diff --git a/README.md b/README.md index 66bfb87d..c77d66b6 100644 --- a/README.md +++ b/README.md @@ -127,10 +127,11 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser ## Change History -### 17 Apr. 2023, 2023/4/17: - -- Added the `--recursive` option to each script in the `finetune` folder to process folders recursively. Please refer to [PR #400](https://github.com/kohya-ss/sd-scripts/pull/400/) for details. Thanks to Linaqruf! -- `finetune`フォルダ内の各スクリプトに再起的にフォルダを処理するオプション`--recursive`を追加しました。詳細は [PR #400](https://github.com/kohya-ss/sd-scripts/pull/400/) を参照してください。Linaqruf 氏に感謝します。 +### 19 Apr. 2023, 2023/4/19: +- Fixed `lora_interrogator.py` not working. Please refer to [PR #392](https://github.com/kohya-ss/sd-scripts/pull/392) for details. Thank you A2va and heyalexchoi! +- Fixed the handling of tags containing `_` in `tag_images_by_wd14_tagger.py`. +- `lora_interrogator.py`が動作しなくなっていたのを修正しました。詳細は [PR #392](https://github.com/kohya-ss/sd-scripts/pull/392) をご参照ください。A2va氏およびheyalexchoi氏に感謝します。 +- `tag_images_by_wd14_tagger.py`で`_`を含むタグの取り扱いを修正しました。 ### Naming of LoRA @@ -164,6 +165,11 @@ LoRA-LierLa は[Web UI向け拡張](https://github.com/kohya-ss/sd-webui-additio LoRA-C3Liarを使いWeb UIで生成するには拡張を使用してください。 +### 17 Apr. 2023, 2023/4/17: + +- Added the `--recursive` option to each script in the `finetune` folder to process folders recursively. Please refer to [PR #400](https://github.com/kohya-ss/sd-scripts/pull/400/) for details. Thanks to Linaqruf! +- `finetune`フォルダ内の各スクリプトに再起的にフォルダを処理するオプション`--recursive`を追加しました。詳細は [PR #400](https://github.com/kohya-ss/sd-scripts/pull/400/) を参照してください。Linaqruf 氏に感謝します。 + ### 14 Apr. 2023, 2023/4/14: - Fixed a bug that caused an error when loading DyLoRA with the `--network_weight` option in `train_network.py`. - `train_network.py`で、DyLoRAを`--network_weight`オプションで読み込むとエラーになる不具合を修正しました。 diff --git a/finetune/tag_images_by_wd14_tagger.py b/finetune/tag_images_by_wd14_tagger.py index 40bf428c..80a55716 100644 --- a/finetune/tag_images_by_wd14_tagger.py +++ b/finetune/tag_images_by_wd14_tagger.py @@ -141,17 +141,19 @@ def main(args): character_tag_text = "" for i, p in enumerate(prob[4:]): if i < len(general_tags) and p >= args.general_threshold: - tag_name = general_tags[i].replace("_", " ") if args.remove_underscore else general_tags[i] + tag_name = general_tags[i] + if args.remove_underscore and len(tag_name) > 3: # ignore emoji tags like >_< and ^_^ + tag_name = tag_name.replace("_", " ") + if tag_name not in undesired_tags: tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1 general_tag_text += ", " + tag_name combined_tags.append(tag_name) elif i >= len(general_tags) and p >= args.character_threshold: - tag_name = ( - character_tags[i - len(general_tags)].replace("_", " ") - if args.remove_underscore - else character_tags[i - len(general_tags)] - ) + tag_name = character_tags[i - len(general_tags)] + if args.remove_underscore and len(tag_name) > 3: + tag_name = tag_name.replace("_", " ") + if tag_name not in undesired_tags: tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1 character_tag_text += ", " + tag_name diff --git a/networks/lora_interrogator.py b/networks/lora_interrogator.py index 2891798b..beb25181 100644 --- a/networks/lora_interrogator.py +++ b/networks/lora_interrogator.py @@ -2,6 +2,7 @@ from tqdm import tqdm from library import model_util +import library.train_util as train_util import argparse from transformers import CLIPTokenizer import torch @@ -16,16 +17,20 @@ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') def interrogate(args): + weights_dtype = torch.float16 + # いろいろ準備する print(f"loading SD model: {args.sd_model}") - text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model) + args.pretrained_model_name_or_path = args.sd_model + args.vae = None + text_encoder, vae, unet, _ = train_util.load_target_model(args,weights_dtype, DEVICE) print(f"loading LoRA: {args.model}") - network = lora.create_network_from_weights(1.0, args.model, vae, text_encoder, unet) + network, weights_sd = lora.create_network_from_weights(1.0, args.model, vae, text_encoder, unet) # text encoder向けの重みがあるかチェックする：本当はlora側でやるのがいい has_te_weight = False - for key in network.weights_sd.keys(): + for key in weights_sd.keys(): if 'lora_te' in key: has_te_weight = True break @@ -40,9 +45,9 @@ def interrogate(args): else: tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(TOKENIZER_PATH) # , model_max_length=max_token_length + 2) - text_encoder.to(DEVICE) + text_encoder.to(DEVICE, dtype=weights_dtype) text_encoder.eval() - unet.to(DEVICE) + unet.to(DEVICE, dtype=weights_dtype) unet.eval() # U-Netは呼び出さないので不要だけど # トークンをひとつひとつ当たっていく @@ -78,9 +83,14 @@ def interrogate(args): orig_embs = get_all_embeddings(text_encoder) network.apply_to(text_encoder, unet, True, len(network.unet_loras) > 0) - network.to(DEVICE) + info = network.load_state_dict(weights_sd, strict=False) + print(f"Loading LoRA weights: {info}") + + network.to(DEVICE, dtype=weights_dtype) network.eval() + del unet + print("You can ignore warning messages start with '_IncompatibleKeys' (LoRA model does not have alpha because trained by older script) / '_IncompatibleKeys'の警告は無視して構いません（以前のスクリプトで学習されたLoRAモデルのためalphaの定義がありません）") print("get text encoder embeddings with lora.") lora_embs = get_all_embeddings(text_encoder) @@ -107,6 +117,7 @@ def interrogate(args): def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() + parser.add_argument("--v2", action='store_true', help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む') parser.add_argument("--sd_model", type=str, default=None,