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fix(bria_fibo): honor timesteps, fix edit num_images batching, add _no_split_modules, fix docstring #13983

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fix(bria_fibo): honor timesteps, fix edit num_images batching, add _no_split_modules, fix docstring #13983

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c59886d
fix(bria_fibo): honor timesteps, fix edit num_images batching, add _n…
akshan-main Jun 18, 2026
8f26863
fix(bria_fibo): read vae_scale_factor from the vae config
akshan-main Jun 18, 2026
64d31b9
fix(bria_fibo): deprecate timesteps instead of wiring it through
akshan-main Jun 19, 2026
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1 change: 1 addition & 0 deletions src/diffusers/models/transformers/transformer_bria_fibo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -442,6 +442,7 @@ class BriaFiboTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, From
...
"""

_no_split_modules = ["BriaFiboTransformerBlock", "BriaFiboSingleTransformerBlock"]
_supports_gradient_checkpointing = True

@register_to_config
Expand Down
9 changes: 8 additions & 1 deletion src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py
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Expand Up @@ -25,6 +25,7 @@
from ...schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
from ...utils import (
USE_PEFT_BACKEND,
deprecate,
is_torch_xla_available,
logging,
replace_example_docstring,
Expand Down Expand Up @@ -107,7 +108,7 @@ def __init__(
scheduler=scheduler,
)

self.vae_scale_factor = 16
self.vae_scale_factor = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 16
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
self.default_sample_size = 64

Expand Down Expand Up @@ -659,6 +660,12 @@ def __call__(
else:
seq_len = (height // self.vae_scale_factor) * (width // self.vae_scale_factor)

if timesteps is not None:
deprecate(
"timesteps",
"1.0.0",
"Passing `timesteps` to `__call__` is deprecated and has no effect; it will be removed in a future version.",
)
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)

mu = calculate_shift(
Expand Down
15 changes: 12 additions & 3 deletions src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo_edit.py
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Expand Up @@ -27,6 +27,7 @@
from ...schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
from ...utils import (
USE_PEFT_BACKEND,
deprecate,
is_torch_xla_available,
logging,
replace_example_docstring,
Expand All @@ -50,7 +51,6 @@
torch.FloatTensor, Image.Image, List[Image.Image], List[torch.FloatTensor], np.ndarray, List[np.ndarray]
]

# TODO: Update example docstring
EXAMPLE_DOC_STRING = """
Example:
```python
Expand All @@ -59,7 +59,7 @@
from diffusers.modular_pipelines import ModularPipeline

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

pipe = BriaFiboEditPipeline.from_pretrained(
Expand Down Expand Up @@ -265,7 +265,7 @@ def __init__(
scheduler=scheduler,
)

self.vae_scale_factor = 16
self.vae_scale_factor = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 16
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) # * 2)
self.default_sample_size = 32 # 64

Expand Down Expand Up @@ -877,6 +877,12 @@ def __call__(
else:
seq_len = (height // self.vae_scale_factor) * (width // self.vae_scale_factor)

if timesteps is not None:
deprecate(
"timesteps",
"1.0.0",
"Passing `timesteps` to `__call__` is deprecated and has no effect; it will be removed in a future version.",
)
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)

mu = calculate_shift(
Expand Down Expand Up @@ -1039,6 +1045,9 @@ def prepare_image_latents(
image_latents_cthw = torch.concat(latents_scaled, dim=0)
image_latents_bchw = image_latents_cthw[:, :, 0, :, :]

repeat_by = batch_size // image_latents_bchw.shape[0]

@yiyixuxu yiyixuxu Jun 19, 2026

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we talked about standardlize this across pipelines using a function like this https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L115

maybe we can start addingthat while you're doing these pipeline refactors?

image_latents_bchw = image_latents_bchw.repeat_interleave(repeat_by, dim=0)

image_latent_height, image_latent_width = image_latents_bchw.shape[2:]
image_latents_bsd = self._pack_latents_no_patch(
latents=image_latents_bchw,
Expand Down
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