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fix: create LoRA params inside torch.enable_grad() to escape inference_mode

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torch.enable_grad() re-enables grad tracking but nn.Parameters created while
torch.inference_mode() is active are inference tensors that can't enter autograd
regardless. Splitting into _train_inner() and calling it inside enable_grad()
ensures the deepcopy, apply_lora, and the training loop all run with a clean
autograd context.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-04-05 22:36:28 +02:00
parent 505d445eb3
commit 849f31e2a6
1 changed files with 68 additions and 51 deletions
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nodes/selva_lora_trainer.py
+68 -51
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@@ -365,6 +365,25 @@ class SelvaLoraTrainer:
raise ValueError("[LoRA Trainer] No clips could be loaded.")
print(f"[LoRA Trainer] {len(dataset)} clip(s) ready.", flush=True)
# Everything from here runs inside enable_grad: ComfyUI wraps nodes in
# inference_mode, and nn.Parameters created in that context are inference
# tensors that can't enter autograd even with requires_grad=True.
with torch.enable_grad():
return self._train_inner(
model, dataset, feature_utils_orig, seq_cfg,
device, dtype, variant, mode,
data_dir, output_dir, steps, rank, lr,
alpha_val, target_suffixes, warmup_steps,
grad_accum, save_every, resume_path, seed,
)
def _train_inner(
self, model, dataset, feature_utils_orig, seq_cfg,
device, dtype, variant, mode,
data_dir, output_dir, steps, rank, lr,
alpha_val, target_suffixes, warmup_steps,
grad_accum, save_every, resume_path, seed,
):
# --- Prepare generator copy with LoRA ---
generator = copy.deepcopy(model["generator"]).to(device, dtype)
@@ -433,67 +452,65 @@ class SelvaLoraTrainer:
print(f"\n[LoRA Trainer] Training {remaining} steps "
f"(step {start_step + 1} → {steps})\n", flush=True)
# ComfyUI runs nodes inside torch.no_grad() — re-enable gradients for training
with torch.enable_grad():
for step in range(start_step + 1, steps + 1):
x1_cpu, clip_f_cpu, sync_f_cpu, text_clip_cpu = random.choice(dataset)
for step in range(start_step + 1, steps + 1):
x1_cpu, clip_f_cpu, sync_f_cpu, text_clip_cpu = random.choice(dataset)
x1 = x1_cpu.to(device, dtype)
clip_f = clip_f_cpu.to(device, dtype)
sync_f = sync_f_cpu.to(device, dtype)
text_clip = text_clip_cpu.to(device, dtype)
x1 = x1_cpu.to(device, dtype)
clip_f = clip_f_cpu.to(device, dtype)
sync_f = sync_f_cpu.to(device, dtype)
text_clip = text_clip_cpu.to(device, dtype)
generator.normalize(x1)
generator.normalize(x1)
t = torch.rand(1, device=device, dtype=dtype)
x0 = torch.randn_like(x1)
xt = fm.get_conditional_flow(x0, x1, t)
t = torch.rand(1, device=device, dtype=dtype)
x0 = torch.randn_like(x1)
xt = fm.get_conditional_flow(x0, x1, t)
v_pred = generator.forward(xt, clip_f, sync_f, text_clip, t)
loss = fm.loss(v_pred, x0, x1).mean() / grad_accum
loss.backward()
running_loss += loss.item() * grad_accum
v_pred = generator.forward(xt, clip_f, sync_f, text_clip, t)
loss = fm.loss(v_pred, x0, x1).mean() / grad_accum
loss.backward()
running_loss += loss.item() * grad_accum
if step % grad_accum == 0:
torch.nn.utils.clip_grad_norm_(lora_params, max_norm=1.0)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
if step % grad_accum == 0:
torch.nn.utils.clip_grad_norm_(lora_params, max_norm=1.0)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
if step % log_interval == 0:
avg = running_loss / log_interval
loss_history.append(avg)
lr_now = scheduler.get_last_lr()[0]
print(f"[LoRA Trainer] step {step:5d}/{steps} "
f"loss={avg:.4f} lr={lr_now:.2e}", flush=True)
running_loss = 0.0
if step % log_interval == 0:
avg = running_loss / log_interval
loss_history.append(avg)
lr_now = scheduler.get_last_lr()[0]
print(f"[LoRA Trainer] step {step:5d}/{steps} "
f"loss={avg:.4f} lr={lr_now:.2e}", flush=True)
running_loss = 0.0
# Live preview: send updated loss curve to ComfyUI frontend
preview_img = _draw_loss_curve(loss_history, log_interval, start_step)
pbar_train.update_absolute(
step - start_step, remaining, ("JPEG", preview_img, 85)
)
# Live preview: send updated loss curve to ComfyUI frontend
preview_img = _draw_loss_curve(loss_history, log_interval, start_step)
pbar_train.update_absolute(
step - start_step, remaining, ("JPEG", preview_img, 85)
)
if step % save_every == 0 or step == steps:
ckpt_path = output_dir / f"adapter_step{step:05d}.pt"
torch.save({
"state_dict": get_lora_state_dict(generator),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"step": step,
"meta": meta,
}, ckpt_path)
print(f"[LoRA Trainer] Saved {ckpt_path}", flush=True)
if step % save_every == 0 or step == steps:
ckpt_path = output_dir / f"adapter_step{step:05d}.pt"
torch.save({
"state_dict": get_lora_state_dict(generator),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"step": step,
"meta": meta,
}, ckpt_path)
print(f"[LoRA Trainer] Saved {ckpt_path}", flush=True)
# Save a quick eval sample next to the checkpoint
wav, sr = _eval_sample(generator, feature_utils_orig,
dataset, seq_cfg, device, dtype)
if wav is not None:
wav_path = output_dir / f"sample_step{step:05d}.wav"
torchaudio.save(str(wav_path), wav, sr)
print(f"[LoRA Trainer] Sample saved: {wav_path}", flush=True)
# Save a quick eval sample next to the checkpoint
wav, sr = _eval_sample(generator, feature_utils_orig,
dataset, seq_cfg, device, dtype)
if wav is not None:
wav_path = output_dir / f"sample_step{step:05d}.wav"
torchaudio.save(str(wav_path), wav, sr)
print(f"[LoRA Trainer] Sample saved: {wav_path}", flush=True)
pbar_train.update(1)
pbar_train.update(1)
# Save inference adapter (state_dict + meta only — SelvaLoraLoader compatible)
final_path = output_dir / "adapter_final.pt"