diffusers/pr_14409/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](https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON) model or the [FIBO-gemini-prompt-to-JSON](https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON)  to convert your freeform text prompt to a structured JSON prompt.

> [!NOTE]
> Avoid using freeform text prompts directly with FIBO because it does not produce the best results.

Refer to the Bria Fibo Hugging Face [page](https://huggingface.co/briaai/FIBO) 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](https://huggingface.co/briaai/FIBO), 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:

```bash
hf auth login
```

## BriaFiboPipeline[[diffusers.BriaFiboPipeline]]

#### diffusers.BriaFiboPipeline[[diffusers.BriaFiboPipeline]]

```python
diffusers.BriaFiboPipeline(transformer: BriaFiboTransformer2DModel, scheduler: diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler | diffusers.schedulers.scheduling_utils.KarrasDiffusionSchedulers, vae: AutoencoderKLWan, text_encoder: SmolLM3ForCausalLM, tokenizer: AutoTokenizer)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14409/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py#L76)

**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.

#### __call__[[diffusers.BriaFiboPipeline.__call__]]

```python
__call__(prompt: str | list[str] = None, height: int | None = None, width: int | None = None, num_inference_steps: int = 30, timesteps: list = None, guidance_scale: float = 5, negative_prompt: str | list[str] | None = None, num_images_per_prompt: int | None = 1, generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = None, latents: typing.Optional[torch.FloatTensor] = None, output_type: str | None = 'pil', return_dict: bool = True, joint_attention_kwargs: dict[str, typing.Any] | None = None, callback_on_step_end: typing.Optional[typing.Callable[[int, int], NoneType]] = None, callback_on_step_end_tensor_inputs: list = ['latents'], max_sequence_length: int = 3000, do_patching = False)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14409/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py#L445)

**Parameters:**

prompt (`str` or `list[str]`) : The prompt or prompts to guide the image generation.

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](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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. 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)](https://pytorch.org/docs/stable/generated/torch.Generator.html) 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`.

output_type (`str`, *optional*, defaults to `"pil"`) : The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

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.

**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.

Function invoked when calling the pipeline for generation.

Example:
```python
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")
```

#### encode_prompt[[diffusers.BriaFiboPipeline.encode_prompt]]

```python
encode_prompt(prompt: str | list[str], device: typing.Optional[torch.device] = None, num_images_per_prompt: int = 1, guidance_scale: float = 5, negative_prompt: str | list[str] | None = None, max_sequence_length: int = 3000, lora_scale: float | None = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14409/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py#L201)

**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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).