trained_betas ignored in some schedulers (#635)

* correcting the beta value assignment

* updating DDIM and LMSDiscreteFlax schedulers

* bringing back the changes that were lost as part of main branch merge

Author: vishnu-anirudh

src/diffusers/schedulers/scheduling_ddim.py

@@ -131,7 +131,7 @@ def __init__(
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
-        if beta_schedule == "linear":
+        elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -86,7 +86,7 @@ def __init__(
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
-        if beta_schedule == "linear":
+        elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.

src/diffusers/schedulers/scheduling_lms_discrete_flax.py

@@ -74,7 +74,7 @@ def __init__(
     ):
         if trained_betas is not None:
             self.betas = jnp.asarray(trained_betas)
-        if beta_schedule == "linear":
+        elif beta_schedule == "linear":
             self.betas = jnp.linspace(beta_start, beta_end, num_train_timesteps, dtype=jnp.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.

src/diffusers/schedulers/scheduling_pndm.py

@@ -111,7 +111,7 @@ def __init__(
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
-        if beta_schedule == "linear":
+        elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.

src/diffusers/schedulers/scheduling_pndm_flax.py

@@ -132,7 +132,7 @@ def __init__(
     ):
         if trained_betas is not None:
             self.betas = jnp.asarray(trained_betas)
-        if beta_schedule == "linear":
+        elif beta_schedule == "linear":
             self.betas = jnp.linspace(beta_start, beta_end, num_train_timesteps, dtype=jnp.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.