Define SchedulerOutput to use torch or flax arrays.

src/diffusers/schedulers/scheduling_utils.py

@@ -14,26 +14,43 @@
 import warnings
 from dataclasses import dataclass
 
-import torch
-
 from ..utils import BaseOutput
+from ..utils import is_torch_available, is_flax_available
 
 
 SCHEDULER_CONFIG_NAME = "scheduler_config.json"
 
 
-@dataclass
-class SchedulerOutput(BaseOutput):
-    """
-    Base class for the scheduler's step function output.
+if is_torch_available():
+    import torch
 
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-    """
+    @dataclass
+    class SchedulerOutput(BaseOutput):
+        """
+        Base class for the scheduler's step function output.
 
-    prev_sample: torch.FloatTensor
+        Args:
+            prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+                Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
+                denoising loop.
+        """
+
+        prev_sample: torch.FloatTensor
+
+if is_flax_available():
+    import jax.numpy as jnp
+
+    class SchedulerOutput(BaseOutput):
+        """
+        Base class for the scheduler's step function output.
+
+        Args:
+            prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
+                Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
+                denoising loop.
+        """
+
+        prev_sample: jnp.ndarray
 
 
 class SchedulerMixin: