[docs] Add missing copied from statements in TCD Scheduler (#7360)

* add missing copied from statements in tcd scheduler

* update docstring

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: a-r-r-o-w

src/diffusers/schedulers/scheduling_tcd.py

@@ -307,6 +307,7 @@ def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] =
                 The input sample.
             timestep (`int`, *optional*):
                 The current timestep in the diffusion chain.
+
         Returns:
             `torch.FloatTensor`:
                 A scaled input sample.
@@ -364,7 +365,7 @@ def set_timesteps(
         device: Union[str, torch.device] = None,
         original_inference_steps: Optional[int] = None,
         timesteps: Optional[List[int]] = None,
-        strength: int = 1.0,
+        strength: float = 1.0,
     ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
@@ -384,6 +385,8 @@ def set_timesteps(
                 Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
                 timestep spacing strategy of equal spacing between timesteps on the training/distillation timestep
                 schedule is used. If `timesteps` is passed, `num_inference_steps` must be `None`.
+            strength (`float`, *optional*, defaults to 1.0):
+                Used to determine the number of timesteps used for inference when using img2img, inpaint, etc.
         """
         # 0. Check inputs
         if num_inference_steps is None and timesteps is None:
@@ -624,14 +627,18 @@ def step(
 
         return TCDSchedulerOutput(prev_sample=prev_sample, pred_noised_sample=pred_noised_sample)
 
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
     def add_noise(
         self,
         original_samples: torch.FloatTensor,
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
+        # for the subsequent add_noise calls
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)
+        alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)
         timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
@@ -647,11 +654,13 @@ def add_noise(
         noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples
 
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
     def get_velocity(
         self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
     ) -> torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
+        self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
+        alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
         timesteps = timesteps.to(sample.device)
 
         sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
@@ -670,6 +679,7 @@ def get_velocity(
     def __len__(self):
         return self.config.num_train_timesteps
 
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep
     def previous_timestep(self, timestep):
         if self.custom_timesteps:
             index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]