import{S as hn,i as gn,s as bn,e as o,k as h,w as v,t as c,M as _n,c as s,d as n,m as g,a as l,x as $,h as p,b as m,G as e,g as w,y as I,q as P,o as T,B as D,v as wn,L as Qt}from"../../../../chunks/vendor-hf-doc-builder.js";import{T as Rt}from"../../../../chunks/Tip-hf-doc-builder.js";import{D as be}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as Yt}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as At}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as qt}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function yn(x){let i,b,f,r,_;return{c(){i=o("p"),b=c("Make sure to check out the Stable Diffusion "),f=o("a"),r=c("Tips"),_=c(" section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),this.h()},l(a){i=s(a,"P",{});var d=l(i);b=p(d,"Make sure to check out the Stable Diffusion "),f=s(d,"A",{href:!0});var k=l(f);r=p(k,"Tips"),k.forEach(n),_=p(d," section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),d.forEach(n),this.h()},h(){m(f,"href","./overview#tips")},m(a,d){w(a,i,d),e(i,b),e(i,f),e(f,r),e(i,_)},d(a){a&&n(i)}}}function vn(x){let i,b,f,r,_;return r=new Yt({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionImageVariationPipeline
<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
<span class="hljs-keyword">import</span> requests

pipe = StableDiffusionImageVariationPipeline.from_pretrained(
    <span class="hljs-string">&quot;lambdalabs/sd-image-variations-diffusers&quot;</span>, revision=<span class="hljs-string">&quot;v2.0&quot;</span>
)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

url = <span class="hljs-string">&quot;https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200&quot;</span>

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

out = pipe(image, num_images_per_prompt=<span class="hljs-number">3</span>, guidance_scale=<span class="hljs-number">15</span>)
out[<span class="hljs-string">&quot;images&quot;</span>][<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;result.jpg&quot;</span>)`}}),{c(){i=o("p"),b=c("Examples:"),f=h(),v(r.$$.fragment)},l(a){i=s(a,"P",{});var d=l(i);b=p(d,"Examples:"),d.forEach(n),f=g(a),$(r.$$.fragment,a)},m(a,d){w(a,i,d),e(i,b),w(a,f,d),I(r,a,d),_=!0},p:Qt,i(a){_||(P(r.$$.fragment,a),_=!0)},o(a){T(r.$$.fragment,a),_=!1},d(a){a&&n(i),a&&n(f),D(r,a)}}}function $n(x){let i,b,f,r,_;return{c(){i=o("p"),b=c("\u26A0\uFE0F Don\u2019t enable attention slicing if you\u2019re already using "),f=o("code"),r=c("scaled_dot_product_attention"),_=c(` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won\u2019t need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!`)},l(a){i=s(a,"P",{});var d=l(i);b=p(d,"\u26A0\uFE0F Don\u2019t enable attention slicing if you\u2019re already using "),f=s(d,"CODE",{});var k=l(f);r=p(k,"scaled_dot_product_attention"),k.forEach(n),_=p(d,` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won\u2019t need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!`),d.forEach(n)},m(a,d){w(a,i,d),e(i,b),e(i,f),e(f,r),e(i,_)},d(a){a&&n(i)}}}function In(x){let i,b,f,r,_;return r=new Yt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>,
<span class="hljs-meta">... </span>    torch_dtype=torch.float16,
<span class="hljs-meta">... </span>    use_safetensors=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_attention_slicing()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt).images[<span class="hljs-number">0</span>]`}}),{c(){i=o("p"),b=c("Examples:"),f=h(),v(r.$$.fragment)},l(a){i=s(a,"P",{});var d=l(i);b=p(d,"Examples:"),d.forEach(n),f=g(a),$(r.$$.fragment,a)},m(a,d){w(a,i,d),e(i,b),w(a,f,d),I(r,a,d),_=!0},p:Qt,i(a){_||(P(r.$$.fragment,a),_=!0)},o(a){T(r.$$.fragment,a),_=!1},d(a){a&&n(i),a&&n(f),D(r,a)}}}function Pn(x){let i,b;return{c(){i=o("p"),b=c(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(f){i=s(f,"P",{});var r=l(i);b=p(r,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),r.forEach(n)},m(f,r){w(f,i,r),e(i,b)},d(f){f&&n(i)}}}function Tn(x){let i,b,f,r,_;return r=new Yt({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwRGlmZnVzaW9uUGlwZWxpbmUlMEFmcm9tJTIweGZvcm1lcnMub3BzJTIwaW1wb3J0JTIwTWVtb3J5RWZmaWNpZW50QXR0ZW50aW9uRmxhc2hBdHRlbnRpb25PcCUwQSUwQXBpcGUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLTItMSUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiklMEFwaXBlJTIwJTNEJTIwcGlwZS50byglMjJjdWRhJTIyKSUwQXBpcGUuZW5hYmxlX3hmb3JtZXJzX21lbW9yeV9lZmZpY2llbnRfYXR0ZW50aW9uKGF0dGVudGlvbl9vcCUzRE1lbW9yeUVmZmljaWVudEF0dGVudGlvbkZsYXNoQXR0ZW50aW9uT3ApJTBBJTIzJTIwV29ya2Fyb3VuZCUyMGZvciUyMG5vdCUyMGFjY2VwdGluZyUyMGF0dGVudGlvbiUyMHNoYXBlJTIwdXNpbmclMjBWQUUlMjBmb3IlMjBGbGFzaCUyMEF0dGVudGlvbiUwQXBpcGUudmFlLmVuYWJsZV94Zm9ybWVyc19tZW1vcnlfZWZmaWNpZW50X2F0dGVudGlvbihhdHRlbnRpb25fb3AlM0ROb25lKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){i=o("p"),b=c("Examples:"),f=h(),v(r.$$.fragment)},l(a){i=s(a,"P",{});var d=l(i);b=p(d,"Examples:"),d.forEach(n),f=g(a),$(r.$$.fragment,a)},m(a,d){w(a,i,d),e(i,b),w(a,f,d),I(r,a,d),_=!0},p:Qt,i(a){_||(P(r.$$.fragment,a),_=!0)},o(a){T(r.$$.fragment,a),_=!1},d(a){a&&n(i),a&&n(f),D(r,a)}}}function Dn(x){let i,b,f,r,_,a,d,k,Oe,Ue,E,He,O,Ke,et,H,tt,nt,Ze,V,at,K,it,ot,ee,st,rt,We,L,je,Z,N,ve,te,lt,$e,ft,Ge,y,ne,ct,Ie,pt,dt,ae,mt,_e,ut,ht,gt,U,ie,bt,Pe,_t,wt,C,yt,J,oe,vt,Te,$t,It,B,Pt,F,Tt,z,se,Dt,re,St,De,xt,Jt,Mt,M,le,kt,fe,Et,ce,Vt,Ut,Zt,X,Wt,R,jt,A,pe,Gt,de,Lt,me,Nt,Ct,Le,W,q,Se,ue,Bt,xe,Ft,Ne,j,he,zt,Je,Xt,Ce;return a=new At({}),L=new Rt({props:{$$slots:{default:[yn]},$$scope:{ctx:x}}}),te=new At({}),ne=new be({props:{name:"class diffusers.StableDiffusionImageVariationPipeline",anchor:"diffusers.StableDiffusionImageVariationPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"image_encoder",val:": CLIPVisionModelWithProjection"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.StableDiffusionImageVariationPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/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.StableDiffusionImageVariationPipeline.image_encoder",description:`<strong>image_encoder</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPVisionModelWithProjection" rel="nofollow">CLIPVisionModelWithProjection</a>) &#x2014;
Frozen CLIP image-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"image_encoder"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.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.StableDiffusionImageVariationPipeline.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.StableDiffusionImageVariationPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/main/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionSafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow">model card</a> for more details
about a model&#x2019;s potential harms.`,name:"safety_checker"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py#L37"}}),ie=new be({props:{name:"__call__",anchor:"diffusers.StableDiffusionImageVariationPipeline.__call__",parameters:[{name:"image",val:": typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image], torch.FloatTensor]"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 7.5"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"}],parametersDescription:[{anchor:"diffusers.StableDiffusionImageVariationPipeline.__call__.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>List[PIL.Image.Image]</code> or <code>torch.FloatTensor</code>) &#x2014;
Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
<a href="https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json" rel="nofollow"><code>CLIPImageProcessor</code></a>.`,name:"image"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__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.StableDiffusionImageVariationPipeline.__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.StableDiffusionImageVariationPipeline.__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. This parameter is modulated by <code>strength</code>.`,name:"num_inference_steps"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__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>.`,name:"guidance_scale"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__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.StableDiffusionImageVariationPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__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.StableDiffusionImageVariationPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__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.StableDiffusionImageVariationPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.StableDiffusionImageVariationPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py#L242",returnDescription:`
<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img#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
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