diffusers/pr_12652/en/api/pipelines/bria_fibo.md

Bria Fibo

Text-to-image models have mastered imagination - but not control. FIBO changes that.

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.

With only 8 billion parameters, FIBO provides a new level of image quality, prompt adherence and proffesional control.

FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts. you can use the FIBO-VLM-prompt-to-JSON model or the FIBO-gemini-prompt-to-JSON to convert your freeform text prompt to a structured JSON prompt.

Avoid using freeform text prompts directly with FIBO because it does not produce the best results.

Refer to the Bria Fibo Hugging Face page to learn more.

Usage

As the model is gated, before using it with diffusers you first need to go to the Bria Fibo Hugging Face page, 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.

Use the command below to log in:

hf auth login

BriaFiboPipeline[[diffusers.BriaFiboPipeline]]

diffusers.BriaFiboPipeline[[diffusers.BriaFiboPipeline]]

Source

__call__diffusers.BriaFiboPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py#L458[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 30"}, {"name": "timesteps", "val": ": list = None"}, {"name": "guidance_scale", "val": ": float = 5"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "num_images_per_prompt", "val": ": int | None = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.FloatTensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 3000"}, {"name": "do_patching", "val": " = False"}]- prompt (str or list[str], optional) -- The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.

• height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) -- The height in pixels of the generated image. This is set to 1024 by default for the best results.
• width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) -- The width in pixels of the generated image. This is set to 1024 by default for the best results.
• num_inference_steps (int, optional, defaults to 50) -- The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
• timesteps (list[int], optional) -- Custom timesteps to use for the denoising process with schedulers which support a timesteps argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. Must be in descending order.
• guidance_scale (float, optional, defaults to 5.0) -- Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
• negative_prompt (str or list[str], optional) -- The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• num_images_per_prompt (int, optional, defaults to 1) -- The number of images to generate per prompt.
• generator (torch.Generator or list[torch.Generator], optional) -- One or a list of torch generator(s) to make generation deterministic.
• latents (torch.FloatTensor, optional) -- 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 generator.
• prompt_embeds (torch.FloatTensor, optional) -- Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.FloatTensor, optional) -- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput instead of a plain tuple.
• joint_attention_kwargs (dict, optional) -- A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
• callback_on_step_end (Callable, optional) -- A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
• callback_on_step_end_tensor_inputs (List, optional) -- The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class.
• max_sequence_length (int defaults to 3000) -- Maximum sequence length to use with the prompt.
• do_patching (bool, optional, defaults to False) -- Whether to use patching.0~pipelines.flux.BriaFiboPipelineOutput or tuple``~pipelines.flux.BriaFiboPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Example:

import torch
from diffusers import BriaFiboPipeline
from diffusers.modular_pipelines import ModularPipeline

torch.set_grad_enabled(False)
vlm_pipe = ModularPipeline.from_pretrained("briaai/FIBO-VLM-prompt-to-JSON", trust_remote_code=True)

pipe = BriaFiboPipeline.from_pretrained(
    "briaai/FIBO",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

with torch.inference_mode():
    # 1. Create a prompt to generate an initial image
    output = vlm_pipe(prompt="a beautiful dog")
    json_prompt_generate = output.values["json_prompt"]

    # Generate the image from the structured json prompt
    results_generate = pipe(prompt=json_prompt_generate, num_inference_steps=50, guidance_scale=5)
    results_generate.images[0].save("image_generate.png")

Parameters:

transformer (BriaFiboTransformer2DModel) : The transformer model for 2D diffusion modeling.

scheduler (FlowMatchEulerDiscreteScheduler or KarrasDiffusionSchedulers) : Scheduler to be used with transformer to denoise the encoded latents.

vae (AutoencoderKLWan) : Variational Auto-Encoder for encoding and decoding images to and from latent representations.

text_encoder (SmolLM3ForCausalLM) : Text encoder for processing input prompts.

tokenizer (AutoTokenizer) : Tokenizer used for processing the input text prompts for the text_encoder.

Returns:

~pipelines.flux.BriaFiboPipelineOutput` or `tuple

~pipelines.flux.BriaFiboPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images.

encode_prompt[[diffusers.BriaFiboPipeline.encode_prompt]]

Source

Parameters:

prompt (str or list[str], optional) : prompt to be encoded

device : (torch.device): torch device

num_images_per_prompt (int) : number of images that should be generated per prompt

guidance_scale (float) : Guidance scale for classifier free guidance.

negative_prompt (str or list[str], optional) : The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).

prompt_embeds (torch.FloatTensor, optional) : Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.

negative_prompt_embeds (torch.FloatTensor, optional) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.