Flax: add shape argument to set_timesteps (huggingface#690)

* Flax: add shape argument to set_timesteps

* style

schedulers/scheduling_ddim_flax.py

@@ -156,7 +156,7 @@ def _get_variance(self, timestep, prev_timestep, alphas_cumprod):
 
         return variance
 
-    def set_timesteps(self, state: DDIMSchedulerState, num_inference_steps: int) -> DDIMSchedulerState:
+    def set_timesteps(self, state: DDIMSchedulerState, num_inference_steps: int, shape: Tuple) -> DDIMSchedulerState:
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 

schedulers/scheduling_ddpm_flax.py

@@ -133,7 +133,7 @@ def __init__(
 
         self.variance_type = variance_type
 
-    def set_timesteps(self, state: DDPMSchedulerState, num_inference_steps: int) -> DDPMSchedulerState:
+    def set_timesteps(self, state: DDPMSchedulerState, num_inference_steps: int, shape: Tuple) -> DDPMSchedulerState:
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 

schedulers/scheduling_karras_ve_flax.py

@@ -99,7 +99,9 @@ def __init__(
     ):
         self.state = KarrasVeSchedulerState.create()
 
-    def set_timesteps(self, state: KarrasVeSchedulerState, num_inference_steps: int) -> KarrasVeSchedulerState:
+    def set_timesteps(
+        self, state: KarrasVeSchedulerState, num_inference_steps: int, shape: Tuple
+    ) -> KarrasVeSchedulerState:
         """
         Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
 

schedulers/scheduling_lms_discrete_flax.py

@@ -111,7 +111,9 @@ def lms_derivative(tau):
 
         return integrated_coeff
 
-    def set_timesteps(self, state: LMSDiscreteSchedulerState, num_inference_steps: int) -> LMSDiscreteSchedulerState:
+    def set_timesteps(
+        self, state: LMSDiscreteSchedulerState, num_inference_steps: int, shape: Tuple
+    ) -> LMSDiscreteSchedulerState:
         """
         Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
 

schedulers/scheduling_pndm_flax.py

@@ -156,7 +156,7 @@ def __init__(
     def create_state(self):
         return PNDMSchedulerState.create(num_train_timesteps=self.config.num_train_timesteps)
 
-    def set_timesteps(self, state: PNDMSchedulerState, shape: Tuple, num_inference_steps: int) -> PNDMSchedulerState:
+    def set_timesteps(self, state: PNDMSchedulerState, num_inference_steps: int, shape: Tuple) -> PNDMSchedulerState:
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 

schedulers/scheduling_sde_ve_flax.py

@@ -95,7 +95,7 @@ def __init__(
         self.state = self.set_sigmas(state, num_train_timesteps, sigma_min, sigma_max, sampling_eps)
 
     def set_timesteps(
-        self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, sampling_eps: float = None
+        self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, shape: Tuple, sampling_eps: float = None
     ) -> ScoreSdeVeSchedulerState:
         """
         Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.