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Freenoise change vae_batch_size to decode_chunk_size #9110

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Aug 7, 2024
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Freenoise change vae_batch_size to decode_chunk_size #9110

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DN6 Aug 7, 2024
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DN6 Aug 7, 2024
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12 changes: 6 additions & 6 deletions src/diffusers/pipelines/animatediff/pipeline_animatediff.py
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Original file line number Diff line number Diff line change
Expand Up @@ -396,15 +396,15 @@ def prepare_ip_adapter_image_embeds(

return ip_adapter_image_embeds

def decode_latents(self, latents, vae_batch_size: int = 16):
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)

video = []
for i in range(0, latents.shape[0], vae_batch_size):
batch_latents = latents[i : i + vae_batch_size]
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)

Expand Down Expand Up @@ -582,7 +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,
decode_chunk_size: int = 16,
**kwargs,
):
r"""
Expand Down Expand Up @@ -651,7 +651,7 @@ 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`):
decode_chunk_size (`int`, defaults to `16`):
The number of frames to decode at a time when calling `decode_latents` method.

Examples:
Expand Down Expand Up @@ -824,7 +824,7 @@ def __call__(
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, vae_batch_size)
video_tensor = self.decode_latents(latents, decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)

# 10. Offload all models
Expand Down
10 changes: 5 additions & 5 deletions src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -435,15 +435,15 @@ def prepare_ip_adapter_image_embeds(
return ip_adapter_image_embeds

# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
def decode_latents(self, latents, vae_batch_size: int = 16):
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)

video = []
for i in range(0, latents.shape[0], vae_batch_size):
batch_latents = latents[i : i + vae_batch_size]
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)

Expand Down Expand Up @@ -728,7 +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"],
vae_batch_size: int = 16,
decode_chunk_size: int = 16,
):
r"""
The call function to the pipeline for generation.
Expand Down Expand Up @@ -1064,7 +1064,7 @@ def __call__(
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, vae_batch_size)
video_tensor = self.decode_latents(latents, decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)

# 10. Offload all models
Expand Down
27 changes: 14 additions & 13 deletions src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -500,24 +500,24 @@ def prepare_ip_adapter_image_embeds(

return ip_adapter_image_embeds

def encode_video(self, video, generator, vae_batch_size: int = 16) -> torch.Tensor:
def encode_video(self, video, generator, decode_chunk_size: int = 16) -> torch.Tensor:
latents = []
for i in range(0, len(video), vae_batch_size):
batch_video = video[i : i + vae_batch_size]
for i in range(0, len(video), decode_chunk_size):
batch_video = video[i : i + decode_chunk_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):
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)

video = []
for i in range(0, latents.shape[0], vae_batch_size):
batch_latents = latents[i : i + vae_batch_size]
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)

Expand Down Expand Up @@ -638,7 +638,7 @@ def prepare_latents(
device,
generator,
latents=None,
vae_batch_size: int = 16,
decode_chunk_size: int = 16,
):
if latents is None:
num_frames = video.shape[1]
Expand Down Expand Up @@ -673,10 +673,11 @@ def prepare_latents(
)

init_latents = [
self.encode_video(video[i], generator[i], vae_batch_size).unsqueeze(0) for i in range(batch_size)
self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
for i in range(batch_size)
]
else:
init_latents = [self.encode_video(vid, generator, vae_batch_size).unsqueeze(0) for vid in video]
init_latents = [self.encode_video(vid, generator, decode_chunk_size).unsqueeze(0) for vid in video]

init_latents = torch.cat(init_latents, dim=0)

Expand Down Expand Up @@ -761,7 +762,7 @@ 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,
decode_chunk_size: int = 16,
):
r"""
The call function to the pipeline for generation.
Expand Down Expand Up @@ -837,7 +838,7 @@ 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`):
decode_chunk_size (`int`, defaults to `16`):
The number of frames to decode at a time when calling `decode_latents` method.

Examples:
Expand Down Expand Up @@ -940,7 +941,7 @@ def __call__(
device=device,
generator=generator,
latents=latents,
vae_batch_size=vae_batch_size,
decode_chunk_size=decode_chunk_size,
)

# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
Expand Down Expand Up @@ -1008,7 +1009,7 @@ def __call__(
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, vae_batch_size)
video_tensor = self.decode_latents(latents, decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)

# 10. Offload all models
Expand Down
10 changes: 5 additions & 5 deletions src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -407,15 +407,15 @@ def prepare_ip_adapter_image_embeds(
return ip_adapter_image_embeds

# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
def decode_latents(self, latents, vae_batch_size: int = 16):
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)

video = []
for i in range(0, latents.shape[0], vae_batch_size):
batch_latents = latents[i : i + vae_batch_size]
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)

Expand Down Expand Up @@ -588,7 +588,7 @@ 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,
decode_chunk_size: int = 16,
pag_scale: float = 3.0,
pag_adaptive_scale: float = 0.0,
):
Expand Down Expand Up @@ -847,7 +847,7 @@ def __call__(
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, vae_batch_size)
video_tensor = self.decode_latents(latents, decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)

# 10. Offload all models
Expand Down
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