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change order to match upstream

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rockerBOO
2025-03-19 14:27:43 -04:00
parent 5d5a7d2acf
commit f974c6b257
1 changed files with 2 additions and 4 deletions
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6
library/flux_train_utils.py
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@@ -413,8 +413,6 @@ def get_noisy_model_input_and_timesteps(
args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
bsz, _, h, w = latents.shape bsz, _, h, w = latents.shape
sigmas = None
if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid": if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
# Simple random t-based noise sampling # Simple random t-based noise sampling
if args.timestep_sampling == "sigmoid": if args.timestep_sampling == "sigmoid":
@@ -463,9 +461,9 @@ def get_noisy_model_input_and_timesteps(
ip_noise_gamma = (torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma) ip_noise_gamma = (torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma)
else: else:
ip_noise_gamma = args.ip_noise_gamma ip_noise_gamma = args.ip_noise_gamma
noisy_model_input = sigmas * (noise + ip_noise_gamma * xi) + (1.0 - sigmas) * latents noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)
else: else:
noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas