diffusers/pr_12507/en/_app/immutable/nodes/114.a64ff7df.js

import{s as $e,o as Je,n as Pe}from"../chunks/scheduler.53228c21.js";import{S as Ie,i as ke,e as l,s as i,c as g,h as Ce,a as p,d as n,b as a,f as Q,g as f,j as x,k as q,l as F,m as o,n as _,t as h,o as b,p as y}from"../chunks/index.100fac89.js";import{C as Ue}from"../chunks/CopyLLMTxtMenu.35976da3.js";import{D as ge}from"../chunks/Docstring.39b25342.js";import{C as Fe}from"../chunks/CodeBlock.d30a6509.js";import{E as je}from"../chunks/ExampleCodeBlock.bbcb67c2.js";import{H as fe,E as Ne}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d4cb73c3.js";function Ze(Y){let s,$="Example:",w,d,c;return d=new Fe({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> BriaFiboPipeline
<span class="hljs-keyword">from</span> diffusers.modular_pipelines <span class="hljs-keyword">import</span> ModularPipeline

torch.set_grad_enabled(<span class="hljs-literal">False</span>)
vlm_pipe = ModularPipeline.from_pretrained(<span class="hljs-string">&quot;briaai/FIBO-VLM-prompt-to-JSON&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>)

pipe = BriaFiboPipeline.from_pretrained(
    <span class="hljs-string">&quot;briaai/FIBO&quot;</span>,
    trust_remote_code=<span class="hljs-literal">True</span>,
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

<span class="hljs-keyword">with</span> torch.inference_mode():
    <span class="hljs-comment"># 1. Create a prompt to generate an initial image</span>
    output = vlm_pipe(prompt=<span class="hljs-string">&quot;a beautiful dog&quot;</span>)
    json_prompt_generate = output.values[<span class="hljs-string">&quot;json_prompt&quot;</span>]

    <span class="hljs-comment"># Generate the image from the structured json prompt</span>
    results_generate = pipe(prompt=json_prompt_generate, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">5</span>)
    results_generate.images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;image_generate.png&quot;</span>)`,wrap:!1}}),{c(){s=l("p"),s.textContent=$,w=i(),g(d.$$.fragment)},l(r){s=p(r,"P",{"data-svelte-h":!0}),x(s)!=="svelte-11lpom8"&&(s.textContent=$),w=a(r),f(d.$$.fragment,r)},m(r,m){o(r,s,m),o(r,w,m),_(d,r,m),c=!0},p:Pe,i(r){c||(h(d.$$.fragment,r),c=!0)},o(r){b(d.$$.fragment,r),c=!1},d(r){r&&(n(s),n(w)),y(d,r)}}}function Ge(Y){let s,$,w,d,c,r,m,R,J,_e="Text-to-image models have mastered imagination - but not control. FIBO changes that.",D,P,he="FIBO is trained on structured JSON captions up to 1,000+ words and designed to understand and control different visual parameters such as lighting, composition, color, and camera settings, enabling precise and reproducible outputs.",A,I,be="With only 8 billion parameters, FIBO provides a new level of image quality, prompt adherence and proffesional control.",z,k,ye=`FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts.
you can use the <a href="https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON" rel="nofollow">FIBO-VLM-prompt-to-JSON</a> model or the <a href="https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON" rel="nofollow">FIBO-gemini-prompt-to-JSON</a>  to convert your freeform text prompt to a structured JSON prompt.`,K,B,we="<p>Avoid using freeform text prompts directly with FIBO because it does not produce the best results.</p>",ee,C,ve='Refer to the Bria Fibo Hugging Face <a href="https://huggingface.co/briaai/FIBO" rel="nofollow">page</a> to learn more.',te,U,ne,j,xe='<em>As the model is gated, before using it with diffusers you first need to go to the <a href="https://huggingface.co/briaai/FIBO" rel="nofollow">Bria Fibo Hugging Face page</a>, fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate.</em>',oe,N,Te="Use the command below to log in:",ie,Z,ae,G,re,u,L,de,v,S,ce,O,Be="Function invoked when calling the pipeline for generation.",me,M,ue,X,E,se,W,le,V,pe;return c=new Ue({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),m=new fe({props:{title:"Bria Fibo",local:"bria-fibo",headingTag:"h1"}}),U=new fe({props:{title:"Usage",local:"usage",headingTag:"h2"}}),Z=new Fe({props:{code:"aGYlMjBhdXRoJTIwbG9naW4=",highlighted:"hf auth login",wrap:!1}}),G=new fe({props:{title:"BriaFiboPipeline",local:"diffusers.BriaFiboPipeline",headingTag:"h2"}}),L=new ge({props:{name:"class diffusers.BriaFiboPipeline",anchor:"diffusers.BriaFiboPipeline",parameters:[{name:"transformer",val:": BriaFiboTransformer2DModel"},{name:"scheduler",val:": typing.Union[diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler, diffusers.schedulers.scheduling_utils.KarrasDiffusionSchedulers]"},{name:"vae",val:": AutoencoderKLWan"},{name:"text_encoder",val:": SmolLM3ForCausalLM"},{name:"tokenizer",val:": AutoTokenizer"}],parametersDescription:[{anchor:"diffusers.BriaFiboPipeline.transformer",description:`<strong>transformer</strong> (<code>BriaFiboTransformer2DModel</code>) &#x2014;
The transformer model for 2D diffusion modeling.`,name:"transformer"},{anchor:"diffusers.BriaFiboPipeline.scheduler",description:`<strong>scheduler</strong> (<code>FlowMatchEulerDiscreteScheduler</code> or <code>KarrasDiffusionSchedulers</code>) &#x2014;
Scheduler to be used with <code>transformer</code> to denoise the encoded latents.`,name:"scheduler"},{anchor:"diffusers.BriaFiboPipeline.vae",description:`<strong>vae</strong> (<code>AutoencoderKLWan</code>) &#x2014;
Variational Auto-Encoder for encoding and decoding images to and from latent representations.`,name:"vae"},{anchor:"diffusers.BriaFiboPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>SmolLM3ForCausalLM</code>) &#x2014;
Text encoder for processing input prompts.`,name:"text_encoder"},{anchor:"diffusers.BriaFiboPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>AutoTokenizer</code>) &#x2014;
Tokenizer used for processing the input text prompts for the text_encoder.`,name:"tokenizer"}],source:"https://github.com/huggingface/diffusers/blob/vr_12507/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py#L76"}}),S=new ge({props:{name:"__call__",anchor:"diffusers.BriaFiboPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 30"},{name:"timesteps",val:": typing.List[int] = None"},{name:"guidance_scale",val:": float = 5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"joint_attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 3000"},{name:"do_patching",val:" = False"}],parametersDescription:[{anchor:"diffusers.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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://huggingface.co/papers/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://huggingface.co/papers/2205.11487" 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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__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.BriaFiboPipeline.__call__.max_sequence_length",description:"<strong>max_sequence_length</strong> (<code>int</code> defaults to 3000) &#x2014; Maximum sequence length to use with the <code>prompt</code>.",name:"max_sequence_length"},{anchor:"diffusers.BriaFiboPipeline.__call__.do_patching",description:"<strong>do_patching</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014; Whether to use patching.",name:"do_patching"}],source:"https://github.com/huggingface/diffusers/blob/vr_12507/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py#L458",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~pipelines.flux.BriaFiboPipelineOutput</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.BriaFiboPipelineOutput</code> or <code>tuple</code></p>
`}}),M=new je({props:{anchor:"diffusers.BriaFiboPipeline.__call__.example",$$slots:{default:[Ze]},$$scope:{ctx:Y}}}),E=new ge({props:{name:"encode_prompt",anchor:"diffusers.BriaFiboPipeline.encode_prompt",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"guidance_scale",val:": float = 5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"max_sequence_length",val:": int = 3000"},{name:"lora_scale",val:": typing.Optional[float] = None"}],parametersDescription:[{anchor:"diffusers.BriaFiboPipeline.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.BriaFiboPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>):
torch device`,name:"device"},{anchor:"diffusers.BriaFiboPipeline.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.BriaFiboPipeline.encode_prompt.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>) &#x2014;
Guidance scale for classifier free guidance.`,name:"guidance_scale"},{anchor:"diffusers.BriaFiboPipeline.encode_prompt.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.BriaFiboPipeline.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.BriaFiboPipeline.encode_prompt.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
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