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Move IP noise gamma to noise creation to remove complexity and align noise for target loss

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rockerBOO
2025-03-18 21:36:55 -04:00
parent 6f4d365775
commit b81bcd0b01
2 changed files with 13 additions and 15 deletions
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19
library/flux_train_utils.py
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@@ -415,16 +415,6 @@ def get_noisy_model_input_and_timesteps(
bsz, _, h, w = latents.shape
sigmas = None
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
if args.ip_noise_gamma:
if args.ip_noise_gamma_random_strength:
ip_noise = (torch.rand(1, device=latents.device) * args.ip_noise_gamma) * torch.randn_like(latents)
else:
ip_noise = args.ip_noise_gamma * torch.randn_like(latents)
else:
ip_noise = torch.zeros_like(latents)
if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
# Simple random t-based noise sampling
if args.timestep_sampling == "sigmoid":
@@ -435,7 +425,7 @@ def get_noisy_model_input_and_timesteps(
timesteps = t * 1000.0
t = t.view(-1, 1, 1, 1)
noisy_model_input = (1 - t) * latents + t * (noise + ip_noise)
noisy_model_input = (1 - t) * latents + t * noise
elif args.timestep_sampling == "shift":
shift = args.discrete_flow_shift
logits_norm = torch.randn(bsz, device=device)
@@ -445,7 +435,7 @@ def get_noisy_model_input_and_timesteps(
t = timesteps.view(-1, 1, 1, 1)
timesteps = timesteps * 1000.0
noisy_model_input = (1 - t) * latents + t * (noise + ip_noise)
noisy_model_input = (1 - t) * latents + t * noise
elif args.timestep_sampling == "flux_shift":
logits_norm = torch.randn(bsz, device=device)
logits_norm = logits_norm * args.sigmoid_scale # larger scale for more uniform sampling
@@ -455,7 +445,7 @@ def get_noisy_model_input_and_timesteps(
t = timesteps.view(-1, 1, 1, 1)
timesteps = timesteps * 1000.0
noisy_model_input = (1 - t) * latents + t * (noise + ip_noise)
noisy_model_input = (1 - t) * latents + t * noise
else:
# Sample a random timestep for each image
# for weighting schemes where we sample timesteps non-uniformly
@@ -471,8 +461,7 @@ def get_noisy_model_input_and_timesteps(
# Add noise according to flow matching.
sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
noisy_model_input = sigmas * (noise + ip_noise) + (1.0 - sigmas) * latents
noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas