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"},{name:"ip_adapter_image_embeds",val:": list[torch.Tensor] | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"cross_attention_kwargs",val:": dict[str, typing.Any] | None = None"},{name:"clip_skip",val:": int | None = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__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.LatentConsistencyModelImg2ImgPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.Tensor</code>, <code>PIL.Image.Image</code>, <code>np.ndarray</code>, <code>list[torch.Tensor]</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 also accept image
latents as <code>image</code>, but if passing latents directly it is not encoded again.`,name:"image"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__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.LatentConsistencyModelImg2ImgPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.8) &#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.LatentConsistencyModelImg2ImgPipeline.__call__.original_inference_steps",description:`<strong>original_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The original number of inference steps use to generate a linearly-spaced timestep schedule, from which
we will draw <code>num_inference_steps</code> evenly spaced timesteps from as our final timestep schedule,
following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
scheduler&#x2019;s <code>original_inference_steps</code> attribute.`,name:"original_inference_steps"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__.timesteps",description:`<strong>timesteps</strong> (<code>list[int]</code>, <em>optional</em>) &#x2014;
Custom timesteps to use for the denoising process. If not defined, equal spaced <code>num_inference_steps</code>
timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending
order.`,name:"timesteps"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__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>.
Note that the original latent consistency models paper uses a different CFG formulation where the
guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when <code>guidance_scale &gt; 0</code>).`,name:"guidance_scale"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__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.LatentConsistencyModelImg2ImgPipeline.__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.LatentConsistencyModelImg2ImgPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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.LatentConsistencyModelImg2ImgPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</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.`,name:"prompt_embeds"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__.ip_adapter_image",description:`<strong>ip_adapter_image</strong> &#x2014; (<code>PipelineImageInput</code>, <em>optional</em>):
Optional image input to work with IP Adapters.`,name:"ip_adapter_image"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__.ip_adapter_image_embeds",description:`<strong>ip_adapter_image_embeds</strong> (<code>list[torch.Tensor]</code>, <em>optional</em>) &#x2014;
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape <code>(batch_size, num_images, emb_dim)</code>. It should
contain the negative image embedding if <code>do_classifier_free_guidance</code> is set to <code>True</code>. If not
provided, embeddings are computed from the <code>ip_adapter_image</code> input argument.`,name:"ip_adapter_image_embeds"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__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.LatentConsistencyModelImg2ImgPipeline.__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/pr_13830/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__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.LatentConsistencyModelImg2ImgPipeline.__call__.clip_skip",description:`<strong>clip_skip</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.`,name:"clip_skip"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>list</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py#L709",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/pr_13830/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> is returned,
otherwise a <code>tuple</code> is returned where the first element is a list with the generated images and the
second element is a list of <code>bool</code>s indicating whether the corresponding generated image contains
“not-safe-for-work” (nsfw) content.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_13830/en/api/pipelines/stable_diffusion/upscale#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),fe=new Ra({props:{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.__call__.example",$$slots:{default:[dl]},$$scope:{ctx:W}}}),at=new B({props:{name:"enable_freeu",anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.enable_freeu",parameters:[{name:"s1",val:": float"},{name:"s2",val:": float"},{name:"b1",val:": float"},{name:"b2",val:": float"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.enable_freeu.s1",description:`<strong>s1</strong> (<code>float</code>) &#x2014;
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
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Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
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prompt to be encoded`,name:"prompt"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>):
torch device`,name:"device"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>) &#x2014;
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</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.LatentConsistencyModelImg2ImgPipeline.encode_prompt.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</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.LatentConsistencyModelImg2ImgPipeline.encode_prompt.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.`,name:"lora_scale"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.encode_prompt.clip_skip",description:`<strong>clip_skip</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.`,name:"clip_skip"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py#L241"}}),ct=new B({props:{name:"get_guidance_scale_embedding",anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.get_guidance_scale_embedding",parameters:[{name:"w",val:": Tensor"},{name:"embedding_dim",val:": int = 512"},{name:"dtype",val:": dtype = torch.float32"}],parametersDescription:[{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.get_guidance_scale_embedding.w",description:`<strong>w</strong> (<code>torch.Tensor</code>) &#x2014;
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.`,name:"w"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.get_guidance_scale_embedding.embedding_dim",description:`<strong>embedding_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimension of the embeddings to generate.`,name:"embedding_dim"},{anchor:"diffusers.LatentConsistencyModelImg2ImgPipeline.get_guidance_scale_embedding.dtype",description:`<strong>dtype</strong> (<code>torch.dtype</code>, <em>optional</em>, defaults to <code>torch.float32</code>) &#x2014;
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<p>Embedding vectors with shape <code>(len(w), embedding_dim)</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),mt=new Q({props:{title:"StableDiffusionPipelineOutput",local:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",headingTag:"h2"}}),ft=new B({props:{name:"class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",parameters:[{name:"images",val:": list[PIL.Image.Image] | numpy.ndarray"},{name:"nsfw_content_detected",val:": list[bool] | None"}],parametersDescription:[{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.images",description:`<strong>images</strong> (<code>list[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
list of denoised PIL images of length <code>batch_size</code> or NumPy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"},{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.nsfw_content_detected",description:`<strong>nsfw_content_detected</strong> (<code>list[bool]</code>) &#x2014;
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<code>None</code> if safety checking could not be performed.`,name:"nsfw_content_detected"}],source:"https://github.com/huggingface/diffusers/blob/vr_13830/src/diffusers/pipelines/stable_diffusion/pipeline_output.py#L10"}}),ut=new 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