Type checks subclasses and fixed type warnings

Author: guiyrt

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -224,7 +224,7 @@ def __init__(
         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 @@ def __init__(
         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 @@ def __init__(
             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/dance_diffusion/pipeline_dance_diffusion.py

@@ -17,6 +17,8 @@
 
 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 @@
 
 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 @@
 
 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 @@ def __init__(
         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 @@
 from typing import List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoModel, AutoTokenizer
+from transformers import PreTrainedModel, PreTrainedTokenizerBase
 
 from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL
@@ -143,13 +143,13 @@ class LuminaText2ImgPipeline(DiffusionPipeline):
     Args:
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`AutoModel`]):
+        text_encoder ([`PreTrainedModel`]):
             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 (`AutoTokenizer`):
             Tokenizer of class
-            [AutoModel](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel).
+            [AutoTokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel).
         transformer ([`Transformer2DModel`]):
             A text conditioned `Transformer2DModel` to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
@@ -180,8 +180,8 @@ def __init__(
         transformer: LuminaNextDiT2DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,
         vae: AutoencoderKL,
-        text_encoder: AutoModel,
-        tokenizer: AutoTokenizer,
+        text_encoder: PreTrainedModel,
+        tokenizer: PreTrainedTokenizerBase,
     ):
         super().__init__()
 

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -20,7 +20,7 @@
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import PreTrainedModel, PreTrainedTokenizerBase
 
 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: PreTrainedTokenizerBase,
+        text_encoder: PreTrainedModel,
         vae: AutoencoderDC,
         transformer: SanaTransformer2DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,