[Type Hints] DDIM pipelines (#345)

* type hints

* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -15,7 +15,7 @@
 
 
 import warnings
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
 
 import torch
 
@@ -31,11 +31,11 @@ class DDIMPipeline(DiffusionPipeline):
     @torch.no_grad()
     def __call__(
         self,
-        batch_size=1,
-        generator=None,
-        eta=0.0,
-        num_inference_steps=50,
-        output_type="pil",
+        batch_size: int = 1,
+        generator: Optional[torch.Generator] = None,
+        eta: float = 0.0,
+        num_inference_steps: int = 50,
+        output_type: Optional[str] = "pil",
         return_dict: bool = True,
         **kwargs,
     ) -> Union[ImagePipelineOutput, Tuple]:

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -15,7 +15,7 @@
 
 
 import warnings
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
 
 import torch
 
@@ -30,7 +30,12 @@ class DDPMPipeline(DiffusionPipeline):
 
     @torch.no_grad()
     def __call__(
-        self, batch_size=1, generator=None, output_type="pil", return_dict: bool = True, **kwargs
+        self,
+        batch_size: int = 1,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        **kwargs,
     ) -> Union[ImagePipelineOutput, Tuple]:
         if "torch_device" in kwargs:
             device = kwargs.pop("torch_device")

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -10,13 +10,23 @@ from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_outputs import BaseModelOutput
 from transformers.modeling_utils import PreTrainedModel
+from transformers.tokenization_utils import PreTrainedTokenizer
 from transformers.utils import logging
 
+from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
 from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 
 
 class LDMTextToImagePipeline(DiffusionPipeline):
-    def __init__(self, vqvae, bert, tokenizer, unet, scheduler):
+    def __init__(
+        self,
+        vqvae: Union[VQModel, AutoencoderKL],
+        bert: PreTrainedModel,
+        tokenizer: PreTrainedTokenizer,
+        unet: Union[UNet2DModel, UNet2DConditionModel],
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+    ):
         super().__init__()
         scheduler = scheduler.set_format("pt")
         self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
@@ -618,7 +628,7 @@ class LDMBertEncoder(LDMBertPreTrainedModel):
 
 
 class LDMBertModel(LDMBertPreTrainedModel):
-    def __init__(self, config):
+    def __init__(self, config: LDMBertConfig):
         super().__init__(config)
         self.model = LDMBertEncoder(config)
         self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)