[core] FreeNoise

src/diffusers/models/unets/unet_motion_model.py

@@ -343,6 +343,7 @@ def custom_forward(*inputs):
 
             else:
                 hidden_states = resnet(hidden_states, temb)
+
             hidden_states = motion_module(hidden_states, num_frames=num_frames)
 
             output_states = output_states + (hidden_states,)
@@ -536,6 +537,7 @@ def custom_forward(*inputs):
                 )[0]
             else:
                 hidden_states = resnet(hidden_states, temb)
+
                 hidden_states = attn(
                     hidden_states,
                     encoder_hidden_states=encoder_hidden_states,
@@ -761,6 +763,7 @@ def custom_forward(*inputs):
                 )[0]
             else:
                 hidden_states = resnet(hidden_states, temb)
+
                 hidden_states = attn(
                     hidden_states,
                     encoder_hidden_states=encoder_hidden_states,
@@ -921,9 +924,9 @@ def custom_forward(*inputs):
                     hidden_states = torch.utils.checkpoint.checkpoint(
                         create_custom_forward(resnet), hidden_states, temb
                     )
-
             else:
                 hidden_states = resnet(hidden_states, temb)
+
             hidden_states = motion_module(hidden_states, num_frames=num_frames)
 
         if self.upsamplers is not None:
@@ -1923,7 +1926,6 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
 
-    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
     def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
         """
         Sets the attention processor to use [feed forward
@@ -1953,7 +1955,6 @@ def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int
         for module in self.children():
             fn_recursive_feed_forward(module, chunk_size, dim)
 
-    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
     def disable_forward_chunking(self) -> None:
         def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
             if hasattr(module, "set_chunk_feed_forward"):

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -42,6 +42,7 @@
 from ...utils.torch_utils import 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
 
@@ -72,6 +73,7 @@ class AnimateDiffPipeline(
     IPAdapterMixin,
     StableDiffusionLoraLoaderMixin,
     FreeInitMixin,
+    AnimateDiffFreeNoiseMixin,
 ):
     r"""
     Pipeline for text-to-video generation.
@@ -394,15 +396,20 @@ def prepare_ip_adapter_image_embeds(
 
         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):
+    def decode_latents(self, latents, vae_batch_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], vae_batch_size):
+            batch_latents = latents[i : i + vae_batch_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
@@ -495,22 +502,28 @@ def check_inputs(
                     f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
                 )
 
-    # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.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,
             num_frames,
             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)
@@ -569,6 +582,7 @@ def __call__(
         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"],
+        vae_batch_size: int = 16,
         **kwargs,
     ):
         r"""
@@ -637,6 +651,8 @@ def __call__(
                 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.
+            vae_batch_size (`int`, defaults to `16`):
+                The number of frames to decode at a time when calling `decode_latents` method.
 
         Examples:
 
@@ -808,7 +824,7 @@ def __call__(
         if output_type == "latent":
             video = latents
         else:
-            video_tensor = self.decode_latents(latents)
+            video_tensor = self.decode_latents(latents, vae_batch_size)
             video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
 
         # 10. Offload all models

src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py

@@ -30,6 +30,7 @@
 from ...video_processor import VideoProcessor
 from ..controlnet.multicontrolnet import MultiControlNetModel
 from ..free_init_utils import FreeInitMixin
+from ..free_noise_utils import AnimateDiffFreeNoiseMixin
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
@@ -109,6 +110,7 @@ class AnimateDiffControlNetPipeline(
     IPAdapterMixin,
     StableDiffusionLoraLoaderMixin,
     FreeInitMixin,
+    AnimateDiffFreeNoiseMixin,
 ):
     r"""
     Pipeline for text-to-video generation with ControlNet guidance.
@@ -432,15 +434,16 @@ def prepare_ip_adapter_image_embeds(
 
         return ip_adapter_image_embeds
 
-    def decode_latents(self, latents, decode_batch_size: int = 16):
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
+    def decode_latents(self, latents, vae_batch_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)
 
         video = []
-        for i in range(0, latents.shape[0], decode_batch_size):
-            batch_latents = latents[i : i + decode_batch_size]
+        for i in range(0, latents.shape[0], vae_batch_size):
+            batch_latents = latents[i : i + vae_batch_size]
             batch_latents = self.vae.decode(batch_latents).sample
             video.append(batch_latents)
 
@@ -608,22 +611,29 @@ def check_inputs(
             if end > 1.0:
                 raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
 
-    # 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,
             num_frames,
             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)
@@ -718,7 +728,7 @@ def __call__(
         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_batch_size: int = 16,
+        vae_batch_size: int = 16,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -1054,7 +1064,7 @@ def __call__(
         if output_type == "latent":
             video = latents
         else:
-            video_tensor = self.decode_latents(latents, decode_batch_size)
+            video_tensor = self.decode_latents(latents, vae_batch_size)
             video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
 
         # 10. Offload all models

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -35,6 +35,7 @@
 from ...utils.torch_utils import 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
 
@@ -176,6 +177,7 @@ class AnimateDiffVideoToVideoPipeline(
     IPAdapterMixin,
     StableDiffusionLoraLoaderMixin,
     FreeInitMixin,
+    AnimateDiffFreeNoiseMixin,
 ):
     r"""
     Pipeline for video-to-video generation.
@@ -498,15 +500,29 @@ def prepare_ip_adapter_image_embeds(
 
         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):
+    def encode_video(self, video, generator, vae_batch_size: int = 16) -> torch.Tensor:
+        latents = []
+        for i in range(0, len(video), vae_batch_size):
+            batch_video = video[i : i + vae_batch_size]
+            batch_video = retrieve_latents(self.vae.encode(batch_video), generator=generator)
+            latents.append(batch_video)
+        return torch.cat(latents)
+
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
+    def decode_latents(self, latents, vae_batch_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], vae_batch_size):
+            batch_latents = latents[i : i + vae_batch_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
@@ -622,6 +638,7 @@ def prepare_latents(
         device,
         generator,
         latents=None,
+        vae_batch_size: int = 16,
     ):
         if latents is None:
             num_frames = video.shape[1]
@@ -656,13 +673,10 @@ def prepare_latents(
                     )
 
                 init_latents = [
-                    retrieve_latents(self.vae.encode(video[i]), generator=generator[i]).unsqueeze(0)
-                    for i in range(batch_size)
+                    self.encode_video(video[i], generator[i], vae_batch_size).unsqueeze(0) for i in range(batch_size)
                 ]
             else:
-                init_latents = [
-                    retrieve_latents(self.vae.encode(vid), generator=generator).unsqueeze(0) for vid in video
-                ]
+                init_latents = [self.encode_video(vid, generator, vae_batch_size).unsqueeze(0) for vid in video]
 
             init_latents = torch.cat(init_latents, dim=0)
 
@@ -747,6 +761,7 @@ def __call__(
         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"],
+        vae_batch_size: int = 16,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -822,6 +837,8 @@ def __call__(
                 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.
+            vae_batch_size (`int`, defaults to `16`):
+                The number of frames to decode at a time when calling `decode_latents` method.
 
         Examples:
 
@@ -923,6 +940,7 @@ def __call__(
             device=device,
             generator=generator,
             latents=latents,
+            vae_batch_size=vae_batch_size,
         )
 
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
@@ -990,7 +1008,7 @@ def __call__(
         if output_type == "latent":
             video = latents
         else:
-            video_tensor = self.decode_latents(latents)
+            video_tensor = self.decode_latents(latents, vae_batch_size)
             video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
 
         # 10. Offload all models