From 9f5f70cb2a8919cb07821f264bf0fd75bfa10584 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sat, 8 Apr 2023 17:38:49 +0200
Subject: Update

---
 training/strategy/lora.py |  2 +-
 training/strategy/ti.py   | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

(limited to 'training')

diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index cfdc504..ae85401 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -93,7 +93,7 @@ def lora_strategy_callbacks(
         if use_emb_decay:
             params = [
                 p
-                for p in text_encoder.text_model.embeddings.token_override_embedding.params
+                for p in text_encoder.text_model.embeddings.token_override_embedding.parameters()
                 if p.grad is not None
             ]
             return torch.stack(params) if len(params) != 0 else None
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 720ebf3..289d6bd 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -72,7 +72,7 @@ def textual_inversion_strategy_callbacks(
 
     if use_ema:
         ema_embeddings = EMAModel(
-            text_encoder.text_model.embeddings.token_override_embedding.params.parameters(),
+            text_encoder.text_model.embeddings.token_override_embedding.parameters(),
             inv_gamma=ema_inv_gamma,
             power=ema_power,
             max_value=ema_max_decay,
@@ -84,20 +84,20 @@ def textual_inversion_strategy_callbacks(
     def ema_context():
         if ema_embeddings is not None:
             return ema_embeddings.apply_temporary(
-                text_encoder.text_model.embeddings.token_override_embedding.params.parameters()
+                text_encoder.text_model.embeddings.token_override_embedding.parameters()
             )
         else:
             return nullcontext()
 
     @contextmanager
     def on_train(epoch: int):
-        text_encoder.text_model.embeddings.token_override_embedding.params.train()
+        text_encoder.train()
         tokenizer.train()
         yield
 
     @contextmanager
     def on_eval():
-        text_encoder.text_model.embeddings.token_override_embedding.params.eval()
+        text_encoder.eval()
         tokenizer.eval()
 
         with ema_context():
@@ -108,7 +108,7 @@ def textual_inversion_strategy_callbacks(
         if use_emb_decay:
             params = [
                 p
-                for p in text_encoder.text_model.embeddings.token_override_embedding.params
+                for p in text_encoder.text_model.embeddings.token_override_embedding.parameters()
                 if p.grad is not None
             ]
             return torch.stack(params) if len(params) != 0 else None
@@ -116,7 +116,7 @@ def textual_inversion_strategy_callbacks(
     @torch.no_grad()
     def on_after_optimize(w, lrs: dict[str, float]):
         if ema_embeddings is not None:
-            ema_embeddings.step(text_encoder.text_model.embeddings.token_override_embedding.params.parameters())
+            ema_embeddings.step(text_encoder.text_model.embeddings.token_override_embedding.parameters())
 
         if use_emb_decay and w is not None:
             lr = lrs["emb"] or lrs["0"]
-- 
cgit v1.2.3-54-g00ecf