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chore: default timestep_mode back to uniform

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logit_normal reaches lower loss but perceptual improvement over uniform
is dataset-dependent. Keeping uniform as default to match original MMAudio
training behavior; logit_normal remains available as an option.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-04-06 01:21:08 +02:00
parent 8e919c0459
commit 57fae4a8ce
2 changed files with 8 additions and 8 deletions
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nodes/selva_lora_trainer.py
+6 -6
View File
@@ -271,11 +271,11 @@ class SelvaLoraTrainer:
"tooltip": "Path to a step checkpoint (.pt) to resume training from.", "tooltip": "Path to a step checkpoint (.pt) to resume training from.",
}), }),
"seed": ("INT", {"default": 42}), "seed": ("INT", {"default": 42}),
"timestep_mode": (["logit_normal", "uniform"], { "timestep_mode": (["uniform", "logit_normal"], {
"default": "logit_normal", "default": "uniform",
"tooltip": "How to sample training timesteps. " "tooltip": "How to sample training timesteps. "
"logit_normal concentrates steps near t=0.5 (recommended — reduces white noise artifacts). " "uniform samples all timesteps equally (default, matches original MMAudio training). "
"uniform samples all timesteps equally (original behavior).", "logit_normal concentrates steps near t=0.5 — reaches lower loss but perceptual improvement is dataset-dependent.",
}), }),
"logit_normal_sigma": ("FLOAT", { "logit_normal_sigma": ("FLOAT", {
"default": 1.0, "min": 0.1, "max": 3.0, "step": 0.1, "default": 1.0, "min": 0.1, "max": 3.0, "step": 0.1,
@@ -305,7 +305,7 @@ class SelvaLoraTrainer:
def train(self, model, data_dir, output_dir, steps, rank, lr, def train(self, model, data_dir, output_dir, steps, rank, lr,
alpha=0.0, target="attn.qkv", batch_size=4, warmup_steps=100, alpha=0.0, target="attn.qkv", batch_size=4, warmup_steps=100,
grad_accum=1, save_every=500, resume_path="", seed=42, grad_accum=1, save_every=500, resume_path="", seed=42,
timestep_mode="logit_normal", logit_normal_sigma=1.0): timestep_mode="uniform", logit_normal_sigma=1.0):
torch.manual_seed(seed) torch.manual_seed(seed)
random.seed(seed) random.seed(seed)
@@ -451,7 +451,7 @@ class SelvaLoraTrainer:
data_dir, output_dir, steps, rank, lr, data_dir, output_dir, steps, rank, lr,
alpha_val, target_suffixes, batch_size, warmup_steps, alpha_val, target_suffixes, batch_size, warmup_steps,
grad_accum, save_every, resume_path, seed, grad_accum, save_every, resume_path, seed,
timestep_mode="logit_normal", logit_normal_sigma=1.0, timestep_mode="uniform", logit_normal_sigma=1.0,
): ):
# --- Prepare generator copy with LoRA --- # --- Prepare generator copy with LoRA ---
generator = copy.deepcopy(model["generator"]).to(device, dtype) generator = copy.deepcopy(model["generator"]).to(device, dtype)
train_lora.py
+2 -2
View File
@@ -167,8 +167,8 @@ def main():
help="Path to a step checkpoint (.pt) to resume training from.") help="Path to a step checkpoint (.pt) to resume training from.")
parser.add_argument("--precision", default="bf16", choices=["bf16", "fp16", "fp32"]) parser.add_argument("--precision", default="bf16", choices=["bf16", "fp16", "fp32"])
parser.add_argument("--seed", type=int, default=42) parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--timestep_mode", default="logit_normal", choices=["logit_normal", "uniform"], parser.add_argument("--timestep_mode", default="uniform", choices=["uniform", "logit_normal"],
help="Timestep sampling distribution. logit_normal reduces white noise artifacts.") help="Timestep sampling distribution. uniform matches original MMAudio training. logit_normal reaches lower loss but perceptual improvement is dataset-dependent.")
parser.add_argument("--logit_normal_sigma", type=float, default=1.0, parser.add_argument("--logit_normal_sigma", type=float, default=1.0,
help="Spread of logit-normal distribution (only used with --timestep_mode logit_normal).") help="Spread of logit-normal distribution (only used with --timestep_mode logit_normal).")
args = parser.parse_args() args = parser.parse_args()