[schedulers] hanlde dtype in add_noise (#767)

* handle dtype in vae and image2image pipeline

* handle dtype in add noise

* don't modify vae and pipeline

* remove the if

src/diffusers/schedulers/scheduling_ddim.py

@@ -301,11 +301,9 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.FloatTensor:
-        if self.alphas_cumprod.device != original_samples.device:
-            self.alphas_cumprod = self.alphas_cumprod.to(original_samples.device)
-
-        if timesteps.device != original_samples.device:
-            timesteps = timesteps.to(original_samples.device)
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = sqrt_alpha_prod.flatten()

src/diffusers/schedulers/scheduling_ddpm.py

@@ -294,11 +294,9 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.FloatTensor:
-        if self.alphas_cumprod.device != original_samples.device:
-            self.alphas_cumprod = self.alphas_cumprod.to(original_samples.device)
-
-        if timesteps.device != original_samples.device:
-            timesteps = timesteps.to(original_samples.device)
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = sqrt_alpha_prod.flatten()

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -257,9 +257,13 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.FloatTensor,
         timesteps: torch.FloatTensor,
     ) -> torch.FloatTensor:
-        sigmas = self.sigmas.to(original_samples.device)
-        schedule_timesteps = self.timesteps.to(original_samples.device)
+        # Make sure sigmas and timesteps have the same device and dtype as original_samples
+        self.sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
+        self.timesteps = self.timesteps.to(original_samples.device)
         timesteps = timesteps.to(original_samples.device)
+
+        schedule_timesteps = self.timesteps
+
         if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
             deprecate(
                 "timesteps as indices",
@@ -273,7 +277,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         else:
             step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
 
-        sigma = sigmas[step_indices].flatten()
+        sigma = self.sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):
             sigma = sigma.unsqueeze(-1)
 

src/diffusers/schedulers/scheduling_pndm.py

@@ -400,11 +400,9 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.Tensor:
-        if self.alphas_cumprod.device != original_samples.device:
-            self.alphas_cumprod = self.alphas_cumprod.to(original_samples.device)
-
-        if timesteps.device != original_samples.device:
-            timesteps = timesteps.to(original_samples.device)
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = sqrt_alpha_prod.flatten()