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Running
on
Zero
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import torch
import torch.nn.functional as F
from utils.func_utils import tensor_to_vae_latent, sample_noise
def DebiasedTemporalLoss(
train_loss_temporal,
accelerator,
optimizers,
lr_schedulers,
unet,
vae,
text_encoder,
noise_scheduler,
batch,
step,
config
):
cache_latents = config.train.cache_latents
if not cache_latents:
latents = tensor_to_vae_latent(batch["pixel_values"], vae)
else:
latents = batch["latents"]
# Sample noise that we'll add to the latents
# use_offset_noise = use_offset_noise and not rescale_schedule
noise = sample_noise(latents, 0.1, False)
bsz = latents.shape[0]
# Sample a random timestep for each video
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# *Potentially* Fixes gradient checkpointing training.
# See: https://github.com/prigoyal/pytorch_memonger/blob/master/tutorial/Checkpointing_for_PyTorch_models.ipynb
# if kwargs.get('eval_train', False):
# unet.eval()
# text_encoder.eval()
# Encode text embeddings
token_ids = batch['prompt_ids']
encoder_hidden_states = text_encoder(token_ids)[0]
detached_encoder_state = encoder_hidden_states.clone().detach()
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
encoder_hidden_states = detached_encoder_state
# optimization
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
loss_temporal = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
beta = 1
alpha = (beta ** 2 + 1) ** 0.5
ran_idx = torch.randint(0, model_pred.shape[2], (1,)).item()
model_pred_decent = alpha * model_pred - beta * model_pred[:, :, ran_idx, :, :].unsqueeze(2)
target_decent = alpha * target - beta * target[:, :, ran_idx, :, :].unsqueeze(2)
loss_ad_temporal = F.mse_loss(model_pred_decent.float(), target_decent.float(), reduction="mean")
loss_temporal = loss_temporal + loss_ad_temporal
avg_loss_temporal = accelerator.gather(loss_temporal.repeat(config.train.train_batch_size)).mean()
train_loss_temporal += avg_loss_temporal.item() / config.train.gradient_accumulation_steps
accelerator.backward(loss_temporal)
optimizers[0].step()
lr_schedulers[0].step()
return loss_temporal, train_loss_temporal |