Remove dead code related to an old symmetry feature.

invokeai/app/invocations/latent.py

@@ -58,7 +58,6 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     T2IAdapterData,
     image_resized_to_grid_as_tensor,
 )
-from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
@@ -374,12 +373,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             cond_text_embedding_masks=cond_text_embedding_masks,
             guidance_scale=self.cfg_scale,
             guidance_rescale_multiplier=self.cfg_rescale_multiplier,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=0.0,  # threshold,
-                warmup=0.2,  # warmup,
-                h_symmetry_time_pct=None,  # h_symmetry_time_pct,
-                v_symmetry_time_pct=None,  # v_symmetry_time_pct,
-            ),
         )
 
         conditioning_data = conditioning_data.add_scheduler_args_if_applicable(

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -461,15 +461,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     ip_adapter_unet_patcher=ip_adapter_unet_patcher,
                 )
                 latents = step_output.prev_sample
-
-                latents = self.invokeai_diffuser.do_latent_postprocessing(
-                    postprocessing_settings=conditioning_data.postprocessing_settings,
-                    latents=latents,
-                    sigma=batched_t,
-                    step_index=i,
-                    total_step_count=len(timesteps),
-                )
-
                 predicted_original = getattr(step_output, "pred_original_sample", None)
 
                 if callback is not None:

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -43,14 +43,6 @@ class SDXLConditioningInfo(BasicConditioningInfo):
         return super().to(device=device, dtype=dtype)
 
 
-@dataclass(frozen=True)
-class PostprocessingSettings:
-    threshold: float
-    warmup: float
-    h_symmetry_time_pct: Optional[float]
-    v_symmetry_time_pct: Optional[float]
-
-
 @dataclass
 class IPAdapterConditioningInfo:
     cond_image_prompt_embeds: torch.Tensor
@@ -82,10 +74,6 @@ class ConditioningData:
     """
     guidance_rescale_multiplier: float = 0
     scheduler_args: dict[str, Any] = field(default_factory=dict)
-    """
-    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
-    """
-    postprocessing_settings: Optional[PostprocessingSettings] = None
 
     ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]] = None
 

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -14,7 +14,6 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ConditioningData,
     ExtraConditioningInfo,
-    PostprocessingSettings,
     SDXLConditioningInfo,
 )
 from invokeai.backend.stable_diffusion.diffusion.regional_prompt_attention import Range, RegionalPromptData
@@ -374,19 +373,6 @@ class InvokeAIDiffuserComponent:
 
         return unconditioned_next_x, conditioned_next_x
 
-    def do_latent_postprocessing(
-        self,
-        postprocessing_settings: PostprocessingSettings,
-        latents: torch.Tensor,
-        sigma,
-        step_index,
-        total_step_count,
-    ) -> torch.Tensor:
-        if postprocessing_settings is not None:
-            percent_through = step_index / total_step_count
-            latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
-        return latents
-
     def _concat_conditionings_for_batch(self, unconditioning, conditioning):
         def _pad_conditioning(cond, target_len, encoder_attention_mask):
             conditioning_attention_mask = torch.ones(
@@ -676,64 +662,3 @@ class InvokeAIDiffuserComponent:
         scaled_delta = (conditioned_next_x - unconditioned_next_x) * guidance_scale
         combined_next_x = unconditioned_next_x + scaled_delta
         return combined_next_x
-
-    def apply_symmetry(
-        self,
-        postprocessing_settings: PostprocessingSettings,
-        latents: torch.Tensor,
-        percent_through: float,
-    ) -> torch.Tensor:
-        # Reset our last percent through if this is our first step.
-        if percent_through == 0.0:
-            self.last_percent_through = 0.0
-
-        if postprocessing_settings is None:
-            return latents
-
-        # Check for out of bounds
-        h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
-        if h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0):
-            h_symmetry_time_pct = None
-
-        v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
-        if v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0):
-            v_symmetry_time_pct = None
-
-        dev = latents.device.type
-
-        latents.to(device="cpu")
-
-        if (
-            h_symmetry_time_pct is not None
-            and self.last_percent_through < h_symmetry_time_pct
-            and percent_through >= h_symmetry_time_pct
-        ):
-            # Horizontal symmetry occurs on the 3rd dimension of the latent
-            width = latents.shape[3]
-            x_flipped = torch.flip(latents, dims=[3])
-            latents = torch.cat(
-                [
-                    latents[:, :, :, 0 : int(width / 2)],
-                    x_flipped[:, :, :, int(width / 2) : int(width)],
-                ],
-                dim=3,
-            )
-
-        if (
-            v_symmetry_time_pct is not None
-            and self.last_percent_through < v_symmetry_time_pct
-            and percent_through >= v_symmetry_time_pct
-        ):
-            # Vertical symmetry occurs on the 2nd dimension of the latent
-            height = latents.shape[2]
-            y_flipped = torch.flip(latents, dims=[2])
-            latents = torch.cat(
-                [
-                    latents[:, :, 0 : int(height / 2)],
-                    y_flipped[:, :, int(height / 2) : int(height)],
-                ],
-                dim=2,
-            )
-
-        self.last_percent_through = percent_through
-        return latents.to(device=dev)