diffusers/main/ko/_app/pages/optimization/torch2.0.mdx-hf-doc-builder.js

import{S as CA,i as YA,s as AA,e as l,k as i,w as g,t as n,M as FA,c as r,d as e,m as c,a,x as f,h as o,b as s,N as zA,G as t,g as p,y as u,L as VA,q as v,o as m,B as T,v as QA}from"../../chunks/vendor-hf-doc-builder.js";import{I as _}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as $r}from"../../chunks/CodeBlock-hf-doc-builder.js";function xA(FG){let 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dn=new _({}),vn=new $r({props:{code:"cGlwJTIwaW5zdGFsbCUyMC0tdXBncmFkZSUyMHRvcmNoJTIwZGlmZnVzZXJz",highlighted:"pip install --upgrade torch diffusers"}}),mn=new _({}),bn=new $r({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwRGlmZnVzaW9uUGlwZWxpbmUlMEElMEFwaXBlJTIwJTNEJTIwRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KSUwQXBpcGUlMjAlM0QlMjBwaXBlLnRvKCUyMmN1ZGElMjIpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyYSUyMHBob3RvJTIwb2YlMjBhbiUyMGFzdHJvbmF1dCUyMHJpZGluZyUyMGElMjBob3JzZSUyMG9uJTIwbWFycyUyMiUwQWltYWdlJTIwJTNEJTIwcGlwZShwcm9tcHQpLmltYWdlcyU1QjAlNUQ=",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

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

prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
image = pipe(prompt).images[<span class="hljs-number">0</span>]`}}),yn=new $r({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwRGlmZnVzaW9uUGlwZWxpbmUlMEElMkIlMjBmcm9tJTIwZGlmZnVzZXJzLm1vZGVscy5hdHRlbnRpb25fcHJvY2Vzc29yJTIwaW1wb3J0JTIwQXR0blByb2Nlc3NvcjJfMCUwQSUwQXBpcGUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpLnRvKCUyMmN1ZGElMjIpJTBBJTJCJTIwcGlwZS51bmV0LnNldF9hdHRuX3Byb2Nlc3NvcihBdHRuUHJvY2Vzc29yMl8wKCkpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyYSUyMHBob3RvJTIwb2YlMjBhbiUyMGFzdHJvbmF1dCUyMHJpZGluZyUyMGElMjBob3JzZSUyMG9uJTIwbWFycyUyMiUwQWltYWdlJTIwJTNEJTIwcGlwZShwcm9tcHQpLmltYWdlcyU1QjAlNUQ=",highlighted:`import torch
from diffusers import DiffusionPipeline
<span class="hljs-addition">+ from diffusers.models.attention_processor import AttnProcessor2_0</span>

pipe = DiffusionPipeline.from_pretrained(&quot;runwayml/stable-diffusion-v1-5&quot;, torch_dtype=torch.float16).to(&quot;cuda&quot;)
<span class="hljs-addition">+ pipe.unet.set_attn_processor(AttnProcessor2_0())</span>

prompt = &quot;a photo of an astronaut riding a horse on mars&quot;
image = pipe(prompt).images[0]`}}),wn=new $r({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
<span class="hljs-keyword">from</span> diffusers.models.attention_processor <span class="hljs-keyword">import</span> AttnProcessor

pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.unet.set_default_attn_processor()

prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
image = pipe(prompt).images[<span class="hljs-number">0</span>]`}}),Rn=new $r({props:{code:"cGlwZS51bmV0JTIwJTNEJTIwdG9yY2guY29tcGlsZShwaXBlLnVuZXQlMkMlMjBtb2RlJTNEJTIycmVkdWNlLW92ZXJoZWFkJTIyJTJDJTIwZnVsbGdyYXBoJTNEVHJ1ZSklMEFpbWFnZXMlMjAlM0QlMjBwaXBlKHByb21wdCUyQyUyMG51bV9pbmZlcmVuY2Vfc3RlcHMlM0RzdGVwcyUyQyUyMG51bV9pbWFnZXNfcGVyX3Byb21wdCUzRGJhdGNoX3NpemUpLmltYWdlcw==",highlighted:`pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)
images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images`}}),Sn=new _({}),jn=new $r({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

path = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>

run_compile = <span class="hljs-literal">True</span>  <span class="hljs-comment"># Set True / False</span>

pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.unet.to(memory_format=torch.channels_last)

<span class="hljs-keyword">if</span> run_compile:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Run torch compile&quot;</span>)
    pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)

prompt = <span class="hljs-string">&quot;ghibli style, a fantasy landscape with castles&quot;</span>

<span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>):
    images = pipe(prompt=prompt).images`}}),Zn=new _({}),Gn=new $r({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionImg2ImgPipeline
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>

response = requests.get(url)
init_image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
init_image = init_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

path = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>

run_compile = <span class="hljs-literal">True</span>  <span class="hljs-comment"># Set True / False</span>

pipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.unet.to(memory_format=torch.channels_last)

<span class="hljs-keyword">if</span> run_compile:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Run torch compile&quot;</span>)
    pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)

prompt = <span class="hljs-string">&quot;ghibli style, a fantasy landscape with castles&quot;</span>

<span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>):
    image = pipe(prompt=prompt, image=init_image).images[<span class="hljs-number">0</span>]`}}),Wn=new _({}),Nn=new $r({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionInpaintPipeline
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>

<span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url</span>):
    response = requests.get(url)
    <span class="hljs-keyword">return</span> Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)


img_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png&quot;</span>
mask_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png&quot;</span>

init_image = download_image(img_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
mask_image = download_image(mask_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

path = <span class="hljs-string">&quot;runwayml/stable-diffusion-inpainting&quot;</span>

run_compile = <span class="hljs-literal">True</span>  <span class="hljs-comment"># Set True / False</span>

pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.unet.to(memory_format=torch.channels_last)

<span class="hljs-keyword">if</span> run_compile:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Run torch compile&quot;</span>)
    pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)

prompt = <span class="hljs-string">&quot;ghibli style, a fantasy landscape with castles&quot;</span>

<span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>):
    image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[<span class="hljs-number">0</span>]`}}),In=new _({}),kn=new $r({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionControlNetPipeline, ControlNetModel
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>

response = requests.get(url)
init_image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
init_image = init_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

path = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>

run_compile = <span class="hljs-literal">True</span>  <span class="hljs-comment"># Set True / False</span>
controlnet = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;lllyasviel/sd-controlnet-canny&quot;</span>, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    path, controlnet=controlnet, torch_dtype=torch.float16
)

pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.unet.to(memory_format=torch.channels_last)
pipe.controlnet.to(memory_format=torch.channels_last)

<span class="hljs-keyword">if</span> run_compile:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Run torch compile&quot;</span>)
    pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)
    pipe.controlnet = torch.<span class="hljs-built_in">compile</span>(pipe.controlnet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)

prompt = <span class="hljs-string">&quot;ghibli style, a fantasy landscape with castles&quot;</span>

<span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>):
    image = pipe(prompt=prompt, image=init_image).images[<span class="hljs-number">0</span>]`}}),Xn=new _({}),Hn=new $r({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

run_compile = <span class="hljs-literal">True</span>  <span class="hljs-comment"># Set True / False</span>

pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;DeepFloyd/IF-I-M-v1.0&quot;</span>, variant=<span class="hljs-string">&quot;fp16&quot;</span>, text_encoder=<span class="hljs-literal">None</span>, torch_dtype=torch.float16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe_2 = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;DeepFloyd/IF-II-M-v1.0&quot;</span>, variant=<span class="hljs-string">&quot;fp16&quot;</span>, text_encoder=<span class="hljs-literal">None</span>, torch_dtype=torch.float16)
pipe_2.to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe_3 = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-x4-upscaler&quot;</span>, torch_dtype=torch.float16)
pipe_3.to(<span class="hljs-string">&quot;cuda&quot;</span>)


pipe.unet.to(memory_format=torch.channels_last)
pipe_2.unet.to(memory_format=torch.channels_last)
pipe_3.unet.to(memory_format=torch.channels_last)

<span class="hljs-keyword">if</span> run_compile:
    pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)
    pipe_2.unet = torch.<span class="hljs-built_in">compile</span>(pipe_2.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)
    pipe_3.unet = torch.<span class="hljs-built_in">compile</span>(pipe_3.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)

prompt = <span class="hljs-string">&quot;the blue hulk&quot;</span>

prompt_embeds = torch.randn((<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">4096</span>), dtype=torch.float16)
neg_prompt_embeds = torch.randn((<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">4096</span>), dtype=torch.float16)

<span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>):
    image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type=<span class="hljs-string">&quot;pt&quot;</span>).images
    image_2 = pipe_2(image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type=<span class="hljs-string">&quot;pt&quot;</span>).images
    image_3 = pipe_3(prompt=prompt, image=image, noise_level=<span class="hljs-number">100</span>).images`}}),zn=new _({}),Vn=new _({}),qn=new _({}),lo=new _({}),so=new _({}),go=new _({}),To=new _({}),Mo=new _({}),Uo=new _({}),Go=new _({}),Xo=new _({}),Yo=new _({}),xo=new _({}),Ko=new _({}),as=new _({}),{c(){zr=l("meta"),Ou=i(),Cr=l("h1"),ua=l("a"),Pg=l("span"),g(dn.$$.fragment),om=i(),Og=l("span"),sm=n("Diffusers\uC5D0\uC11C\uC758 PyTorch 2.0 \uAC00\uC18D\uD654 \uC9C0\uC6D0"),qu=i(),Yr=l("p"),qg=l("code"),im=n("0.13.0"),cm=n(" \uBC84\uC804\uBD80\uD130 Diffusers\uB294 "),hn=l("a"),dm=n("PyTorch 2.0"),hm=n("\uC5D0\uC11C\uC758 \uCD5C\uC2E0 \uCD5C\uC801\uD654\uB97C \uC9C0\uC6D0\uD569\uB2C8\uB2E4. \uC774\uB294 \uB2E4\uC74C\uC744 \uD3EC\uD568\uB429\uB2C8\uB2E4."),Lu=i(),va=l("ol"),pn=l("li"),pm=n("momory-efficient attention\uC744 \uC0AC\uC6A9\uD55C \uAC00\uC18D\uD654\uB41C \uD2B8\uB79C\uC2A4\uD3EC\uBA38 \uC9C0\uC6D0 - "),Lg=l("code"),gm=n("xformers"),fm=n("\uAC19\uC740 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\uC644\uB8CC\uD558\uB294 \uB370 \uC57D\uAC04\uC758 \uC2DC\uAC04\uC774 \uAC78\uB9AC\uBBC0\uB85C, \uD30C\uC774\uD504\uB77C\uC778\uC744 \uD55C \uBC88 \uC900\uBE44\uD55C \uB2E4\uC74C \uB3D9\uC77C\uD55C \uC720\uD615\uC758 \uCD94\uB860 \uC791\uC5C5\uC744 \uC5EC\uB7EC \uBC88 \uC218\uD589\uD574\uC57C \uD558\uB294 \uC0C1\uD669\uC5D0 \uAC00\uC7A5 \uC801\uD569\uD569\uB2C8\uB2E4. \uB2E4\uB978 \uC774\uBBF8\uC9C0 \uD06C\uAE30\uC5D0\uC11C \uCEF4\uD30C\uC77C\uB41C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uD638\uCD9C\uD558\uBA74 \uC2DC\uAC04\uC801 \uBE44\uC6A9\uC774 \uB9CE\uC774 \uB4E4 \uC218 \uC788\uB294 \uCEF4\uD30C\uC77C \uC791\uC5C5\uC774 \uB2E4\uC2DC \uD2B8\uB9AC\uAC70\uB429\uB2C8\uB2E4."),r3=i(),Un=l("h2"),RT=n("\uBCA4\uCE58\uB9C8\uD06C"),a3=i(),be=l("p"),UT=n("PyTorch 2.0\uC758 \uD6A8\uC728\uC801\uC778 \uC5B4\uD150\uC158 \uAD6C\uD604\uACFC "),bf=l("code"),BT=n("torch.compile"),ST=n("\uC744 \uC0AC\uC6A9\uD558\uC5EC \uAC00\uC7A5 \uB9CE\uC774 \uC0AC\uC6A9\uB418\uB294 5\uAC1C\uC758 \uD30C\uC774\uD504\uB77C\uC778\uC5D0 \uB300\uD574 \uB2E4\uC591\uD55C GPU\uC640 \uBC30\uCE58 \uD06C\uAE30\uC5D0 \uAC78\uCCD0 \uD3EC\uAD04\uC801\uC778 \uBCA4\uCE58\uB9C8\uD06C\uB97C \uC218\uD589\uD588\uC2B5\uB2C8\uB2E4. \uC5EC\uAE30\uC11C\uB294 "),_a=l("a"),yf=l("code"),jT=n("torch.compile()"),ZT=n("\uC774 \uCD5C\uC801\uC73C\uB85C \uD65C\uC6A9\uB418\uB3C4\uB85D \uD558\uB294"),GT=i(),Mf=l("code"),WT=n("diffusers 0.17.0.dev0"),NT=n("\uC744 \uC0AC\uC6A9\uD588\uC2B5\uB2C8\uB2E4."),n3=i(),Bn=l("h3"),IT=n("\uBCA4\uCE58\uB9C8\uD0B9 \uCF54\uB4DC"),o3=i(),Qr=l("h4"),Ea=l("a"),Df=l("span"),g(Sn.$$.fragment),kT=i(),wf=l("span"),XT=n("Stable Diffusion text-to-image"),s3=i(),g(jn.$$.fragment),i3=i(),xr=l("h4"),ba=l("a"),Jf=l("span"),g(Zn.$$.fragment),HT=i(),Rf=l("span"),$T=n("Stable Diffusion image-to-image"),c3=i(),g(Gn.$$.fragment),d3=i(),Pr=l("h4"),ya=l("a"),Uf=l("span"),g(Wn.$$.fragment),zT=i(),Bf=l("span"),CT=n("Stable Diffusion - inpainting"),h3=i(),g(Nn.$$.fragment),p3=i(),Or=l("h4"),Ma=l("a"),Sf=l("span"),g(In.$$.fragment),YT=i(),jf=l("span"),AT=n("ControlNet"),g3=i(),g(kn.$$.fragment),f3=i(),qr=l("h4"),Da=l("a"),Zf=l("span"),g(Xn.$$.fragment),FT=i(),Gf=l("span"),VT=n("IF text-to-image + upscaling"),u3=i(),g(Hn.$$.fragment),v3=i(),we=l("p"),QT=n("PyTorch 2.0 \uBC0F "),Wf=l("code"),xT=n("torch.compile()"),PT=n("\uB85C \uC5BB\uC744 \uC218 \uC788\uB294 \uAC00\uB2A5\uD55C \uC18D\uB3C4 \uD5A5\uC0C1\uC5D0 \uB300\uD574, "),ms=l("a"),OT=n("Stable Diffusion text-to-image pipeline"),qT=n("\uC5D0 \uB300\uD55C \uC0C1\uB300\uC801\uC778 \uC18D\uB3C4 \uD5A5\uC0C1\uC744 \uBCF4\uC5EC\uC8FC\uB294 \uCC28\uD2B8\uB97C 5\uAC1C\uC758 \uC11C\uB85C \uB2E4\uB978 GPU \uC81C\uD488\uAD70(\uBC30\uCE58 \uD06C\uAE30 4)\uC5D0 \uB300\uD574 \uB098\uD0C0\uB0C5\uB2C8\uB2E4:"),m3=i(),Ts=l("p"),_s=l("img"),T3=i(),wa=l("p"),LT=n(`To give you an even better idea of how this speed-up holds for the other pipelines presented above, consider the following
plot that shows the benchmarking numbers from an A100 across three different batch sizes
(with PyTorch 2.0 nightly and `),Nf=l("code"),KT=n("torch.compile()"),t5=n("):\n\uC774 \uC18D\uB3C4 \uD5A5\uC0C1\uC774 \uC704\uC5D0 \uC81C\uC2DC\uB41C \uB2E4\uB978 \uD30C\uC774\uD504\uB77C\uC778\uC5D0 \uB300\uD574\uC11C\uB3C4 \uC5B4\uB5BB\uAC8C \uC720\uC9C0\uB418\uB294\uC9C0 \uB354 \uC798 \uC774\uD574\uD558\uAE30 \uC704\uD574, \uC138 \uAC00\uC9C0\uC758 \uB2E4\uB978 \uBC30\uCE58 \uD06C\uAE30\uC5D0 \uAC78\uCCD0 A100\uC758 \uBCA4\uCE58\uB9C8\uD0B9(PyTorch 2.0 nightly \uBC0F `torch.compile() \uC0AC\uC6A9) \uC218\uCE58\uB97C \uBCF4\uC5EC\uC8FC\uB294 \uCC28\uD2B8\uB97C \uBCF4\uC785\uB2C8\uB2E4:"),_3=i(),Es=l("p"),bs=l("img"),E3=i(),ys=l("p"),$n=l("em"),e5=n("(\uC704 \uCC28\uD2B8\uC758 \uBCA4\uCE58\uB9C8\uD06C \uBA54\uD2B8\uB9AD\uC740 "),If=l("strong"),l5=n("\uCD08\uB2F9 iteration \uC218(iterations/second)"),r5=n("\uC785\uB2C8\uB2E4)"),b3=i(),Ms=l("p"),a5=n("\uADF8\uB7EC\uB098 \uD22C\uBA85\uC131\uC744 \uC704\uD574 \uBAA8\uB4E0 \uBCA4\uCE58\uB9C8\uD0B9 \uC218\uCE58\uB97C \uACF5\uAC1C\uD569\uB2C8\uB2E4!"),y3=i(),Ja=l("p"),n5=n("\uB2E4\uC74C \uD45C\uB4E4\uC5D0\uC11C\uB294, "),kf=l("strong"),Xf=l("em"),o5=n("\uCD08\uB2F9 \uCC98\uB9AC\uB418\uB294 iteration"),s5=n(" \uC218 \uCE21\uBA74\uC5D0\uC11C\uC758 \uACB0\uACFC\uB97C \uBCF4\uC5EC\uC90D\uB2C8\uB2E4."),M3=i(),Lr=l("h3"),Ra=l("a"),Hf=l("span"),g(zn.$$.fragment),i5=i(),$f=l("span"),c5=n("A100 (batch size: 1)"),D3=i(),Ua=l("table"),zf=l("thead"),D=l("tr"),Ds=l("th"),Cf=l("strong"),d5=n("Pipeline"),h5=i(),ws=l("th"),Cn=l("strong"),p5=n("torch 2.0 - 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plot that shows the benchmarking numbers from an A100 across three different batch sizes
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