diffusers/pr_10567/en/_app/immutable/nodes/69.739e53cd.js

import{s as xe,o as Ze,n as Fe}from"../chunks/scheduler.8c3d61f6.js";import{S as Ie,i as $e,g as p,s,r as g,A as Ce,h as d,f as n,c as r,j as Y,u as h,x as U,k as H,y as f,a as o,v as _,d as w,t as y,w as b}from"../chunks/index.da70eac4.js";import{T as Ae}from"../chunks/Tip.1d9b8c37.js";import{D as ge}from"../chunks/Docstring.6b390b9a.js";import{C as _e}from"../chunks/CodeBlock.00a903b3.js";import{E as je}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as he,E as ke}from"../chunks/EditOnGithub.1e64e623.js";function Be(W){let a,v='AuraFlow 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.';return{c(){a=p("p"),a.innerHTML=v},l(l){a=d(l,"P",{"data-svelte-h":!0}),U(a)!=="svelte-q1wg22"&&(a.innerHTML=v)},m(l,m){o(l,a,m)},p:Fe,d(l){l&&n(a)}}}function Ge(W){let a,v="Examples:",l,m,c;return m=new _e({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQXVyYUZsb3dQaXBlbGluZSUwQSUwQXBpcGUlMjAlM0QlMjBBdXJhRmxvd1BpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJmYWwlMkZBdXJhRmxvdyUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiklMEFwaXBlJTIwJTNEJTIwcGlwZS50byglMjJjdWRhJTIyKSUwQXByb21wdCUyMCUzRCUyMCUyMkElMjBjYXQlMjBob2xkaW5nJTIwYSUyMHNpZ24lMjB0aGF0JTIwc2F5cyUyMGhlbGxvJTIwd29ybGQlMjIlMEFpbWFnZSUyMCUzRCUyMHBpcGUocHJvbXB0KS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2Uuc2F2ZSglMjJhdXJhX2Zsb3cucG5nJTIyKQ==",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> AuraFlowPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AuraFlowPipeline.from_pretrained(<span class="hljs-string">&quot;fal/AuraFlow&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>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>image = pipe(prompt).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;aura_flow.png&quot;</span>)`,wrap:!1}}),{c(){a=p("p"),a.textContent=v,l=s(),g(m.$$.fragment)},l(i){a=d(i,"P",{"data-svelte-h":!0}),U(a)!=="svelte-kvfsh7"&&(a.textContent=v),l=r(i),h(m.$$.fragment,i)},m(i,T){o(i,a,T),o(i,l,T),_(m,i,T),c=!0},p:Fe,i(i){c||(w(m.$$.fragment,i),c=!0)},o(i){y(m.$$.fragment,i),c=!1},d(i){i&&(n(a),n(l)),b(m,i)}}}function Xe(W){let a,v,l,m,c,i,T,we='AuraFlow is inspired by <a href="../pipelines/stable_diffusion/stable_diffusion_3">Stable Diffusion 3</a> and is by far the largest text-to-image generation model that comes with an Apache 2.0 license. This model achieves state-of-the-art results on the <a href="https://github.com/djghosh13/geneval" rel="nofollow">GenEval</a> benchmark.',L,$,ye='It was developed by the Fal team and more details about it can be found in <a href="https://blog.fal.ai/auraflow/" rel="nofollow">this blog post</a>.',S,x,D,C,O,A,be="Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.",K,j,Te='Refer to the <a href="../../quantization/overview">Quantization</a> overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized <a href="/docs/diffusers/pr_10567/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a> for inference with bitsandbytes.',ee,k,te,B,Me='Loading <a href="https://huggingface.co/docs/diffusers/quantization/gguf" rel="nofollow">GGUF checkpoints</a> are also supported:',ne,G,oe,X,ae,M,E,pe,u,P,de,q,Je="Function invoked when calling the pipeline for generation.",me,Z,ce,R,ve=`Returns: <a href="/docs/diffusers/pr_10567/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> or <code>tuple</code>:
If <code>return_dict</code> is <code>True</code>, <a href="/docs/diffusers/pr_10567/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> is returned, otherwise a <code>tuple</code> is returned
where the first element is a list with the generated images.`,ue,I,Q,fe,z,Ue="Encodes the prompt into text encoder hidden states.",se,N,re,V,ie;return c=new he({props:{title:"AuraFlow",local:"auraflow",headingTag:"h1"}}),x=new Ae({props:{$$slots:{default:[Be]},$$scope:{ctx:W}}}),C=new he({props:{title:"Quantization",local:"quantization",headingTag:"h2"}}),k=new _e({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> BitsAndBytesConfig <span class="hljs-keyword">as</span> DiffusersBitsAndBytesConfig, AuraFlowTransformer2DModel, AuraFlowPipeline
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BitsAndBytesConfig <span class="hljs-keyword">as</span> BitsAndBytesConfig, T5EncoderModel

quant_config = BitsAndBytesConfig(load_in_8bit=<span class="hljs-literal">True</span>)
text_encoder_8bit = T5EncoderModel.from_pretrained(
    <span class="hljs-string">&quot;fal/AuraFlow&quot;</span>,
    subfolder=<span class="hljs-string">&quot;text_encoder&quot;</span>,
    quantization_config=quant_config,
    torch_dtype=torch.float16,
)

quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=<span class="hljs-literal">True</span>)
transformer_8bit = AuraFlowTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;fal/AuraFlow&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quant_config,
    torch_dtype=torch.float16,
)

pipeline = AuraFlowPipeline.from_pretrained(
    <span class="hljs-string">&quot;fal/AuraFlow&quot;</span>,
    text_encoder=text_encoder_8bit,
    transformer=transformer_8bit,
    torch_dtype=torch.float16,
    device_map=<span class="hljs-string">&quot;balanced&quot;</span>,
)

prompt = <span class="hljs-string">&quot;a tiny astronaut hatching from an egg on the moon&quot;</span>
image = pipeline(prompt).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;auraflow.png&quot;</span>)`,wrap:!1}}),G=new _e({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwKCUwQSUyMCUyMCUyMCUyMEF1cmFGbG93UGlwZWxpbmUlMkMlMEElMjAlMjAlMjAlMjBHR1VGUXVhbnRpemF0aW9uQ29uZmlnJTJDJTBBJTIwJTIwJTIwJTIwQXVyYUZsb3dUcmFuc2Zvcm1lcjJETW9kZWwlMkMlMEEpJTBBJTBBdHJhbnNmb3JtZXIlMjAlM0QlMjBBdXJhRmxvd1RyYW5zZm9ybWVyMkRNb2RlbC5mcm9tX3NpbmdsZV9maWxlKCUwQSUyMCUyMCUyMCUyMCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZjaXR5OTYlMkZBdXJhRmxvdy12MC4zLWdndWYlMkZibG9iJTJGbWFpbiUyRmF1cmFfZmxvd18wLjMtUTJfSy5nZ3VmJTIyJTJDJTBBJTIwJTIwJTIwJTIwcXVhbnRpemF0aW9uX2NvbmZpZyUzREdHVUZRdWFudGl6YXRpb25Db25maWcoY29tcHV0ZV9kdHlwZSUzRHRvcmNoLmJmbG9hdDE2KSUyQyUwQSUyMCUyMCUyMCUyMHRvcmNoX2R0eXBlJTNEdG9yY2guYmZsb2F0MTYlMkMlMEEpJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBBdXJhRmxvd1BpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJmYWwlMkZBdXJhRmxvdy12MC4zJTIyJTJDJTBBJTIwJTIwJTIwJTIwdHJhbnNmb3JtZXIlM0R0cmFuc2Zvcm1lciUyQyUwQSUyMCUyMCUyMCUyMHRvcmNoX2R0eXBlJTNEdG9yY2guYmZsb2F0MTYlMkMlMEEpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyYSUyMGN1dGUlMjBwb255JTIwaW4lMjBhJTIwZmllbGQlMjBvZiUyMGZsb3dlcnMlMjIlMEFpbWFnZSUyMCUzRCUyMHBpcGVsaW5lKHByb21wdCkuaW1hZ2VzJTVCMCU1RCUwQWltYWdlLnNhdmUoJTIyYXVyYWZsb3cucG5nJTIyKQ==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> (
    AuraFlowPipeline,
    GGUFQuantizationConfig,
    AuraFlowTransformer2DModel,
)

transformer = AuraFlowTransformer2DModel.from_single_file(
    <span class="hljs-string">&quot;https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf&quot;</span>,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
)

pipeline = AuraFlowPipeline.from_pretrained(
    <span class="hljs-string">&quot;fal/AuraFlow-v0.3&quot;</span>,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)

prompt = <span class="hljs-string">&quot;a cute pony in a field of flowers&quot;</span>
image = pipeline(prompt).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;auraflow.png&quot;</span>)`,wrap:!1}}),X=new he({props:{title:"AuraFlowPipeline",local:"diffusers.AuraFlowPipeline",headingTag:"h2"}}),E=new ge({props:{name:"class diffusers.AuraFlowPipeline",anchor:"diffusers.AuraFlowPipeline",parameters:[{name:"tokenizer",val:": T5Tokenizer"},{name:"text_encoder",val:": UMT5EncoderModel"},{name:"vae",val:": AutoencoderKL"},{name:"transformer",val:": AuraFlowTransformer2DModel"},{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"}],parametersDescription:[{anchor:"diffusers.AuraFlowPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>T5TokenizerFast</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer" rel="nofollow">T5Tokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.AuraFlowPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>T5EncoderModel</code>) &#x2014;
Frozen text-encoder. AuraFlow uses
<a href="https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel" rel="nofollow">T5</a>, specifically the
<a href="https://huggingface.co/EleutherAI/pile-t5-xl" rel="nofollow">EleutherAI/pile-t5-xl</a> variant.`,name:"text_encoder"},{anchor:"diffusers.AuraFlowPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/pr_10567/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.AuraFlowPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/pr_10567/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a>) &#x2014;
Conditional Transformer (MMDiT and DiT) architecture to denoise the encoded image latents.`,name:"transformer"},{anchor:"diffusers.AuraFlowPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_10567/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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10567/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py#L114"}}),P=new ge({props:{name:"__call__",anchor:"diffusers.AuraFlowPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"sigmas",val:": typing.List[float] = None"},{name:"guidance_scale",val:": float = 3.5"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"height",val:": typing.Optional[int] = 1024"},{name:"width",val:": typing.Optional[int] = 1024"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"max_sequence_length",val:": int = 256"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.AuraFlowPipeline.__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.AuraFlowPipeline.__call__.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.AuraFlowPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.transformer.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image. This is set to 1024 by default for best results.`,name:"height"},{anchor:"diffusers.AuraFlowPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.transformer.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image. This is set to 1024 by default for best results.`,name:"width"},{anchor:"diffusers.AuraFlowPipeline.__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.AuraFlowPipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
Custom sigmas used to override the timestep spacing strategy of the scheduler. If <code>sigmas</code> is passed,
<code>num_inference_steps</code> and <code>timesteps</code> must be <code>None</code>.`,name:"sigmas"},{anchor:"diffusers.AuraFlowPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 5.0) &#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.AuraFlowPipeline.__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.AuraFlowPipeline.__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.AuraFlowPipeline.__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>.`,name:"latents"},{anchor:"diffusers.AuraFlowPipeline.__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.AuraFlowPipeline.__call__.prompt_attention_mask",description:`<strong>prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated attention mask for text embeddings.`,name:"prompt_attention_mask"},{anchor:"diffusers.AuraFlowPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</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.AuraFlowPipeline.__call__.negative_prompt_attention_mask",description:`<strong>negative_prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated attention mask for negative text embeddings.`,name:"negative_prompt_attention_mask"},{anchor:"diffusers.AuraFlowPipeline.__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.AuraFlowPipeline.__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.stable_diffusion_xl.StableDiffusionXLPipelineOutput</code> instead
of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.AuraFlowPipeline.__call__.max_sequence_length",description:"<strong>max_sequence_length</strong> (<code>int</code> defaults to 256) &#x2014; Maximum sequence length to use with the <code>prompt</code>.",name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_10567/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py#L388"}}),Z=new je({props:{anchor:"diffusers.AuraFlowPipeline.__call__.example",$$slots:{default:[Ge]},$$scope:{ctx:W}}}),Q=new ge({props:{name:"encode_prompt",anchor:"diffusers.AuraFlowPipeline.encode_prompt",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"do_classifier_free_guidance",val:": bool = True"},{name:"num_images_per_prompt",val:": int = 1"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"max_sequence_length",val:": int = 256"}],parametersDescription:[{anchor:"diffusers.AuraFlowPipeline.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.AuraFlowPipeline.encode_prompt.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt 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.AuraFlowPipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
whether to use classifier free guidance or not`,name:"do_classifier_free_guidance"},{anchor:"diffusers.AuraFlowPipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
number of images that should be generated per prompt`,name:"num_images_per_prompt"},{anchor:"diffusers.AuraFlowPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>, <em>optional</em>):
torch device to place the resulting embeddings on`,name:"device"},{anchor:"diffusers.AuraFlowPipeline.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.AuraFlowPipeline.encode_prompt.prompt_attention_mask",description:`<strong>prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated attention mask for text embeddings.`,name:"prompt_attention_mask"},{anchor:"diffusers.AuraFlowPipeline.encode_prompt.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AuraFlowPipeline.encode_prompt.negative_prompt_attention_mask",description:`<strong>negative_prompt_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated attention mask for negative text embeddings.`,name:"negative_prompt_attention_mask"},{anchor:"diffusers.AuraFlowPipeline.encode_prompt.max_sequence_length",description:"<strong>max_sequence_length</strong> (<code>int</code>, defaults to 256) &#x2014; Maximum sequence length to use for the prompt.",name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_10567/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py#L210"}}),N=new ke({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/aura_flow.md"}}),{c(){a=p("meta"),v=s(),l=p("p"),m=s(),g(c.$$.fragment),i=s(),T=p("p"),T.innerHTML=we,L=s(),$=p("p"),$.innerHTML=ye,S=s(),g(x.$$.fragment),D=s(),g(C.$$.fragment),O=s(),A=p("p"),A.textContent=be,K=s(),j=p("p"),j.innerHTML=Te,ee=s(),g(k.$$.fragment),te=s(),B=p("p"),B.innerHTML=Me,ne=s(),g(G.$$.fragment),oe=s(),g(X.$$.fragment),ae=s(),M=p("div"),g(E.$$.fragment),pe=s(),u=p("div"),g(P.$$.fragment),de=s(),q=p("p"),q.textContent=Je,me=s(),g(Z.$$.fragment),ce=s(),R=p("p"),R.innerHTML=ve,ue=s(),I=p("div"),g(Q.$$.fragment),fe=s(),z=p("p"),z.textContent=Ue,se=s(),g(N.$$.fragment),re=s(),V=p("p"),this.h()},l(e){const t=Ce("svelte-u9bgzb",document.head);a=d(t,"META",{name:!0,content:!0}),t.forEach(n),v=r(e),l=d(e,"P",{}),Y(l).forEach(n),m=r(e),h(c.$$.fragment,e),i=r(e),T=d(e,"P",{"data-svelte-h":!0}),U(T)!=="svelte-mz8sy4"&&(T.innerHTML=we),L=r(e),$=d(e,"P",{"data-svelte-h":!0}),U($)!=="svelte-1tuv1oa"&&($.innerHTML=ye),S=r(e),h(x.$$.fragment,e),D=r(e),h(C.$$.fragment,e),O=r(e),A=d(e,"P",{"data-svelte-h":!0}),U(A)!=="svelte-1ou2pxc"&&(A.textContent=be),K=r(e),j=d(e,"P",{"data-svelte-h":!0}),U(j)!=="svelte-1r4t9hp"&&(j.innerHTML=Te),ee=r(e),h(k.$$.fragment,e),te=r(e),B=d(e,"P",{"data-svelte-h":!0}),U(B)!=="svelte-9873at"&&(B.innerHTML=Me),ne=r(e),h(G.$$.fragment,e),oe=r(e),h(X.$$.fragment,e),ae=r(e),M=d(e,"DIV",{class:!0});var F=Y(M);h(E.$$.fragment,F),pe=r(F),u=d(F,"DIV",{class:!0});var J=Y(u);h(P.$$.fragment,J),de=r(J),q=d(J,"P",{"data-svelte-h":!0}),U(q)!=="svelte-v78lg8"&&(q.textContent=Je),me=r(J),h(Z.$$.fragment,J),ce=r(J),R=d(J,"P",{"data-svelte-h":!0}),U(R)!=="svelte-2abp8y"&&(R.innerHTML=ve),J.forEach(n),ue=r(F),I=d(F,"DIV",{class:!0});var le=Y(I);h(Q.$$.fragment,le),fe=r(le),z=d(le,"P",{"data-svelte-h":!0}),U(z)!=="svelte-16q0ax1"&&(z.textContent=Ue),le.forEach(n),F.forEach(n),se=r(e),h(N.$$.fragment,e),re=r(e),V=d(e,"P",{}),Y(V).forEach(n),this.h()},h(){H(a,"name","hf:doc:metadata"),H(a,"content",Ee),H(u,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(M,"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){f(document.head,a),o(e,v,t),o(e,l,t),o(e,m,t),_(c,e,t),o(e,i,t),o(e,T,t),o(e,L,t),o(e,$,t),o(e,S,t),_(x,e,t),o(e,D,t),_(C,e,t),o(e,O,t),o(e,A,t),o(e,K,t),o(e,j,t),o(e,ee,t),_(k,e,t),o(e,te,t),o(e,B,t),o(e,ne,t),_(G,e,t),o(e,oe,t),_(X,e,t),o(e,ae,t),o(e,M,t),_(E,M,null),f(M,pe),f(M,u),_(P,u,null),f(u,de),f(u,q),f(u,me),_(Z,u,null),f(u,ce),f(u,R),f(M,ue),f(M,I),_(Q,I,null),f(I,fe),f(I,z),o(e,se,t),_(N,e,t),o(e,re,t),o(e,V,t),ie=!0},p(e,[t]){const F={};t&2&&(F.$$scope={dirty:t,ctx:e}),x.$set(F);const J={};t&2&&(J.$$scope={dirty:t,ctx:e}),Z.$set(J)},i(e){ie||(w(c.$$.fragment,e),w(x.$$.fragment,e),w(C.$$.fragment,e),w(k.$$.fragment,e),w(G.$$.fragment,e),w(X.$$.fragment,e),w(E.$$.fragment,e),w(P.$$.fragment,e),w(Z.$$.fragment,e),w(Q.$$.fragment,e),w(N.$$.fragment,e),ie=!0)},o(e){y(c.$$.fragment,e),y(x.$$.fragment,e),y(C.$$.fragment,e),y(k.$$.fragment,e),y(G.$$.fragment,e),y(X.$$.fragment,e),y(E.$$.fragment,e),y(P.$$.fragment,e),y(Z.$$.fragment,e),y(Q.$$.fragment,e),y(N.$$.fragment,e),ie=!1},d(e){e&&(n(v),n(l),n(m),n(i),n(T),n(L),n($),n(S),n(D),n(O),n(A),n(K),n(j),n(ee),n(te),n(B),n(ne),n(oe),n(ae),n(M),n(se),n(re),n(V)),n(a),b(c,e),b(x,e),b(C,e),b(k,e),b(G,e),b(X,e),b(E),b(P),b(Z),b(Q),b(N,e)}}}const Ee='{"title":"AuraFlow","local":"auraflow","sections":[{"title":"Quantization","local":"quantization","sections":[],"depth":2},{"title":"AuraFlowPipeline","local":"diffusers.AuraFlowPipeline","sections":[],"depth":2}],"depth":1}';function Pe(W){return Ze(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ye extends Ie{constructor(a){super(),$e(this,a,Pe,Xe,xe,{})}}export{Ye as component};