From b78c0e2a69e52ce6c79abc6c8c82d1a9cabcf05c Mon Sep 17 00:00:00 2001
From: Kohya S <ykumeykume@gmail.com>
Date: Tue, 25 Jul 2023 19:07:26 +0900
Subject: [PATCH] remove unused func

---
 library/sdxl_train_util.py | 48 --------------------------------------
 1 file changed, 48 deletions(-)

diff --git a/library/sdxl_train_util.py b/library/sdxl_train_util.py
index 54774f88..6ff0d48f 100644
--- a/library/sdxl_train_util.py
+++ b/library/sdxl_train_util.py
@@ -286,54 +286,6 @@ def save_sd_model_on_epoch_end_or_stepwise(
     )
 
 
-# TextEncoderの出力をキャッシュする
-# weight_dtypeを指定するとText Encoderそのもの、およひ出力がweight_dtypeになる
-def cache_text_encoder_outputs(args, accelerator, tokenizers, text_encoders, dataset, weight_dtype):
-    print("caching text encoder outputs")
-
-    tokenizer1, tokenizer2 = tokenizers
-    text_encoder1, text_encoder2 = text_encoders
-    text_encoder1.to(accelerator.device)
-    text_encoder2.to(accelerator.device)
-    if weight_dtype is not None:
-        text_encoder1.to(dtype=weight_dtype)
-        text_encoder2.to(dtype=weight_dtype)
-
-    text_encoder1_cache = {}
-    text_encoder2_cache = {}
-    for batch in tqdm(dataset):
-        input_ids1_batch = batch["input_ids"].to(accelerator.device)
-        input_ids2_batch = batch["input_ids2"].to(accelerator.device)
-
-        # split batch to avoid OOM
-        # TODO specify batch size by args
-        for input_id1, input_id2 in zip(input_ids1_batch.split(1), input_ids2_batch.split(1)):
-            # remove input_ids already in cache
-            input_id1_cache_key = tuple(input_id1.flatten().tolist())
-            input_id2_cache_key = tuple(input_id2.flatten().tolist())
-            if input_id1_cache_key in text_encoder1_cache:
-                assert input_id2_cache_key in text_encoder2_cache
-                continue
-
-            with torch.no_grad():
-                encoder_hidden_states1, encoder_hidden_states2, pool2 = get_hidden_states(
-                    args,
-                    input_id1,
-                    input_id2,
-                    tokenizer1,
-                    tokenizer2,
-                    text_encoder1,
-                    text_encoder2,
-                    None if not args.full_fp16 else weight_dtype,
-                )
-            encoder_hidden_states1 = encoder_hidden_states1.detach().to("cpu").squeeze(0)  # n*75+2,768
-            encoder_hidden_states2 = encoder_hidden_states2.detach().to("cpu").squeeze(0)  # n*75+2,1280
-            pool2 = pool2.detach().to("cpu").squeeze(0)  # 1280
-            text_encoder1_cache[input_id1_cache_key] = encoder_hidden_states1
-            text_encoder2_cache[input_id2_cache_key] = (encoder_hidden_states2, pool2)
-    return text_encoder1_cache, text_encoder2_cache
-
-
 def add_sdxl_training_arguments(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--cache_text_encoder_outputs", action="store_true", help="cache text encoder outputs / text encoderの出力をキャッシュする"