Merge branch 'main' into enable-cp-kernels

Remove `*pooled_*` mentions from Chroma as it has just one TE.

docker/diffusers-pytorch-cuda/Dockerfile

@@ -2,7 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 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/cu121
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -2,7 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 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/cu121
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

src/diffusers/models/attention_dispatch.py

@@ -260,6 +260,10 @@ class _HubKernelConfig:
     function_attr: str
     revision: Optional[str] = None
     kernel_fn: Optional[Callable] = None
+    wrapped_forward_attr: Optional[str] = None
+    wrapped_backward_attr: Optional[str] = None
+    wrapped_forward_fn: Optional[Callable] = None
+    wrapped_backward_fn: Optional[Callable] = None
 
 
 # Registry for hub-based attention kernels
@@ -274,7 +278,11 @@ _HUB_KERNELS_REGISTRY: Dict["AttentionBackendName", _HubKernelConfig] = {
         # revision="fake-ops-return-probs",
     ),
     AttentionBackendName.FLASH_HUB: _HubKernelConfig(
-        repo_id="kernels-community/flash-attn2", function_attr="flash_attn_func", revision=None
+        repo_id="kernels-community/flash-attn2",
+        function_attr="flash_attn_func",
+        revision=None,
+        wrapped_forward_attr="flash_attn_interface._wrapped_flash_attn_forward",
+        wrapped_backward_attr="flash_attn_interface._wrapped_flash_attn_backward",
     ),
     AttentionBackendName.FLASH_VARLEN_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn2", function_attr="flash_attn_varlen_func", revision=None
@@ -599,22 +607,39 @@ def _flex_attention_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
 
 
 # ===== Helpers for downloading kernels =====
+def _resolve_kernel_attr(module, attr_path: str):
+    target = module
+    for attr in attr_path.split("."):
+        if not hasattr(target, attr):
+            raise AttributeError(f"Kernel module '{module.__name__}' does not define attribute path '{attr_path}'.")
+        target = getattr(target, attr)
+    return target
+
+
 def _maybe_download_kernel_for_backend(backend: AttentionBackendName) -> None:
     if backend not in _HUB_KERNELS_REGISTRY:
         return
     config = _HUB_KERNELS_REGISTRY[backend]
 
-    if config.kernel_fn is not None:
+    needs_kernel = config.kernel_fn is None
+    needs_wrapped_forward = config.wrapped_forward_attr is not None and config.wrapped_forward_fn is None
+    needs_wrapped_backward = config.wrapped_backward_attr is not None and config.wrapped_backward_fn is None
+
+    if not (needs_kernel or needs_wrapped_forward or needs_wrapped_backward):
         return
 
     try:
         from kernels import get_kernel
 
         kernel_module = get_kernel(config.repo_id, revision=config.revision)
-        kernel_func = getattr(kernel_module, config.function_attr)
+        if needs_kernel:
+            config.kernel_fn = _resolve_kernel_attr(kernel_module, config.function_attr)
 
-        # Cache the downloaded kernel function in the config object
-        config.kernel_fn = kernel_func
+        if needs_wrapped_forward:
+            config.wrapped_forward_fn = _resolve_kernel_attr(kernel_module, config.wrapped_forward_attr)
+
+        if needs_wrapped_backward:
+            config.wrapped_backward_fn = _resolve_kernel_attr(kernel_module, config.wrapped_backward_attr)
 
     except Exception as e:
         logger.error(f"An error occurred while fetching kernel '{config.repo_id}' from the Hub: {e}")
@@ -1065,6 +1090,231 @@ def _flash_attention_backward_op(
     return grad_query, grad_key, grad_value
 
 
+def _flash_attention_hub_forward_op(
+    ctx: torch.autograd.function.FunctionCtx,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: Optional[float] = None,
+    enable_gqa: bool = False,
+    return_lse: bool = False,
+    _save_ctx: bool = True,
+    _parallel_config: Optional["ParallelConfig"] = None,
+):
+    if attn_mask is not None:
+        raise ValueError("`attn_mask` is not yet supported for flash-attn hub kernels.")
+    if enable_gqa:
+        raise ValueError("`enable_gqa` is not yet supported for flash-attn hub kernels.")
+
+    config = _HUB_KERNELS_REGISTRY[AttentionBackendName.FLASH_HUB]
+    wrapped_forward_fn = config.wrapped_forward_fn
+    wrapped_backward_fn = config.wrapped_backward_fn
+    if wrapped_forward_fn is None or wrapped_backward_fn is None:
+        raise RuntimeError(
+            "Flash attention hub kernels must expose `_wrapped_flash_attn_forward` and `_wrapped_flash_attn_backward` "
+            "for context parallel execution."
+        )
+
+    if scale is None:
+        scale = query.shape[-1] ** (-0.5)
+
+    window_size = (-1, -1)
+    softcap = 0.0
+    alibi_slopes = None
+    deterministic = False
+    grad_enabled = any(x.requires_grad for x in (query, key, value))
+
+    if grad_enabled or (_parallel_config is not None and _parallel_config.context_parallel_config._world_size > 1):
+        dropout_p = dropout_p if dropout_p > 0 else 1e-30
+
+    with torch.set_grad_enabled(grad_enabled):
+        out, lse, S_dmask, rng_state = wrapped_forward_fn(
+            query,
+            key,
+            value,
+            dropout_p,
+            scale,
+            is_causal,
+            window_size[0],
+            window_size[1],
+            softcap,
+            alibi_slopes,
+            return_lse,
+        )
+        lse = lse.permute(0, 2, 1).contiguous()
+
+    if _save_ctx:
+        ctx.save_for_backward(query, key, value, out, lse, rng_state)
+        ctx.dropout_p = dropout_p
+        ctx.scale = scale
+        ctx.is_causal = is_causal
+        ctx.window_size = window_size
+        ctx.softcap = softcap
+        ctx.alibi_slopes = alibi_slopes
+        ctx.deterministic = deterministic
+
+    return (out, lse) if return_lse else out
+
+
+def _flash_attention_hub_backward_op(
+    ctx: torch.autograd.function.FunctionCtx,
+    grad_out: torch.Tensor,
+    *args,
+    **kwargs,
+):
+    config = _HUB_KERNELS_REGISTRY[AttentionBackendName.FLASH_HUB]
+    wrapped_backward_fn = config.wrapped_backward_fn
+    if wrapped_backward_fn is None:
+        raise RuntimeError(
+            "Flash attention hub kernels must expose `_wrapped_flash_attn_backward` for context parallel execution."
+        )
+
+    query, key, value, out, lse, rng_state = ctx.saved_tensors
+    grad_query, grad_key, grad_value = torch.empty_like(query), torch.empty_like(key), torch.empty_like(value)
+
+    _ = wrapped_backward_fn(
+        grad_out,
+        query,
+        key,
+        value,
+        out,
+        lse,
+        grad_query,
+        grad_key,
+        grad_value,
+        ctx.dropout_p,
+        ctx.scale,
+        ctx.is_causal,
+        ctx.window_size[0],
+        ctx.window_size[1],
+        ctx.softcap,
+        ctx.alibi_slopes,
+        ctx.deterministic,
+        rng_state,
+    )
+
+    grad_query = grad_query[..., : grad_out.shape[-1]]
+    grad_key = grad_key[..., : grad_out.shape[-1]]
+    grad_value = grad_value[..., : grad_out.shape[-1]]
+
+    return grad_query, grad_key, grad_value
+
+
+def _flash_attention_3_hub_forward_op(
+    ctx: torch.autograd.function.FunctionCtx,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: Optional[float] = None,
+    enable_gqa: bool = False,
+    return_lse: bool = False,
+    _save_ctx: bool = True,
+    _parallel_config: Optional["ParallelConfig"] = None,
+    *,
+    window_size: Tuple[int, int] = (-1, -1),
+    softcap: float = 0.0,
+    num_splits: int = 1,
+    pack_gqa: Optional[bool] = None,
+    deterministic: bool = False,
+    sm_margin: int = 0,
+):
+    if attn_mask is not None:
+        raise ValueError("`attn_mask` is not yet supported for flash-attn 3 hub kernels.")
+    if dropout_p != 0.0:
+        raise ValueError("`dropout_p` is not yet supported for flash-attn 3 hub kernels.")
+    if enable_gqa:
+        raise ValueError("`enable_gqa` is not yet supported for flash-attn 3 hub kernels.")
+
+    func = _HUB_KERNELS_REGISTRY[AttentionBackendName._FLASH_3_HUB].kernel_fn
+    out = func(
+        q=query,
+        k=key,
+        v=value,
+        softmax_scale=scale,
+        causal=is_causal,
+        qv=None,
+        q_descale=None,
+        k_descale=None,
+        v_descale=None,
+        window_size=window_size,
+        softcap=softcap,
+        num_splits=num_splits,
+        pack_gqa=pack_gqa,
+        deterministic=deterministic,
+        sm_margin=sm_margin,
+        return_attn_probs=return_lse,
+    )
+
+    lse = None
+    if return_lse:
+        out, lse = out
+        lse = lse.permute(0, 2, 1).contiguous()
+
+    if _save_ctx:
+        ctx.save_for_backward(query, key, value)
+        ctx.scale = scale
+        ctx.is_causal = is_causal
+        ctx._hub_kernel = func
+
+    return (out, lse) if return_lse else out
+
+
+def _flash_attention_3_hub_backward_op(
+    ctx: torch.autograd.function.FunctionCtx,
+    grad_out: torch.Tensor,
+    *args,
+    window_size: Tuple[int, int] = (-1, -1),
+    softcap: float = 0.0,
+    num_splits: int = 1,
+    pack_gqa: Optional[bool] = None,
+    deterministic: bool = False,
+    sm_margin: int = 0,
+):
+    query, key, value = ctx.saved_tensors
+    kernel_fn = ctx._hub_kernel
+    with torch.enable_grad():
+        query_r = query.detach().requires_grad_(True)
+        key_r = key.detach().requires_grad_(True)
+        value_r = value.detach().requires_grad_(True)
+
+        out = kernel_fn(
+            q=query_r,
+            k=key_r,
+            v=value_r,
+            softmax_scale=ctx.scale,
+            causal=ctx.is_causal,
+            qv=None,
+            q_descale=None,
+            k_descale=None,
+            v_descale=None,
+            window_size=window_size,
+            softcap=softcap,
+            num_splits=num_splits,
+            pack_gqa=pack_gqa,
+            deterministic=deterministic,
+            sm_margin=sm_margin,
+            return_attn_probs=False,
+        )
+        if isinstance(out, tuple):
+            out = out[0]
+
+        grad_query, grad_key, grad_value = torch.autograd.grad(
+            out,
+            (query_r, key_r, value_r),
+            grad_out,
+            retain_graph=False,
+            allow_unused=False,
+        )
+
+    return grad_query, grad_key, grad_value
+
+
 def _sage_attention_forward_op(
     ctx: torch.autograd.function.FunctionCtx,
     query: torch.Tensor,
@@ -1103,6 +1353,46 @@ def _sage_attention_forward_op(
     return (out, lse) if return_lse else out
 
 
+def _sage_attention_hub_forward_op(
+    ctx: torch.autograd.function.FunctionCtx,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: Optional[float] = None,
+    enable_gqa: bool = False,
+    return_lse: bool = False,
+    _save_ctx: bool = True,
+    _parallel_config: Optional["ParallelConfig"] = None,
+):
+    if attn_mask is not None:
+        raise ValueError("`attn_mask` is not yet supported for Sage attention.")
+    if dropout_p > 0.0:
+        raise ValueError("`dropout_p` is not yet supported for Sage attention.")
+    if enable_gqa:
+        raise ValueError("`enable_gqa` is not yet supported for Sage attention.")
+
+    func = _HUB_KERNELS_REGISTRY[AttentionBackendName.SAGE_HUB].kernel_fn
+    out = func(
+        q=query,
+        k=key,
+        v=value,
+        tensor_layout="NHD",
+        is_causal=is_causal,
+        sm_scale=scale,
+        return_lse=return_lse,
+    )
+
+    lse = None
+    if return_lse:
+        out, lse, *_ = out
+        lse = lse.permute(0, 2, 1).contiguous()
+
+    return (out, lse) if return_lse else out
+
+
 def _sage_attention_backward_op(
     ctx: torch.autograd.function.FunctionCtx,
     grad_out: torch.Tensor,
@@ -1695,7 +1985,7 @@ def _flash_attention(
 @_AttentionBackendRegistry.register(
     AttentionBackendName.FLASH_HUB,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-    supports_context_parallel=False,
+    supports_context_parallel=True,
 )
 def _flash_attention_hub(
     query: torch.Tensor,
@@ -1713,17 +2003,35 @@ def _flash_attention_hub(
         raise ValueError("`attn_mask` is not supported for flash-attn 2.")
 
     func = _HUB_KERNELS_REGISTRY[AttentionBackendName.FLASH_HUB].kernel_fn
-    out = func(
-        q=query,
-        k=key,
-        v=value,
-        dropout_p=dropout_p,
-        softmax_scale=scale,
-        causal=is_causal,
-        return_attn_probs=return_lse,
-    )
-    if return_lse:
-        out, lse, *_ = out
+    if _parallel_config is None:
+        out = func(
+            q=query,
+            k=key,
+            v=value,
+            dropout_p=dropout_p,
+            softmax_scale=scale,
+            causal=is_causal,
+            return_attn_probs=return_lse,
+        )
+        if return_lse:
+            out, lse, *_ = out
+    else:
+        out = _templated_context_parallel_attention(
+            query,
+            key,
+            value,
+            None,
+            dropout_p,
+            is_causal,
+            scale,
+            False,
+            return_lse,
+            forward_op=_flash_attention_hub_forward_op,
+            backward_op=_flash_attention_hub_backward_op,
+            _parallel_config=_parallel_config,
+        )
+        if return_lse:
+            out, lse = out
 
     return (out, lse) if return_lse else out
 
@@ -1870,7 +2178,7 @@ def _flash_attention_3(
 @_AttentionBackendRegistry.register(
     AttentionBackendName._FLASH_3_HUB,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-    supports_context_parallel=False,
+    supports_context_parallel=True,
 )
 def _flash_attention_3_hub(
     query: torch.Tensor,
@@ -1885,33 +2193,68 @@ def _flash_attention_3_hub(
     return_attn_probs: bool = False,
     _parallel_config: Optional["ParallelConfig"] = None,
 ) -> torch.Tensor:
-    if _parallel_config:
-        raise NotImplementedError(f"{AttentionBackendName._FLASH_3_HUB.value} is not implemented for parallelism yet.")
     if attn_mask is not None:
         raise ValueError("`attn_mask` is not supported for flash-attn 3.")
 
     func = _HUB_KERNELS_REGISTRY[AttentionBackendName._FLASH_3_HUB].kernel_fn
-    out = func(
-        q=query,
-        k=key,
-        v=value,
-        softmax_scale=scale,
-        causal=is_causal,
-        qv=None,
-        q_descale=None,
-        k_descale=None,
-        v_descale=None,
+    if _parallel_config is None:
+        out = func(
+            q=query,
+            k=key,
+            v=value,
+            softmax_scale=scale,
+            causal=is_causal,
+            qv=None,
+            q_descale=None,
+            k_descale=None,
+            v_descale=None,
+            window_size=window_size,
+            softcap=softcap,
+            num_splits=1,
+            pack_gqa=None,
+            deterministic=deterministic,
+            sm_margin=0,
+            return_attn_probs=return_attn_probs,
+        )
+        return (out[0], out[1]) if return_attn_probs else out
+
+    forward_op = functools.partial(
+        _flash_attention_3_hub_forward_op,
         window_size=window_size,
         softcap=softcap,
         num_splits=1,
         pack_gqa=None,
         deterministic=deterministic,
         sm_margin=0,
-        return_attn_probs=return_attn_probs,
     )
-    # When `return_attn_probs` is True, the above returns a tuple of
-    # actual outputs and lse.
-    return (out[0], out[1]) if return_attn_probs else out
+    backward_op = functools.partial(
+        _flash_attention_3_hub_backward_op,
+        window_size=window_size,
+        softcap=softcap,
+        num_splits=1,
+        pack_gqa=None,
+        deterministic=deterministic,
+        sm_margin=0,
+    )
+    out = _templated_context_parallel_attention(
+        query,
+        key,
+        value,
+        None,
+        0.0,
+        is_causal,
+        scale,
+        False,
+        return_attn_probs,
+        forward_op=forward_op,
+        backward_op=backward_op,
+        _parallel_config=_parallel_config,
+    )
+    if return_attn_probs:
+        out, lse = out
+        return out, lse
+
+    return out
 
 
 @_AttentionBackendRegistry.register(
@@ -2542,7 +2885,7 @@ def _sage_attention(
 @_AttentionBackendRegistry.register(
     AttentionBackendName.SAGE_HUB,
     constraints=[_check_device_cuda, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-    supports_context_parallel=False,
+    supports_context_parallel=True,
 )
 def _sage_attention_hub(
     query: torch.Tensor,
@@ -2570,6 +2913,23 @@ def _sage_attention_hub(
         )
         if return_lse:
             out, lse, *_ = out
+    else:
+        out = _templated_context_parallel_attention(
+            query,
+            key,
+            value,
+            None,
+            0.0,
+            is_causal,
+            scale,
+            False,
+            return_lse,
+            forward_op=_sage_attention_hub_forward_op,
+            backward_op=_sage_attention_backward_op,
+            _parallel_config=_parallel_config,
+        )
+        if return_lse:
+            out, lse = out
 
     return (out, lse) if return_lse else out
 

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/resnet.py

@@ -366,7 +366,12 @@ class ResnetBlock2D(nn.Module):
         hidden_states = self.conv2(hidden_states)
 
         if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor.contiguous())
+            # Only use contiguous() during training to avoid DDP gradient stride mismatch warning.
+            # In inference mode (eval or no_grad), skip contiguous() for better performance, especially on CPU.
+            # Issue: https://github.com/huggingface/diffusers/issues/12975
+            if self.training:
+                input_tensor = input_tensor.contiguous()
+            input_tensor = self.conv_shortcut(input_tensor)
 
         output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
 

src/diffusers/models/transformers/transformer_longcat_image.py

@@ -23,7 +23,7 @@ from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
 from ...utils import logging
 from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import AttentionModuleMixin, FeedForward
+from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
 from ..cache_utils import CacheMixin
 from ..embeddings import TimestepEmbedding, Timesteps, apply_rotary_emb, get_1d_rotary_pos_embed
@@ -400,12 +400,14 @@ class LongCatImageTransformer2DModel(
     PeftAdapterMixin,
     FromOriginalModelMixin,
     CacheMixin,
+    AttentionMixin,
 ):
     """
     The Transformer model introduced in Longcat-Image.
     """
 
     _supports_gradient_checkpointing = True
+    _repeated_blocks = ["LongCatImageTransformerBlock", "LongCatImageSingleTransformerBlock"]
 
     @register_to_config
     def __init__(

src/diffusers/pipelines/chroma/pipeline_chroma_inpainting.py

@@ -482,8 +482,6 @@ class ChromaInpaintPipeline(
         negative_prompt=None,
         prompt_embeds=None,
         negative_prompt_embeds=None,
-        pooled_prompt_embeds=None,
-        negative_pooled_prompt_embeds=None,
         callback_on_step_end_tensor_inputs=None,
         padding_mask_crop=None,
         max_sequence_length=None,
@@ -531,15 +529,6 @@ class ChromaInpaintPipeline(
                     f" {negative_prompt_embeds.shape}."
                 )
 
-        if prompt_embeds is not None and pooled_prompt_embeds is None:
-            raise ValueError(
-                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
-            )
-        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
-            raise ValueError(
-                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
-            )
-
         if prompt_embeds is not None and prompt_attention_mask is None:
             raise ValueError("Cannot provide `prompt_embeds` without also providing `prompt_attention_mask")
 
@@ -793,13 +782,11 @@ class ChromaInpaintPipeline(
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
         ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
         negative_ip_adapter_image: Optional[PipelineImageInput] = None,
         negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,

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

@@ -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)
 
@@ -713,11 +707,6 @@ 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 +772,6 @@ 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 +1061,6 @@ 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,

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/schedulers/scheduling_ddim_parallel.py

@@ -101,7 +101,7 @@ 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)
 
@@ -266,7 +266,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def _get_variance(self, timestep, prev_timestep=None):
+    def _get_variance(self, timestep: int, prev_timestep: Optional[int] = None) -> torch.Tensor:
         if prev_timestep is None:
             prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
 
@@ -279,7 +279,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
 
         return variance
 
-    def _batch_get_variance(self, t, prev_t):
+    def _batch_get_variance(self, t: torch.Tensor, prev_t: torch.Tensor) -> torch.Tensor:
         alpha_prod_t = self.alphas_cumprod[t]
         alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]
         alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)
@@ -335,7 +335,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         return sample
 
     # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.set_timesteps
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -392,7 +392,7 @@ class DDIMParallelScheduler(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[DDIMParallelSchedulerOutput, Tuple]:
@@ -406,11 +406,13 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
             sample (`torch.Tensor`):
                 current instance of sample being created by diffusion process.
             eta (`float`): weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`): if `True`, compute "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` will have not effect.
-            generator: random number generator.
+            use_clipped_model_output (`bool`, defaults to `False`):
+                If `True`, compute "corrected" `model_output` from the clipped predicted original sample. This
+                correction is necessary because the predicted original sample is clipped to [-1, 1] when
+                `self.config.clip_sample` is `True`. If no clipping occurred, the "corrected" `model_output` matches
+                the input and `use_clipped_model_output` has no effect.
+            generator (`torch.Generator`, *optional*):
+                Random number generator.
             variance_noise (`torch.Tensor`): instead of generating noise for the variance using `generator`, we
                 can directly provide the noise for the variance itself. This is useful for methods such as
                 CycleDiffusion. (https://huggingface.co/papers/2210.05559)
@@ -496,7 +498,10 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
 
             if variance_noise is None:
                 variance_noise = randn_tensor(
-                    model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
+                    model_output.shape,
+                    generator=generator,
+                    device=model_output.device,
+                    dtype=model_output.dtype,
                 )
             variance = std_dev_t * variance_noise
 
@@ -513,7 +518,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
     def batch_step_no_noise(
         self,
         model_output: torch.Tensor,
-        timesteps: List[int],
+        timesteps: torch.Tensor,
         sample: torch.Tensor,
         eta: float = 0.0,
         use_clipped_model_output: bool = False,
@@ -528,7 +533,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
 
         Args:
             model_output (`torch.Tensor`): direct output from learned diffusion model.
-            timesteps (`List[int]`):
+            timesteps (`torch.Tensor`):
                 current discrete timesteps in the diffusion chain. This is now a list of integers.
             sample (`torch.Tensor`):
                 current instance of sample being created by diffusion process.
@@ -696,5 +701,5 @@ class DDIMParallelScheduler(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_ddpm.py

@@ -281,7 +281,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
         Args:
-            num_inference_steps (`int`):
+            num_inference_steps (`int`, *optional*):
                 The number of diffusion steps used when generating samples with a pre-trained model. If used,
                 `timesteps` must be `None`.
             device (`str` or `torch.device`, *optional*):
@@ -646,7 +646,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
     def __len__(self) -> int:
         return self.config.num_train_timesteps
 
-    def previous_timestep(self, timestep: int) -> int:
+    def previous_timestep(self, timestep: int) -> Union[int, torch.Tensor]:
         """
         Compute the previous timestep in the diffusion chain.
 
@@ -655,7 +655,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
                 The current timestep.
 
         Returns:
-            `int`:
+            `int` or `torch.Tensor`:
                 The previous timestep.
         """
         if self.custom_timesteps or self.num_inference_steps:

src/diffusers/schedulers/scheduling_ddpm_flax.py

@@ -22,6 +22,7 @@ import jax
 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 DDPMSchedulerState:
     common: CommonSchedulerState
@@ -42,7 +46,12 @@ class DDPMSchedulerState:
     num_inference_steps: Optional[int] = None
 
     @classmethod
-    def create(cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray):
+    def create(
+        cls,
+        common: CommonSchedulerState,
+        init_noise_sigma: jnp.ndarray,
+        timesteps: jnp.ndarray,
+    ):
         return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps)
 
 
@@ -105,6 +114,10 @@ class FlaxDDPMScheduler(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) -> DDPMSchedulerState:
@@ -123,7 +136,10 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
         )
 
     def scale_model_input(
-        self, state: DDPMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
+        self,
+        state: DDPMSchedulerState,
+        sample: jnp.ndarray,
+        timestep: Optional[int] = None,
     ) -> jnp.ndarray:
         """
         Args:

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -149,38 +149,41 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
     For more details, see the original paper: https://huggingface.co/papers/2006.11239
 
     Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        beta_start (`float`, defaults to 0.0001):
+            The starting `beta` value of inference.
+        beta_end (`float`, defaults to 0.02):
+            The final `beta` value.
+        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`, `squaredcos_cap_v2` or `sigmoid`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        variance_type (`str`):
-            options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
+        trained_betas (`np.ndarray`, *optional*):
+            Option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
+        variance_type (`str`, defaults to `"fixed_small"`):
+            Options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
             `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample for numerical stability.
-        clip_sample_range (`float`, default `1.0`):
-            the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+        clip_sample (`bool`, defaults to `True`):
+            Option to clip predicted sample for numerical stability.
+        prediction_type (`str`, defaults to `"epsilon"`):
+            Prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
             process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
             https://huggingface.co/papers/2210.02303)
-        thresholding (`bool`, default `False`):
-            whether to use the "dynamic thresholding" method (introduced by Imagen,
+        thresholding (`bool`, defaults to `False`):
+            Whether to use the "dynamic thresholding" method (introduced by Imagen,
             https://huggingface.co/papers/2205.11487). Note that the thresholding method is unsuitable for latent-space
             diffusion models (such as stable-diffusion).
-        dynamic_thresholding_ratio (`float`, default `0.995`):
-            the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
+        dynamic_thresholding_ratio (`float`, defaults to 0.995):
+            The ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
             (https://huggingface.co/papers/2205.11487). Valid only when `thresholding=True`.
-        sample_max_value (`float`, default `1.0`):
-            the threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, default `"leading"`):
+        clip_sample_range (`float`, defaults to 1.0):
+            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
+        sample_max_value (`float`, defaults to 1.0):
+            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
+        timestep_spacing (`str`, defaults to `"leading"`):
             The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample
             Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        steps_offset (`int`, default `0`):
+        steps_offset (`int`, defaults to 0):
             An offset added to the inference steps, as required by some model families.
         rescale_betas_zero_snr (`bool`, defaults to `False`):
             Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
@@ -293,7 +296,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
         Args:
-            num_inference_steps (`int`):
+            num_inference_steps (`int`, *optional*):
                 The number of diffusion steps used when generating samples with a pre-trained model. If used,
                 `timesteps` must be `None`.
             device (`str` or `torch.device`, *optional*):
@@ -478,7 +481,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         model_output: torch.Tensor,
         timestep: int,
         sample: torch.Tensor,
-        generator=None,
+        generator: Optional[torch.Generator] = None,
         return_dict: bool = True,
     ) -> Union[DDPMParallelSchedulerOutput, Tuple]:
         """
@@ -490,7 +493,8 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
             timestep (`int`): current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 current instance of sample being created by diffusion process.
-            generator: random number generator.
+            generator (`torch.Generator`, *optional*):
+                Random number generator.
             return_dict (`bool`): option for returning tuple rather than DDPMParallelSchedulerOutput class
 
         Returns:
@@ -503,7 +507,10 @@ class DDPMParallelScheduler(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
@@ -552,7 +559,10 @@ class DDPMParallelScheduler(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
@@ -575,7 +585,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
     def batch_step_no_noise(
         self,
         model_output: torch.Tensor,
-        timesteps: List[int],
+        timesteps: torch.Tensor,
         sample: torch.Tensor,
     ) -> torch.Tensor:
         """
@@ -588,8 +598,8 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
 
         Args:
             model_output (`torch.Tensor`): direct output from learned diffusion model.
-            timesteps (`List[int]`):
-                current discrete timesteps in the diffusion chain. This is now a list of integers.
+            timesteps (`torch.Tensor`):
+                Current discrete timesteps in the diffusion chain. This is a tensor of integers.
             sample (`torch.Tensor`):
                 current instance of sample being created by diffusion process.
 
@@ -603,7 +613,10 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         t = t.view(-1, *([1] * (model_output.ndim - 1)))
         prev_t = prev_t.view(-1, *([1] * (model_output.ndim - 1)))
 
-        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:
             pass
@@ -734,7 +747,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         return self.config.num_train_timesteps
 
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep
-    def previous_timestep(self, timestep):
+    def previous_timestep(self, timestep: int) -> Union[int, torch.Tensor]:
         """
         Compute the previous timestep in the diffusion chain.
 
@@ -743,7 +756,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
                 The current timestep.
 
         Returns:
-            `int`:
+            `int` or `torch.Tensor`:
                 The previous timestep.
         """
         if self.custom_timesteps or self.num_inference_steps:

src/diffusers/schedulers/scheduling_lcm.py

@@ -722,7 +722,7 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
                 The current timestep.
 
         Returns:
-            `int`:
+            `int` or `torch.Tensor`:
                 The previous timestep.
         """
         if self.custom_timesteps or self.num_inference_steps:

src/diffusers/schedulers/scheduling_tcd.py

@@ -777,7 +777,7 @@ class TCDScheduler(SchedulerMixin, ConfigMixin):
                 The current timestep.
 
         Returns:
-            `int`:
+            `int` or `torch.Tensor`:
                 The previous timestep.
         """
         if self.custom_timesteps or self.num_inference_steps:

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

tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py

@@ -248,6 +248,9 @@ class KandinskyV22InpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCas
     def test_float16_inference(self):
         super().test_float16_inference(expected_max_diff=5e-1)
 
+    def test_save_load_dduf(self):
+        super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
+
     @is_flaky()
     def test_model_cpu_offload_forward_pass(self):
         super().test_inference_batch_single_identical(expected_max_diff=8e-4)

tests/pipelines/kandinsky3/test_kandinsky3_img2img.py

@@ -191,6 +191,9 @@ class Kandinsky3Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
     def test_inference_batch_single_identical(self):
         super().test_inference_batch_single_identical(expected_max_diff=1e-2)
 
+    def test_save_load_dduf(self):
+        super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
+
 
 @slow
 @require_torch_accelerator