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README.md
12
README.md
@@ -249,6 +249,18 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
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## Change History
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### Jan 27, 2024 / 2024/1/27: v0.8.3
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- Fixed a bug that the training crashes when `--fp8_base` is specified with `--save_state`. PR [#1079](https://github.com/kohya-ss/sd-scripts/pull/1079) Thanks to feffy380!
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- `safetensors` is updated. Please see [Upgrade](#upgrade) and update the library.
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- Fixed a bug that the training crashes when `network_multiplier` is specified with multi-GPU training. PR [#1084](https://github.com/kohya-ss/sd-scripts/pull/1084) Thanks to fireicewolf!
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- Fixed a bug that the training crashes when training ControlNet-LLLite.
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- `--fp8_base` 指定時に `--save_state` での保存がエラーになる不具合が修正されました。 PR [#1079](https://github.com/kohya-ss/sd-scripts/pull/1079) feffy380 氏に感謝します。
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- `safetensors` がバージョンアップされていますので、[Upgrade](#upgrade) を参照し更新をお願いします。
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- 複数 GPU での学習時に `network_multiplier` を指定するとクラッシュする不具合が修正されました。 PR [#1084](https://github.com/kohya-ss/sd-scripts/pull/1084) fireicewolf 氏に感謝します。
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- ControlNet-LLLite の学習がエラーになる不具合を修正しました。
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### Jan 23, 2024 / 2024/1/23: v0.8.2
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- [Experimental] The `--fp8_base` option is added to the training scripts for LoRA etc. The base model (U-Net, and Text Encoder when training modules for Text Encoder) can be trained with fp8. PR [#1057](https://github.com/kohya-ss/sd-scripts/pull/1057) Thanks to KohakuBlueleaf!
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@@ -1774,6 +1774,7 @@ class ControlNetDataset(BaseDataset):
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tokenizer,
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max_token_length,
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resolution,
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network_multiplier,
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enable_bucket,
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min_bucket_reso,
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max_bucket_reso,
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@@ -8,7 +8,7 @@ einops==0.6.1
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pytorch-lightning==1.9.0
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# bitsandbytes==0.39.1
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tensorboard==2.10.1
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safetensors==0.3.1
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safetensors==0.4.2
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# gradio==3.16.2
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altair==4.2.2
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easygui==0.98.3
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@@ -774,7 +774,7 @@ class NetworkTrainer:
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else:
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raise NotImplementedError("multipliers for each sample is not supported yet")
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# print(f"set multiplier: {multipliers}")
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network.set_multiplier(multipliers)
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accelerator.unwrap_model(network).set_multiplier(multipliers)
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with torch.set_grad_enabled(train_text_encoder), accelerator.autocast():
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# Get the text embedding for conditioning
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