From 96638bbd54ca7f91d44c938fae7275d3ecaa6add Mon Sep 17 00:00:00 2001 From: Volpeon Date: Tue, 21 Feb 2023 14:08:49 +0100 Subject: Fixed TI normalization order --- training/strategy/dreambooth.py | 11 +++++------ training/strategy/ti.py | 15 ++++++++++----- 2 files changed, 15 insertions(+), 11 deletions(-) (limited to 'training/strategy') diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py index fcf5c0d..0290327 100644 --- a/training/strategy/dreambooth.py +++ b/training/strategy/dreambooth.py @@ -115,14 +115,13 @@ def dreambooth_strategy_callbacks( yield def on_before_optimize(lr: float, epoch: int): - if accelerator.sync_gradients: - params_to_clip = [unet.parameters()] - if epoch < train_text_encoder_epochs: - params_to_clip.append(text_encoder.parameters()) - accelerator.clip_grad_norm_(itertools.chain(*params_to_clip), max_grad_norm) + params_to_clip = [unet.parameters()] + if epoch < train_text_encoder_epochs: + params_to_clip.append(text_encoder.parameters()) + accelerator.clip_grad_norm_(itertools.chain(*params_to_clip), max_grad_norm) @torch.no_grad() - def on_after_optimize(lr: float): + def on_after_optimize(_, lr: float): if ema_unet is not None: ema_unet.step(unet.parameters()) diff --git a/training/strategy/ti.py b/training/strategy/ti.py index 09beec4..732cd74 100644 --- a/training/strategy/ti.py +++ b/training/strategy/ti.py @@ -115,6 +115,15 @@ def textual_inversion_strategy_callbacks( @torch.no_grad() def on_before_optimize(lr: float, epoch: int): + if use_emb_decay: + w = text_encoder.text_model.embeddings.temp_token_embedding.weight + return torch.all(w.grad == 0, dim=1) + + @torch.no_grad() + def on_after_optimize(zero_ids, lr: float): + if ema_embeddings is not None: + ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters()) + if use_emb_decay: lambda_ = emb_decay * lr @@ -123,15 +132,11 @@ def textual_inversion_strategy_callbacks( mask = torch.zeros(w.size(0), dtype=torch.bool) mask[text_encoder.text_model.embeddings.temp_token_ids] = True - mask[torch.all(w.grad == 0, dim=1)] = False + mask[zero_ids] = False norm = w[mask, :].norm(dim=-1, keepdim=True) w[mask].add_((w[mask] / norm.clamp_min(1e-12)) * lambda_ * (emb_decay_target - norm)) - def on_after_optimize(lr: float): - if ema_embeddings is not None: - ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters()) - def on_log(): if ema_embeddings is not None: return {"ema_decay": ema_embeddings.decay} -- cgit v1.2.3-54-g00ecf