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Fix video DOP methods

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This commit is contained in:
Jaret Burkett
2026-08-26 10:05:14 -06:00
parent 8436c407f6
commit 8a912564ce
1 changed files with 57 additions and 69 deletions
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126
toolkit/models/diffusion_feature_extraction.py
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@@ -526,20 +526,15 @@ class DiffusionFeatureExtractor4(nn.Module):
device = self.vae.device
tensors = batch.tensor.to(device, dtype=dtype)
is_video = False
# stack time for video models on the batch dimension
if len(noise_pred.shape) == 5:
# (B, C, T, H, W): fold every frame into the batch dim so the loss covers all
# frames, and repeat the per-sample timestep for each of its frames
num_frames = noise_pred.shape[2]
noise = _fold_frames_to_batch(noise)
noise_pred = _fold_frames_to_batch(noise_pred)
noisy_latents = _fold_frames_to_batch(noisy_latents)
timesteps = timesteps.repeat_interleave(num_frames)
# (B, C, T, H, W): video VAEs decode whole clips (latent frames do
# not map 1:1 to pixel frames), so the latents stay 5D through the
# VAE and the decoded PIXEL frames fold into the batch dim below,
# matching the folded target frames
is_video = True
if len(tensors.shape) == 5:
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W), matching
# the frame order of the folded predictions above
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W)
tensors = tensors.reshape(-1, *tensors.shape[2:])
if model is not None and hasattr(model, 'get_stepped_pred'):
@@ -563,16 +558,15 @@ class DiffusionFeatureExtractor4(nn.Module):
scaling_factor = self.vae.config.scaling_factor if hasattr(self.vae.config, 'scaling_factor') else 1.0
shift_factor = self.vae.config.shift_factor if hasattr(self.vae.config, 'shift_factor') else 0.0
latents = (latents / scaling_factor) + shift_factor
if is_video:
# if video, we need to unsqueeze the latents to match the vae input shape
latents = latents.unsqueeze(2)
# video latents stay 5D (B, C, T, H, W): the video VAE decodes the whole
# clip, and the decoded PIXEL frames fold into the batch dim to match
# the folded target frames
tensors_n1p1 = self.vae.decode(latents) # -1 to 1
if hasattr(tensors_n1p1, 'sample'):
tensors_n1p1 = tensors_n1p1.sample
if is_video:
# if video, we need to squeeze the tensors to match the output shape
tensors_n1p1 = tensors_n1p1.squeeze(2)
tensors_n1p1 = _fold_frames_to_batch(tensors_n1p1)
pred_images = (tensors_n1p1 + 1) / 2 # 0 to 1
@@ -751,20 +745,15 @@ class DiffusionFeatureExtractor6(nn.Module):
device = self.vae.device
tensors = batch.tensor.to(device, dtype=dtype)
is_video = False
# stack time for video models on the batch dimension
if len(noise_pred.shape) == 5:
# (B, C, T, H, W): fold every frame into the batch dim so the loss covers all
# frames, and repeat the per-sample timestep for each of its frames
num_frames = noise_pred.shape[2]
noise = _fold_frames_to_batch(noise)
noise_pred = _fold_frames_to_batch(noise_pred)
noisy_latents = _fold_frames_to_batch(noisy_latents)
timesteps = timesteps.repeat_interleave(num_frames)
# (B, C, T, H, W): video VAEs decode whole clips (latent frames do
# not map 1:1 to pixel frames), so the latents stay 5D through the
# VAE and the decoded PIXEL frames fold into the batch dim below,
# matching the folded target frames
is_video = True
if len(tensors.shape) == 5:
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W), matching
# the frame order of the folded predictions above
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W)
tensors = tensors.reshape(-1, *tensors.shape[2:])
with torch.no_grad():
@@ -785,16 +774,15 @@ class DiffusionFeatureExtractor6(nn.Module):
scaling_factor = self.vae.config.scaling_factor if hasattr(self.vae.config, 'scaling_factor') else 1.0
shift_factor = self.vae.config.shift_factor if hasattr(self.vae.config, 'shift_factor') else 0.0
latents = (latents / scaling_factor) + shift_factor
if is_video:
# if video, we need to unsqueeze the latents to match the vae input shape
latents = latents.unsqueeze(2)
# video latents stay 5D (B, C, T, H, W): the video VAE decodes the whole
# clip, and the decoded PIXEL frames fold into the batch dim to match
# the folded target frames
tensors_n1p1 = self.vae.decode(latents) # -1 to 1
if hasattr(tensors_n1p1, 'sample'):
tensors_n1p1 = tensors_n1p1.sample
if is_video:
# if video, we need to squeeze the tensors to match the output shape
tensors_n1p1 = tensors_n1p1.squeeze(2)
tensors_n1p1 = _fold_frames_to_batch(tensors_n1p1)
pred_images = (tensors_n1p1 + 1) / 2 # 0 to 1
@@ -925,20 +913,15 @@ class DiffusionFeatureExtractor7(nn.Module):
device = self.sd_ref().vae.device
tensors = batch.tensor.to(device, dtype=dtype)
is_video = False
# stack time for video models on the batch dimension
if len(noise_pred.shape) == 5:
# (B, C, T, H, W): fold every frame into the batch dim so the loss covers all
# frames, and repeat the per-sample timestep for each of its frames
num_frames = noise_pred.shape[2]
noise = _fold_frames_to_batch(noise)
noise_pred = _fold_frames_to_batch(noise_pred)
noisy_latents = _fold_frames_to_batch(noisy_latents)
timesteps = timesteps.repeat_interleave(num_frames)
# (B, C, T, H, W): video VAEs decode whole clips (latent frames do
# not map 1:1 to pixel frames), so the latents stay 5D through the
# VAE and the decoded PIXEL frames fold into the batch dim below,
# matching the folded target frames
is_video = True
if len(tensors.shape) == 5:
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W), matching
# the frame order of the folded predictions above
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W)
tensors = tensors.reshape(-1, *tensors.shape[2:])
with torch.no_grad():
@@ -969,17 +952,22 @@ class DiffusionFeatureExtractor7(nn.Module):
with torch.no_grad():
# make a noisy target at next timestep
target_latents = batch.latents.to(self.sd_ref().vae.device, dtype=self.sd_ref().vae.dtype)
if target_latents.dim() == 5:
# fold frames to match the folded noise/predictions
target_latents = _fold_frames_to_batch(target_latents)
# add noise
target_latents = (1.0 - next_step) * target_latents + next_step * noise
target_n1p1 = self.sd_ref().decode_latents(target_latents)
if is_video:
target_n1p1 = _fold_frames_to_batch(target_n1p1)
target_0_1 = (target_n1p1 + 1) / 2 # 0 to 1
latents = stepped_latents.to(self.sd_ref().vae.device, dtype=self.sd_ref().vae.dtype)
tensors_n1p1 = self.sd_ref().decode_latents(latents)
if is_video:
# (B, 3, T_px, H, W) -> (B*T_px, 3, H, W), and repeat each sample's
# timestep weight for every one of its decoded frames
b, t_px = tensors_n1p1.shape[0], tensors_n1p1.shape[2]
tensors_n1p1 = _fold_frames_to_batch(tensors_n1p1)
tv = tv.reshape(b, 1, 1, 1).repeat_interleave(t_px, dim=0)
pred_images = (tensors_n1p1 + 1) / 2 # 0 to 1
@@ -1128,20 +1116,15 @@ class DiffusionFeatureExtractor9(nn.Module):
device = self.sd_ref().vae.device
tensors = batch.tensor.to(device, dtype=dtype)
is_video = False
# stack time for video models on the batch dimension
if len(noise_pred.shape) == 5:
# (B, C, T, H, W): fold every frame into the batch dim so the loss covers all
# frames, and repeat the per-sample timestep for each of its frames
num_frames = noise_pred.shape[2]
noise = _fold_frames_to_batch(noise)
noise_pred = _fold_frames_to_batch(noise_pred)
noisy_latents = _fold_frames_to_batch(noisy_latents)
timesteps = timesteps.repeat_interleave(num_frames)
# (B, C, T, H, W): video VAEs decode whole clips (latent frames do
# not map 1:1 to pixel frames), so the latents stay 5D through the
# VAE and the decoded PIXEL frames fold into the batch dim below,
# matching the folded target frames
is_video = True
if len(tensors.shape) == 5:
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W), matching
# the frame order of the folded predictions above
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W)
tensors = tensors.reshape(-1, *tensors.shape[2:])
with torch.no_grad():
@@ -1172,17 +1155,22 @@ class DiffusionFeatureExtractor9(nn.Module):
with torch.no_grad():
# make a noisy target at next timestep
target_latents = batch.latents.to(self.sd_ref().vae.device, dtype=self.sd_ref().vae.dtype)
if target_latents.dim() == 5:
# fold frames to match the folded noise/predictions
target_latents = _fold_frames_to_batch(target_latents)
# add noise
target_latents = (1.0 - next_step) * target_latents + next_step * noise
target_n1p1 = self.sd_ref().decode_latents(target_latents)
if is_video:
target_n1p1 = _fold_frames_to_batch(target_n1p1)
target_0_1 = (target_n1p1 + 1) / 2 # 0 to 1
latents = stepped_latents.to(self.sd_ref().vae.device, dtype=self.sd_ref().vae.dtype)
tensors_n1p1 = self.sd_ref().decode_latents(latents)
if is_video:
# (B, 3, T_px, H, W) -> (B*T_px, 3, H, W), and repeat each sample's
# timestep weight for every one of its decoded frames
b, t_px = tensors_n1p1.shape[0], tensors_n1p1.shape[2]
tensors_n1p1 = _fold_frames_to_batch(tensors_n1p1)
tv = tv.reshape(b, 1, 1, 1).repeat_interleave(t_px, dim=0)
pred_images = (tensors_n1p1 + 1) / 2 # 0 to 1
@@ -1291,20 +1279,15 @@ class DiffusionFeatureExtractor10(nn.Module):
device = self.sd_ref().vae.device
tensors = batch.tensor.to(device, dtype=dtype)
is_video = False
# stack time for video models on the batch dimension
if len(noise_pred.shape) == 5:
# (B, C, T, H, W): fold every frame into the batch dim so the loss covers all
# frames, and repeat the per-sample timestep for each of its frames
num_frames = noise_pred.shape[2]
noise = _fold_frames_to_batch(noise)
noise_pred = _fold_frames_to_batch(noise_pred)
noisy_latents = _fold_frames_to_batch(noisy_latents)
timesteps = timesteps.repeat_interleave(num_frames)
# (B, C, T, H, W): video VAEs decode whole clips (latent frames do
# not map 1:1 to pixel frames), so the latents stay 5D through the
# VAE and the decoded PIXEL frames fold into the batch dim below,
# matching the folded target frames
is_video = True
if len(tensors.shape) == 5:
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W), matching
# the frame order of the folded predictions above
# batch tensor is frames-first (B, T, C, H, W): fold to (B*T, C, H, W)
tensors = tensors.reshape(-1, *tensors.shape[2:])
with torch.no_grad():
@@ -1335,17 +1318,22 @@ class DiffusionFeatureExtractor10(nn.Module):
with torch.no_grad():
# make a noisy target at next timestep
target_latents = batch.latents.to(self.sd_ref().vae.device, dtype=self.sd_ref().vae.dtype)
if target_latents.dim() == 5:
# fold frames to match the folded noise/predictions
target_latents = _fold_frames_to_batch(target_latents)
# add noise
target_latents = (1.0 - next_step) * target_latents + next_step * noise
target_n1p1 = self.sd_ref().decode_latents(target_latents)
if is_video:
target_n1p1 = _fold_frames_to_batch(target_n1p1)
target_0_1 = (target_n1p1 + 1) / 2 # 0 to 1
latents = stepped_latents.to(self.sd_ref().vae.device, dtype=self.sd_ref().vae.dtype)
tensors_n1p1 = self.sd_ref().decode_latents(latents)
if is_video:
# (B, 3, T_px, H, W) -> (B*T_px, 3, H, W), and repeat each sample's
# timestep weight for every one of its decoded frames
b, t_px = tensors_n1p1.shape[0], tensors_n1p1.shape[2]
tensors_n1p1 = _fold_frames_to_batch(tensors_n1p1)
tv = tv.reshape(b, 1, 1, 1).repeat_interleave(t_px, dim=0)
pred_images = (tensors_n1p1 + 1) / 2 # 0 to 1