Fix SharkEulerDiscrete (#2022)

apps/stable_diffusion/src/schedulers/shark_eulerdiscrete.py

@@ -48,7 +48,7 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
             steps_offset,
         )
         # TODO: make it dynamic so we dont have to worry about batch size
-        self.batch_size = None
+        self.batch_size = 1
 
     def compile(self, batch_size=1):
         SCHEDULER_BUCKET = "gs://shark_tank/stable_diffusion/schedulers"
@@ -171,8 +171,9 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
                 _import(self)
 
     def scale_model_input(self, sample, timestep):
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
+        if self.step_index is None:
+            self._init_step_index(timestep)
+        sigma = self.sigmas[self.step_index]
         return self.scaling_model(
             "forward",
             (
@@ -213,21 +214,25 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
             else noise_pred
         )
 
-        if gamma > 0:
-            noise = randn_tensor(
-                torch.Size(noise_pred.shape),
-                dtype=torch.float16,
-                device="cpu",
-                generator=generator,
-            )
+        noise = randn_tensor(
+            torch.Size(noise_pred.shape),
+            dtype=torch.float16,
+            device="cpu",
+            generator=generator,
+        )
 
-            eps = noise * s_noise
+        eps = noise * s_noise
+
+        if gamma > 0:
             latent = latent + eps * (sigma_hat**2 - sigma**2) ** 0.5
 
         if self.config.prediction_type == "v_prediction":
             sigma_hat = sigma
 
         dt = self.sigmas[self.step_index + 1] - sigma_hat
+
+        self._step_index += 1
+
         return self.step_model(
             "forward",
             (