import{s as ol,o as ml,n as yl}from"../chunks/scheduler.53228c21.js";import{S as Jl,i as cl,e as u,s as y,c as j,h as Ul,a as b,d as e,b as o,f as nl,g as f,j as W,k as pl,l as rl,m as a,n as Z,t as G,o as g,p as B}from"../chunks/index.100fac89.js";import{C as hl}from"../chunks/CopyLLMTxtMenu.4f0519ef.js";import{C as X}from"../chunks/CodeBlock.d30a6509.js";import{H as il,E as wl}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.62dbb7c4.js";import{H as Tl,a as Ml}from"../chunks/HfOption.fad27e59.js";function ul(Q){let p,d="For text-to-image, pass a list of prompts to the pipeline.",J,m,t,M,T="To generate multiple variations of one prompt, use the <code>num_images_per_prompt</code> argument.",w,c,C,U,I="Combine both approaches to generate different variations of different prompts.",k,r,h;return m=new X({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

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

prompts = [
    <span class="hljs-string">&quot;cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed&quot;</span>,
    <span class="hljs-string">&quot;cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain&quot;</span>,
    <span class="hljs-string">&quot;pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics&quot;</span>
]

images = pipeline(
    prompt=prompts,
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
plt.show()`,wrap:!1}}),c=new X({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

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

images = pipeline(
    prompt=<span class="hljs-string">&quot;pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics&quot;</span>,
    num_images_per_prompt=<span class="hljs-number">4</span>
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
plt.show()`,wrap:!1}}),r=new X({props:{code:"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",highlighted:`images = pipeline(
    prompt=prompts,
    num_images_per_prompt=<span class="hljs-number">2</span>,
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
plt.show()`,wrap:!1}}),{c(){p=u("p"),p.textContent=d,J=y(),j(m.$$.fragment),t=y(),M=u("p"),M.innerHTML=T,w=y(),j(c.$$.fragment),C=y(),U=u("p"),U.textContent=I,k=y(),j(r.$$.fragment)},l(s){p=b(s,"P",{"data-svelte-h":!0}),W(p)!=="svelte-1u13a2m"&&(p.textContent=d),J=o(s),f(m.$$.fragment,s),t=o(s),M=b(s,"P",{"data-svelte-h":!0}),W(M)!=="svelte-1wlqa91"&&(M.innerHTML=T),w=o(s),f(c.$$.fragment,s),C=o(s),U=b(s,"P",{"data-svelte-h":!0}),W(U)!=="svelte-1tfp3na"&&(U.textContent=I),k=o(s),f(r.$$.fragment,s)},m(s,i){a(s,p,i),a(s,J,i),Z(m,s,i),a(s,t,i),a(s,M,i),a(s,w,i),Z(c,s,i),a(s,C,i),a(s,U,i),a(s,k,i),Z(r,s,i),h=!0},p:yl,i(s){h||(G(m.$$.fragment,s),G(c.$$.fragment,s),G(r.$$.fragment,s),h=!0)},o(s){g(m.$$.fragment,s),g(c.$$.fragment,s),g(r.$$.fragment,s),h=!1},d(s){s&&(e(p),e(J),e(t),e(M),e(w),e(C),e(U),e(k)),B(m,s),B(c,s),B(r,s)}}}function bl(Q){let p,d="For image-to-image, pass a list of input images and prompts to the pipeline.",J,m,t,M,T="To generate multiple variations of one prompt, use the <code>num_images_per_prompt</code> argument.",w,c,C,U,I="Combine both approaches to generate different variations of different prompts.",k,r,h;return m=new X({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

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

input_images = [
    load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png&quot;</span>),
    load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png&quot;</span>),
    load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png&quot;</span>)
]

prompts = [
    <span class="hljs-string">&quot;cinematic photo of a beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed&quot;</span>,
    <span class="hljs-string">&quot;cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain&quot;</span>,
    <span class="hljs-string">&quot;pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics&quot;</span>
]

images = pipeline(
    prompt=prompts,
    image=input_images,
    guidance_scale=<span class="hljs-number">8.0</span>,
    strength=<span class="hljs-number">0.5</span>
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
plt.show()`,wrap:!1}}),c=new X({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

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

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

images = pipeline(
    prompt=<span class="hljs-string">&quot;pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics&quot;</span>,
    image=input_image,
    num_images_per_prompt=<span class="hljs-number">4</span>
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
plt.show()`,wrap:!1}}),r=new X({props:{code:"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",highlighted:`input_images = [
    load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png&quot;</span>),
    load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png&quot;</span>)
]

prompts = [
    <span class="hljs-string">&quot;cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain&quot;</span>,
    <span class="hljs-string">&quot;pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics&quot;</span>
]

images = pipeline(
    prompt=prompts,
    image=input_images,
    num_images_per_prompt=<span class="hljs-number">2</span>,
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
plt.show()`,wrap:!1}}),{c(){p=u("p"),p.textContent=d,J=y(),j(m.$$.fragment),t=y(),M=u("p"),M.innerHTML=T,w=y(),j(c.$$.fragment),C=y(),U=u("p"),U.textContent=I,k=y(),j(r.$$.fragment)},l(s){p=b(s,"P",{"data-svelte-h":!0}),W(p)!=="svelte-s3yhbz"&&(p.textContent=d),J=o(s),f(m.$$.fragment,s),t=o(s),M=b(s,"P",{"data-svelte-h":!0}),W(M)!=="svelte-1wlqa91"&&(M.innerHTML=T),w=o(s),f(c.$$.fragment,s),C=o(s),U=b(s,"P",{"data-svelte-h":!0}),W(U)!=="svelte-1tfp3na"&&(U.textContent=I),k=o(s),f(r.$$.fragment,s)},m(s,i){a(s,p,i),a(s,J,i),Z(m,s,i),a(s,t,i),a(s,M,i),a(s,w,i),Z(c,s,i),a(s,C,i),a(s,U,i),a(s,k,i),Z(r,s,i),h=!0},p:yl,i(s){h||(G(m.$$.fragment,s),G(c.$$.fragment,s),G(r.$$.fragment,s),h=!0)},o(s){g(m.$$.fragment,s),g(c.$$.fragment,s),g(r.$$.fragment,s),h=!1},d(s){s&&(e(p),e(J),e(t),e(M),e(w),e(C),e(U),e(k)),B(m,s),B(c,s),B(r,s)}}}function dl(Q){let p,d,J,m;return p=new Ml({props:{id:"usage",option:"text-to-image",$$slots:{default:[ul]},$$scope:{ctx:Q}}}),J=new Ml({props:{id:"usage",option:"image-to-image",$$slots:{default:[bl]},$$scope:{ctx:Q}}}),{c(){j(p.$$.fragment),d=y(),j(J.$$.fragment)},l(t){f(p.$$.fragment,t),d=o(t),f(J.$$.fragment,t)},m(t,M){Z(p,t,M),a(t,d,M),Z(J,t,M),m=!0},p(t,M){const T={};M&2&&(T.$$scope={dirty:M,ctx:t}),p.$set(T);const w={};M&2&&(w.$$scope={dirty:M,ctx:t}),J.$set(w)},i(t){m||(G(p.$$.fragment,t),G(J.$$.fragment,t),m=!0)},o(t){g(p.$$.fragment,t),g(J.$$.fragment,t),m=!1},d(t){t&&e(d),B(p,t),B(J,t)}}}function jl(Q){let p,d,J,m,t,M,T,w,c,C="Batch inference processes multiple prompts at a time to increase throughput. It is more efficient because processing multiple prompts at once maximizes GPU usage versus processing a single prompt and underutilizing the GPU.",U,I,k="The downside is increased latency because you must wait for the entire batch to complete, and more GPU memory is required for large batches.",r,h,s,i,F,x,O='Enable reproducible batch generation by passing a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">Generator’s</a> to the pipeline and tie each <code>Generator</code> to a seed to reuse it.',N,R,ll="Use a list comprehension to iterate over the batch size specified in <code>range()</code> to create a unique <code>Generator</code> object for each image in the batch.",H,E,sl="Don’t multiply the <code>Generator</code> by the batch size because that only creates one <code>Generator</code> object that is used sequentially for each image in the batch.",_,Y,A,V,el="Pass the <code>generator</code> to the pipeline.",q,S,L,z,al="You can use this to iteratively select an image associated with a seed and then improve on it by crafting a more detailed prompt.",D,v,K,$,P;return t=new hl({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new il({props:{title:"Batch inference",local:"batch-inference",headingTag:"h1"}}),h=new Tl({props:{id:"usage",options:["text-to-image","image-to-image"],$$slots:{default:[dl]},$$scope:{ctx:Q}}}),i=new il({props:{title:"Deterministic generation",local:"deterministic-generation",headingTag:"h2"}}),Y=new X({props:{code:"Z2VuZXJhdG9yJTIwJTNEJTIwJTVCdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmN1ZGElMjIpLm1hbnVhbF9zZWVkKDApJTVEJTIwKiUyMDM=",highlighted:'generator = [torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)] * <span class="hljs-number">3</span>',wrap:!1}}),S=new X({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

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

generator = [torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(i) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>)]
prompts = [
    <span class="hljs-string">&quot;cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed&quot;</span>,
    <span class="hljs-string">&quot;cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain&quot;</span>,
    <span class="hljs-string">&quot;pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics&quot;</span>
]

images = pipeline(
    prompt=prompts,
    generator=generator
).images

fig, axes = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">12</span>))
axes = axes.flatten()

<span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
    axes[i].imshow(image)
    axes[i].set_title(<span class="hljs-string">f&quot;Image <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&quot;</span>)
    axes[i].axis(<span class="hljs-string">&#x27;off&#x27;</span>)

plt.tight_layout()
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