update

src/diffusers/models/controlnet_sparsectrl.py

@@ -691,7 +691,6 @@ class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
 
         emb = self.time_embedding(t_emb, timestep_cond)
         emb = emb.repeat_interleave(sample_num_frames, dim=0)
-        encoder_hidden_states = encoder_hidden_states.repeat_interleave(sample_num_frames, dim=0)
 
         # 2. pre-process
         batch_size, channels, num_frames, height, width = sample.shape

src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py

@@ -38,6 +38,7 @@ from ...utils import (
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ...video_processor import VideoProcessor
 from ..free_init_utils import FreeInitMixin
+from ..free_noise_utils import AnimateDiffFreeNoiseMixin
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
@@ -127,6 +128,7 @@ class AnimateDiffSparseControlNetPipeline(
     IPAdapterMixin,
     StableDiffusionLoraLoaderMixin,
     FreeInitMixin,
+    AnimateDiffFreeNoiseMixin,
 ):
     r"""
     Pipeline for controlled text-to-video generation using the method described in [SparseCtrl: Adding Sparse Controls
@@ -448,15 +450,21 @@ class AnimateDiffSparseControlNetPipeline(
 
         return ip_adapter_image_embeds
 
-    # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
-    def decode_latents(self, latents):
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
+    def decode_latents(self, latents, decode_chunk_size: int = 16):
         latents = 1 / self.vae.config.scaling_factor * latents
 
         batch_size, channels, num_frames, height, width = latents.shape
         latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
 
-        image = self.vae.decode(latents).sample
-        video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)
+        video = []
+        for i in range(0, latents.shape[0], decode_chunk_size):
+            batch_latents = latents[i : i + decode_chunk_size]
+            batch_latents = self.vae.decode(batch_latents).sample
+            video.append(batch_latents)
+
+        video = torch.cat(video)
+        video = video[None, :].reshape((batch_size, num_frames, -1) + video.shape[2:]).permute(0, 2, 1, 3, 4)
         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         video = video.float()
         return video
@@ -611,10 +619,22 @@ class AnimateDiffSparseControlNetPipeline(
                 f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
             )
 
-    # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.prepare_latents
     def prepare_latents(
         self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
     ):
+        # If FreeNoise is enabled, generate latents as described in Equation (7) of [FreeNoise](https://arxiv.org/abs/2310.15169)
+        if self.free_noise_enabled:
+            latents = self._prepare_latents_free_noise(
+                batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents
+            )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
         shape = (
             batch_size,
             num_channels_latents,
@@ -622,11 +642,6 @@ class AnimateDiffSparseControlNetPipeline(
             height // self.vae_scale_factor,
             width // self.vae_scale_factor,
         )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
 
         if latents is None:
             latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
@@ -728,6 +743,7 @@ class AnimateDiffSparseControlNetPipeline(
         clip_skip: Optional[int] = None,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        decode_chunk_size: int = 16,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -806,6 +822,8 @@ class AnimateDiffSparseControlNetPipeline(
                 The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                 `._callback_tensor_inputs` attribute of your pipeline class.
+            decode_chunk_size (`int`, defaults to `16`):
+                The number of frames to decode at a time when calling `decode_latents` method.
 
         Examples:
 
@@ -996,7 +1014,7 @@ class AnimateDiffSparseControlNetPipeline(
         if output_type == "latent":
             video = latents
         else:
-            video_tensor = self.decode_latents(latents)
+            video_tensor = self.decode_latents(latents, decode_chunk_size)
             video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
 
         # 12. Offload all models

src/diffusers/pipelines/free_noise_utils.py

@@ -221,15 +221,25 @@ class AnimateDiffFreeNoiseMixin:
         self._free_noise_noise_type = noise_type
 
         blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
+        if hasattr(self, "controlnet"):
+            blocks.extend([*self.controlnet.down_blocks, self.controlnet.mid_block])
+
         for block in blocks:
-            self._enable_free_noise_in_block(block)
+            if hasattr(block, "motion_modules"):
+                print(block.__class__.__name__)
+                self._enable_free_noise_in_block(block)
 
     def disable_free_noise(self) -> None:
         self._free_noise_context_length = None
 
         blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
+        if hasattr(self, "controlnet"):
+            blocks.extend([*self.controlnet.down_blocks, self.controlnet.mid_block])
+
         for block in blocks:
-            self._disable_free_noise_in_block(block)
+            if hasattr(block, "motion_modules"):
+                print(block.__class__.__name__)
+                self._disable_free_noise_in_block(block)
 
     @property
     def free_noise_enabled(self):