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improve flux2 training performance

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Artiprocher
2026-01-21 15:44:15 +08:00
parent 37fbb3248a
commit b61131c693
1 changed files with 7 additions and 2 deletions
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9
diffsynth/diffusion/flow_match.py
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@@ -89,13 +89,18 @@ class FlowMatchScheduler():
return float(mu)
@staticmethod
def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=1024//16*1024//16):
def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None):
sigma_min = 1 / num_inference_steps
sigma_max = 1.0
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
if dynamic_shift_len is None:
# If you ask me why I set mu=0.8,
# I can only say that it yields better training results.
mu = 0.8
else:
mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps