up

docs: improve docstring scheduling_ddim_inverse.py

docker/diffusers-pytorch-cuda/Dockerfile

@@ -1,8 +1,8 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+FROM nvidia/cuda:12.8.0-runtime-ubuntu22.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.11
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
@@ -32,10 +32,12 @@ RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# Install torch, torchvision, and torchaudio together to ensure compatibility
 RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio
+    torchaudio \
+    --index-url https://download.pytorch.org/whl/cu128
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -1,8 +1,8 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+FROM nvidia/cuda:12.8.0-runtime-ubuntu22.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.11
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
@@ -32,10 +32,12 @@ RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# Install torch, torchvision, and torchaudio together to ensure compatibility
 RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio
+    torchaudio \
+    --index-url https://download.pytorch.org/whl/cu128
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

docs/source/en/_toctree.yml

@@ -496,6 +496,8 @@
         title: Bria 3.2
       - local: api/pipelines/bria_fibo
         title: Bria Fibo
+      - local: api/pipelines/bria_fibo_edit
+        title: Bria Fibo Edit
       - local: api/pipelines/chroma
         title: Chroma
       - local: api/pipelines/cogview3

docs/source/en/api/pipelines/bria_fibo_edit.md

@@ -0,0 +1,33 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Bria Fibo Edit
+
+Fibo Edit is an 8B parameter image-to-image model that introduces a new paradigm of structured control, operating on JSON inputs paired with source images to enable deterministic and repeatable editing workflows.
+Featuring native masking for granular precision, it moves beyond simple prompt-based diffusion to offer explicit, interpretable control optimized for production environments.
+Its lightweight architecture is designed for deep customization, empowering researchers to build specialized "Edit" models for domain-specific tasks while delivering top-tier aesthetic quality
+
+## 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-Edit), 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
+```
+
+
+## BriaFiboEditPipeline
+
+[[autodoc]] BriaFiboEditPipeline
+	- all
+	- __call__

src/diffusers/__init__.py

@@ -457,6 +457,7 @@ else:
             "AuraFlowPipeline",
             "BlipDiffusionControlNetPipeline",
             "BlipDiffusionPipeline",
+            "BriaFiboEditPipeline",
             "BriaFiboPipeline",
             "BriaPipeline",
             "ChromaImg2ImgPipeline",
@@ -1185,6 +1186,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDM2UNet2DConditionModel,
             AudioLDMPipeline,
             AuraFlowPipeline,
+            BriaFiboEditPipeline,
             BriaFiboPipeline,
             BriaPipeline,
             ChromaImg2ImgPipeline,

src/diffusers/loaders/peft.py

@@ -478,7 +478,7 @@ class PeftAdapterMixin:
         Args:
             adapter_names (`List[str]` or `str`):
                 The names of the adapters to use.
-            adapter_weights (`Union[List[float], float]`, *optional*):
+            weights (`Union[List[float], float]`, *optional*):
                 The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the
                 adapters.
 
@@ -495,7 +495,7 @@ class PeftAdapterMixin:
             "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
         )
         pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
-        pipeline.unet.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
+        pipeline.unet.set_adapters(["cinematic", "pixel"], weights=[0.5, 0.5])
         ```
         """
         if not USE_PEFT_BACKEND:

src/diffusers/models/transformers/transformer_longcat_image.py

@@ -406,6 +406,7 @@ class LongCatImageTransformer2DModel(
     """
 
     _supports_gradient_checkpointing = True
+    _repeated_blocks = ["LongCatImageTransformerBlock", "LongCatImageSingleTransformerBlock"]
 
     @register_to_config
     def __init__(

src/diffusers/pipelines/__init__.py

@@ -129,7 +129,7 @@ else:
         "AnimateDiffVideoToVideoControlNetPipeline",
     ]
     _import_structure["bria"] = ["BriaPipeline"]
-    _import_structure["bria_fibo"] = ["BriaFiboPipeline"]
+    _import_structure["bria_fibo"] = ["BriaFiboPipeline", "BriaFiboEditPipeline"]
     _import_structure["flux2"] = ["Flux2Pipeline", "Flux2KleinPipeline"]
     _import_structure["flux"] = [
         "FluxControlPipeline",
@@ -597,7 +597,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .aura_flow import AuraFlowPipeline
         from .blip_diffusion import BlipDiffusionPipeline
         from .bria import BriaPipeline
-        from .bria_fibo import BriaFiboPipeline
+        from .bria_fibo import BriaFiboEditPipeline, BriaFiboPipeline
         from .chroma import ChromaImg2ImgPipeline, ChromaInpaintPipeline, ChromaPipeline
         from .chronoedit import ChronoEditPipeline
         from .cogvideo import (

src/diffusers/pipelines/bria_fibo/__init__.py

@@ -23,6 +23,8 @@ except OptionalDependencyNotAvailable:
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
     _import_structure["pipeline_bria_fibo"] = ["BriaFiboPipeline"]
+    _import_structure["pipeline_bria_fibo_edit"] = ["BriaFiboEditPipeline"]
+
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -33,6 +35,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ...utils.dummy_torch_and_transformers_objects import *
     else:
         from .pipeline_bria_fibo import BriaFiboPipeline
+        from .pipeline_bria_fibo_edit import BriaFiboEditPipeline
 
 else:
     import sys

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -84,7 +84,6 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
         >>> from diffusers.utils import load_image
 
-
         >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
         >>> feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
         >>> controlnet = ControlNetModel.from_pretrained(

src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py

@@ -53,7 +53,6 @@ EXAMPLE_DOC_STRING = """
         >>> from transformers import AutoTokenizer, LlamaForCausalLM
         >>> from diffusers import HiDreamImagePipeline
 
-
         >>> tokenizer_4 = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
         >>> text_encoder_4 = LlamaForCausalLM.from_pretrained(
         ...     "meta-llama/Meta-Llama-3.1-8B-Instruct",

src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py

@@ -85,7 +85,6 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers import ControlNetModel, StableDiffusionXLControlNetPAGImg2ImgPipeline, AutoencoderKL
         >>> from diffusers.utils import load_image
 
-
         >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
         >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
         >>> controlnet = ControlNetModel.from_pretrained(

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -459,7 +459,6 @@ class StableDiffusionLatentUpscalePipeline(DiffusionPipeline, StableDiffusionMix
         >>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
         >>> import torch
 
-
         >>> pipeline = StableDiffusionPipeline.from_pretrained(
         ...     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
         ... )

src/diffusers/schedulers/scheduling_consistency_decoder.py

@@ -14,7 +14,7 @@ from .scheduling_utils import SchedulerMixin
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -28,8 +28,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_ddim.py

@@ -51,7 +51,7 @@ class DDIMSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_ddim_cogvideox.py

@@ -51,7 +51,7 @@ class DDIMSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:
@@ -100,14 +100,13 @@ def betas_for_alpha_bar(
     return torch.tensor(betas, dtype=torch.float32)
 
 
-def rescale_zero_terminal_snr(alphas_cumprod):
+def rescale_zero_terminal_snr(alphas_cumprod: torch.Tensor) -> torch.Tensor:
     """
-    Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
-
+    Rescales betas to have zero terminal SNR Based on (Algorithm 1)[https://huggingface.co/papers/2305.08891]
 
     Args:
-        betas (`torch.Tensor`):
-            the betas that the scheduler is being initialized with.
+        alphas_cumprod (`torch.Tensor`):
+            The alphas cumulative products that the scheduler is being initialized with.
 
     Returns:
         `torch.Tensor`: rescaled betas with zero terminal SNR
@@ -142,11 +141,11 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
     Args:
         num_train_timesteps (`int`, defaults to 1000):
             The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
+        beta_start (`float`, defaults to 0.00085):
             The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
+        beta_end (`float`, defaults to 0.0120):
             The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
+        beta_schedule (`str`, defaults to `"scaled_linear"`):
             The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
             `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
         trained_betas (`np.ndarray`, *optional*):
@@ -179,6 +178,8 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
             Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
             dark samples instead of limiting it to samples with medium brightness. Loosely related to
             [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
+        snr_shift_scale (`float`, defaults to 3.0):
+            Shift scale for SNR.
     """
 
     _compatibles = [e.name for e in KarrasDiffusionSchedulers]
@@ -190,15 +191,15 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         num_train_timesteps: int = 1000,
         beta_start: float = 0.00085,
         beta_end: float = 0.0120,
-        beta_schedule: str = "scaled_linear",
+        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "scaled_linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         clip_sample: bool = True,
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
-        prediction_type: str = "epsilon",
+        prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
         clip_sample_range: float = 1.0,
         sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
+        timestep_spacing: Literal["linspace", "leading", "trailing"] = "leading",
         rescale_betas_zero_snr: bool = False,
         snr_shift_scale: float = 3.0,
     ):
@@ -208,7 +209,15 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float64) ** 2
+            self.betas = (
+                torch.linspace(
+                    beta_start**0.5,
+                    beta_end**0.5,
+                    num_train_timesteps,
+                    dtype=torch.float64,
+                )
+                ** 2
+            )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -238,7 +247,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         self.num_inference_steps = None
         self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
 
-    def _get_variance(self, timestep, prev_timestep):
+    def _get_variance(self, timestep: int, prev_timestep: int) -> torch.Tensor:
         alpha_prod_t = self.alphas_cumprod[timestep]
         alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
         beta_prod_t = 1 - alpha_prod_t
@@ -265,7 +274,11 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -317,7 +330,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         sample: torch.Tensor,
         eta: float = 0.0,
         use_clipped_model_output: bool = False,
-        generator=None,
+        generator: Optional[torch.Generator] = None,
         variance_noise: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ) -> Union[DDIMSchedulerOutput, Tuple]:
@@ -328,7 +341,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         Args:
             model_output (`torch.Tensor`):
                 The direct output from learned diffusion model.
-            timestep (`float`):
+            timestep (`int`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
@@ -487,5 +500,5 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
         return velocity
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -22,6 +22,7 @@ import flax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import logging
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -32,6 +33,9 @@ from .scheduling_utils_flax import (
 )
 
 
+logger = logging.get_logger(__name__)
+
+
 @flax.struct.dataclass
 class DDIMSchedulerState:
     common: CommonSchedulerState
@@ -125,6 +129,10 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type: str = "epsilon",
         dtype: jnp.dtype = jnp.float32,
     ):
+        logger.warning(
+            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
+            "recommend migrating to PyTorch classes or pinning your version of Diffusers."
+        )
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:
@@ -152,7 +160,10 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         )
 
     def scale_model_input(
-        self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
+        self,
+        state: DDIMSchedulerState,
+        sample: jnp.ndarray,
+        timestep: Optional[int] = None,
     ) -> jnp.ndarray:
         """
         Args:
@@ -190,7 +201,9 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
     def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep):
         alpha_prod_t = state.common.alphas_cumprod[timestep]
         alpha_prod_t_prev = jnp.where(
-            prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod
+            prev_timestep >= 0,
+            state.common.alphas_cumprod[prev_timestep],
+            state.final_alpha_cumprod,
         )
         beta_prod_t = 1 - alpha_prod_t
         beta_prod_t_prev = 1 - alpha_prod_t_prev

src/diffusers/schedulers/scheduling_ddim_inverse.py

@@ -49,7 +49,7 @@ class DDIMSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -63,8 +63,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:
@@ -99,7 +99,7 @@ def betas_for_alpha_bar(
 
 
 # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
-def rescale_zero_terminal_snr(betas):
+def rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:
     """
     Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
 
@@ -187,14 +187,14 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         num_train_timesteps: int = 1000,
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
-        beta_schedule: str = "linear",
+        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         clip_sample: bool = True,
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
-        prediction_type: str = "epsilon",
+        prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
         clip_sample_range: float = 1.0,
-        timestep_spacing: str = "leading",
+        timestep_spacing: Literal["leading", "trailing"] = "leading",
         rescale_betas_zero_snr: bool = False,
         **kwargs,
     ):
@@ -210,7 +210,15 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            self.betas = (
+                torch.linspace(
+                    beta_start**0.5,
+                    beta_end**0.5,
+                    num_train_timesteps,
+                    dtype=torch.float32,
+                )
+                ** 2
+            )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -256,7 +264,11 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -308,20 +320,10 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         Args:
             model_output (`torch.Tensor`):
                 The direct output from learned diffusion model.
-            timestep (`float`):
+            timestep (`int`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            variance_noise (`torch.Tensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or
                 `tuple`.
@@ -335,7 +337,8 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         # 1. get previous step value (=t+1)
         prev_timestep = timestep
         timestep = min(
-            timestep - self.config.num_train_timesteps // self.num_inference_steps, self.config.num_train_timesteps - 1
+            timestep - self.config.num_train_timesteps // self.num_inference_steps,
+            self.config.num_train_timesteps - 1,
         )
 
         # 2. compute alphas, betas
@@ -378,5 +381,5 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_ddim_parallel.py

@@ -51,7 +51,7 @@ class DDIMParallelSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_ddpm.py

@@ -48,7 +48,7 @@ class DDPMSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -62,8 +62,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:
@@ -192,7 +192,12 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2", "sigmoid"] = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         variance_type: Literal[
-            "fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"
+            "fixed_small",
+            "fixed_small_log",
+            "fixed_large",
+            "fixed_large_log",
+            "learned",
+            "learned_range",
         ] = "fixed_small",
         clip_sample: bool = True,
         prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
@@ -210,7 +215,15 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            self.betas = (
+                torch.linspace(
+                    beta_start**0.5,
+                    beta_end**0.5,
+                    num_train_timesteps,
+                    dtype=torch.float32,
+                )
+                ** 2
+            )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -337,7 +350,14 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         t: int,
         predicted_variance: Optional[torch.Tensor] = None,
         variance_type: Optional[
-            Literal["fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"]
+            Literal[
+                "fixed_small",
+                "fixed_small_log",
+                "fixed_large",
+                "fixed_large_log",
+                "learned",
+                "learned_range",
+            ]
         ] = None,
     ) -> torch.Tensor:
         """
@@ -472,7 +492,10 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
 
         prev_t = self.previous_timestep(t)
 
-        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
+        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [
+            "learned",
+            "learned_range",
+        ]:
             model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
         else:
             predicted_variance = None
@@ -521,7 +544,10 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         if t > 0:
             device = model_output.device
             variance_noise = randn_tensor(
-                model_output.shape, generator=generator, device=device, dtype=model_output.dtype
+                model_output.shape,
+                generator=generator,
+                device=device,
+                dtype=model_output.dtype,
             )
             if self.variance_type == "fixed_small_log":
                 variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -50,7 +50,7 @@ class DDPMParallelSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -64,8 +64,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:
@@ -202,7 +202,12 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2", "sigmoid"] = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         variance_type: Literal[
-            "fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"
+            "fixed_small",
+            "fixed_small_log",
+            "fixed_large",
+            "fixed_large_log",
+            "learned",
+            "learned_range",
         ] = "fixed_small",
         clip_sample: bool = True,
         prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
@@ -220,7 +225,15 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            self.betas = (
+                torch.linspace(
+                    beta_start**0.5,
+                    beta_end**0.5,
+                    num_train_timesteps,
+                    dtype=torch.float32,
+                )
+                ** 2
+            )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -350,7 +363,14 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         t: int,
         predicted_variance: Optional[torch.Tensor] = None,
         variance_type: Optional[
-            Literal["fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"]
+            Literal[
+                "fixed_small",
+                "fixed_small_log",
+                "fixed_large",
+                "fixed_large_log",
+                "learned",
+                "learned_range",
+            ]
         ] = None,
     ) -> torch.Tensor:
         """

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -34,7 +34,7 @@ if is_scipy_available():
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_dpm_cogvideox.py

@@ -52,7 +52,7 @@ class DDIMSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -66,8 +66,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -34,7 +34,7 @@ if is_scipy_available():
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -34,7 +34,7 @@ if is_scipy_available():
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_dpmsolver_sde.py

@@ -117,7 +117,7 @@ class BrownianTreeNoiseSampler:
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -131,8 +131,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -36,7 +36,7 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -50,8 +50,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -51,7 +51,7 @@ class EulerAncestralDiscreteSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -54,7 +54,7 @@ class EulerDiscreteSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -68,8 +68,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_heun_discrete.py

@@ -51,7 +51,7 @@ class HeunDiscreteSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py

@@ -52,7 +52,7 @@ class KDPM2AncestralDiscreteSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -66,8 +66,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py

@@ -51,7 +51,7 @@ class KDPM2DiscreteSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_lcm.py

@@ -53,7 +53,7 @@ class LCMSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -67,8 +67,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -49,7 +49,7 @@ class LMSDiscreteSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -63,8 +63,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_pndm.py

@@ -28,7 +28,7 @@ from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, Schedul
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -42,8 +42,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_repaint.py

@@ -47,7 +47,7 @@ class RePaintSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -61,8 +61,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_sasolver.py

@@ -35,7 +35,7 @@ if is_scipy_available():
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -49,8 +49,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_tcd.py

@@ -52,7 +52,7 @@ class TCDSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -66,8 +66,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_unclip.py

@@ -48,7 +48,7 @@ class UnCLIPSchedulerOutput(BaseOutput):
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -62,8 +62,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -34,7 +34,7 @@ if is_scipy_available():
 def betas_for_alpha_bar(
     num_diffusion_timesteps: int,
     max_beta: float = 0.999,
-    alpha_transform_type: Literal["cosine", "exp"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
 ) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
             The number of betas to produce.
         max_beta (`float`, defaults to `0.999`):
             The maximum beta to use; use values lower than 1 to avoid numerical instability.
-        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
+        alpha_transform_type (`str`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
 
     Returns:
         `torch.Tensor`:
@@ -226,6 +226,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         time_shift_type: Literal["exponential"] = "exponential",
         sigma_min: Optional[float] = None,
         sigma_max: Optional[float] = None,
+        shift_terminal: Optional[float] = None,
     ) -> None:
         if self.config.use_beta_sigmas and not is_scipy_available():
             raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
@@ -245,6 +246,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
+        if shift_terminal is not None and not use_flow_sigmas:
+            raise ValueError("`shift_terminal` is only supported when `use_flow_sigmas=True`.")
 
         if rescale_betas_zero_snr:
             self.betas = rescale_zero_terminal_snr(self.betas)
@@ -313,8 +316,12 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = begin_index
 
     def set_timesteps(
-        self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None, mu: Optional[float] = None
-    ) -> None:
+        self,
+        num_inference_steps: Optional[int] = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -323,13 +330,24 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 The number of diffusion steps used when generating samples with a pre-trained model.
             device (`str` or `torch.device`, *optional*):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+            sigmas (`List[float]`, *optional*):
+                Custom values for sigmas to be used for each diffusion step. If `None`, the sigmas are computed
+                automatically.
             mu (`float`, *optional*):
                 Optional mu parameter for dynamic shifting when using exponential time shift type.
         """
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError("`mu` must be passed when `use_dynamic_shifting` is set to be `True`")
+
+        if sigmas is not None:
+            if not self.config.use_flow_sigmas:
+                raise ValueError(
+                    "Passing `sigmas` is only supported when `use_flow_sigmas=True`. "
+                    "Please set `use_flow_sigmas=True` during scheduler initialization."
+                )
+            num_inference_steps = len(sigmas)
+
         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
-        if mu is not None:
-            assert self.config.use_dynamic_shifting and self.config.time_shift_type == "exponential"
-            self.config.flow_shift = np.exp(mu)
         if self.config.timestep_spacing == "linspace":
             timesteps = (
                 np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
@@ -354,8 +372,9 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
             )
 
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         if self.config.use_karras_sigmas:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -375,6 +394,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_exponential_sigmas:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -389,6 +410,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_beta_sigmas:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -403,9 +426,18 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_flow_sigmas:
-            alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)
-            sigmas = 1.0 - alphas
-            sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
+            if sigmas is None:
+                sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1]
+            if self.config.use_dynamic_shifting:
+                sigmas = self.time_shift(mu, 1.0, sigmas)
+            else:
+                sigmas = self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas)
+            if self.config.shift_terminal:
+                sigmas = self.stretch_shift_to_terminal(sigmas)
+            eps = 1e-6
+            if np.fabs(sigmas[0] - 1) < eps:
+                # to avoid inf torch.log(alpha_si) in multistep_uni_p_bh_update during first/second update
+                sigmas[0] -= eps
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = sigmas[-1]
@@ -417,6 +449,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         else:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
@@ -446,6 +480,43 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = None
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        if self.config.time_shift_type == "exponential":
+            return self._time_shift_exponential(mu, sigma, t)
+        elif self.config.time_shift_type == "linear":
+            return self._time_shift_linear(mu, sigma, t)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.stretch_shift_to_terminal
+    def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor:
+        r"""
+        Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_terminal` config
+        value.
+
+        Reference:
+        https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51
+
+        Args:
+            t (`torch.Tensor`):
+                A tensor of timesteps to be stretched and shifted.
+
+        Returns:
+            `torch.Tensor`:
+                A tensor of adjusted timesteps such that the final value equals `self.config.shift_terminal`.
+        """
+        one_minus_z = 1 - t
+        scale_factor = one_minus_z[-1] / (1 - self.config.shift_terminal)
+        stretched_t = 1 - (one_minus_z / scale_factor)
+        return stretched_t
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_exponential
+    def _time_shift_exponential(self, mu, sigma, t):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_linear
+    def _time_shift_linear(self, mu, sigma, t):
+        return mu / (mu + (1 / t - 1) ** sigma)
+
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
     def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
         """

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -587,6 +587,21 @@ class AuraFlowPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class BriaFiboEditPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class BriaFiboPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/bria_fibo_edit/test_pipeline_bria_fibo_edit.py

@@ -0,0 +1,192 @@
+# Copyright 2024 Bria AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoTokenizer
+from transformers.models.smollm3.modeling_smollm3 import SmolLM3Config, SmolLM3ForCausalLM
+
+from diffusers import (
+    AutoencoderKLWan,
+    BriaFiboEditPipeline,
+    FlowMatchEulerDiscreteScheduler,
+)
+from diffusers.models.transformers.transformer_bria_fibo import BriaFiboTransformer2DModel
+from tests.pipelines.test_pipelines_common import PipelineTesterMixin
+
+from ...testing_utils import (
+    enable_full_determinism,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+class BriaFiboPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = BriaFiboEditPipeline
+    params = frozenset(["prompt", "height", "width", "guidance_scale"])
+    batch_params = frozenset(["prompt"])
+    test_xformers_attention = False
+    test_layerwise_casting = False
+    test_group_offloading = False
+    supports_dduf = False
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = BriaFiboTransformer2DModel(
+            patch_size=1,
+            in_channels=16,
+            num_layers=1,
+            num_single_layers=1,
+            attention_head_dim=8,
+            num_attention_heads=2,
+            joint_attention_dim=64,
+            text_encoder_dim=32,
+            pooled_projection_dim=None,
+            axes_dims_rope=[0, 4, 4],
+        )
+
+        vae = AutoencoderKLWan(
+            base_dim=80,
+            decoder_base_dim=128,
+            dim_mult=[1, 2, 4, 4],
+            dropout=0.0,
+            in_channels=12,
+            latents_mean=[0.0] * 16,
+            latents_std=[1.0] * 16,
+            is_residual=True,
+            num_res_blocks=2,
+            out_channels=12,
+            patch_size=2,
+            scale_factor_spatial=16,
+            scale_factor_temporal=4,
+            temperal_downsample=[False, True, True],
+            z_dim=16,
+        )
+        scheduler = FlowMatchEulerDiscreteScheduler()
+        text_encoder = SmolLM3ForCausalLM(SmolLM3Config(hidden_size=32))
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        components = {
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "transformer": transformer,
+            "vae": vae,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device="cpu").manual_seed(seed)
+        inputs = {
+            "prompt": '{"text": "A painting of a squirrel eating a burger","edit_instruction": "A painting of a squirrel eating a burger"}',
+            "negative_prompt": "bad, ugly",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "height": 192,
+            "width": 336,
+            "output_type": "np",
+        }
+        image = Image.new("RGB", (336, 192), (255, 255, 255))
+        inputs["image"] = image
+        return inputs
+
+    @unittest.skip(reason="will not be supported due to dim-fusion")
+    def test_encode_prompt_works_in_isolation(self):
+        pass
+
+    @unittest.skip(reason="Batching is not supported yet")
+    def test_num_images_per_prompt(self):
+        pass
+
+    @unittest.skip(reason="Batching is not supported yet")
+    def test_inference_batch_consistent(self):
+        pass
+
+    @unittest.skip(reason="Batching is not supported yet")
+    def test_inference_batch_single_identical(self):
+        pass
+
+    def test_bria_fibo_different_prompts(self):
+        pipe = self.pipeline_class(**self.get_dummy_components())
+        pipe = pipe.to(torch_device)
+        inputs = self.get_dummy_inputs(torch_device)
+        output_same_prompt = pipe(**inputs).images[0]
+
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["prompt"] = {"edit_instruction": "a different prompt"}
+        output_different_prompts = pipe(**inputs).images[0]
+
+        max_diff = np.abs(output_same_prompt - output_different_prompts).max()
+        assert max_diff > 1e-6
+
+    def test_image_output_shape(self):
+        pipe = self.pipeline_class(**self.get_dummy_components())
+        pipe = pipe.to(torch_device)
+        inputs = self.get_dummy_inputs(torch_device)
+
+        height_width_pairs = [(32, 32), (64, 64), (32, 64)]
+        for height, width in height_width_pairs:
+            expected_height = height
+            expected_width = width
+
+            inputs.update({"height": height, "width": width})
+            image = pipe(**inputs).images[0]
+            output_height, output_width, _ = image.shape
+            assert (output_height, output_width) == (expected_height, expected_width)
+
+    def test_bria_fibo_edit_mask(self):
+        pipe = self.pipeline_class(**self.get_dummy_components())
+        pipe = pipe.to(torch_device)
+        inputs = self.get_dummy_inputs(torch_device)
+
+        mask = Image.fromarray((np.ones((192, 336)) * 255).astype(np.uint8), mode="L")
+
+        inputs.update({"mask": mask})
+        output = pipe(**inputs).images[0]
+
+        assert output.shape == (192, 336, 3)
+
+    def test_bria_fibo_edit_mask_image_size_mismatch(self):
+        pipe = self.pipeline_class(**self.get_dummy_components())
+        pipe = pipe.to(torch_device)
+        inputs = self.get_dummy_inputs(torch_device)
+
+        mask = Image.fromarray((np.ones((64, 64)) * 255).astype(np.uint8), mode="L")
+
+        inputs.update({"mask": mask})
+        with self.assertRaisesRegex(ValueError, "Mask and image must have the same size"):
+            pipe(**inputs)
+
+    def test_bria_fibo_edit_mask_no_image(self):
+        pipe = self.pipeline_class(**self.get_dummy_components())
+        pipe = pipe.to(torch_device)
+        inputs = self.get_dummy_inputs(torch_device)
+
+        mask = Image.fromarray((np.ones((32, 32)) * 255).astype(np.uint8), mode="L")
+
+        # Remove image from inputs if it's there (it shouldn't be by default from get_dummy_inputs)
+        inputs.pop("image", None)
+        inputs.update({"mask": mask})
+
+        with self.assertRaisesRegex(ValueError, "If mask is provided, image must also be provided"):
+            pipe(**inputs)