Merge branch 'main' into requirements-custom-blocks

src/diffusers/commands/custom_blocks.py

@@ -89,8 +89,6 @@ class CustomBlocksCommand(BaseDiffusersCLICommand):
         # automap = self._create_automap(parent_class=parent_class, child_class=child_class)
         # with open(CONFIG, "w") as f:
         #     json.dump(automap, f)
-        with open("requirements.txt", "w") as f:
-            f.write("")
 
     def _choose_block(self, candidates, chosen=None):
         for cls, base in candidates:

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -39,6 +39,7 @@ from .modular_pipeline_utils import (
     InputParam,
     InsertableDict,
     OutputParam,
+    _validate_requirements,
     format_components,
     format_configs,
     make_doc_string,
@@ -242,6 +243,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 
     config_name = "modular_config.json"
     model_name = None
+    _requirements: Optional[Dict[str, str]] = None
 
     @classmethod
     def _get_signature_keys(cls, obj):
@@ -304,6 +306,19 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         trust_remote_code: bool = False,
         **kwargs,
     ):
+        config = cls.load_config(pretrained_model_name_or_path)
+        has_remote_code = "auto_map" in config and cls.__name__ in config["auto_map"]
+        trust_remote_code = resolve_trust_remote_code(
+            trust_remote_code, pretrained_model_name_or_path, has_remote_code
+        )
+        if not (has_remote_code and trust_remote_code):
+            raise ValueError(
+                "Selected model repository does not happear to have any custom code or does not have a valid `config.json` file."
+            )
+
+        if "requirements" in config and config["requirements"] is not None:
+            _ = _validate_requirements(config["requirements"])
+
         hub_kwargs_names = [
             "cache_dir",
             "force_download",
@@ -316,16 +331,6 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         ]
         hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
 
-        config = cls.load_config(pretrained_model_name_or_path, **hub_kwargs)
-        has_remote_code = "auto_map" in config and cls.__name__ in config["auto_map"]
-        trust_remote_code = resolve_trust_remote_code(
-            trust_remote_code, pretrained_model_name_or_path, has_remote_code
-        )
-        if not has_remote_code and trust_remote_code:
-            raise ValueError(
-                "Selected model repository does not happear to have any custom code or does not have a valid `config.json` file."
-            )
-
         class_ref = config["auto_map"][cls.__name__]
         module_file, class_name = class_ref.split(".")
         module_file = module_file + ".py"
@@ -350,8 +355,13 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         module = full_mod.rsplit(".", 1)[-1].replace("__dynamic__", "")
         parent_module = self.save_pretrained.__func__.__qualname__.split(".", 1)[0]
         auto_map = {f"{parent_module}": f"{module}.{cls_name}"}
-
         self.register_to_config(auto_map=auto_map)
+
+        # resolve requirements
+        requirements = _validate_requirements(getattr(self, "_requirements", None))
+        if requirements:
+            self.register_to_config(requirements=requirements)
+
         self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
         config = dict(self.config)
         self._internal_dict = FrozenDict(config)
@@ -1154,6 +1164,14 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             expected_configs=self.expected_configs,
         )
 
+    @property
+    def _requirements(self) -> Dict[str, str]:
+        requirements = {}
+        for block_name, block in self.sub_blocks.items():
+            if getattr(block, "_requirements", None):
+                requirements[block_name] = block._requirements
+        return requirements
+
 
 class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
     """
@@ -1552,11 +1570,11 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             else:
                 logger.warning(f"`blocks` is `None`, no default blocks class found for {self.__class__.__name__}")
 
-        self._blocks = blocks
+        self.blocks = blocks
         self._components_manager = components_manager
         self._collection = collection
-        self._component_specs = {spec.name: deepcopy(spec) for spec in self._blocks.expected_components}
-        self._config_specs = {spec.name: deepcopy(spec) for spec in self._blocks.expected_configs}
+        self._component_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_components}
+        self._config_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_configs}
 
         # update component_specs and config_specs based on modular_model_index.json
         if modular_config_dict is not None:
@@ -1603,9 +1621,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         for name, config_spec in self._config_specs.items():
             default_configs[name] = config_spec.default
         self.register_to_config(**default_configs)
-        self.register_to_config(
-            _blocks_class_name=self._blocks.__class__.__name__ if self._blocks is not None else None
-        )
+        self.register_to_config(_blocks_class_name=self.blocks.__class__.__name__ if self.blocks is not None else None)
 
     @property
     def default_call_parameters(self) -> Dict[str, Any]:
@@ -1614,7 +1630,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             - Dictionary mapping input names to their default values
         """
         params = {}
-        for input_param in self._blocks.inputs:
+        for input_param in self.blocks.inputs:
             params[input_param.name] = input_param.default
         return params
 
@@ -1777,15 +1793,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         Returns:
             - The docstring of the pipeline blocks
         """
-        return self._blocks.doc
-
-    @property
-    def blocks(self) -> ModularPipelineBlocks:
-        """
-        Returns:
-            - A copy of the pipeline blocks
-        """
-        return deepcopy(self._blocks)
+        return self.blocks.doc
 
     def register_components(self, **kwargs):
         """
@@ -2519,7 +2527,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         )
 
     def set_progress_bar_config(self, **kwargs):
-        for sub_block_name, sub_block in self._blocks.sub_blocks.items():
+        for sub_block_name, sub_block in self.blocks.sub_blocks.items():
             if hasattr(sub_block, "set_progress_bar_config"):
                 sub_block.set_progress_bar_config(**kwargs)
 
@@ -2573,7 +2581,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
 
         # Add inputs to state, using defaults if not provided in the kwargs or the state
         # if same input already in the state, will override it if provided in the kwargs
-        for expected_input_param in self._blocks.inputs:
+        for expected_input_param in self.blocks.inputs:
             name = expected_input_param.name
             default = expected_input_param.default
             kwargs_type = expected_input_param.kwargs_type
@@ -2592,9 +2600,9 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         # Run the pipeline
         with torch.no_grad():
             try:
-                _, state = self._blocks(self, state)
+                _, state = self.blocks(self, state)
             except Exception:
-                error_msg = f"Error in block: ({self._blocks.__class__.__name__}):\n"
+                error_msg = f"Error in block: ({self.blocks.__class__.__name__}):\n"
                 logger.error(error_msg)
                 raise
 

src/diffusers/modular_pipelines/modular_pipeline_utils.py

@@ -19,10 +19,12 @@ from dataclasses import dataclass, field, fields
 from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
+from packaging.specifiers import InvalidSpecifier, SpecifierSet
 
 from ..configuration_utils import ConfigMixin, FrozenDict
 from ..loaders.single_file_utils import _is_single_file_path_or_url
 from ..utils import is_torch_available, logging
+from ..utils.import_utils import _is_package_available
 
 
 if is_torch_available():
@@ -690,3 +692,86 @@ def make_doc_string(
     output += format_output_params(outputs, indent_level=2)
 
     return output
+
+
+def _validate_requirements(reqs):
+    if reqs is None:
+        normalized_reqs = {}
+    else:
+        if not isinstance(reqs, dict):
+            raise ValueError(
+                "Requirements must be provided as a dictionary mapping package names to version specifiers."
+            )
+        normalized_reqs = _normalize_requirements(reqs)
+
+    if not normalized_reqs:
+        return {}
+
+    final: Dict[str, str] = {}
+    for req, specified_ver in normalized_reqs.items():
+        req_available, req_actual_ver = _is_package_available(req)
+        if not req_available:
+            logger.warning(f"{req} was specified in the requirements but wasn't found in the current environment.")
+
+        if specified_ver:
+            try:
+                specifier = SpecifierSet(specified_ver)
+            except InvalidSpecifier as err:
+                raise ValueError(f"Requirement specifier '{specified_ver}' for {req} is invalid.") from err
+
+            if req_actual_ver == "N/A":
+                logger.warning(
+                    f"Version of {req} could not be determined to validate requirement '{specified_ver}'. Things might work unexpected."
+                )
+            elif not specifier.contains(req_actual_ver, prereleases=True):
+                logger.warning(
+                    f"{req} requirement '{specified_ver}' is not satisfied by the installed version {req_actual_ver}. Things might work unexpected."
+                )
+
+        final[req] = specified_ver
+
+    return final
+
+
+def _normalize_requirements(reqs):
+    if not reqs:
+        return {}
+
+    normalized: "OrderedDict[str, str]" = OrderedDict()
+
+    def _accumulate(mapping: Dict[str, Any]):
+        for pkg, spec in mapping.items():
+            if isinstance(spec, dict):
+                # This is recursive because blocks are composable. This way, we can merge requirements
+                # from multiple blocks.
+                _accumulate(spec)
+                continue
+
+            pkg_name = str(pkg).strip()
+            if not pkg_name:
+                raise ValueError("Requirement package name cannot be empty.")
+
+            spec_str = "" if spec is None else str(spec).strip()
+            if spec_str and not spec_str.startswith(("<", ">", "=", "!", "~")):
+                spec_str = f"=={spec_str}"
+
+            existing_spec = normalized.get(pkg_name)
+            if existing_spec is not None:
+                if not existing_spec and spec_str:
+                    normalized[pkg_name] = spec_str
+                elif existing_spec and spec_str and existing_spec != spec_str:
+                    try:
+                        combined_spec = SpecifierSet(",".join(filter(None, [existing_spec, spec_str])))
+                    except InvalidSpecifier:
+                        logger.warning(
+                            f"Conflicting requirements for '{pkg_name}' detected: '{existing_spec}' vs '{spec_str}'. Keeping '{existing_spec}'."
+                        )
+                    else:
+                        normalized[pkg_name] = str(combined_spec)
+                continue
+
+            normalized[pkg_name] = spec_str
+
+    _accumulate(reqs)
+
+    return normalized

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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     Returns:
         `torch.Tensor`:
@@ -100,13 +100,14 @@ def betas_for_alpha_bar(
     return torch.tensor(betas, dtype=torch.float32)
 
 
-def rescale_zero_terminal_snr(alphas_cumprod: torch.Tensor) -> torch.Tensor:
+def rescale_zero_terminal_snr(alphas_cumprod):
     """
-    Rescales betas to have zero terminal SNR Based on (Algorithm 1)[https://huggingface.co/papers/2305.08891]
+    Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
+
 
     Args:
-        alphas_cumprod (`torch.Tensor`):
-            The alphas cumulative products that the scheduler is being initialized with.
+        betas (`torch.Tensor`):
+            the betas that the scheduler is being initialized with.
 
     Returns:
         `torch.Tensor`: rescaled betas with zero terminal SNR
@@ -141,11 +142,11 @@ 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 0.00085):
+        beta_start (`float`, defaults to 0.0001):
             The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.0120):
+        beta_end (`float`, defaults to 0.02):
             The final `beta` value.
-        beta_schedule (`str`, defaults to `"scaled_linear"`):
+        beta_schedule (`str`, defaults to `"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*):
@@ -178,8 +179,6 @@ 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]
@@ -191,15 +190,15 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         num_train_timesteps: int = 1000,
         beta_start: float = 0.00085,
         beta_end: float = 0.0120,
-        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "scaled_linear",
+        beta_schedule: str = "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: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
+        prediction_type: str = "epsilon",
         clip_sample_range: float = 1.0,
         sample_max_value: float = 1.0,
-        timestep_spacing: Literal["linspace", "leading", "trailing"] = "leading",
+        timestep_spacing: str = "leading",
         rescale_betas_zero_snr: bool = False,
         snr_shift_scale: float = 3.0,
     ):
@@ -209,15 +208,7 @@ 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)
@@ -247,7 +238,7 @@ 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: int, prev_timestep: int) -> torch.Tensor:
+    def _get_variance(self, timestep, prev_timestep):
         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
@@ -274,11 +265,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def set_timesteps(
-        self,
-        num_inference_steps: int,
-        device: Optional[Union[str, torch.device]] = None,
-    ) -> None:
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -330,7 +317,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         sample: torch.Tensor,
         eta: float = 0.0,
         use_clipped_model_output: bool = False,
-        generator: Optional[torch.Generator] = None,
+        generator=None,
         variance_noise: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ) -> Union[DDIMSchedulerOutput, Tuple]:
@@ -341,7 +328,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         Args:
             model_output (`torch.Tensor`):
                 The direct output from learned diffusion model.
-            timestep (`int`):
+            timestep (`float`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
@@ -500,5 +487,5 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
         return velocity
 
-    def __len__(self) -> int:
+    def __len__(self):
         return self.config.num_train_timesteps

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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     Returns:
         `torch.Tensor`:

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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     Returns:
         `torch.Tensor`:
@@ -192,12 +192,7 @@ 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",
@@ -215,15 +210,7 @@ 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)
@@ -350,14 +337,7 @@ 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:
         """
@@ -492,10 +472,7 @@ 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
@@ -544,10 +521,7 @@ 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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     Returns:
         `torch.Tensor`:
@@ -202,12 +202,7 @@ 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",
@@ -225,15 +220,7 @@ 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)
@@ -363,14 +350,7 @@ 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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     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", "laplace"] = "cosine",
+    alpha_transform_type: Literal["cosine", "exp"] = "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 (`str`, defaults to `"cosine"`):
-            The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
+        alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
+            The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
 
     Returns:
         `torch.Tensor`:

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -1,4 +1,6 @@
 import gc
+import json
+import os
 import tempfile
 from typing import Callable, Union
 
@@ -8,9 +10,16 @@ import torch
 import diffusers
 from diffusers import ComponentsManager, ModularPipeline, ModularPipelineBlocks
 from diffusers.guiders import ClassifierFreeGuidance
+from diffusers.modular_pipelines import SequentialPipelineBlocks
 from diffusers.utils import logging
 
-from ..testing_utils import backend_empty_cache, numpy_cosine_similarity_distance, require_accelerator, torch_device
+from ..testing_utils import (
+    CaptureLogger,
+    backend_empty_cache,
+    numpy_cosine_similarity_distance,
+    require_accelerator,
+    torch_device,
+)
 
 
 class ModularPipelineTesterMixin:
@@ -335,3 +344,53 @@ class ModularGuiderTesterMixin:
         assert out_cfg.shape == out_no_cfg.shape
         max_diff = torch.abs(out_cfg - out_no_cfg).max()
         assert max_diff > expected_max_diff, "Output with CFG must be different from normal inference"
+
+
+class TestCustomBlockRequirements:
+    def get_dummy_block_pipe(self):
+        class DummyBlockOne:
+            # keep two arbitrary deps so that we can test warnings.
+            _requirements = {"xyz": ">=0.8.0", "abc": ">=10.0.0"}
+
+        class DummyBlockTwo:
+            # keep two dependencies that will be available during testing.
+            _requirements = {"transformers": ">=4.44.0", "diffusers": ">=0.2.0"}
+
+        pipe = SequentialPipelineBlocks.from_blocks_dict(
+            {"dummy_block_one": DummyBlockOne, "dummy_block_two": DummyBlockTwo}
+        )
+        return pipe
+
+    def test_custom_requirements_save_load(self):
+        pipe = self.get_dummy_block_pipe()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            config_path = os.path.join(tmpdir, "modular_config.json")
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+        assert "requirements" in config
+        requirements = config["requirements"]
+
+        expected_requirements = {
+            "xyz": ">=0.8.0",
+            "abc": ">=10.0.0",
+            "transformers": ">=4.44.0",
+            "diffusers": ">=0.2.0",
+        }
+        assert expected_requirements == requirements
+
+    def test_warnings(self):
+        pipe = self.get_dummy_block_pipe()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            logger = logging.get_logger("diffusers.modular_pipelines.modular_pipeline_utils")
+            logger.setLevel(30)
+
+            with CaptureLogger(logger) as cap_logger:
+                pipe.save_pretrained(tmpdir)
+
+            template = "{req} was specified in the requirements but wasn't found in the current environment"
+            msg_xyz = template.format(req="xyz")
+            msg_abc = template.format(req="abc")
+            assert msg_xyz in str(cap_logger.out)
+            assert msg_abc in str(cap_logger.out)