From dee4c7135754543f1eb7ea616ee3847d34a85b51 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sun, 16 Oct 2022 14:39:39 +0200
Subject: Update

---
 dreambooth_plus.py | 33 ++++++++++++++++++++-------------
 1 file changed, 20 insertions(+), 13 deletions(-)

(limited to 'dreambooth_plus.py')

diff --git a/dreambooth_plus.py b/dreambooth_plus.py
index eeee424..42994af 100644
--- a/dreambooth_plus.py
+++ b/dreambooth_plus.py
@@ -118,7 +118,7 @@ def parse_args():
     parser.add_argument(
         "--max_train_steps",
         type=int,
-        default=1300,
+        default=1200,
         help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
@@ -141,7 +141,7 @@ def parse_args():
     parser.add_argument(
         "--learning_rate_text",
         type=float,
-        default=5e-6,
+        default=1e-6,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
@@ -153,7 +153,7 @@ def parse_args():
     parser.add_argument(
         "--lr_scheduler",
         type=str,
-        default="cosine",
+        default="cosine_with_restarts",
         help=(
             'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
             ' "constant", "constant_with_warmup"]'
@@ -162,9 +162,15 @@ def parse_args():
     parser.add_argument(
         "--lr_warmup_steps",
         type=int,
-        default=500,
+        default=300,
         help="Number of steps for the warmup in the lr scheduler."
     )
+    parser.add_argument(
+        "--lr_cycles",
+        type=int,
+        default=2,
+        help="Number of restart cycles in the lr scheduler."
+    )
     parser.add_argument(
         "--use_ema",
         action="store_true",
@@ -179,7 +185,7 @@ def parse_args():
     parser.add_argument(
         "--ema_power",
         type=float,
-        default=6 / 7
+        default=9 / 10
     )
     parser.add_argument(
         "--ema_max_decay",
@@ -565,6 +571,7 @@ def main():
 
     # Initialise the newly added placeholder token with the embeddings of the initializer token
     token_embeds = text_encoder.get_input_embeddings().weight.data
+    original_token_embeds = token_embeds.detach().clone().to(accelerator.device)
     initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids)
     token_embeds[placeholder_token_id] = initializer_token_embeddings
 
@@ -717,11 +724,10 @@ def main():
 
     if args.lr_scheduler == "cosine_with_restarts":
         lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
-            args.lr_scheduler,
             optimizer=optimizer,
             num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
             num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-            num_cycles=num_update_steps_per_epoch,
+            num_cycles=args.lr_cycles,
         )
     else:
         lr_scheduler = get_scheduler(
@@ -857,15 +863,16 @@ def main():
 
                     accelerator.backward(loss)
 
-                    # Zero out the gradients for all token embeddings except the newly added
+                    # Keep the token embeddings fixed except the newly added
                     # embeddings for the concept, as we only want to optimize the concept embeddings
                     if accelerator.num_processes > 1:
-                        grads = text_encoder.module.get_input_embeddings().weight.grad
+                        token_embeds = text_encoder.module.get_input_embeddings().weight
                     else:
-                        grads = text_encoder.get_input_embeddings().weight.grad
-                    # Get the index for tokens that we want to zero the grads for
-                    index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
-                    grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
+                        token_embeds = text_encoder.get_input_embeddings().weight
+
+                    # Get the index for tokens that we want to freeze
+                    index_fixed_tokens = torch.arange(len(tokenizer)) != placeholder_token_id
+                    token_embeds.data[index_fixed_tokens, :] = original_token_embeds[index_fixed_tokens, :]
 
                     if accelerator.sync_gradients:
                         accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
-- 
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