Adding pred_original_sample to SchedulerOutput for some samplers

src/diffusers/schedulers/scheduling_ddim.py

@@ -17,13 +17,33 @@
 
 import math
 import warnings
+from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils import SchedulerMixin, SchedulerOutput
+from ..utils import BaseOutput
+from .scheduling_utils import SchedulerMixin
+
+
+@dataclass
+class DDIMSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's step function output.
+
+    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.
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    prev_sample: torch.FloatTensor
+    pred_original_sample: Optional[torch.FloatTensor] = None
 
 
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
@@ -179,7 +199,7 @@ def step(
         use_clipped_model_output: bool = False,
         generator=None,
         return_dict: bool = True,
-    ) -> Union[SchedulerOutput, Tuple]:
+    ) -> Union[DDIMSchedulerOutput, Tuple]:
         """
         Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
         process from the learned model outputs (most often the predicted noise).
@@ -192,11 +212,11 @@ def step(
             eta (`float`): weight of noise for added noise in diffusion step.
             use_clipped_model_output (`bool`): TODO
             generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+            return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
 
         Returns:
-            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
             returning a tuple, the first element is the sample tensor.
 
         """
@@ -261,7 +281,7 @@ def step(
         if not return_dict:
             return (prev_sample,)
 
-        return SchedulerOutput(prev_sample=prev_sample)
+        return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
 
     def add_noise(
         self,

src/diffusers/schedulers/scheduling_ddpm.py

@@ -15,13 +15,33 @@
 # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
 
 import math
+from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils import SchedulerMixin, SchedulerOutput
+from ..utils import BaseOutput
+from .scheduling_utils import SchedulerMixin
+
+
+@dataclass
+class DDPMSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's step function output.
+
+    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.
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    prev_sample: torch.FloatTensor
+    pred_original_sample: Optional[torch.FloatTensor] = None
 
 
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
@@ -177,7 +197,7 @@ def step(
         predict_epsilon=True,
         generator=None,
         return_dict: bool = True,
-    ) -> Union[SchedulerOutput, Tuple]:
+    ) -> Union[DDPMSchedulerOutput, Tuple]:
         """
         Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
         process from the learned model outputs (most often the predicted noise).
@@ -190,11 +210,11 @@ def step(
             predict_epsilon (`bool`):
                 optional flag to use when model predicts the samples directly instead of the noise, epsilon.
             generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+            return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
 
         Returns:
-            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
             returning a tuple, the first element is the sample tensor.
 
         """
@@ -242,7 +262,7 @@ def step(
         if not return_dict:
             return (pred_prev_sample,)
 
-        return SchedulerOutput(prev_sample=pred_prev_sample)
+        return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
 
     def add_noise(
         self,

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -35,10 +35,14 @@ class KarrasVeOutput(BaseOutput):
             denoising loop.
         derivative (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
             Derivative of predicted original image sample (x_0).
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
     """
 
     prev_sample: torch.FloatTensor
     derivative: torch.FloatTensor
+    pred_original_sample: Optional[torch.FloatTensor] = None
 
 
 class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
@@ -153,7 +157,7 @@ def step(
             sigma_hat (`float`): TODO
             sigma_prev (`float`): TODO
             sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+            return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class
 
             KarrasVeOutput: updated sample in the diffusion chain and derivative (TODO double check).
         Returns:
@@ -170,7 +174,9 @@ def step(
         if not return_dict:
             return (sample_prev, derivative)
 
-        return KarrasVeOutput(prev_sample=sample_prev, derivative=derivative)
+        return KarrasVeOutput(
+            prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
+        )
 
     def step_correct(
         self,
@@ -192,7 +198,7 @@ def step_correct(
             sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
             sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO
             derivative (`torch.FloatTensor` or `np.ndarray`): TODO
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+            return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class
 
         Returns:
             prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO
@@ -205,7 +211,9 @@ def step_correct(
         if not return_dict:
             return (sample_prev, derivative)
 
-        return KarrasVeOutput(prev_sample=sample_prev, derivative=derivative)
+        return KarrasVeOutput(
+            prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
+        )
 
     def add_noise(self, original_samples, noise, timesteps):
         raise NotImplementedError()

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import numpy as np
@@ -20,7 +21,26 @@
 from scipy import integrate
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils import SchedulerMixin, SchedulerOutput
+from ..utils import BaseOutput
+from .scheduling_utils import SchedulerMixin
+
+
+@dataclass
+class LMSDiscreteSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's step function output.
+
+    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.
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    prev_sample: torch.FloatTensor
+    pred_original_sample: Optional[torch.FloatTensor] = None
 
 
 class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
@@ -133,7 +153,7 @@ def step(
         sample: Union[torch.FloatTensor, np.ndarray],
         order: int = 4,
         return_dict: bool = True,
-    ) -> Union[SchedulerOutput, Tuple]:
+    ) -> Union[LMSDiscreteSchedulerOutput, Tuple]:
         """
         Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
         process from the learned model outputs (most often the predicted noise).
@@ -144,12 +164,12 @@ def step(
             sample (`torch.FloatTensor` or `np.ndarray`):
                 current instance of sample being created by diffusion process.
             order: coefficient for multi-step inference.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+            return_dict (`bool`): option for returning tuple rather than LMSDiscreteSchedulerOutput class
 
         Returns:
-            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
+            [`~schedulers.scheduling_utils.LMSDiscreteSchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.LMSDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.
+            When returning a tuple, the first element is the sample tensor.
 
         """
         sigma = self.sigmas[timestep]
@@ -175,7 +195,7 @@ def step(
         if not return_dict:
             return (prev_sample,)
 
-        return SchedulerOutput(prev_sample=prev_sample)
+        return LMSDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
 
     def add_noise(
         self,