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fix pin_memory with different latent sampling method

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flamelaw
2022-11-21 10:15:46 +09:00
parent 2d22d72cda
commit 5b57f61ba4
3 changed files with 24 additions and 11 deletions
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7
modules/textual_inversion/textual_inversion.py
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@@ -277,7 +277,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
latent_sampling_method = ds.latent_sampling_method
dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=False)
dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)
@@ -333,11 +333,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
# go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
#print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
#scaler.unscale_(optimizer)
#print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
#torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=1.0)
#print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
scaler.step(optimizer)
scaler.update()
embedding.step += 1