From d2105d96fdd18da035d2ad412e3fb6f579d5571a Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sat, 24 Dec 2022 14:30:15 +0100
Subject: Fixed Textual Inversion

---
 train_dreambooth.py |  9 ++++++++-
 train_ti.py         | 10 ++++++++--
 training/ti.py      | 15 +++++----------
 3 files changed, 21 insertions(+), 13 deletions(-)

diff --git a/train_dreambooth.py b/train_dreambooth.py
index 51e881a..8cb6414 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -568,9 +568,16 @@ def main():
         print(f"Training entire text encoder.")
     else:
         print(f"Training added text embeddings")
-        text_encoder.requires_grad_(False)
+
         patch_trainable_embeddings(text_encoder, placeholder_token_id)
 
+        freeze_params(itertools.chain(
+            text_encoder.text_model.encoder.parameters(),
+            text_encoder.text_model.final_layer_norm.parameters(),
+            text_encoder.text_model.embeddings.position_embedding.parameters(),
+            text_encoder.text_model.embeddings.token_embedding.parameters(),
+        ))
+
     prompt_processor = PromptProcessor(tokenizer, text_encoder)
 
     if args.scale_lr:
diff --git a/train_ti.py b/train_ti.py
index a12b889..5f37d54 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -25,7 +25,7 @@ from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from data.csv import CSVDataModule, CSVDataItem
 from training.optimization import get_one_cycle_schedule
 from training.ti import patch_trainable_embeddings
-from training.util import AverageMeter, CheckpointerBase, save_args
+from training.util import AverageMeter, CheckpointerBase, save_args, freeze_params
 from models.clip.prompt import PromptProcessor
 
 logger = get_logger(__name__)
@@ -515,10 +515,16 @@ def main():
 
     vae.requires_grad_(False)
     unet.requires_grad_(False)
-    text_encoder.requires_grad_(False)
 
     patch_trainable_embeddings(text_encoder, placeholder_token_id)
 
+    freeze_params(itertools.chain(
+        text_encoder.text_model.encoder.parameters(),
+        text_encoder.text_model.final_layer_norm.parameters(),
+        text_encoder.text_model.embeddings.position_embedding.parameters(),
+        text_encoder.text_model.embeddings.token_embedding.parameters(),
+    ))
+
     prompt_processor = PromptProcessor(tokenizer, text_encoder)
 
     if args.scale_lr:
diff --git a/training/ti.py b/training/ti.py
index 8b2fdd6..dc33e5e 100644
--- a/training/ti.py
+++ b/training/ti.py
@@ -8,26 +8,21 @@ from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
 
 
 def patch_trainable_embeddings(text_encoder: CLIPTextModel, new_ids: list[int]):
-    text_embeddings = TrainableEmbeddings(text_encoder.config, new_ids)
-
-    text_embeddings.token_embedding = text_encoder.text_model.embeddings.token_embedding
-    text_embeddings.token_embedding.weight.requires_grad = False
-
-    text_embeddings.position_embedding = text_encoder.text_model.embeddings.position_embedding
-    text_embeddings.position_embedding.weight.requires_grad = False
-
+    text_embeddings = TrainableEmbeddings(text_encoder.config, text_encoder.text_model.embeddings, new_ids)
     text_encoder.text_model.embeddings = text_embeddings
 
 
 class TrainableEmbeddings(CLIPTextEmbeddings):
-    def __init__(self, config: CLIPTextConfig, new_ids: list[int]):
+    def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, new_ids: list[int]):
         super().__init__(config)
 
         self.train_indices = torch.tensor(new_ids)
 
         self.trainable_embedding = nn.Embedding(self.token_embedding.num_embeddings, self.token_embedding.embedding_dim)
+
+        self.token_embedding = embeddings.token_embedding
+        self.position_embedding = embeddings.position_embedding
         self.trainable_embedding.weight.data = self.token_embedding.weight.data.clone()
-        self.trainable_embedding.weight.requires_grad = True
 
     def forward(
         self,
-- 
cgit v1.2.3-54-g00ecf