From e68cb3542e08c9f22ce8a94fd88bebe0c121ca17 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Mon, 3 Apr 2023 18:52:30 +0200 Subject: TI: Delta learning --- training/functional.py | 4 ++-- training/strategy/ti.py | 23 ----------------------- 2 files changed, 2 insertions(+), 25 deletions(-) (limited to 'training') diff --git a/training/functional.py b/training/functional.py index 96ecbc1..1d8e2ee 100644 --- a/training/functional.py +++ b/training/functional.py @@ -73,7 +73,7 @@ def make_grid(images, rows, cols): return grid -def get_models(pretrained_model_name_or_path: str): +def get_models(pretrained_model_name_or_path: str, emb_alpha: float = 1.0): tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer') text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder') vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae') @@ -82,7 +82,7 @@ def get_models(pretrained_model_name_or_path: str): sample_scheduler = UniPCMultistepScheduler.from_pretrained( pretrained_model_name_or_path, subfolder='scheduler') - embeddings = patch_managed_embeddings(text_encoder) + embeddings = patch_managed_embeddings(text_encoder, emb_alpha) return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings diff --git a/training/strategy/ti.py b/training/strategy/ti.py index c7520ed..16baa34 100644 --- a/training/strategy/ti.py +++ b/training/strategy/ti.py @@ -31,10 +31,6 @@ def textual_inversion_strategy_callbacks( seed: int, placeholder_tokens: list[str], placeholder_token_ids: list[list[int]], - gradient_checkpointing: bool = False, - use_emb_decay: bool = False, - emb_decay_target: float = 0.4, - emb_decay: float = 1e-2, use_ema: bool = False, ema_inv_gamma: float = 1.0, ema_power: int = 1, @@ -105,29 +101,11 @@ def textual_inversion_strategy_callbacks( with ema_context(): yield - @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 - - if lambda_ != 0: - w = text_encoder.text_model.embeddings.temp_token_embedding.weight - - mask = torch.ones(w.shape[0], dtype=torch.bool) - 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_log(): if ema_embeddings is not None: return {"ema_decay": ema_embeddings.decay} @@ -171,7 +149,6 @@ def textual_inversion_strategy_callbacks( on_accum_model=on_accum_model, on_train=on_train, on_eval=on_eval, - on_before_optimize=on_before_optimize, on_after_optimize=on_after_optimize, on_log=on_log, on_checkpoint=on_checkpoint, -- cgit v1.2.3-54-g00ecf