diffusers/pr_9064/en/_app/immutable/nodes/67.fc3cd5c1.js

import{s as Ie,o as Ge,n as Pe}from"../chunks/scheduler.8c3d61f6.js";import{S as Ee,i as Le,g as r,s as l,r as f,A as Xe,h as p,f as n,c as a,j as S,u as g,x as y,k as D,y as w,a as o,v as h,d as _,t as b,w as x}from"../chunks/index.da70eac4.js";import{T as We}from"../chunks/Tip.1d9b8c37.js";import{D as we}from"../chunks/Docstring.ee4b6913.js";import{C as ye}from"../chunks/CodeBlock.00a903b3.js";import{E as ze}from"../chunks/ExampleCodeBlock.f7bd2c1f.js";import{H as fe,E as Ne}from"../chunks/EditOnGithub.1e64e623.js";function Ye(A){let s,v='Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out <a href="https://huggingface.co/blog/sd3#memory-optimizations-for-sd3" rel="nofollow">this section</a> for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to <a href="https://huggingface.co/blog/quanto-diffusers" rel="nofollow">this blog post</a> to learn more.  For an exhaustive list of resources, check out <a href="https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c" rel="nofollow">this gist</a>.';return{c(){s=r("p"),s.innerHTML=v},l(c){s=p(c,"P",{"data-svelte-h":!0}),y(s)!=="svelte-1nl2vrj"&&(s.innerHTML=v)},m(c,m){o(c,s,m)},p:Pe,d(c){c&&n(s)}}}function Ae(A){let s,v="Examples:",c,m,M;return m=new ye({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> FluxPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = FluxPipeline.from_pretrained(<span class="hljs-string">&quot;black-forest-labs/FLUX.1-schnell&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A cat holding a sign that says hello world&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Depending on the variant being used, the pipeline call will slightly vary.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Refer to the pipeline documentation for more details.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt, num_inference_steps=<span class="hljs-number">4</span>, guidance_scale=<span class="hljs-number">0.0</span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;flux.png&quot;</span>)`,wrap:!1}}),{c(){s=r("p"),s.textContent=v,c=l(),f(m.$$.fragment)},l(i){s=p(i,"P",{"data-svelte-h":!0}),y(s)!=="svelte-kvfsh7"&&(s.textContent=v),c=a(i),g(m.$$.fragment,i)},m(i,T){o(i,s,T),o(i,c,T),h(m,i,T),M=!0},p:Pe,i(i){M||(_(m.$$.fragment,i),M=!0)},o(i){b(m.$$.fragment,i),M=!1},d(i){i&&(n(s),n(c)),x(m,i)}}}function Qe(A){let s,v,c,m,M,i,T,Te='Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original <a href="https://blackforestlabs.ai/announcing-black-forest-labs/" rel="nofollow">blog post</a> by the creators of Flux, Black Forest Labs.',O,U,ve='Original model checkpoints for Flux can be found <a href="https://huggingface.co/black-forest-labs" rel="nofollow">here</a>. Original inference code can be found <a href="https://github.com/black-forest-labs/flux" rel="nofollow">here</a>.',K,$,ee,J,je="Flux comes in two variants:",te,C,ke="<li>Timestep-distilled (<code>black-forest-labs/FLUX.1-schnell</code>)</li> <li>Guidance-distilled (<code>black-forest-labs/FLUX.1-dev</code>)</li>",ne,Z,$e="Both checkpoints have slightly difference usage which we detail below.",oe,B,se,P,Fe="<li><code>max_sequence_length</code> cannot be more than 256.</li> <li><code>guidance_scale</code> needs to be 0.</li> <li>As this is a timestep-distilled model, it benefits from fewer sampling steps.</li>",le,I,ae,G,ie,E,Ue="<li>The guidance-distilled variant takes about 50 sampling steps for good-quality generation.</li> <li>It doesn’t have any limitations around the <code>max_sequence_length</code>.</li>",re,L,pe,X,ce,d,W,ge,Q,Je="The Flux pipeline for text-to-image generation.",he,R,Ce='Reference: <a href="https://blackforestlabs.ai/announcing-black-forest-labs/" rel="nofollow">https://blackforestlabs.ai/announcing-black-forest-labs/</a>',_e,j,z,be,q,Ze="Function invoked when calling the pipeline for generation.",xe,F,Me,H,N,de,Y,me,V,ue;return M=new fe({props:{title:"Flux",local:"flux",headingTag:"h1"}}),$=new We({props:{$$slots:{default:[Ye]},$$scope:{ctx:A}}}),B=new fe({props:{title:"Timestep-distilled",local:"timestep-distilled",headingTag:"h3"}}),I=new ye({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>  FluxPipeline

pipe = FluxPipeline.from_pretrained(<span class="hljs-string">&quot;black-forest-labs/FLUX.1-schnell&quot;</span>, torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A cat holding a sign that says hello world&quot;</span>
out = pipe(
    prompt=prompt,
    guidance_scale=<span class="hljs-number">0.</span>,
    height=<span class="hljs-number">768</span>,
    width=<span class="hljs-number">1360</span>,
    num_inference_steps=<span class="hljs-number">4</span>,
    max_sequence_length=<span class="hljs-number">256</span>,
).images[<span class="hljs-number">0</span>]
out.save(<span class="hljs-string">&quot;image.png&quot;</span>)`,wrap:!1}}),G=new fe({props:{title:"Guidance-distilled",local:"guidance-distilled",headingTag:"h3"}}),L=new ye({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwJTIwRmx1eFBpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyMEZsdXhQaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyYmxhY2stZm9yZXN0LWxhYnMlMkZGTFVYLjEtZGV2JTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5iZmxvYXQxNiklMEFwaXBlLmVuYWJsZV9tb2RlbF9jcHVfb2ZmbG9hZCgpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyYSUyMHRpbnklMjBhc3Ryb25hdXQlMjBoYXRjaGluZyUyMGZyb20lMjBhbiUyMGVnZyUyMG9uJTIwdGhlJTIwbW9vbiUyMiUwQW91dCUyMCUzRCUyMHBpcGUoJTBBJTIwJTIwJTIwJTIwcHJvbXB0JTNEcHJvbXB0JTJDJTBBJTIwJTIwJTIwJTIwZ3VpZGFuY2Vfc2NhbGUlM0QzLjUlMkMlMEElMjAlMjAlMjAlMjBoZWlnaHQlM0Q3NjglMkMlMEElMjAlMjAlMjAlMjB3aWR0aCUzRDEzNjAlMkMlMEElMjAlMjAlMjAlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNENTAlMkMlMEEpLmltYWdlcyU1QjAlNUQlMEFvdXQuc2F2ZSglMjJpbWFnZS5wbmclMjIp",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span>  FluxPipeline

pipe = FluxPipeline.from_pretrained(<span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>, torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;a tiny astronaut hatching from an egg on the moon&quot;</span>
out = pipe(
    prompt=prompt,
    guidance_scale=<span class="hljs-number">3.5</span>,
    height=<span class="hljs-number">768</span>,
    width=<span class="hljs-number">1360</span>,
    num_inference_steps=<span class="hljs-number">50</span>,
).images[<span class="hljs-number">0</span>]
out.save(<span class="hljs-string">&quot;image.png&quot;</span>)`,wrap:!1}}),X=new fe({props:{title:"FluxPipeline",local:"diffusers.FluxPipeline",headingTag:"h2"}}),W=new we({props:{name:"class diffusers.FluxPipeline",anchor:"diffusers.FluxPipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder_2",val:": T5EncoderModel"},{name:"tokenizer_2",val:": T5TokenizerFast"},{name:"transformer",val:": FluxTransformer2DModel"}],parametersDescription:[{anchor:"diffusers.FluxPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/pr_9064/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>) &#x2014;
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.`,name:"transformer"},{anchor:"diffusers.FluxPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_9064/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>transformer</code> to denoise the encoded image latents.`,name:"scheduler"},{anchor:"diffusers.FluxPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/pr_9064/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.FluxPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIP</a>, specifically
the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.FluxPipeline.text_encoder_2",description:`<strong>text_encoder_2</strong> (<code>T5EncoderModel</code>) &#x2014;
<a href="https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel" rel="nofollow">T5</a>, specifically
the <a href="https://huggingface.co/google/t5-v1_1-xxl" rel="nofollow">google/t5-v1_1-xxl</a> variant.`,name:"text_encoder_2"},{anchor:"diffusers.FluxPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.FluxPipeline.tokenizer_2",description:`<strong>tokenizer_2</strong> (<code>T5TokenizerFast</code>) &#x2014;
Second Tokenizer of class
<a href="https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast" rel="nofollow">T5TokenizerFast</a>.`,name:"tokenizer_2"}],source:"https://github.com/huggingface/diffusers/blob/vr_9064/src/diffusers/pipelines/flux/pipeline_flux.py#L140"}}),z=new we({props:{name:"__call__",anchor:"diffusers.FluxPipeline.__call__",parameters:[{name:"prompt",val:": Union = None"},{name:"prompt_2",val:": Union = None"},{name:"height",val:": Optional = None"},{name:"width",val:": Optional = None"},{name:"num_inference_steps",val:": int = 28"},{name:"timesteps",val:": List = None"},{name:"guidance_scale",val:": float = 7.0"},{name:"num_images_per_prompt",val:": Optional = 1"},{name:"generator",val:": Union = None"},{name:"latents",val:": Optional = None"},{name:"prompt_embeds",val:": Optional = None"},{name:"pooled_prompt_embeds",val:": Optional = None"},{name:"output_type",val:": Optional = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"joint_attention_kwargs",val:": Optional = None"},{name:"callback_on_step_end",val:": Optional = None"},{name:"callback_on_step_end_tensor_inputs",val:": List = ['latents']"},{name:"max_sequence_length",val:": int = 512"}],parametersDescription:[{anchor:"diffusers.FluxPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.FluxPipeline.__call__.prompt_2",description:`<strong>prompt_2</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to be sent to <code>tokenizer_2</code> and <code>text_encoder_2</code>. If not defined, <code>prompt</code> is
will be used instead`,name:"prompt_2"},{anchor:"diffusers.FluxPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image. This is set to 1024 by default for the best results.`,name:"height"},{anchor:"diffusers.FluxPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image. This is set to 1024 by default for the best results.`,name:"width"},{anchor:"diffusers.FluxPipeline.__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.FluxPipeline.__call__.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Custom timesteps to use for the denoising process with schedulers which support a <code>timesteps</code> argument
in their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is
passed will be used. Must be in descending order.`,name:"timesteps"},{anchor:"diffusers.FluxPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.FluxPipeline.__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.FluxPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.FluxPipeline.__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 will ge generated by sampling using the supplied random <code>generator</code>. Note: This pipeline expects
<code>latents</code> to be in a packed format. If you&#x2019;re providing custom latents, make sure to use the
<code>_pack_latents</code> method to prepare them. Packed latents should be a 3D tensor of shape
(batch_size, num_patches, channels).`,name:"latents"},{anchor:"diffusers.FluxPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <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.FluxPipeline.__call__.pooled_prompt_embeds",description:`<strong>pooled_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting.
If not provided, pooled text embeddings will be generated from <code>prompt</code> input argument.`,name:"pooled_prompt_embeds"},{anchor:"diffusers.FluxPipeline.__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 generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.FluxPipeline.__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 <code>~pipelines.flux.FluxPipelineOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.FluxPipeline.__call__.joint_attention_kwargs",description:`<strong>joint_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 under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow">diffusers.models.attention_processor</a>.`,name:"joint_attention_kwargs"},{anchor:"diffusers.FluxPipeline.__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.FluxPipeline.__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"},{anchor:"diffusers.FluxPipeline.__call__.max_sequence_length",description:"<strong>max_sequence_length</strong> (<code>int</code> defaults to 512) &#x2014; Maximum sequence length to use with the <code>prompt</code>.",name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_9064/src/diffusers/pipelines/flux/pipeline_flux.py#L527",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~pipelines.flux.FluxPipelineOutput</code> if <code>return_dict</code>
is True, otherwise a <code>tuple</code>. When returning a tuple, the first element is a list with the generated
images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~pipelines.flux.FluxPipelineOutput</code> or <code>tuple</code></p>
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The prompt or prompts to be sent to the <code>tokenizer_2</code> and <code>text_encoder_2</code>. If not defined, <code>prompt</code> is
used in all text-encoders
device &#x2014; (<code>torch.device</code>):
torch device`,name:"prompt_2"},{anchor:"diffusers.FluxPipeline.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.FluxPipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <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.FluxPipeline.encode_prompt.pooled_prompt_embeds",description:`<strong>pooled_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting.
If not provided, pooled text embeddings will be generated from <code>prompt</code> input argument.`,name:"pooled_prompt_embeds"},{anchor:"diffusers.FluxPipeline.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_9064/src/diffusers/pipelines/flux/pipeline_flux.py#L288"}}),Y=new Ne({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/flux.md"}}),{c(){s=r("meta"),v=l(),c=r("p"),m=l(),f(M.$$.fragment),i=l(),T=r("p"),T.innerHTML=Te,O=l(),U=r("p"),U.innerHTML=ve,K=l(),f($.$$.fragment),ee=l(),J=r("p"),J.textContent=je,te=l(),C=r("ul"),C.innerHTML=ke,ne=l(),Z=r("p"),Z.textContent=$e,oe=l(),f(B.$$.fragment),se=l(),P=r("ul"),P.innerHTML=Fe,le=l(),f(I.$$.fragment),ae=l(),f(G.$$.fragment),ie=l(),E=r("ul"),E.innerHTML=Ue,re=l(),f(L.$$.fragment),pe=l(),f(X.$$.fragment),ce=l(),d=r("div"),f(W.$$.fragment),ge=l(),Q=r("p"),Q.textContent=Je,he=l(),R=r("p"),R.innerHTML=Ce,_e=l(),j=r("div"),f(z.$$.fragment),be=l(),q=r("p"),q.textContent=Ze,xe=l(),f(F.$$.fragment),Me=l(),H=r("div"),f(N.$$.fragment),de=l(),f(Y.$$.fragment),me=l(),V=r("p"),this.h()},l(e){const t=Xe("svelte-u9bgzb",document.head);s=p(t,"META",{name:!0,content:!0}),t.forEach(n),v=a(e),c=p(e,"P",{}),S(c).forEach(n),m=a(e),g(M.$$.fragment,e),i=a(e),T=p(e,"P",{"data-svelte-h":!0}),y(T)!=="svelte-mlg237"&&(T.innerHTML=Te),O=a(e),U=p(e,"P",{"data-svelte-h":!0}),y(U)!=="svelte-pdc76o"&&(U.innerHTML=ve),K=a(e),g($.$$.fragment,e),ee=a(e),J=p(e,"P",{"data-svelte-h":!0}),y(J)!=="svelte-3w7f1n"&&(J.textContent=je),te=a(e),C=p(e,"UL",{"data-svelte-h":!0}),y(C)!=="svelte-g4tu0n"&&(C.innerHTML=ke),ne=a(e),Z=p(e,"P",{"data-svelte-h":!0}),y(Z)!=="svelte-r8tuq5"&&(Z.textContent=$e),oe=a(e),g(B.$$.fragment,e),se=a(e),P=p(e,"UL",{"data-svelte-h":!0}),y(P)!=="svelte-459kcz"&&(P.innerHTML=Fe),le=a(e),g(I.$$.fragment,e),ae=a(e),g(G.$$.fragment,e),ie=a(e),E=p(e,"UL",{"data-svelte-h":!0}),y(E)!=="svelte-k8komj"&&(E.innerHTML=Ue),re=a(e),g(L.$$.fragment,e),pe=a(e),g(X.$$.fragment,e),ce=a(e),d=p(e,"DIV",{class:!0});var u=S(d);g(W.$$.fragment,u),ge=a(u),Q=p(u,"P",{"data-svelte-h":!0}),y(Q)!=="svelte-77uxl4"&&(Q.textContent=Je),he=a(u),R=p(u,"P",{"data-svelte-h":!0}),y(R)!=="svelte-mxgguy"&&(R.innerHTML=Ce),_e=a(u),j=p(u,"DIV",{class:!0});var k=S(j);g(z.$$.fragment,k),be=a(k),q=p(k,"P",{"data-svelte-h":!0}),y(q)!=="svelte-v78lg8"&&(q.textContent=Ze),xe=a(k),g(F.$$.fragment,k),k.forEach(n),Me=a(u),H=p(u,"DIV",{class:!0});var Be=S(H);g(N.$$.fragment,Be),Be.forEach(n),u.forEach(n),de=a(e),g(Y.$$.fragment,e),me=a(e),V=p(e,"P",{}),S(V).forEach(n),this.h()},h(){D(s,"name","hf:doc:metadata"),D(s,"content",Re),D(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),D(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),D(d,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){w(document.head,s),o(e,v,t),o(e,c,t),o(e,m,t),h(M,e,t),o(e,i,t),o(e,T,t),o(e,O,t),o(e,U,t),o(e,K,t),h($,e,t),o(e,ee,t),o(e,J,t),o(e,te,t),o(e,C,t),o(e,ne,t),o(e,Z,t),o(e,oe,t),h(B,e,t),o(e,se,t),o(e,P,t),o(e,le,t),h(I,e,t),o(e,ae,t),h(G,e,t),o(e,ie,t),o(e,E,t),o(e,re,t),h(L,e,t),o(e,pe,t),h(X,e,t),o(e,ce,t),o(e,d,t),h(W,d,null),w(d,ge),w(d,Q),w(d,he),w(d,R),w(d,_e),w(d,j),h(z,j,null),w(j,be),w(j,q),w(j,xe),h(F,j,null),w(d,Me),w(d,H),h(N,H,null),o(e,de,t),h(Y,e,t),o(e,me,t),o(e,V,t),ue=!0},p(e,[t]){const u={};t&2&&(u.$$scope={dirty:t,ctx:e}),$.$set(u);const k={};t&2&&(k.$$scope={dirty:t,ctx:e}),F.$set(k)},i(e){ue||(_(M.$$.fragment,e),_($.$$.fragment,e),_(B.$$.fragment,e),_(I.$$.fragment,e),_(G.$$.fragment,e),_(L.$$.fragment,e),_(X.$$.fragment,e),_(W.$$.fragment,e),_(z.$$.fragment,e),_(F.$$.fragment,e),_(N.$$.fragment,e),_(Y.$$.fragment,e),ue=!0)},o(e){b(M.$$.fragment,e),b($.$$.fragment,e),b(B.$$.fragment,e),b(I.$$.fragment,e),b(G.$$.fragment,e),b(L.$$.fragment,e),b(X.$$.fragment,e),b(W.$$.fragment,e),b(z.$$.fragment,e),b(F.$$.fragment,e),b(N.$$.fragment,e),b(Y.$$.fragment,e),ue=!1},d(e){e&&(n(v),n(c),n(m),n(i),n(T),n(O),n(U),n(K),n(ee),n(J),n(te),n(C),n(ne),n(Z),n(oe),n(se),n(P),n(le),n(ae),n(ie),n(E),n(re),n(pe),n(ce),n(d),n(de),n(me),n(V)),n(s),x(M,e),x($,e),x(B,e),x(I,e),x(G,e),x(L,e),x(X,e),x(W),x(z),x(F),x(N),x(Y,e)}}}const Re='{"title":"Flux","local":"flux","sections":[{"title":"Timestep-distilled","local":"timestep-distilled","sections":[],"depth":3},{"title":"Guidance-distilled","local":"guidance-distilled","sections":[],"depth":3},{"title":"FluxPipeline","local":"diffusers.FluxPipeline","sections":[],"depth":2}],"depth":1}';function qe(A){return Ge(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class tt extends Ee{constructor(s){super(),Le(this,s,qe,Qe,Ie,{})}}export{tt as component};