Merge branch 'main' into device-map-direct

docs: improve docstring scheduling_ddim_inverse.py

src/diffusers/models/modeling_utils.py

@@ -675,7 +675,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         variant: Optional[str] = None,
         max_shard_size: Union[int, str] = "10GB",
         push_to_hub: bool = False,
-        use_flashpack: bool = False,
         **kwargs,
     ):
         """
@@ -708,9 +707,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
-            use_flashpack (`bool`, *optional*, defaults to `False`):
-                Whether to save the model in [FlashPack](https://github.com/fal-ai/flashpack) format. FlashPack is a
-                binary format that allows for faster loading. Requires the `flashpack` library to be installed.
             kwargs (`Dict[str, Any]`, *optional*):
                 Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
@@ -731,6 +727,12 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     " the logger on the traceback to understand the reason why the quantized model is not serializable."
                 )
 
+        weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+        weights_name = _add_variant(weights_name, variant)
+        weights_name_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+            ".safetensors", "{suffix}.safetensors"
+        )
+
         os.makedirs(save_directory, exist_ok=True)
 
         if push_to_hub:
@@ -744,80 +746,67 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         # Only save the model itself if we are using distributed training
         model_to_save = self
 
+        # Attach architecture to the config
         # Save the config
         if is_main_process:
             model_to_save.save_config(save_directory)
 
-        if use_flashpack:
-            if not is_main_process:
-                return
+        # Save the model
+        state_dict = model_to_save.state_dict()
 
-            from ..utils.flashpack_utils import save_flashpack
+        # Save the model
+        state_dict_split = split_torch_state_dict_into_shards(
+            state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern
+        )
 
-            save_flashpack(model_to_save, save_directory, variant=variant)
-        else:
-            weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
-            weights_name = _add_variant(weights_name, variant)
-            weights_name_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
-                ".safetensors", "{suffix}.safetensors"
-            )
+        # Clean the folder from a previous save
+        if is_main_process:
+            for filename in os.listdir(save_directory):
+                if filename in state_dict_split.filename_to_tensors.keys():
+                    continue
+                full_filename = os.path.join(save_directory, filename)
+                if not os.path.isfile(full_filename):
+                    continue
+                weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")
+                weights_without_ext = weights_without_ext.replace("{suffix}", "")
+                filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")
+                # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
+                if (
+                    filename.startswith(weights_without_ext)
+                    and _REGEX_SHARD.fullmatch(filename_without_ext) is not None
+                ):
+                    os.remove(full_filename)
 
-            state_dict = model_to_save.state_dict()
-            state_dict_split = split_torch_state_dict_into_shards(
-                state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern
-            )
-
-            # Clean the folder from a previous save
-            if is_main_process:
-                for filename in os.listdir(save_directory):
-                    if filename in state_dict_split.filename_to_tensors.keys():
-                        continue
-                    full_filename = os.path.join(save_directory, filename)
-                    if not os.path.isfile(full_filename):
-                        continue
-                    weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")
-                    weights_without_ext = weights_without_ext.replace("{suffix}", "")
-                    filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")
-                    # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
-                    if (
-                        filename.startswith(weights_without_ext)
-                        and _REGEX_SHARD.fullmatch(filename_without_ext) is not None
-                    ):
-                        os.remove(full_filename)
-
-            # Save each shard
-            for filename, tensors in state_dict_split.filename_to_tensors.items():
-                shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
-                filepath = os.path.join(save_directory, filename)
-                if safe_serialization:
-                    # At some point we will need to deal better with save_function (used for TPU and other distributed
-                    # joyfulness), but for now this enough.
-                    safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
-                else:
-                    torch.save(shard, filepath)
-
-            # Save index file if sharded
-            if state_dict_split.is_sharded:
-                index = {
-                    "metadata": state_dict_split.metadata,
-                    "weight_map": state_dict_split.tensor_to_filename,
-                }
-                save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
-                save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
-                # Save the index as well
-                with open(save_index_file, "w", encoding="utf-8") as f:
-                    content = json.dumps(index, indent=2, sort_keys=True) + "\n"
-                    f.write(content)
-                logger.info(
-                    f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
-                    f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
-                    f"index located at {save_index_file}."
-                )
+        for filename, tensors in state_dict_split.filename_to_tensors.items():
+            shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
+            filepath = os.path.join(save_directory, filename)
+            if safe_serialization:
+                # At some point we will need to deal better with save_function (used for TPU and other distributed
+                # joyfulness), but for now this enough.
+                safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
             else:
-                path_to_weights = os.path.join(save_directory, weights_name)
-                logger.info(f"Model weights saved in {path_to_weights}")
+                torch.save(shard, filepath)
+
+        if state_dict_split.is_sharded:
+            index = {
+                "metadata": state_dict_split.metadata,
+                "weight_map": state_dict_split.tensor_to_filename,
+            }
+            save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+            save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
+            # Save the index as well
+            with open(save_index_file, "w", encoding="utf-8") as f:
+                content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+                f.write(content)
+            logger.info(
+                f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
+                f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
+                f"index located at {save_index_file}."
+            )
+        else:
+            path_to_weights = os.path.join(save_directory, weights_name)
+            logger.info(f"Model weights saved in {path_to_weights}")
 
-        # Push to hub if requested (common to both paths)
         if push_to_hub:
             # Create a new empty model card and eventually tag it
             model_card = load_or_create_model_card(repo_id, token=token)
@@ -950,10 +939,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
                 `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
                 weights. If set to `False`, `safetensors` weights are not loaded.
-            use_flashpack (`bool`, *optional*, defaults to `False`):
-                If set to `True`, the model is first loaded from `flashpack` (https://github.com/fal-ai/flashpack)
-                weights if a compatible `.flashpack` file is found. If flashpack is unavailable or the `.flashpack`
-                file cannot be used, automatic fallback to the standard loading path (for example, `safetensors`).
             disable_mmap ('bool', *optional*, defaults to 'False'):
                 Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
                 is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
@@ -997,7 +982,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
-        use_flashpack = kwargs.pop("use_flashpack", False)
         quantization_config = kwargs.pop("quantization_config", None)
         dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
@@ -1215,31 +1199,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model
 
             model = load_flax_checkpoint_in_pytorch_model(model, resolved_model_file)
-
-        flashpack_file = None
-        if use_flashpack:
-            try:
-                flashpack_file = _get_model_file(
-                    pretrained_model_name_or_path,
-                    weights_name=_add_variant("model.flashpack", variant),
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    token=token,
-                    revision=revision,
-                    subfolder=subfolder,
-                    user_agent=user_agent,
-                    commit_hash=commit_hash,
-                    dduf_entries=dduf_entries,
-                )
-            except EnvironmentError:
-                flashpack_file = None
-                logger.warning(
-                    "`use_flashpack` was specified to be True but not flashpack file was found. Resorting to non-flashpack alternatives."
-                )
-
-        if flashpack_file is None:
+        else:
             # in the case it is sharded, we have already the index
             if is_sharded:
                 resolved_model_file, sharded_metadata = _get_checkpoint_shard_files(
@@ -1255,7 +1215,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     dduf_entries=dduf_entries,
                 )
             elif use_safetensors:
-                logger.warning("Trying to load model weights with safetensors format.")
                 try:
                     resolved_model_file = _get_model_file(
                         pretrained_model_name_or_path,
@@ -1321,29 +1280,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         if dtype_orig is not None:
             torch.set_default_dtype(dtype_orig)
 
-        if flashpack_file is not None:
-            from ..utils.flashpack_utils import load_flashpack
-
-            # Even when using FlashPack, we preserve `low_cpu_mem_usage` behavior by initializing
-            # the model with meta tensors. Since FlashPack cannot write into meta tensors, we
-            # explicitly materialize parameters before loading to ensure correctness and parity
-            # with the standard loading path.
-            if any(p.device.type == "meta" for p in model.parameters()):
-                model.to_empty(device="cpu")
-            load_flashpack(model, flashpack_file)
-            model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-            model.eval()
-
-            if output_loading_info:
-                return model, {
-                    "missing_keys": [],
-                    "unexpected_keys": [],
-                    "mismatched_keys": [],
-                    "error_msgs": [],
-                }
-
-            return model
-
         state_dict = None
         if not is_sharded:
             # Time to load the checkpoint
@@ -1391,6 +1327,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             keep_in_fp32_modules=keep_in_fp32_modules,
             dduf_entries=dduf_entries,
             is_parallel_loading_enabled=is_parallel_loading_enabled,
+            disable_mmap=disable_mmap,
         )
         loading_info = {
             "missing_keys": missing_keys,
@@ -1436,8 +1373,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         if output_loading_info:
             return model, loading_info
 
-        logger.warning(f"Model till end {pretrained_model_name_or_path} loaded successfully")
-
         return model
 
     # Adapted from `transformers`.

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/pipeline_loading_utils.py

@@ -756,7 +756,6 @@ def load_sub_model(
     low_cpu_mem_usage: bool,
     cached_folder: Union[str, os.PathLike],
     use_safetensors: bool,
-    use_flashpack: bool,
     dduf_entries: Optional[Dict[str, DDUFEntry]],
     provider_options: Any,
     disable_mmap: bool,
@@ -839,9 +838,6 @@ def load_sub_model(
         loading_kwargs["variant"] = model_variants.pop(name, None)
         loading_kwargs["use_safetensors"] = use_safetensors
 
-        if is_diffusers_model:
-            loading_kwargs["use_flashpack"] = use_flashpack
-
         if from_flax:
             loading_kwargs["from_flax"] = True
 

src/diffusers/pipelines/pipeline_utils.py

@@ -111,7 +111,7 @@ LIBRARIES = []
 for library in LOADABLE_CLASSES:
     LIBRARIES.append(library)
 
-SUPPORTED_DEVICE_MAP = ["balanced"] + [get_device()]
+SUPPORTED_DEVICE_MAP = ["balanced"] + [get_device(), "cpu"]
 
 logger = logging.get_logger(__name__)
 
@@ -243,7 +243,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         variant: Optional[str] = None,
         max_shard_size: Optional[Union[int, str]] = None,
         push_to_hub: bool = False,
-        use_flashpack: bool = False,
         **kwargs,
     ):
         """
@@ -269,9 +268,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
-            use_flashpack (`bool`, *optional*, defaults to `False`):
-                Whether or not to use `flashpack` to save the model weights. Requires the `flashpack` library: `pip
-                install flashpack`.
+
             kwargs (`Dict[str, Any]`, *optional*):
                 Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
@@ -343,7 +340,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             save_method_accept_safe = "safe_serialization" in save_method_signature.parameters
             save_method_accept_variant = "variant" in save_method_signature.parameters
             save_method_accept_max_shard_size = "max_shard_size" in save_method_signature.parameters
-            save_method_accept_flashpack = "use_flashpack" in save_method_signature.parameters
 
             save_kwargs = {}
             if save_method_accept_safe:
@@ -353,8 +349,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             if save_method_accept_max_shard_size and max_shard_size is not None:
                 # max_shard_size is expected to not be None in ModelMixin
                 save_kwargs["max_shard_size"] = max_shard_size
-            if save_method_accept_flashpack:
-                save_kwargs["use_flashpack"] = use_flashpack
 
             save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs)
 
@@ -473,8 +467,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         pipeline_is_sequentially_offloaded = any(
             module_is_sequentially_offloaded(module) for _, module in self.components.items()
         )
-
-        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self._is_pipeline_device_mapped()
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline which doesn't allow explicit device placement using `to()`. You can call `reset_device_map()` to remove the existing device map from the pipeline."
@@ -713,11 +706,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                 safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                 weights. If set to `False`, safetensors weights are not loaded.
-            use_flashpack (`bool`, *optional*, defaults to `False`):
-                If set to `True`, the model is first loaded from `flashpack` weights if a compatible `.flashpack` file
-                is found. If flashpack is unavailable or the `.flashpack` file cannot be used, automatic fallback to
-                the standard loading path (for example, `safetensors`). Requires the `flashpack` library: `pip install
-                flashpack`.
             use_onnx (`bool`, *optional*, defaults to `None`):
                 If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
                 will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
@@ -783,7 +771,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         variant = kwargs.pop("variant", None)
         dduf_file = kwargs.pop("dduf_file", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
-        use_flashpack = kwargs.pop("use_flashpack", False)
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
         quantization_config = kwargs.pop("quantization_config", None)
@@ -1073,7 +1060,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     low_cpu_mem_usage=low_cpu_mem_usage,
                     cached_folder=cached_folder,
                     use_safetensors=use_safetensors,
-                    use_flashpack=use_flashpack,
                     dduf_entries=dduf_entries,
                     provider_options=provider_options,
                     disable_mmap=disable_mmap,
@@ -1200,7 +1186,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         """
         self._maybe_raise_error_if_group_offload_active(raise_error=True)
 
-        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self._is_pipeline_device_mapped()
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline so calling `enable_model_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_model_cpu_offload()`."
@@ -1324,7 +1310,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
         self.remove_all_hooks()
 
-        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self._is_pipeline_device_mapped()
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline so calling `enable_sequential_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_sequential_cpu_offload()`."
@@ -2213,6 +2199,21 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 return True
         return False
 
+    def _is_pipeline_device_mapped(self):
+        # We support passing `device_map="cuda"`, for example. This is helpful, in case
+        # users want to pass `device_map="cpu"` when initializing a pipeline. This explicit declaration is desirable
+        # in limited VRAM environments because quantized models often initialize directly on the accelerator.
+        device_map = self.hf_device_map
+        is_device_type_map = False
+        if isinstance(device_map, str):
+            try:
+                torch.device(device_map)
+                is_device_type_map = True
+            except RuntimeError:
+                pass
+
+        return not is_device_type_map and isinstance(device_map, dict) and len(device_map) > 1
+
 
 class StableDiffusionMixin:
     r"""

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

@@ -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/utils/flashpack_utils.py

@@ -1,81 +0,0 @@
-import json
-import os
-from typing import Optional
-
-from ..utils import _add_variant
-from .import_utils import is_flashpack_available
-from .logging import get_logger
-
-
-logger = get_logger(__name__)
-
-
-def save_flashpack(
-    model,
-    save_directory: str,
-    variant: Optional[str] = None,
-    is_main_process: bool = True,
-):
-    """
-    Save model weights in FlashPack format along with a metadata config.
-
-    Args:
-        model: Diffusers model instance
-        save_directory (`str`): Directory to save weights
-        variant (`str`, *optional*): Model variant
-    """
-    if not is_flashpack_available():
-        raise ImportError(
-            "The `use_flashpack=True` argument requires the `flashpack` package. "
-            "Install it with `pip install flashpack`."
-        )
-
-    from flashpack import pack_to_file
-
-    os.makedirs(save_directory, exist_ok=True)
-
-    weights_name = _add_variant("model.flashpack", variant)
-    weights_path = os.path.join(save_directory, weights_name)
-    config_path = os.path.join(save_directory, "flashpack_config.json")
-
-    try:
-        target_dtype = getattr(model, "dtype", None)
-        logger.warning(f"Dtype used for FlashPack save: {target_dtype}")
-
-        # 1. Save binary weights
-        pack_to_file(model, weights_path, target_dtype=target_dtype)
-
-        # 2. Save config metadata (best-effort)
-        if hasattr(model, "config"):
-            try:
-                if hasattr(model.config, "to_dict"):
-                    config_data = model.config.to_dict()
-                else:
-                    config_data = dict(model.config)
-
-                with open(config_path, "w") as f:
-                    json.dump(config_data, f, indent=4)
-
-            except Exception as config_err:
-                logger.warning(f"FlashPack weights saved, but config serialization failed: {config_err}")
-
-    except Exception as e:
-        logger.error(f"Failed to save weights in FlashPack format: {e}")
-        raise
-
-
-def load_flashpack(model, flashpack_file: str):
-    """
-    Assign FlashPack weights from a file into an initialized PyTorch model.
-    """
-    if not is_flashpack_available():
-        raise ImportError("FlashPack weights require the `flashpack` package. Install with `pip install flashpack`.")
-
-    from flashpack import assign_from_file
-
-    logger.warning(f"Loading FlashPack weights from {flashpack_file}")
-
-    try:
-        assign_from_file(model, flashpack_file)
-    except Exception as e:
-        raise RuntimeError(f"Failed to load FlashPack weights from {flashpack_file}") from e

src/diffusers/utils/import_utils.py

@@ -231,7 +231,6 @@ _aiter_available, _aiter_version = _is_package_available("aiter")
 _kornia_available, _kornia_version = _is_package_available("kornia")
 _nvidia_modelopt_available, _nvidia_modelopt_version = _is_package_available("modelopt", get_dist_name=True)
 _av_available, _av_version = _is_package_available("av")
-_flashpack_available, _flashpack_version = _is_package_available("flashpack")
 
 
 def is_torch_available():
@@ -426,10 +425,6 @@ def is_av_available():
     return _av_available
 
 
-def is_flashpack_available():
-    return _flashpack_available
-
-
 # docstyle-ignore
 FLAX_IMPORT_ERROR = """
 {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
@@ -947,16 +942,6 @@ def is_aiter_version(operation: str, version: str):
     return compare_versions(parse(_aiter_version), operation, version)
 
 
-@cache
-def is_flashpack_version(operation: str, version: str):
-    """
-    Compares the current flashpack version to a given reference with an operation.
-    """
-    if not _flashpack_available:
-        return False
-    return compare_versions(parse(_flashpack_version), operation, version)
-
-
 def get_objects_from_module(module):
     """
     Returns a dict of object names and values in a module, while skipping private/internal objects