[refactor] Make guiders return their inputs

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -18,7 +18,7 @@
 import torch
 
 from ..configuration_utils import register_to_config
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -92,7 +92,7 @@ def prepare_inputs(
             data_batches.append(data_batch)
         return data_batches
 
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
         if not self._is_apg_enabled():
@@ -111,7 +111,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] =
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/auto_guidance.py

@@ -20,7 +20,7 @@
 from ..configuration_utils import register_to_config
 from ..hooks import HookRegistry, LayerSkipConfig
 from ..hooks.layer_skip import _apply_layer_skip_hook
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -145,7 +145,7 @@ def prepare_inputs(
             data_batches.append(data_batch)
         return data_batches
 
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
         if not self._is_ag_enabled():
@@ -158,7 +158,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] =
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/classifier_free_guidance.py

@@ -18,7 +18,7 @@
 import torch
 
 from ..configuration_utils import register_to_config
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -96,7 +96,7 @@ def prepare_inputs(
             data_batches.append(data_batch)
         return data_batches
 
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
         if not self._is_cfg_enabled():
@@ -109,7 +109,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] =
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/classifier_free_zero_star_guidance.py

@@ -18,7 +18,7 @@
 import torch
 
 from ..configuration_utils import register_to_config
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -89,7 +89,7 @@ def prepare_inputs(
             data_batches.append(data_batch)
         return data_batches
 
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
         if self._step < self.zero_init_steps:
@@ -109,7 +109,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] =
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/frequency_decoupled_guidance.py

@@ -19,7 +19,7 @@
 
 from ..configuration_utils import register_to_config
 from ..utils import is_kornia_available
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -230,7 +230,7 @@ def prepare_inputs(
             data_batches.append(data_batch)
         return data_batches
 
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
         if not self._is_fdg_enabled():
@@ -277,7 +277,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] =
             if self.guidance_rescale_space == "data" and self.guidance_rescale[0] > 0.0:
                 pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale[0])
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/guider_utils.py

@@ -20,7 +20,7 @@
 from typing_extensions import Self
 
 from ..configuration_utils import ConfigMixin
-from ..utils import PushToHubMixin, get_logger
+from ..utils import BaseOutput, PushToHubMixin, get_logger
 
 
 if TYPE_CHECKING:
@@ -284,6 +284,12 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub:
         self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
 
 
+class GuiderOutput(BaseOutput):
+    pred: torch.Tensor
+    pred_cond: Optional[torch.Tensor]
+    pred_uncond: Optional[torch.Tensor]
+
+
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     r"""
     Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on

src/diffusers/guiders/perturbed_attention_guidance.py

@@ -21,7 +21,7 @@
 from ..hooks import HookRegistry, LayerSkipConfig
 from ..hooks.layer_skip import _apply_layer_skip_hook
 from ..utils import get_logger
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -197,7 +197,7 @@ def forward(
         pred_cond: torch.Tensor,
         pred_uncond: Optional[torch.Tensor] = None,
         pred_cond_skip: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
+    ) -> GuiderOutput:
         pred = None
 
         if not self._is_cfg_enabled() and not self._is_slg_enabled():
@@ -219,7 +219,7 @@ def forward(
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.is_conditional

src/diffusers/guiders/skip_layer_guidance.py

@@ -20,7 +20,7 @@
 from ..configuration_utils import register_to_config
 from ..hooks import HookRegistry, LayerSkipConfig
 from ..hooks.layer_skip import _apply_layer_skip_hook
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -192,7 +192,7 @@ def forward(
         pred_cond: torch.Tensor,
         pred_uncond: Optional[torch.Tensor] = None,
         pred_cond_skip: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
+    ) -> GuiderOutput:
         pred = None
 
         if not self._is_cfg_enabled() and not self._is_slg_enabled():
@@ -214,7 +214,7 @@ def forward(
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/smoothed_energy_guidance.py

@@ -20,7 +20,7 @@
 from ..configuration_utils import register_to_config
 from ..hooks import HookRegistry
 from ..hooks.smoothed_energy_guidance_utils import SmoothedEnergyGuidanceConfig, _apply_smoothed_energy_guidance_hook
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -181,7 +181,7 @@ def forward(
         pred_cond: torch.Tensor,
         pred_uncond: Optional[torch.Tensor] = None,
         pred_cond_seg: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
+    ) -> GuiderOutput:
         pred = None
 
         if not self._is_cfg_enabled() and not self._is_seg_enabled():
@@ -203,7 +203,7 @@ def forward(
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/guiders/tangential_classifier_free_guidance.py

@@ -18,7 +18,7 @@
 import torch
 
 from ..configuration_utils import register_to_config
-from .guider_utils import BaseGuidance, rescale_noise_cfg
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
 
 
 if TYPE_CHECKING:
@@ -78,7 +78,7 @@ def prepare_inputs(
             data_batches.append(data_batch)
         return data_batches
 
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
         if not self._is_tcfg_enabled():
@@ -89,7 +89,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] =
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
-        return pred, {}
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
 
     @property
     def is_conditional(self) -> bool:

src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py

@@ -238,7 +238,7 @@ def __call__(
             components.guider.cleanup_models(components.unet)
 
         # Perform guidance
-        block_state.noise_pred, block_state.scheduler_step_kwargs = components.guider(guider_state)
+        block_state.noise_pred = components.guider(guider_state)[0]
 
         return components, block_state
 
@@ -433,7 +433,7 @@ def __call__(self, components: StableDiffusionXLModularPipeline, block_state: Bl
             components.guider.cleanup_models(components.unet)
 
         # Perform guidance
-        block_state.noise_pred, block_state.scheduler_step_kwargs = components.guider(guider_state)
+        block_state.noise_pred = components.guider(guider_state)[0]
 
         return components, block_state
 
@@ -492,7 +492,6 @@ def __call__(self, components: StableDiffusionXLModularPipeline, block_state: Bl
             t,
             block_state.latents,
             **block_state.extra_step_kwargs,
-            **block_state.scheduler_step_kwargs,
             return_dict=False,
         )[0]
 
@@ -590,7 +589,6 @@ def __call__(self, components: StableDiffusionXLModularPipeline, block_state: Bl
             t,
             block_state.latents,
             **block_state.extra_step_kwargs,
-            **block_state.scheduler_step_kwargs,
             return_dict=False,
         )[0]
 

src/diffusers/modular_pipelines/wan/denoise.py

@@ -127,7 +127,7 @@ def __call__(
             components.guider.cleanup_models(components.transformer)
 
         # Perform guidance
-        block_state.noise_pred, block_state.scheduler_step_kwargs = components.guider(guider_state)
+        block_state.noise_pred = components.guider(guider_state)[0]
 
         return components, block_state
 
@@ -171,7 +171,6 @@ def __call__(self, components: WanModularPipeline, block_state: BlockState, i: i
             block_state.noise_pred.float(),
             t,
             block_state.latents.float(),
-            **block_state.scheduler_step_kwargs,
             return_dict=False,
         )[0]