update

* More robust from_pretrained init_kwargs type checking

* Corrected for Python 3.10

* Type checks subclasses and fixed type warnings

* More type corrections and skip tokenizer type checking

* make style && make quality

* Updated docs and types for Lumina pipelines

* Fixed check for empty signature

* changed location of helper functions

* make style

---------

Co-authored-by: hlky <hlky@hlky.ac>

src/diffusers/hooks/_cfg_parallel.py

@@ -0,0 +1,65 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.distributed as dist
+
+from ..utils import get_logger
+from ._common import _BATCHED_INPUT_IDENTIFIERS
+from .hooks import HookRegistry, ModelHook
+
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+_CFG_PARALLEL = "cfg_parallel"
+
+
+class CFGParallelHook(ModelHook):
+    def initialize_hook(self, module):
+        if not dist.is_initialized():
+            raise RuntimeError("Distributed environment not initialized.")
+        return module
+
+    def new_forward(self, module: torch.nn.Module, *args, **kwargs):
+        if len(args) > 0:
+            logger.warning(
+                "CFGParallelHook is an example hook that does not work with batched positional arguments. Please use with caution."
+            )
+
+        world_size = dist.get_world_size()
+        rank = dist.get_rank()
+
+        assert world_size == 2, "This is an example hook designed to only work with 2 processes."
+
+        for key in list(kwargs.keys()):
+            if key not in _BATCHED_INPUT_IDENTIFIERS or kwargs[key] is None:
+                continue
+            kwargs[key] = torch.chunk(kwargs[key], world_size, dim=0)[rank].contiguous()
+
+        output = self.fn_ref.original_forward(*args, **kwargs)
+        sample = output[0]
+        sample_list = [torch.empty_like(sample) for _ in range(world_size)]
+        dist.all_gather(sample_list, sample)
+        sample = torch.cat(sample_list, dim=0).contiguous()
+
+        return_dict = kwargs.get("return_dict", False)
+        if not return_dict:
+            return (sample, *output[1:])
+        return output.__class__(sample, *output[1:])
+
+
+def apply_cfg_parallel(module: torch.nn.Module) -> None:
+    registry = HookRegistry.check_if_exists_or_initialize(module)
+    hook = CFGParallelHook()
+    registry.register_hook(hook, _CFG_PARALLEL)

src/diffusers/hooks/_common.py

@@ -0,0 +1,26 @@
+from ..models.attention_processor import Attention, MochiAttention
+
+
+_ATTENTION_CLASSES = (Attention, MochiAttention)
+
+_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "single_transformer_blocks", "layers")
+_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("temporal_transformer_blocks",)
+_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "layers")
+
+_ALL_TRANSFORMER_BLOCK_IDENTIFIERS = tuple(
+    {
+        *_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+        *_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+        *_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS,
+    }
+)
+
+_BATCHED_INPUT_IDENTIFIERS = (
+    "hidden_states",
+    "encoder_hidden_states",
+    "pooled_projections",
+    "timestep",
+    "attention_mask",
+    "encoder_attention_mask",
+    "guidance",
+)

src/diffusers/hooks/pyramid_attention_broadcast.py

@@ -20,19 +20,18 @@ import torch
 
 from ..models.attention_processor import Attention, MochiAttention
 from ..utils import logging
+from ._common import (
+    _ATTENTION_CLASSES,
+    _CROSS_TRANSFORMER_BLOCK_IDENTIFIERS,
+    _SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+    _TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+)
 from .hooks import HookRegistry, ModelHook
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-_ATTENTION_CLASSES = (Attention, MochiAttention)
-
-_SPATIAL_ATTENTION_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "single_transformer_blocks")
-_TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS = ("temporal_transformer_blocks",)
-_CROSS_ATTENTION_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks")
-
-
 @dataclass
 class PyramidAttentionBroadcastConfig:
     r"""
@@ -76,9 +75,9 @@ class PyramidAttentionBroadcastConfig:
     temporal_attention_timestep_skip_range: Tuple[int, int] = (100, 800)
     cross_attention_timestep_skip_range: Tuple[int, int] = (100, 800)
 
-    spatial_attention_block_identifiers: Tuple[str, ...] = _SPATIAL_ATTENTION_BLOCK_IDENTIFIERS
-    temporal_attention_block_identifiers: Tuple[str, ...] = _TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS
-    cross_attention_block_identifiers: Tuple[str, ...] = _CROSS_ATTENTION_BLOCK_IDENTIFIERS
+    spatial_attention_block_identifiers: Tuple[str, ...] = _SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS
+    temporal_attention_block_identifiers: Tuple[str, ...] = _TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS
+    cross_attention_block_identifiers: Tuple[str, ...] = _CROSS_TRANSFORMER_BLOCK_IDENTIFIERS
 
     current_timestep_callback: Callable[[], int] = None
 

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -224,7 +224,7 @@ class AnimateDiffVideoToVideoPipeline(
         vae: AutoencoderKL,
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
+        unet: Union[UNet2DConditionModel, UNetMotionModel],
         motion_adapter: MotionAdapter,
         scheduler: Union[
             DDIMScheduler,

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video_controlnet.py

@@ -246,7 +246,7 @@ class AnimateDiffVideoToVideoControlNetPipeline(
         vae: AutoencoderKL,
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
+        unet: Union[UNet2DConditionModel, UNetMotionModel],
         motion_adapter: MotionAdapter,
         controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
         scheduler: Union[

src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py

@@ -232,8 +232,8 @@ class HunyuanDiTControlNetPipeline(DiffusionPipeline):
             Tuple[HunyuanDiT2DControlNetModel],
             HunyuanDiT2DMultiControlNetModel,
         ],
-        text_encoder_2=T5EncoderModel,
-        tokenizer_2=MT5Tokenizer,
+        text_encoder_2: Optional[T5EncoderModel] = None,
+        tokenizer_2: Optional[MT5Tokenizer] = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py

@@ -17,10 +17,10 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import (
-    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
-    PreTrainedModel,
+    SiglipImageProcessor,
+    SiglipVisionModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
@@ -178,9 +178,9 @@ class StableDiffusion3ControlNetPipeline(
             Provides additional conditioning to the `unet` during the denoising process. If you set multiple
             ControlNets as a list, the outputs from each ControlNet are added together to create one combined
             additional conditioning.
-        image_encoder (`PreTrainedModel`, *optional*):
+        image_encoder (`SiglipVisionModel`, *optional*):
             Pre-trained Vision Model for IP Adapter.
-        feature_extractor (`BaseImageProcessor`, *optional*):
+        feature_extractor (`SiglipImageProcessor`, *optional*):
             Image processor for IP Adapter.
     """
 
@@ -202,8 +202,8 @@ class StableDiffusion3ControlNetPipeline(
         controlnet: Union[
             SD3ControlNetModel, List[SD3ControlNetModel], Tuple[SD3ControlNetModel], SD3MultiControlNetModel
         ],
-        image_encoder: PreTrainedModel = None,
-        feature_extractor: BaseImageProcessor = None,
+        image_encoder: Optional[SiglipVisionModel] = None,
+        feature_extractor: Optional[SiglipImageProcessor] = None,
     ):
         super().__init__()
         if isinstance(controlnet, (list, tuple)):

src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py

@@ -17,10 +17,10 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import (
-    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
-    PreTrainedModel,
+    SiglipImageProcessor,
+    SiglipModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
@@ -223,8 +223,8 @@ class StableDiffusion3ControlNetInpaintingPipeline(
         controlnet: Union[
             SD3ControlNetModel, List[SD3ControlNetModel], Tuple[SD3ControlNetModel], SD3MultiControlNetModel
         ],
-        image_encoder: PreTrainedModel = None,
-        feature_extractor: BaseImageProcessor = None,
+        image_encoder: SiglipModel = None,
+        feature_extractor: Optional[SiglipImageProcessor] = None,
     ):
         super().__init__()
 

src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py

@@ -17,6 +17,8 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
+from ...models import UNet1DModel
+from ...schedulers import SchedulerMixin
 from ...utils import is_torch_xla_available, logging
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
@@ -49,7 +51,7 @@ class DanceDiffusionPipeline(DiffusionPipeline):
 
     model_cpu_offload_seq = "unet"
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet1DModel, scheduler: SchedulerMixin):
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler)
 

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -16,6 +16,7 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
+from ...models import UNet2DModel
 from ...schedulers import DDIMScheduler
 from ...utils import is_torch_xla_available
 from ...utils.torch_utils import randn_tensor
@@ -47,7 +48,7 @@ class DDIMPipeline(DiffusionPipeline):
 
     model_cpu_offload_seq = "unet"
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet2DModel, scheduler: DDIMScheduler):
         super().__init__()
 
         # make sure scheduler can always be converted to DDIM

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -17,6 +17,8 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
+from ...models import UNet2DModel
+from ...schedulers import DDPMScheduler
 from ...utils import is_torch_xla_available
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
@@ -47,7 +49,7 @@ class DDPMPipeline(DiffusionPipeline):
 
     model_cpu_offload_seq = "unet"
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet2DModel, scheduler: DDPMScheduler):
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler)
 

src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py

@@ -91,7 +91,7 @@ class RePaintPipeline(DiffusionPipeline):
     scheduler: RePaintScheduler
     model_cpu_offload_seq = "unet"
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet2DModel, scheduler: RePaintScheduler):
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler)
 

src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py

@@ -207,8 +207,8 @@ class HunyuanDiTPipeline(DiffusionPipeline):
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
-        text_encoder_2=T5EncoderModel,
-        tokenizer_2=MT5Tokenizer,
+        text_encoder_2: Optional[T5EncoderModel] = None,
+        tokenizer_2: Optional[MT5Tokenizer] = None,
     ):
         super().__init__()
 

src/diffusers/pipelines/lumina/pipeline_lumina.py

@@ -20,7 +20,7 @@ import urllib.parse as ul
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoModel, AutoTokenizer
+from transformers import GemmaPreTrainedModel, GemmaTokenizer, GemmaTokenizerFast
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import VaeImageProcessor
@@ -144,13 +144,10 @@ class LuminaText2ImgPipeline(DiffusionPipeline):
     Args:
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`AutoModel`]):
-            Frozen text-encoder. Lumina-T2I uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel), specifically the
-            [t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant.
-        tokenizer (`AutoModel`):
-            Tokenizer of class
-            [AutoModel](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel).
+        text_encoder ([`GemmaPreTrainedModel`]):
+            Frozen Gemma text-encoder.
+        tokenizer (`GemmaTokenizer` or `GemmaTokenizerFast`):
+            Gemma tokenizer.
         transformer ([`Transformer2DModel`]):
             A text conditioned `Transformer2DModel` to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
@@ -185,8 +182,8 @@ class LuminaText2ImgPipeline(DiffusionPipeline):
         transformer: LuminaNextDiT2DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,
         vae: AutoencoderKL,
-        text_encoder: AutoModel,
-        tokenizer: AutoTokenizer,
+        text_encoder: GemmaPreTrainedModel,
+        tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
     ):
         super().__init__()
 

src/diffusers/pipelines/lumina2/pipeline_lumina2.py

@@ -17,7 +17,7 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
-from transformers import AutoModel, AutoTokenizer
+from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFast
 
 from ...image_processor import VaeImageProcessor
 from ...loaders import Lumina2LoraLoaderMixin
@@ -143,13 +143,10 @@ class Lumina2Text2ImgPipeline(DiffusionPipeline, Lumina2LoraLoaderMixin):
     Args:
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`AutoModel`]):
-            Frozen text-encoder. Lumina-T2I uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel), specifically the
-            [t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant.
-        tokenizer (`AutoModel`):
-            Tokenizer of class
-            [AutoModel](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel).
+        text_encoder ([`Gemma2PreTrainedModel`]):
+            Frozen Gemma2 text-encoder.
+        tokenizer (`GemmaTokenizer` or `GemmaTokenizerFast`):
+            Gemma tokenizer.
         transformer ([`Transformer2DModel`]):
             A text conditioned `Transformer2DModel` to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
@@ -165,8 +162,8 @@ class Lumina2Text2ImgPipeline(DiffusionPipeline, Lumina2LoraLoaderMixin):
         transformer: Lumina2Transformer2DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,
         vae: AutoencoderKL,
-        text_encoder: AutoModel,
-        tokenizer: AutoTokenizer,
+        text_encoder: Gemma2PreTrainedModel,
+        tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
     ):
         super().__init__()
 

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -20,7 +20,7 @@ import warnings
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFast
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PixArtImageProcessor
@@ -160,8 +160,8 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
 
     def __init__(
         self,
-        tokenizer: AutoTokenizer,
-        text_encoder: AutoModelForCausalLM,
+        tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
+        text_encoder: Gemma2PreTrainedModel,
         vae: AutoencoderDC,
         transformer: SanaTransformer2DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -17,7 +17,7 @@ import os
 import re
 import warnings
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union, get_args, get_origin
 
 import requests
 import torch
@@ -1059,3 +1059,76 @@ def _maybe_raise_error_for_incorrect_transformers(config_dict):
                 break
     if has_transformers_component and not is_transformers_version(">", "4.47.1"):
         raise ValueError("Please upgrade your `transformers` installation to the latest version to use DDUF.")
+
+
+def _is_valid_type(obj: Any, class_or_tuple: Union[Type, Tuple[Type, ...]]) -> bool:
+    """
+    Checks if an object is an instance of any of the provided types. For collections, it checks if every element is of
+    the correct type as well.
+    """
+    if not isinstance(class_or_tuple, tuple):
+        class_or_tuple = (class_or_tuple,)
+
+    # Unpack unions
+    unpacked_class_or_tuple = []
+    for t in class_or_tuple:
+        if get_origin(t) is Union:
+            unpacked_class_or_tuple.extend(get_args(t))
+        else:
+            unpacked_class_or_tuple.append(t)
+    class_or_tuple = tuple(unpacked_class_or_tuple)
+
+    if Any in class_or_tuple:
+        return True
+
+    obj_type = type(obj)
+    # Classes with obj's type
+    class_or_tuple = {t for t in class_or_tuple if isinstance(obj, get_origin(t) or t)}
+
+    # Singular types (e.g. int, ControlNet, ...)
+    # Untyped collections (e.g. List, but not List[int])
+    elem_class_or_tuple = {get_args(t) for t in class_or_tuple}
+    if () in elem_class_or_tuple:
+        return True
+    # Typed lists or sets
+    elif obj_type in (list, set):
+        return any(all(_is_valid_type(x, t) for x in obj) for t in elem_class_or_tuple)
+    # Typed tuples
+    elif obj_type is tuple:
+        return any(
+            # Tuples with any length and single type (e.g. Tuple[int, ...])
+            (len(t) == 2 and t[-1] is Ellipsis and all(_is_valid_type(x, t[0]) for x in obj))
+            or
+            # Tuples with fixed length and any types (e.g. Tuple[int, str])
+            (len(obj) == len(t) and all(_is_valid_type(x, tt) for x, tt in zip(obj, t)))
+            for t in elem_class_or_tuple
+        )
+    # Typed dicts
+    elif obj_type is dict:
+        return any(
+            all(_is_valid_type(k, kt) and _is_valid_type(v, vt) for k, v in obj.items())
+            for kt, vt in elem_class_or_tuple
+        )
+
+    else:
+        return False
+
+
+def _get_detailed_type(obj: Any) -> Type:
+    """
+    Gets a detailed type for an object, including nested types for collections.
+    """
+    obj_type = type(obj)
+
+    if obj_type in (list, set):
+        obj_origin_type = List if obj_type is list else Set
+        elems_type = Union[tuple({_get_detailed_type(x) for x in obj})]
+        return obj_origin_type[elems_type]
+    elif obj_type is tuple:
+        return Tuple[tuple(_get_detailed_type(x) for x in obj)]
+    elif obj_type is dict:
+        keys_type = Union[tuple({_get_detailed_type(k) for k in obj.keys()})]
+        values_type = Union[tuple({_get_detailed_type(k) for k in obj.values()})]
+        return Dict[keys_type, values_type]
+    else:
+        return obj_type

src/diffusers/pipelines/pipeline_utils.py

@@ -13,7 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import enum
 import fnmatch
 import importlib
 import inspect
@@ -79,10 +78,12 @@ from .pipeline_loading_utils import (
     _fetch_class_library_tuple,
     _get_custom_components_and_folders,
     _get_custom_pipeline_class,
+    _get_detailed_type,
     _get_final_device_map,
     _get_ignore_patterns,
     _get_pipeline_class,
     _identify_model_variants,
+    _is_valid_type,
     _maybe_raise_error_for_incorrect_transformers,
     _maybe_raise_warning_for_inpainting,
     _resolve_custom_pipeline_and_cls,
@@ -876,26 +877,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
 
         init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}
 
-        for key in init_dict.keys():
-            if key not in passed_class_obj:
-                continue
-            if "scheduler" in key:
-                continue
-
-            class_obj = passed_class_obj[key]
-            _expected_class_types = []
-            for expected_type in expected_types[key]:
-                if isinstance(expected_type, enum.EnumMeta):
-                    _expected_class_types.extend(expected_type.__members__.keys())
-                else:
-                    _expected_class_types.append(expected_type.__name__)
-
-            _is_valid_type = class_obj.__class__.__name__ in _expected_class_types
-            if not _is_valid_type:
-                logger.warning(
-                    f"Expected types for {key}: {_expected_class_types}, got {class_obj.__class__.__name__}."
-                )
-
         # Special case: safety_checker must be loaded separately when using `from_flax`
         if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj:
             raise NotImplementedError(
@@ -1015,10 +996,26 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
             )
 
-        # 10. Instantiate the pipeline
+        # 10. Type checking init arguments
+        for kw, arg in init_kwargs.items():
+            # Too complex to validate with type annotation alone
+            if "scheduler" in kw:
+                continue
+            # Many tokenizer annotations don't include its "Fast" variant, so skip this
+            # e.g T5Tokenizer but not T5TokenizerFast
+            elif "tokenizer" in kw:
+                continue
+            elif (
+                arg is not None  # Skip if None
+                and not expected_types[kw] == (inspect.Signature.empty,)  # Skip if no type annotations
+                and not _is_valid_type(arg, expected_types[kw])  # Check type
+            ):
+                logger.warning(f"Expected types for {kw}: {expected_types[kw]}, got {_get_detailed_type(arg)}.")
+
+        # 11. Instantiate the pipeline
         model = pipeline_class(**init_kwargs)
 
-        # 11. Save where the model was instantiated from
+        # 12. Save where the model was instantiated from
         model.register_to_config(_name_or_path=pretrained_model_name_or_path)
         if device_map is not None:
             setattr(model, "hf_device_map", final_device_map)

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -20,7 +20,7 @@ import warnings
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFast
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PixArtImageProcessor
@@ -200,8 +200,8 @@ class SanaPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
 
     def __init__(
         self,
-        tokenizer: AutoTokenizer,
-        text_encoder: AutoModelForCausalLM,
+        tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
+        text_encoder: Gemma2PreTrainedModel,
         vae: AutoencoderDC,
         transformer: SanaTransformer2DModel,
         scheduler: DPMSolverMultistepScheduler,

src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py

@@ -15,7 +15,7 @@
 from typing import Callable, Dict, List, Optional, Union
 
 import torch
-from transformers import CLIPTextModel, CLIPTokenizer
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
 
 from ...models import StableCascadeUNet
 from ...schedulers import DDPMWuerstchenScheduler
@@ -65,7 +65,7 @@ class StableCascadeDecoderPipeline(DiffusionPipeline):
     Args:
         tokenizer (`CLIPTokenizer`):
             The CLIP tokenizer.
-        text_encoder (`CLIPTextModel`):
+        text_encoder (`CLIPTextModelWithProjection`):
             The CLIP text encoder.
         decoder ([`StableCascadeUNet`]):
             The Stable Cascade decoder unet.
@@ -93,7 +93,7 @@ class StableCascadeDecoderPipeline(DiffusionPipeline):
         self,
         decoder: StableCascadeUNet,
         tokenizer: CLIPTokenizer,
-        text_encoder: CLIPTextModel,
+        text_encoder: CLIPTextModelWithProjection,
         scheduler: DDPMWuerstchenScheduler,
         vqgan: PaellaVQModel,
         latent_dim_scale: float = 10.67,

src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py

@@ -15,7 +15,7 @@ from typing import Callable, Dict, List, Optional, Union
 
 import PIL
 import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from ...models import StableCascadeUNet
 from ...schedulers import DDPMWuerstchenScheduler
@@ -52,7 +52,7 @@ class StableCascadeCombinedPipeline(DiffusionPipeline):
     Args:
         tokenizer (`CLIPTokenizer`):
             The decoder tokenizer to be used for text inputs.
-        text_encoder (`CLIPTextModel`):
+        text_encoder (`CLIPTextModelWithProjection`):
             The decoder text encoder to be used for text inputs.
         decoder (`StableCascadeUNet`):
             The decoder model to be used for decoder image generation pipeline.
@@ -60,14 +60,18 @@ class StableCascadeCombinedPipeline(DiffusionPipeline):
             The scheduler to be used for decoder image generation pipeline.
         vqgan (`PaellaVQModel`):
             The VQGAN model to be used for decoder image generation pipeline.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `image_encoder`.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
         prior_prior (`StableCascadeUNet`):
             The prior model to be used for prior pipeline.
+        prior_text_encoder (`CLIPTextModelWithProjection`):
+            The prior text encoder to be used for text inputs.
+        prior_tokenizer (`CLIPTokenizer`):
+            The prior tokenizer to be used for text inputs.
         prior_scheduler (`DDPMWuerstchenScheduler`):
             The scheduler to be used for prior pipeline.
+        prior_feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `image_encoder`.
+        prior_image_encoder ([`CLIPVisionModelWithProjection`]):
+            Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
     """
 
     _load_connected_pipes = True
@@ -76,12 +80,12 @@ class StableCascadeCombinedPipeline(DiffusionPipeline):
     def __init__(
         self,
         tokenizer: CLIPTokenizer,
-        text_encoder: CLIPTextModel,
+        text_encoder: CLIPTextModelWithProjection,
         decoder: StableCascadeUNet,
         scheduler: DDPMWuerstchenScheduler,
         vqgan: PaellaVQModel,
         prior_prior: StableCascadeUNet,
-        prior_text_encoder: CLIPTextModel,
+        prior_text_encoder: CLIPTextModelWithProjection,
         prior_tokenizer: CLIPTokenizer,
         prior_scheduler: DDPMWuerstchenScheduler,
         prior_feature_extractor: Optional[CLIPImageProcessor] = None,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py

@@ -141,7 +141,7 @@ class StableUnCLIPPipeline(
         image_noising_scheduler: KarrasDiffusionSchedulers,
         # regular denoising components
         tokenizer: CLIPTokenizer,
-        text_encoder: CLIPTextModelWithProjection,
+        text_encoder: CLIPTextModel,
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         # vae

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py

@@ -17,10 +17,10 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from transformers import (
-    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
-    PreTrainedModel,
+    SiglipImageProcessor,
+    SiglipVisionModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
@@ -176,9 +176,9 @@ class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingle
         tokenizer_3 (`T5TokenizerFast`):
             Tokenizer of class
             [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-        image_encoder (`PreTrainedModel`, *optional*):
+        image_encoder (`SiglipVisionModel`, *optional*):
             Pre-trained Vision Model for IP Adapter.
-        feature_extractor (`BaseImageProcessor`, *optional*):
+        feature_extractor (`SiglipImageProcessor`, *optional*):
             Image processor for IP Adapter.
     """
 
@@ -197,8 +197,8 @@ class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingle
         tokenizer_2: CLIPTokenizer,
         text_encoder_3: T5EncoderModel,
         tokenizer_3: T5TokenizerFast,
-        image_encoder: PreTrainedModel = None,
-        feature_extractor: BaseImageProcessor = None,
+        image_encoder: SiglipVisionModel = None,
+        feature_extractor: SiglipImageProcessor = None,
     ):
         super().__init__()
 

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py

@@ -18,10 +18,10 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import PIL.Image
 import torch
 from transformers import (
-    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
-    PreTrainedModel,
+    SiglipImageProcessor,
+    SiglipVisionModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
@@ -197,6 +197,10 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         tokenizer_3 (`T5TokenizerFast`):
             Tokenizer of class
             [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        image_encoder (`SiglipVisionModel`, *optional*):
+            Pre-trained Vision Model for IP Adapter.
+        feature_extractor (`SiglipImageProcessor`, *optional*):
+            Image processor for IP Adapter.
     """
 
     model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
@@ -214,8 +218,8 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         tokenizer_2: CLIPTokenizer,
         text_encoder_3: T5EncoderModel,
         tokenizer_3: T5TokenizerFast,
-        image_encoder: PreTrainedModel = None,
-        feature_extractor: BaseImageProcessor = None,
+        image_encoder: Optional[SiglipVisionModel] = None,
+        feature_extractor: Optional[SiglipImageProcessor] = None,
     ):
         super().__init__()
 

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -17,10 +17,10 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from transformers import (
-    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
-    PreTrainedModel,
+    SiglipImageProcessor,
+    SiglipVisionModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
@@ -196,9 +196,9 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         tokenizer_3 (`T5TokenizerFast`):
             Tokenizer of class
             [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-        image_encoder (`PreTrainedModel`, *optional*):
+        image_encoder (`SiglipVisionModel`, *optional*):
             Pre-trained Vision Model for IP Adapter.
-        feature_extractor (`BaseImageProcessor`, *optional*):
+        feature_extractor (`SiglipImageProcessor`, *optional*):
             Image processor for IP Adapter.
     """
 
@@ -217,8 +217,8 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         tokenizer_2: CLIPTokenizer,
         text_encoder_3: T5EncoderModel,
         tokenizer_3: T5TokenizerFast,
-        image_encoder: PreTrainedModel = None,
-        feature_extractor: BaseImageProcessor = None,
+        image_encoder: Optional[SiglipVisionModel] = None,
+        feature_extractor: Optional[SiglipImageProcessor] = None,
     ):
         super().__init__()
 

src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -19,15 +19,31 @@ from typing import Callable, List, Optional, Union
 import torch
 from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
 from k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTokenizer,
+    CLIPTokenizerFast,
+)
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import (
+    StableDiffusionLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
-from ...schedulers import LMSDiscreteScheduler
-from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
-from ..stable_diffusion import StableDiffusionPipelineOutput
+from ..stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -95,13 +111,13 @@ class StableDiffusionKDiffusionPipeline(
 
     def __init__(
         self,
-        vae,
-        text_encoder,
-        tokenizer,
-        unet,
-        scheduler,
-        safety_checker,
-        feature_extractor,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: Union[CLIPTokenizer, CLIPTokenizerFast],
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

tests/fixtures/custom_pipeline/pipeline.py

@@ -18,7 +18,7 @@ from typing import Optional, Tuple, Union
 
 import torch
 
-from diffusers import DiffusionPipeline, ImagePipelineOutput
+from diffusers import DiffusionPipeline, ImagePipelineOutput, SchedulerMixin, UNet2DModel
 
 
 class CustomLocalPipeline(DiffusionPipeline):
@@ -33,7 +33,7 @@ class CustomLocalPipeline(DiffusionPipeline):
             [`DDPMScheduler`], or [`DDIMScheduler`].
     """
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet2DModel, scheduler: SchedulerMixin):
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler)
 

tests/fixtures/custom_pipeline/what_ever.py

@@ -18,6 +18,7 @@ from typing import Optional, Tuple, Union
 
 import torch
 
+from diffusers import SchedulerMixin, UNet2DModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
@@ -33,7 +34,7 @@ class CustomLocalPipeline(DiffusionPipeline):
             [`DDPMScheduler`], or [`DDIMScheduler`].
     """
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet2DModel, scheduler: SchedulerMixin):
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler)
 

tests/pipelines/lumina2/test_pipeline_lumina2.py

@@ -91,10 +91,10 @@ class Lumina2Text2ImgPipelinePipelineFastTests(unittest.TestCase, PipelineTester
         text_encoder = Gemma2Model(config)
 
         components = {
-            "transformer": transformer.eval(),
+            "transformer": transformer,
             "vae": vae.eval(),
             "scheduler": scheduler,
-            "text_encoder": text_encoder.eval(),
+            "text_encoder": text_encoder,
             "tokenizer": tokenizer,
         }
         return components