update README and format code

README.md

@@ -177,6 +177,8 @@ https://github.com/kohya-ss/sd-scripts/pull/1290) Thanks to frodo821!
 
 - Fixed a bug that `caption_separator` cannot be specified in the subset in the dataset settings .toml file.  [#1312](https://github.com/kohya-ss/sd-scripts/pull/1312) and [#1313](https://github.com/kohya-ss/sd-scripts/pull/1312) Thanks to rockerBOO!
 
+- Fixed a potential bug in ControlNet-LLLite training. PR [#1322](https://github.com/kohya-ss/sd-scripts/pull/1322) Thanks to aria1th!
+
 - Fixed some bugs when using DeepSpeed. Related [#1247](https://github.com/kohya-ss/sd-scripts/pull/1247)
 
 - Added a prompt option `--f` to `gen_imgs.py` to specify the file name when saving. Also, Diffusers-based keys for LoRA weights are now supported.
@@ -219,6 +221,8 @@ https://github.com/kohya-ss/sd-scripts/pull/1290) frodo821 氏に感謝します
 
 - データセット設定の .toml ファイルで、`caption_separator` が subset に指定できない不具合が修正されました。 PR [#1312](https://github.com/kohya-ss/sd-scripts/pull/1312) および [#1313](https://github.com/kohya-ss/sd-scripts/pull/1312) rockerBOO 氏に感謝します。
 
+- ControlNet-LLLite 学習時の潜在バグが修正されました。 PR [#1322](https://github.com/kohya-ss/sd-scripts/pull/1322) aria1th 氏に感謝します。
+
 - DeepSpeed 使用時のいくつかのバグを修正しました。関連 [#1247](https://github.com/kohya-ss/sd-scripts/pull/1247)
 
 - `gen_imgs.py` のプロンプトオプションに、保存時のファイル名を指定する `--f` オプションを追加しました。また同スクリプトで Diffusers ベースのキーを持つ LoRA の重みに対応しました。

sdxl_train_control_net_lllite.py

@@ -15,6 +15,7 @@ from tqdm import tqdm
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -439,7 +440,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -458,7 +461,9 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                loss = train_util.conditional_loss(
+                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                )
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight