From e454b7e7df13cf6ce7b96b7dcc107533edf83f6f Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Thu, 12 Jan 2023 08:51:17 +0100
Subject: Fixed TI decay

---
 models/clip/embeddings.py | 10 +++++++++-
 train_ti.py               | 11 +++--------
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/models/clip/embeddings.py b/models/clip/embeddings.py
index 9d8f770..46b414b 100644
--- a/models/clip/embeddings.py
+++ b/models/clip/embeddings.py
@@ -3,6 +3,7 @@ from pathlib import Path
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 from safetensors import safe_open
 from safetensors.torch import save_file
@@ -45,7 +46,7 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
             device=self.token_embedding.weight.device,
             dtype=self.token_embedding.weight.dtype
         )
-        self.temp_token_embedding.weight.data.normal_(mean=0.0, std=self.initializer_factor * 0.02)
+        self.temp_token_embedding.weight.data = self.token_embedding.weight.data.clone().detach()
         self.temp_token_ids = torch.tensor([], dtype=torch.long)
 
     def resize(self, size: int):
@@ -98,6 +99,13 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
 
         return embeds
 
+    def normalize(self, lambda_: float = 1.0):
+        w = self.temp_token_embedding.weight
+        pre_norm = w[self.temp_token_ids, :].norm(dim=-1, keepdim=True)
+        w[self.temp_token_ids] = F.normalize(
+            w[self.temp_token_ids, :], dim=-1
+        ) * (pre_norm + lambda_ * (0.4 - pre_norm))
+
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
diff --git a/train_ti.py b/train_ti.py
index 2c5037f..890c465 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -7,7 +7,6 @@ from pathlib import Path
 from contextlib import contextmanager, nullcontext
 
 import torch
-import torch.nn.functional as F
 import torch.utils.checkpoint
 
 from accelerate import Accelerator
@@ -166,7 +165,7 @@ def parse_args():
     parser.add_argument(
         "--tag_dropout",
         type=float,
-        default=0.1,
+        default=0,
         help="Tag dropout probability.",
     )
     parser.add_argument(
@@ -177,7 +176,7 @@ def parse_args():
     parser.add_argument(
         "--vector_dropout",
         type=int,
-        default=0.1,
+        default=0,
         help="Vector dropout probability.",
     )
     parser.add_argument(
@@ -869,11 +868,7 @@ def main():
 
     @torch.no_grad()
     def on_clip(lr):
-        embeddings = text_encoder.text_model.embeddings.temp_token_embedding
-
-        pre_norm = embeddings.weight.norm(dim=-1, keepdim=True)
-        lambda_ = min(1.0, 100 * lr)
-        embeddings.weight[:] = F.normalize(embeddings.weight, dim=-1) * (pre_norm + lambda_ * (0.4 - pre_norm))
+        text_encoder.text_model.embeddings.normalize(min(1.0, 100 * lr))
 
     loop = partial(
         loss_step,
-- 
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