From 0e4c36889aa6b7ec13320a03728118c7c1a8e716 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Mon, 27 Mar 2023 07:15:46 +0200
Subject: Sparse TI embeddings without sparse tensors
---
training/strategy/ti.py | 18 ++++++++----------
1 file changed, 8 insertions(+), 10 deletions(-)
(limited to 'training')
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index b9a5547..7ac5011 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -108,11 +108,14 @@ 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)
+ return torch.stack([
+ t
+ for t in text_encoder.text_model.embeddings.temp_token_embedding
+ if t.grad is not None
+ ])
@torch.no_grad()
- def on_after_optimize(zero_ids, lr: float):
+ def on_after_optimize(w, lr: float):
if ema_embeddings is not None:
ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
@@ -120,13 +123,8 @@ def textual_inversion_strategy_callbacks(
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))
+ norm = w[:, :].norm(dim=-1, keepdim=True)
+ w[:].add_((w[:] / norm.clamp_min(1e-12)) * lambda_ * (emb_decay_target - norm))
def on_log():
if ema_embeddings is not None:
--
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