diff --git a/src/diffusers/models/transformers/transformer_bria_fibo.py b/src/diffusers/models/transformers/transformer_bria_fibo.py
index 7ddbccfa47c5..481d44fb9fd5 100644
--- a/src/diffusers/models/transformers/transformer_bria_fibo.py
+++ b/src/diffusers/models/transformers/transformer_bria_fibo.py
@@ -442,6 +442,7 @@ class BriaFiboTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, From
         ...
     """
 
+    _no_split_modules = ["BriaFiboTransformerBlock", "BriaFiboSingleTransformerBlock"]
     _supports_gradient_checkpointing = True
 
     @register_to_config
diff --git a/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py b/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py
index 1f178066b17d..93f117105e91 100644
--- a/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py
+++ b/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py
@@ -25,6 +25,7 @@
 from ...schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -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
 
@@ -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(
diff --git a/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo_edit.py b/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo_edit.py
index c2327bbce1c7..74fffb6ff7e8 100644
--- a/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo_edit.py
+++ b/src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo_edit.py
@@ -27,6 +27,7 @@
 from ...schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -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
@@ -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(
@@ -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
 
@@ -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(
@@ -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]
+        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,