Added type hints and improved docstrings for DPMSolverSampler class.

ldm/models/diffusion/dpm_solver/sampler.py

@@ -1,4 +1,3 @@
-"""SAMPLING ONLY."""
 import torch
 
 from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
@@ -8,46 +7,87 @@ MODEL_TYPES = {
     "v": "v"
 }
 
-
 class DPMSolverSampler(object):
-    def __init__(self, model, device=torch.device("cuda"), **kwargs):
+    def __init__(self, model: torch.nn.Module, device: torch.device = torch.device("cuda"), **kwargs) -> None:
+        """
+        Initialize the DPMSolverSampler.
+
+        Args:
+            model (torch.nn.Module): The model to use for sampling.
+            device (torch.device, optional): The device to use. Defaults to torch.device("cuda").
+            **kwargs: Additional keyword arguments.
+        """
         super().__init__()
         self.model = model
         self.device = device
         to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
         self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
 
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
+    def register_buffer(self, name: str, attr: torch.Tensor) -> None:
+        """
+        Register a buffer in the module.
+
+        Args:
+            name (str): The name of the buffer.
+            attr (torch.Tensor): The tensor to register as a buffer.
+        """
+        if isinstance(attr, torch.Tensor):
             if attr.device != self.device:
                 attr = attr.to(self.device)
         setattr(self, name, attr)
 
     @torch.no_grad()
     def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
+               S: int,
+               batch_size: int,
+               shape: tuple,
+               conditioning: dict = None,
+               callback: callable = None,
+               normals_sequence: list = None,
+               img_callback: callable = None,
+               quantize_x0: bool = False,
+               eta: float = 0.,
+               mask: torch.Tensor = None,
+               x0: torch.Tensor = None,
+               temperature: float = 1.,
+               noise_dropout: float = 0.,
+               score_corrector: callable = None,
+               corrector_kwargs: dict = None,
+               verbose: bool = True,
+               x_T: torch.Tensor = None,
+               log_every_t: int = 100,
+               unconditional_guidance_scale: float = 1.,
+               unconditional_conditioning: torch.Tensor = None,
+               **kwargs) -> tuple:
+        """
+        Perform sampling using the DPM Solver.
+
+        Args:
+            S (int): Number of steps.
+            batch_size (int): Batch size for sampling.
+            shape (tuple): Shape of the samples (C, H, W).
+            conditioning (dict, optional): Conditioning information. Defaults to None.
+            callback (callable, optional): Callback function. Defaults to None.
+            normals_sequence (list, optional): Sequence of normals. Defaults to None.
+            img_callback (callable, optional): Image callback function. Defaults to None.
+            quantize_x0 (bool, optional): Flag for quantizing x0. Defaults to False.
+            eta (float, optional): Eta parameter. Defaults to 0..
+            mask (torch.Tensor, optional): Mask tensor. Defaults to None.
+            x0 (torch.Tensor, optional): Initial x0 tensor. Defaults to None.
+            temperature (float, optional): Temperature parameter. Defaults to 1..
+            noise_dropout (float, optional): Noise dropout parameter. Defaults to 0..
+            score_corrector (callable, optional): Score corrector. Defaults to None.
+            corrector_kwargs (dict, optional): Keyword arguments for the score corrector. Defaults to None.
+            verbose (bool, optional): Verbose flag. Defaults to True.
+            x_T (torch.Tensor, optional): Initial x_T tensor. Defaults to None.
+            log_every_t (int, optional): Log interval. Defaults to 100.
+            unconditional_guidance_scale (float, optional): Guidance scale for unconditional sampling. Defaults to 1..
+            unconditional_conditioning (torch.Tensor, optional): Conditioning tensor for unconditional sampling. Defaults to None.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            tuple: Sampled tensor and additional information.
+        """
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 ctmp = conditioning[list(conditioning.keys())[0]]
@@ -94,3 +134,4 @@ class DPMSolverSampler(object):
                               lower_order_final=True)
 
         return x.to(device), None
+