update

Author: a-r-r-o-w

src/diffusers/models/__init__.py

@@ -51,6 +51,11 @@
     _import_structure["controlnets.controlnet_xs"] = ["ControlNetXSAdapter", "UNetControlNetXSModel"]
     _import_structure["controlnets.multicontrolnet"] = ["MultiControlNetModel"]
     _import_structure["embeddings"] = ["ImageProjection"]
+    _import_structure["layerwise_upcasting_utils"] = [
+        "LayerwiseUpcastingGranularity",
+        "apply_layerwise_upcasting",
+        "apply_layerwise_upcasting_hook",
+    ]
     _import_structure["modeling_utils"] = ["ModelMixin"]
     _import_structure["transformers.auraflow_transformer_2d"] = ["AuraFlowTransformer2DModel"]
     _import_structure["transformers.cogvideox_transformer_3d"] = ["CogVideoXTransformer3DModel"]

src/diffusers/models/modeling_utils.py

@@ -321,6 +321,7 @@ def enable_layerwise_upcasting(
         storage_dtype: torch.dtype = torch.float8_e4m3fn,
         compute_dtype: Optional[torch.dtype] = None,
         granularity: LayerwiseUpcastingGranularity = LayerwiseUpcastingGranularity.PYTORCH_LAYER,
+        skip_modules_pattern: Optional[List[str]] = None,
     ) -> None:
         r"""
         Activates layerwise upcasting for the current model.
@@ -364,7 +365,8 @@ def enable_layerwise_upcasting(
                 [`~LayerwiseUpcastingGranularity`] for more information.
         """
 
-        skip_modules_pattern = []
+        if skip_modules_pattern is None:
+            skip_modules_pattern = []
         if self._keep_in_fp32_modules is not None:
             skip_modules_pattern.extend(self._keep_in_fp32_modules)
         if self._always_upcast_modules is not None:

src/diffusers/pipelines/latte/pipeline_latte.py

@@ -836,7 +836,7 @@ def __call__(
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
-        if not output_type == "latents":
+        if not output_type == "latent":
             video = self.decode_latents(latents, video_length, decode_chunk_size=14)
             video = self.video_processor.postprocess_video(video=video, output_type=output_type)
         else: