Update to latest sd-script code
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bmaltais
parent
09ad7961e3
commit
ccae80186a
16
README.md
16
README.md
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@ -41,6 +41,9 @@ If you run on Linux and would like to use the GUI, there is now a port of it as
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## Installation
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### Runpod
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Follow the instructions found in this discussion: https://github.com/bmaltais/kohya_ss/discussions/379
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### Ubuntu
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In the terminal, run
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@ -189,6 +192,19 @@ This will store your a backup file with your current locally installed pip packa
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## Change History
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* 2023/03/19 (v21.3.0)
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- Add a function to load training config with `.toml` to each training script. Thanks to Linaqruf for this great contribution!
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- Specify `.toml` file with `--config_file`. `.toml` file has `key=value` entries. Keys are same as command line options. See [#241](https://github.com/kohya-ss/sd-scripts/pull/241) for details.
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- All sub-sections are combined to a single dictionary (the section names are ignored.)
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- Omitted arguments are the default values for command line arguments.
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- Command line args override the arguments in `.toml`.
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- With `--output_config` option, you can output current command line options to the `.toml` specified with`--config_file`. Please use as a template.
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- Add `--lr_scheduler_type` and `--lr_scheduler_args` arguments for custom LR scheduler to each training script. Thanks to Isotr0py! [#271](https://github.com/kohya-ss/sd-scripts/pull/271)
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- Same as the optimizer.
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- Add sample image generation with weight and no length limit. Thanks to mio2333! [#288](https://github.com/kohya-ss/sd-scripts/pull/288)
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- `( )`, `(xxxx:1.2)` and `[ ]` can be used.
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- Fix exception on training model in diffusers format with `train_network.py` Thanks to orenwang! [#290](https://github.com/kohya-ss/sd-scripts/pull/290)
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- Add warning if you are about to overwrite an existing model: https://github.com/bmaltais/kohya_ss/issues/404
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* 2023/03/19 (v21.2.5):
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- Fix basic captioning logic
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- Add possibility to not train TE in Dreamboot by setting `Step text encoder training` to -1.
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@ -26,6 +26,7 @@ from library.common_gui import (
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gradio_source_model,
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# set_legacy_8bitadam,
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update_my_data,
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check_if_model_exist,
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)
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from library.tensorboard_gui import (
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gradio_tensorboard,
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@ -104,7 +105,8 @@ def save_configuration(
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sample_every_n_steps,
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sample_every_n_epochs,
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sample_sampler,
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sample_prompts,additional_parameters,
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sample_prompts,
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additional_parameters,
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):
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# Get list of function parameters and values
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parameters = list(locals().items())
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@ -210,15 +212,16 @@ def open_configuration(
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sample_every_n_steps,
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sample_every_n_epochs,
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sample_sampler,
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sample_prompts,additional_parameters,
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sample_prompts,
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additional_parameters,
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):
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# Get list of function parameters and values
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parameters = list(locals().items())
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ask_for_file = True if ask_for_file.get('label') == 'True' else False
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original_file_path = file_path
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if ask_for_file:
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file_path = get_file_path(file_path)
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@ -298,7 +301,8 @@ def train_model(
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sample_every_n_steps,
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sample_every_n_epochs,
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sample_sampler,
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sample_prompts,additional_parameters,
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sample_prompts,
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additional_parameters,
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):
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if pretrained_model_name_or_path == '':
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msgbox('Source model information is missing')
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@ -321,6 +325,9 @@ def train_model(
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msgbox('Output folder path is missing')
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return
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if check_if_model_exist(output_name, output_dir, save_model_as):
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return
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# Get a list of all subfolders in train_data_dir
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subfolders = [
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f
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@ -787,7 +794,7 @@ def dreambooth_tab(
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outputs=[config_file_name] + settings_list,
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show_progress=False,
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)
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button_load_config.click(
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open_configuration,
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inputs=[dummy_db_false, config_file_name] + settings_list,
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623
fine_tune.py
623
fine_tune.py
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@ -5,6 +5,7 @@ import argparse
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import gc
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import math
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import os
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import toml
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from tqdm import tqdm
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import torch
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@ -15,351 +16,391 @@ from diffusers import DDPMScheduler
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import library.train_util as train_util
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import library.config_util as config_util
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from library.config_util import (
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ConfigSanitizer,
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BlueprintGenerator,
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ConfigSanitizer,
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BlueprintGenerator,
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)
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def collate_fn(examples):
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return examples[0]
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return examples[0]
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def train(args):
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train_util.verify_training_args(args)
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train_util.prepare_dataset_args(args, True)
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train_util.verify_training_args(args)
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train_util.prepare_dataset_args(args, True)
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cache_latents = args.cache_latents
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cache_latents = args.cache_latents
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if args.seed is not None:
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set_seed(args.seed) # 乱数系列を初期化する
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if args.seed is not None:
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set_seed(args.seed) # 乱数系列を初期化する
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tokenizer = train_util.load_tokenizer(args)
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tokenizer = train_util.load_tokenizer(args)
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blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, True, True))
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if args.dataset_config is not None:
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print(f"Load dataset config from {args.dataset_config}")
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user_config = config_util.load_user_config(args.dataset_config)
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ignored = ["train_data_dir", "in_json"]
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if any(getattr(args, attr) is not None for attr in ignored):
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print("ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(', '.join(ignored)))
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else:
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user_config = {
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"datasets": [{
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"subsets": [{
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"image_dir": args.train_data_dir,
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"metadata_file": args.in_json,
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}]
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}]
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}
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blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
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train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
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if args.debug_dataset:
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train_util.debug_dataset(train_dataset_group)
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return
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if len(train_dataset_group) == 0:
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print("No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。")
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return
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if cache_latents:
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assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
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# acceleratorを準備する
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print("prepare accelerator")
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accelerator, unwrap_model = train_util.prepare_accelerator(args)
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# mixed precisionに対応した型を用意しておき適宜castする
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weight_dtype, save_dtype = train_util.prepare_dtype(args)
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# モデルを読み込む
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text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
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# verify load/save model formats
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if load_stable_diffusion_format:
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src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
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src_diffusers_model_path = None
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else:
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src_stable_diffusion_ckpt = None
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src_diffusers_model_path = args.pretrained_model_name_or_path
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if args.save_model_as is None:
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save_stable_diffusion_format = load_stable_diffusion_format
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use_safetensors = args.use_safetensors
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else:
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save_stable_diffusion_format = args.save_model_as.lower() == 'ckpt' or args.save_model_as.lower() == 'safetensors'
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use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
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# Diffusers版のxformers使用フラグを設定する関数
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def set_diffusers_xformers_flag(model, valid):
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# model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう
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# pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
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# U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
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# 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)
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# Recursively walk through all the children.
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# Any children which exposes the set_use_memory_efficient_attention_xformers method
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# gets the message
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def fn_recursive_set_mem_eff(module: torch.nn.Module):
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if hasattr(module, "set_use_memory_efficient_attention_xformers"):
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module.set_use_memory_efficient_attention_xformers(valid)
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for child in module.children():
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fn_recursive_set_mem_eff(child)
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fn_recursive_set_mem_eff(model)
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# モデルに xformers とか memory efficient attention を組み込む
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if args.diffusers_xformers:
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print("Use xformers by Diffusers")
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set_diffusers_xformers_flag(unet, True)
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else:
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# Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
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print("Disable Diffusers' xformers")
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set_diffusers_xformers_flag(unet, False)
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train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
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# 学習を準備する
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if cache_latents:
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vae.to(accelerator.device, dtype=weight_dtype)
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vae.requires_grad_(False)
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vae.eval()
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with torch.no_grad():
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train_dataset_group.cache_latents(vae)
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vae.to("cpu")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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# 学習を準備する：モデルを適切な状態にする
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training_models = []
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if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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training_models.append(unet)
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if args.train_text_encoder:
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print("enable text encoder training")
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if args.gradient_checkpointing:
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text_encoder.gradient_checkpointing_enable()
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training_models.append(text_encoder)
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else:
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text_encoder.to(accelerator.device, dtype=weight_dtype)
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text_encoder.requires_grad_(False) # text encoderは学習しない
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if args.gradient_checkpointing:
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text_encoder.gradient_checkpointing_enable()
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text_encoder.train() # required for gradient_checkpointing
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blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, True, True))
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if args.dataset_config is not None:
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print(f"Load dataset config from {args.dataset_config}")
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user_config = config_util.load_user_config(args.dataset_config)
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ignored = ["train_data_dir", "in_json"]
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if any(getattr(args, attr) is not None for attr in ignored):
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print(
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"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
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", ".join(ignored)
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)
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)
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else:
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text_encoder.eval()
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user_config = {
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"datasets": [
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{
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"subsets": [
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{
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"image_dir": args.train_data_dir,
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"metadata_file": args.in_json,
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}
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]
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}
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]
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}
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if not cache_latents:
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vae.requires_grad_(False)
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vae.eval()
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vae.to(accelerator.device, dtype=weight_dtype)
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blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
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train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
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for m in training_models:
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m.requires_grad_(True)
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params = []
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for m in training_models:
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params.extend(m.parameters())
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params_to_optimize = params
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if args.debug_dataset:
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train_util.debug_dataset(train_dataset_group)
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return
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if len(train_dataset_group) == 0:
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print(
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"No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。"
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)
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return
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# 学習に必要なクラスを準備する
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print("prepare optimizer, data loader etc.")
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_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
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if cache_latents:
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assert (
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train_dataset_group.is_latent_cacheable()
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), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
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# dataloaderを準備する
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# DataLoaderのプロセス数：0はメインプロセスになる
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n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
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train_dataloader = torch.utils.data.DataLoader(
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train_dataset_group, batch_size=1, shuffle=True, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
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# acceleratorを準備する
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print("prepare accelerator")
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accelerator, unwrap_model = train_util.prepare_accelerator(args)
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# 学習ステップ数を計算する
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if args.max_train_epochs is not None:
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args.max_train_steps = args.max_train_epochs * len(train_dataloader)
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print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
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# mixed precisionに対応した型を用意しておき適宜castする
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weight_dtype, save_dtype = train_util.prepare_dtype(args)
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# lr schedulerを用意する
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lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
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num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
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num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
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# モデルを読み込む
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text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
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# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
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if args.full_fp16:
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assert args.mixed_precision == "fp16", "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
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print("enable full fp16 training.")
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unet.to(weight_dtype)
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text_encoder.to(weight_dtype)
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# verify load/save model formats
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if load_stable_diffusion_format:
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src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
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src_diffusers_model_path = None
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else:
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src_stable_diffusion_ckpt = None
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src_diffusers_model_path = args.pretrained_model_name_or_path
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# acceleratorがなんかよろしくやってくれるらしい
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if args.train_text_encoder:
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unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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unet, text_encoder, optimizer, train_dataloader, lr_scheduler)
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else:
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unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
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if args.save_model_as is None:
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save_stable_diffusion_format = load_stable_diffusion_format
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use_safetensors = args.use_safetensors
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else:
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save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
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use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
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# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
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if args.full_fp16:
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train_util.patch_accelerator_for_fp16_training(accelerator)
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# Diffusers版のxformers使用フラグを設定する関数
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def set_diffusers_xformers_flag(model, valid):
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# model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう
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# pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
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# U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
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# 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)
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# resumeする
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if args.resume is not None:
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print(f"resume training from state: {args.resume}")
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accelerator.load_state(args.resume)
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# Recursively walk through all the children.
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# Any children which exposes the set_use_memory_efficient_attention_xformers method
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# gets the message
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def fn_recursive_set_mem_eff(module: torch.nn.Module):
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if hasattr(module, "set_use_memory_efficient_attention_xformers"):
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module.set_use_memory_efficient_attention_xformers(valid)
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# epoch数を計算する
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||||
for child in module.children():
|
||||
fn_recursive_set_mem_eff(child)
|
||||
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num examples / サンプル数: {train_dataset_group.num_train_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
fn_recursive_set_mem_eff(model)
|
||||
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
# モデルに xformers とか memory efficient attention を組み込む
|
||||
if args.diffusers_xformers:
|
||||
print("Use xformers by Diffusers")
|
||||
set_diffusers_xformers_flag(unet, True)
|
||||
else:
|
||||
# Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
|
||||
print("Disable Diffusers' xformers")
|
||||
set_diffusers_xformers_flag(unet, False)
|
||||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||||
|
||||
noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
|
||||
num_train_timesteps=1000, clip_sample=False)
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
train_dataset_group.cache_latents(vae)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("finetuning")
|
||||
# 学習を準備する：モデルを適切な状態にする
|
||||
training_models = []
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
training_models.append(unet)
|
||||
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
train_dataset_group.set_current_epoch(epoch + 1)
|
||||
if args.train_text_encoder:
|
||||
print("enable text encoder training")
|
||||
if args.gradient_checkpointing:
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
training_models.append(text_encoder)
|
||||
else:
|
||||
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
||||
text_encoder.requires_grad_(False) # text encoderは学習しない
|
||||
if args.gradient_checkpointing:
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
text_encoder.train() # required for gradient_checkpointing
|
||||
else:
|
||||
text_encoder.eval()
|
||||
|
||||
if not cache_latents:
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
|
||||
for m in training_models:
|
||||
m.train()
|
||||
m.requires_grad_(True)
|
||||
params = []
|
||||
for m in training_models:
|
||||
params.extend(m.parameters())
|
||||
params_to_optimize = params
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく
|
||||
with torch.no_grad():
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
# latentに変換
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
|
||||
|
||||
with torch.set_grad_enabled(args.train_text_encoder):
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
encoder_hidden_states = train_util.get_hidden_states(
|
||||
args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype)
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collate_fn,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||||
# 学習ステップ数を計算する
|
||||
if args.max_train_epochs is not None:
|
||||
args.max_train_steps = args.max_train_epochs * len(train_dataloader)
|
||||
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
# lr schedulerを用意する
|
||||
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
|
||||
if args.full_fp16:
|
||||
assert (
|
||||
args.mixed_precision == "fp16"
|
||||
), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
||||
print("enable full fp16 training.")
|
||||
unet.to(weight_dtype)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
if args.train_text_encoder:
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
else:
|
||||
target = noise
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f"resume training from state: {args.resume}")
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = []
|
||||
for m in training_models:
|
||||
params_to_clip.extend(m.parameters())
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num examples / サンプル数: {train_dataset_group.num_train_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
train_util.sample_images(accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
noise_scheduler = DDPMScheduler(
|
||||
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
|
||||
accelerator.log(logs, step=global_step)
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("finetuning")
|
||||
|
||||
# TODO moving averageにする
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step+1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
train_dataset_group.set_current_epoch(epoch + 1)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
for m in training_models:
|
||||
m.train()
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch+1)
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく
|
||||
with torch.no_grad():
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
# latentに変換
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
with torch.set_grad_enabled(args.train_text_encoder):
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
encoder_hidden_states = train_util.get_hidden_states(
|
||||
args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype
|
||||
)
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_epoch_end(args, accelerator, src_path, save_stable_diffusion_format, use_safetensors,
|
||||
save_dtype, epoch, num_train_epochs, global_step, unwrap_model(text_encoder), unwrap_model(unet), vae)
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||||
|
||||
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
unet = unwrap_model(unet)
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
accelerator.end_training()
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
else:
|
||||
target = noise
|
||||
|
||||
if is_main_process:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_train_end(args, src_path, save_stable_diffusion_format, use_safetensors,
|
||||
save_dtype, epoch, global_step, text_encoder, unet, vae)
|
||||
print("model saved.")
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = []
|
||||
for m in training_models:
|
||||
params_to_clip.extend(m.parameters())
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
|
||||
train_util.sample_images(
|
||||
accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
# TODO moving averageにする
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step + 1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_epoch_end(
|
||||
args,
|
||||
accelerator,
|
||||
src_path,
|
||||
save_stable_diffusion_format,
|
||||
use_safetensors,
|
||||
save_dtype,
|
||||
epoch,
|
||||
num_train_epochs,
|
||||
global_step,
|
||||
unwrap_model(text_encoder),
|
||||
unwrap_model(unet),
|
||||
vae,
|
||||
)
|
||||
|
||||
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
unet = unwrap_model(unet)
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
|
||||
if is_main_process:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_train_end(
|
||||
args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae
|
||||
)
|
||||
print("model saved.")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, False, True, True)
|
||||
train_util.add_training_arguments(parser, False)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, False, True, True)
|
||||
train_util.add_training_arguments(parser, False)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
|
||||
parser.add_argument("--diffusers_xformers", action='store_true',
|
||||
help='use xformers by diffusers / Diffusersでxformersを使用する')
|
||||
parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
|
||||
parser.add_argument("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する")
|
||||
parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
|
||||
|
||||
args = parser.parse_args()
|
||||
train(args)
|
||||
args = parser.parse_args()
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from pathlib import Path
|
|||
from typing import List
|
||||
from tqdm import tqdm
|
||||
import library.train_util as train_util
|
||||
|
||||
import os
|
||||
|
||||
def main(args):
|
||||
assert not args.recursive or (args.recursive and args.full_path), "recursive requires full_path / recursiveはfull_pathと同時に指定してください"
|
||||
|
|
@ -29,6 +29,9 @@ def main(args):
|
|||
caption_path = image_path.with_suffix(args.caption_extension)
|
||||
caption = caption_path.read_text(encoding='utf-8').strip()
|
||||
|
||||
if not os.path.exists(caption_path):
|
||||
caption_path = os.path.join(image_path, args.caption_extension)
|
||||
|
||||
image_key = str(image_path) if args.full_path else image_path.stem
|
||||
if image_key not in metadata:
|
||||
metadata[image_key] = {}
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from pathlib import Path
|
|||
from typing import List
|
||||
from tqdm import tqdm
|
||||
import library.train_util as train_util
|
||||
|
||||
import os
|
||||
|
||||
def main(args):
|
||||
assert not args.recursive or (args.recursive and args.full_path), "recursive requires full_path / recursiveはfull_pathと同時に指定してください"
|
||||
|
|
@ -29,6 +29,9 @@ def main(args):
|
|||
tags_path = image_path.with_suffix(args.caption_extension)
|
||||
tags = tags_path.read_text(encoding='utf-8').strip()
|
||||
|
||||
if not os.path.exists(tags_path):
|
||||
tags_path = os.path.join(image_path, args.caption_extension)
|
||||
|
||||
image_key = str(image_path) if args.full_path else image_path.stem
|
||||
if image_key not in metadata:
|
||||
metadata[image_key] = {}
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ def main(args):
|
|||
tag_text = ""
|
||||
for i, p in enumerate(prob[4:]): # numpyとか使うのが良いけど、まあそれほど数も多くないのでループで
|
||||
if p >= args.thresh and i < len(tags):
|
||||
tag_text += ", " + (tags[i].replace("_", " ") if args.replace_underscores else tags[i])
|
||||
tag_text += ", " + tags[i]
|
||||
|
||||
if len(tag_text) > 0:
|
||||
tag_text = tag_text[2:] # 最初の ", " を消す
|
||||
|
|
@ -190,7 +190,6 @@ if __name__ == '__main__':
|
|||
help="extension of caption file (for backward compatibility) / 出力されるキャプションファイルの拡張子（スペルミスしていたのを残してあります）")
|
||||
parser.add_argument("--caption_extension", type=str, default=".txt", help="extension of caption file / 出力されるキャプションファイルの拡張子")
|
||||
parser.add_argument("--debug", action="store_true", help="debug mode")
|
||||
parser.add_argument("--replace_underscores", action="store_true", help="replace underscores in tags with spaces / タグのアンダースコアをスペースに置き換える")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ from library.common_gui import (
|
|||
run_cmd_training,
|
||||
# set_legacy_8bitadam,
|
||||
update_my_data,
|
||||
check_if_model_exist,
|
||||
)
|
||||
from library.tensorboard_gui import (
|
||||
gradio_tensorboard,
|
||||
|
|
@ -102,7 +103,8 @@ def save_configuration(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
|
@ -214,15 +216,16 @@ def open_configuration(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
|
||||
ask_for_file = True if ask_for_file.get('label') == 'True' else False
|
||||
|
||||
original_file_path = file_path
|
||||
|
||||
|
||||
if ask_for_file:
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
|
|
@ -308,8 +311,12 @@ def train_model(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
if check_if_model_exist(output_name, output_dir, save_model_as):
|
||||
return
|
||||
|
||||
# create caption json file
|
||||
if generate_caption_database:
|
||||
if not os.path.exists(train_dir):
|
||||
|
|
@ -677,7 +684,8 @@ def finetune_tab():
|
|||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
noise_offset,additional_parameters,
|
||||
noise_offset,
|
||||
additional_parameters,
|
||||
) = gradio_advanced_training()
|
||||
color_aug.change(
|
||||
color_aug_changed,
|
||||
|
|
@ -770,7 +778,8 @@ def finetune_tab():
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
]
|
||||
|
||||
button_run.click(train_model, inputs=settings_list)
|
||||
|
|
@ -781,7 +790,7 @@ def finetune_tab():
|
|||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
|
||||
button_load_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
|
|
|
|||
|
|
@ -122,7 +122,7 @@ def gradio_basic_caption_gui_tab():
|
|||
label='Replacement text',
|
||||
placeholder='Eg: , by some artist. Leave empty if you just want to replace with nothing',
|
||||
interactive=True,
|
||||
)
|
||||
)
|
||||
caption_button = gr.Button('Caption images')
|
||||
caption_button.click(
|
||||
caption_images,
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from tkinter import filedialog, Tk
|
||||
import os
|
||||
import gradio as gr
|
||||
from easygui import msgbox
|
||||
import easygui
|
||||
import shutil
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
|
@ -31,6 +31,34 @@ V1_MODELS = [
|
|||
ALL_PRESET_MODELS = V2_BASE_MODELS + V_PARAMETERIZATION_MODELS + V1_MODELS
|
||||
|
||||
|
||||
def check_if_model_exist(output_name, output_dir, save_model_as):
|
||||
if save_model_as in ['diffusers', 'diffusers_safetendors']:
|
||||
ckpt_folder = os.path.join(output_dir, output_name)
|
||||
if os.path.isdir(ckpt_folder):
|
||||
msg = f'A diffuser model with the same name {ckpt_folder} already exists. Do you want to overwrite it?'
|
||||
if not easygui.ynbox(msg, 'Overwrite Existing Model?'):
|
||||
print(
|
||||
'Aborting training due to existing model with same name...'
|
||||
)
|
||||
return True
|
||||
elif save_model_as in ['ckpt', 'safetensors']:
|
||||
ckpt_file = os.path.join(output_dir, output_name + '.' + save_model_as)
|
||||
if os.path.isfile(ckpt_file):
|
||||
msg = f'A model with the same file name {ckpt_file} already exists. Do you want to overwrite it?'
|
||||
if not easygui.ynbox(msg, 'Overwrite Existing Model?'):
|
||||
print(
|
||||
'Aborting training due to existing model with same name...'
|
||||
)
|
||||
return True
|
||||
else:
|
||||
print(
|
||||
'Can\'t verify if existing model exist when save model is set a "same as source model", continuing to train model...'
|
||||
)
|
||||
return False
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def update_my_data(my_data):
|
||||
# Update optimizer based on use_8bit_adam flag
|
||||
use_8bit_adam = my_data.get('use_8bit_adam', False)
|
||||
|
|
@ -38,11 +66,16 @@ def update_my_data(my_data):
|
|||
my_data['optimizer'] = 'AdamW8bit'
|
||||
elif 'optimizer' not in my_data:
|
||||
my_data['optimizer'] = 'AdamW'
|
||||
|
||||
|
||||
# Update model_list to custom if empty or pretrained_model_name_or_path is not a preset model
|
||||
model_list = my_data.get('model_list', [])
|
||||
pretrained_model_name_or_path = my_data.get('pretrained_model_name_or_path', '')
|
||||
if not model_list or pretrained_model_name_or_path not in ALL_PRESET_MODELS:
|
||||
pretrained_model_name_or_path = my_data.get(
|
||||
'pretrained_model_name_or_path', ''
|
||||
)
|
||||
if (
|
||||
not model_list
|
||||
or pretrained_model_name_or_path not in ALL_PRESET_MODELS
|
||||
):
|
||||
my_data['model_list'] = 'custom'
|
||||
|
||||
# Convert epoch and save_every_n_epochs values to int if they are strings
|
||||
|
|
@ -78,7 +111,7 @@ def update_my_data(my_data):
|
|||
# # If Pretrained model name or path is not one of the preset models then set the preset_model to custom
|
||||
# if not my_data.get('pretrained_model_name_or_path', '') in ALL_PRESET_MODELS:
|
||||
# my_data['model_list'] = 'custom'
|
||||
|
||||
|
||||
# # Fix old config files that contain epoch as str instead of int
|
||||
# for key in ['epoch', 'save_every_n_epochs']:
|
||||
# value = my_data.get(key, -1)
|
||||
|
|
@ -87,10 +120,10 @@ def update_my_data(my_data):
|
|||
# my_data[key] = int(value)
|
||||
# else:
|
||||
# my_data[key] = -1
|
||||
|
||||
|
||||
# if my_data.get('LoRA_type', 'Standard') == 'LoCon':
|
||||
# my_data['LoRA_type'] = 'LyCORIS/LoCon'
|
||||
|
||||
|
||||
# return my_data
|
||||
|
||||
|
||||
|
|
@ -265,11 +298,11 @@ def get_saveasfilename_path(
|
|||
|
||||
|
||||
def add_pre_postfix(
|
||||
folder: str = '',
|
||||
prefix: str = '',
|
||||
postfix: str = '',
|
||||
caption_file_ext: str = '.caption'
|
||||
) -> None:
|
||||
folder: str = '',
|
||||
prefix: str = '',
|
||||
postfix: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
) -> None:
|
||||
"""
|
||||
Add prefix and/or postfix to the content of caption files within a folder.
|
||||
If no caption files are found, create one with the requested prefix and/or postfix.
|
||||
|
|
@ -285,7 +318,9 @@ def add_pre_postfix(
|
|||
return
|
||||
|
||||
image_extensions = ('.jpg', '.jpeg', '.png', '.webp')
|
||||
image_files = [f for f in os.listdir(folder) if f.lower().endswith(image_extensions)]
|
||||
image_files = [
|
||||
f for f in os.listdir(folder) if f.lower().endswith(image_extensions)
|
||||
]
|
||||
|
||||
for image_file in image_files:
|
||||
caption_file_name = os.path.splitext(image_file)[0] + caption_file_ext
|
||||
|
|
@ -303,7 +338,10 @@ def add_pre_postfix(
|
|||
|
||||
prefix_separator = ' ' if prefix else ''
|
||||
postfix_separator = ' ' if postfix else ''
|
||||
f.write(f'{prefix}{prefix_separator}{content}{postfix_separator}{postfix}')
|
||||
f.write(
|
||||
f'{prefix}{prefix_separator}{content}{postfix_separator}{postfix}'
|
||||
)
|
||||
|
||||
|
||||
# def add_pre_postfix(
|
||||
# folder='', prefix='', postfix='', caption_file_ext='.caption'
|
||||
|
|
@ -335,11 +373,11 @@ def add_pre_postfix(
|
|||
def has_ext_files(folder_path: str, file_extension: str) -> bool:
|
||||
"""
|
||||
Check if there are any files with the specified extension in the given folder.
|
||||
|
||||
|
||||
Args:
|
||||
folder_path (str): Path to the folder containing files.
|
||||
file_extension (str): Extension of the files to look for.
|
||||
|
||||
|
||||
Returns:
|
||||
bool: True if files with the specified extension are found, False otherwise.
|
||||
"""
|
||||
|
|
@ -348,15 +386,16 @@ def has_ext_files(folder_path: str, file_extension: str) -> bool:
|
|||
return True
|
||||
return False
|
||||
|
||||
|
||||
def find_replace(
|
||||
folder_path: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
search_text: str = '',
|
||||
replace_text: str = ''
|
||||
) -> None:
|
||||
folder_path: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
search_text: str = '',
|
||||
replace_text: str = '',
|
||||
) -> None:
|
||||
"""
|
||||
Find and replace text in caption files within a folder.
|
||||
|
||||
|
||||
Args:
|
||||
folder_path (str, optional): Path to the folder containing caption files.
|
||||
caption_file_ext (str, optional): Extension of the caption files.
|
||||
|
|
@ -364,7 +403,7 @@ def find_replace(
|
|||
replace_text (str, optional): Text to replace the search text with.
|
||||
"""
|
||||
print('Running caption find/replace')
|
||||
|
||||
|
||||
if not has_ext_files(folder_path, caption_file_ext):
|
||||
msgbox(
|
||||
f'No files with extension {caption_file_ext} were found in {folder_path}...'
|
||||
|
|
@ -374,10 +413,14 @@ def find_replace(
|
|||
if search_text == '':
|
||||
return
|
||||
|
||||
caption_files = [f for f in os.listdir(folder_path) if f.endswith(caption_file_ext)]
|
||||
|
||||
caption_files = [
|
||||
f for f in os.listdir(folder_path) if f.endswith(caption_file_ext)
|
||||
]
|
||||
|
||||
for caption_file in caption_files:
|
||||
with open(os.path.join(folder_path, caption_file), 'r', errors='ignore') as f:
|
||||
with open(
|
||||
os.path.join(folder_path, caption_file), 'r', errors='ignore'
|
||||
) as f:
|
||||
content = f.read()
|
||||
|
||||
content = content.replace(search_text, replace_text)
|
||||
|
|
@ -385,6 +428,7 @@ def find_replace(
|
|||
with open(os.path.join(folder_path, caption_file), 'w') as f:
|
||||
f.write(content)
|
||||
|
||||
|
||||
# def find_replace(folder='', caption_file_ext='.caption', find='', replace=''):
|
||||
# print('Running caption find/replace')
|
||||
# if not has_ext_files(folder, caption_file_ext):
|
||||
|
|
@ -477,17 +521,15 @@ def set_pretrained_model_name_or_path_input(
|
|||
if (
|
||||
str(pretrained_model_name_or_path) in V1_MODELS
|
||||
or str(pretrained_model_name_or_path) in V2_BASE_MODELS
|
||||
or str(pretrained_model_name_or_path)
|
||||
in V_PARAMETERIZATION_MODELS
|
||||
or str(pretrained_model_name_or_path) in V_PARAMETERIZATION_MODELS
|
||||
):
|
||||
pretrained_model_name_or_path = ''
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
return model_list, pretrained_model_name_or_path, v2, v_parameterization
|
||||
|
||||
def set_v2_checkbox(
|
||||
model_list, v2, v_parameterization
|
||||
):
|
||||
|
||||
def set_v2_checkbox(model_list, v2, v_parameterization):
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
|
||||
if str(model_list) in V2_BASE_MODELS:
|
||||
v2 = True
|
||||
|
|
@ -504,6 +546,7 @@ def set_v2_checkbox(
|
|||
|
||||
return v2, v_parameterization
|
||||
|
||||
|
||||
def set_model_list(
|
||||
model_list,
|
||||
pretrained_model_name_or_path,
|
||||
|
|
@ -515,7 +558,7 @@ def set_model_list(
|
|||
model_list = 'custom'
|
||||
else:
|
||||
model_list = pretrained_model_name_or_path
|
||||
|
||||
|
||||
return model_list, v2, v_parameterization
|
||||
|
||||
|
||||
|
|
@ -538,7 +581,11 @@ def gradio_config():
|
|||
interactive=True,
|
||||
)
|
||||
button_load_config = gr.Button('Load 💾', elem_id='open_folder')
|
||||
config_file_name.change(remove_doublequote, inputs=[config_file_name], outputs=[config_file_name])
|
||||
config_file_name.change(
|
||||
remove_doublequote,
|
||||
inputs=[config_file_name],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
return (
|
||||
button_open_config,
|
||||
button_save_config,
|
||||
|
|
@ -614,8 +661,18 @@ def gradio_source_model():
|
|||
v_parameterization = gr.Checkbox(
|
||||
label='v_parameterization', value=False
|
||||
)
|
||||
v2.change(set_v2_checkbox, inputs=[model_list, v2, v_parameterization], outputs=[v2, v_parameterization],show_progress=False)
|
||||
v_parameterization.change(set_v2_checkbox, inputs=[model_list, v2, v_parameterization], outputs=[v2, v_parameterization],show_progress=False)
|
||||
v2.change(
|
||||
set_v2_checkbox,
|
||||
inputs=[model_list, v2, v_parameterization],
|
||||
outputs=[v2, v_parameterization],
|
||||
show_progress=False,
|
||||
)
|
||||
v_parameterization.change(
|
||||
set_v2_checkbox,
|
||||
inputs=[model_list, v2, v_parameterization],
|
||||
outputs=[v2, v_parameterization],
|
||||
show_progress=False,
|
||||
)
|
||||
model_list.change(
|
||||
set_pretrained_model_name_or_path_input,
|
||||
inputs=[
|
||||
|
|
@ -671,7 +728,9 @@ def gradio_training(
|
|||
step=1,
|
||||
)
|
||||
epoch = gr.Number(label='Epoch', value=1, precision=0)
|
||||
save_every_n_epochs = gr.Number(label='Save every N epochs', value=1, precision=0)
|
||||
save_every_n_epochs = gr.Number(
|
||||
label='Save every N epochs', value=1, precision=0
|
||||
)
|
||||
caption_extension = gr.Textbox(
|
||||
label='Caption Extension',
|
||||
placeholder='(Optional) Extension for caption files. default: .caption',
|
||||
|
|
@ -788,7 +847,7 @@ def run_cmd_training(**kwargs):
|
|||
if kwargs.get('save_precision')
|
||||
else '',
|
||||
f' --seed="{kwargs.get("seed", "")}"'
|
||||
if kwargs.get('seed') != ""
|
||||
if kwargs.get('seed') != ''
|
||||
else '',
|
||||
f' --caption_extension="{kwargs.get("caption_extension", "")}"'
|
||||
if kwargs.get('caption_extension')
|
||||
|
|
@ -807,7 +866,7 @@ def run_cmd_training(**kwargs):
|
|||
def gradio_advanced_training():
|
||||
with gr.Row():
|
||||
additional_parameters = gr.Textbox(
|
||||
label='Additional parameters',
|
||||
label='Additional parameters',
|
||||
placeholder='(Optional) Use to provide additional parameters not handled by the GUI. Eg: --some_parameters "value"',
|
||||
)
|
||||
with gr.Row():
|
||||
|
|
@ -964,7 +1023,7 @@ def run_cmd_advanced_training(**kwargs):
|
|||
f' --noise_offset={float(kwargs.get("noise_offset", 0))}'
|
||||
if not kwargs.get('noise_offset', '') == ''
|
||||
else '',
|
||||
f' {kwargs.get("additional_parameters", "")}'
|
||||
f' {kwargs.get("additional_parameters", "")}',
|
||||
]
|
||||
run_cmd = ''.join(options)
|
||||
return run_cmd
|
||||
|
|
|
|||
|
|
@ -153,6 +153,14 @@ def gradio_extract_lora_tab():
|
|||
|
||||
extract_button.click(
|
||||
extract_lora,
|
||||
inputs=[model_tuned, model_org, save_to, save_precision, dim, v2, conv_dim],
|
||||
inputs=[
|
||||
model_tuned,
|
||||
model_org,
|
||||
save_to,
|
||||
save_precision,
|
||||
dim,
|
||||
v2,
|
||||
conv_dim,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -16,12 +16,23 @@ PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
|
|||
|
||||
|
||||
def extract_lycoris_locon(
|
||||
db_model, base_model, output_name, device,
|
||||
is_v2, mode, linear_dim, conv_dim,
|
||||
linear_threshold, conv_threshold,
|
||||
linear_ratio, conv_ratio,
|
||||
linear_quantile, conv_quantile,
|
||||
use_sparse_bias, sparsity, disable_cp
|
||||
db_model,
|
||||
base_model,
|
||||
output_name,
|
||||
device,
|
||||
is_v2,
|
||||
mode,
|
||||
linear_dim,
|
||||
conv_dim,
|
||||
linear_threshold,
|
||||
conv_threshold,
|
||||
linear_ratio,
|
||||
conv_ratio,
|
||||
linear_quantile,
|
||||
conv_quantile,
|
||||
use_sparse_bias,
|
||||
sparsity,
|
||||
disable_cp,
|
||||
):
|
||||
# Check for caption_text_input
|
||||
if db_model == '':
|
||||
|
|
@ -41,9 +52,7 @@ def extract_lycoris_locon(
|
|||
msgbox('The provided base model is not a file')
|
||||
return
|
||||
|
||||
run_cmd = (
|
||||
f'{PYTHON} "{os.path.join("tools","lycoris_locon_extract.py")}"'
|
||||
)
|
||||
run_cmd = f'{PYTHON} "{os.path.join("tools","lycoris_locon_extract.py")}"'
|
||||
if is_v2:
|
||||
run_cmd += f' --is_v2'
|
||||
run_cmd += f' --device {device}'
|
||||
|
|
@ -89,10 +98,11 @@ def extract_lycoris_locon(
|
|||
# if mode == 'threshold':
|
||||
# return gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=True)
|
||||
|
||||
|
||||
def update_mode(mode):
|
||||
# Create a list of possible mode values
|
||||
modes = ['fixed', 'threshold', 'ratio', 'quantile']
|
||||
|
||||
|
||||
# Initialize an empty list to store visibility updates
|
||||
updates = []
|
||||
|
||||
|
|
@ -104,12 +114,15 @@ def update_mode(mode):
|
|||
# Return the visibility updates as a tuple
|
||||
return tuple(updates)
|
||||
|
||||
|
||||
def gradio_extract_lycoris_locon_tab():
|
||||
with gr.Tab('Extract LyCORIS LoCON'):
|
||||
gr.Markdown(
|
||||
'This utility can extract a LyCORIS LoCon network from a finetuned model.'
|
||||
)
|
||||
lora_ext = gr.Textbox(value='*.safetensors', visible=False) # lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
|
||||
lora_ext = gr.Textbox(
|
||||
value='*.safetensors', visible=False
|
||||
) # lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
|
||||
lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
|
||||
model_ext = gr.Textbox(value='*.safetensors *.ckpt', visible=False)
|
||||
model_ext_name = gr.Textbox(value='Model types', visible=False)
|
||||
|
|
@ -161,14 +174,17 @@ def gradio_extract_lycoris_locon_tab():
|
|||
)
|
||||
device = gr.Dropdown(
|
||||
label='Device',
|
||||
choices=['cpu', 'cuda',],
|
||||
choices=[
|
||||
'cpu',
|
||||
'cuda',
|
||||
],
|
||||
value='cuda',
|
||||
interactive=True,
|
||||
)
|
||||
is_v2 = gr.Checkbox(label='is v2', value=False, interactive=True)
|
||||
mode = gr.Dropdown(
|
||||
label='Mode',
|
||||
choices=['fixed', 'threshold','ratio','quantile'],
|
||||
choices=['fixed', 'threshold', 'ratio', 'quantile'],
|
||||
value='fixed',
|
||||
interactive=True,
|
||||
)
|
||||
|
|
@ -241,7 +257,9 @@ def gradio_extract_lycoris_locon_tab():
|
|||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
use_sparse_bias = gr.Checkbox(label='Use sparse biais', value=False, interactive=True)
|
||||
use_sparse_bias = gr.Checkbox(
|
||||
label='Use sparse biais', value=False, interactive=True
|
||||
)
|
||||
sparsity = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
|
|
@ -250,24 +268,42 @@ def gradio_extract_lycoris_locon_tab():
|
|||
step=0.01,
|
||||
interactive=True,
|
||||
)
|
||||
disable_cp = gr.Checkbox(label='Disable CP decomposition', value=False, interactive=True)
|
||||
disable_cp = gr.Checkbox(
|
||||
label='Disable CP decomposition', value=False, interactive=True
|
||||
)
|
||||
mode.change(
|
||||
update_mode,
|
||||
inputs=[mode],
|
||||
outputs=[
|
||||
fixed, threshold, ratio, quantile,
|
||||
]
|
||||
fixed,
|
||||
threshold,
|
||||
ratio,
|
||||
quantile,
|
||||
],
|
||||
)
|
||||
|
||||
extract_button = gr.Button('Extract LyCORIS LoCon')
|
||||
|
||||
extract_button.click(
|
||||
extract_lycoris_locon,
|
||||
inputs=[db_model, base_model, output_name, device,
|
||||
is_v2, mode, linear_dim, conv_dim,
|
||||
linear_threshold, conv_threshold,
|
||||
linear_ratio, conv_ratio,
|
||||
linear_quantile, conv_quantile,
|
||||
use_sparse_bias, sparsity, disable_cp],
|
||||
inputs=[
|
||||
db_model,
|
||||
base_model,
|
||||
output_name,
|
||||
device,
|
||||
is_v2,
|
||||
mode,
|
||||
linear_dim,
|
||||
conv_dim,
|
||||
linear_threshold,
|
||||
conv_threshold,
|
||||
linear_ratio,
|
||||
conv_ratio,
|
||||
linear_quantile,
|
||||
conv_quantile,
|
||||
use_sparse_bias,
|
||||
sparsity,
|
||||
disable_cp,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -27,7 +27,9 @@ def caption_images(
|
|||
return
|
||||
|
||||
print(f'GIT captioning files in {train_data_dir}...')
|
||||
run_cmd = f'.\\venv\\Scripts\\python.exe "finetune/make_captions_by_git.py"'
|
||||
run_cmd = (
|
||||
f'.\\venv\\Scripts\\python.exe "finetune/make_captions_by_git.py"'
|
||||
)
|
||||
if not model_id == '':
|
||||
run_cmd += f' --model_id="{model_id}"'
|
||||
run_cmd += f' --batch_size="{int(batch_size)}"'
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -30,15 +30,19 @@ def resize_lora(
|
|||
if not os.path.isfile(model):
|
||||
msgbox('The provided model is not a file')
|
||||
return
|
||||
|
||||
|
||||
if dynamic_method == 'sv_ratio':
|
||||
if float(dynamic_param) < 2:
|
||||
msgbox(f'Dynamic parameter for {dynamic_method} need to be 2 or greater...')
|
||||
msgbox(
|
||||
f'Dynamic parameter for {dynamic_method} need to be 2 or greater...'
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
if dynamic_method == 'sv_fro' or dynamic_method == 'sv_cumulative':
|
||||
if float(dynamic_param) < 0 or float(dynamic_param) > 1:
|
||||
msgbox(f'Dynamic parameter for {dynamic_method} need to be between 0 and 1...')
|
||||
msgbox(
|
||||
f'Dynamic parameter for {dynamic_method} need to be between 0 and 1...'
|
||||
)
|
||||
return
|
||||
|
||||
# Check if save_to end with one of the defines extension. If not add .safetensors.
|
||||
|
|
@ -108,25 +112,18 @@ def gradio_resize_lora_tab():
|
|||
|
||||
with gr.Row():
|
||||
dynamic_method = gr.Dropdown(
|
||||
choices=['None',
|
||||
'sv_ratio',
|
||||
'sv_fro',
|
||||
'sv_cumulative'
|
||||
],
|
||||
choices=['None', 'sv_ratio', 'sv_fro', 'sv_cumulative'],
|
||||
value='sv_fro',
|
||||
label='Dynamic method',
|
||||
interactive=True
|
||||
interactive=True,
|
||||
)
|
||||
dynamic_param = gr.Textbox(
|
||||
label='Dynamic parameter',
|
||||
value='0.9',
|
||||
interactive=True,
|
||||
placeholder='Value for the dynamic method selected.'
|
||||
)
|
||||
verbose = gr.Checkbox(
|
||||
label='Verbose',
|
||||
value=False
|
||||
placeholder='Value for the dynamic method selected.',
|
||||
)
|
||||
verbose = gr.Checkbox(label='Verbose', value=False)
|
||||
with gr.Row():
|
||||
save_to = gr.Textbox(
|
||||
label='Save to',
|
||||
|
|
@ -150,7 +147,10 @@ def gradio_resize_lora_tab():
|
|||
)
|
||||
device = gr.Dropdown(
|
||||
label='Device',
|
||||
choices=['cpu', 'cuda',],
|
||||
choices=[
|
||||
'cpu',
|
||||
'cuda',
|
||||
],
|
||||
value='cuda',
|
||||
interactive=True,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -74,18 +74,18 @@ def run_cmd_sample(
|
|||
sample_prompts,
|
||||
output_dir,
|
||||
):
|
||||
output_dir = os.path.join(output_dir, "sample")
|
||||
|
||||
output_dir = os.path.join(output_dir, 'sample')
|
||||
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
|
||||
run_cmd = ''
|
||||
|
||||
|
||||
if sample_every_n_epochs == 0 and sample_every_n_steps == 0:
|
||||
return run_cmd
|
||||
|
||||
# Create the prompt file and get its path
|
||||
sample_prompts_path = os.path.join(output_dir, "prompt.txt")
|
||||
sample_prompts_path = os.path.join(output_dir, 'prompt.txt')
|
||||
|
||||
with open(sample_prompts_path, 'w') as f:
|
||||
f.write(sample_prompts)
|
||||
|
|
|
|||
|
|
@ -163,7 +163,10 @@ def gradio_svd_merge_lora_tab():
|
|||
)
|
||||
device = gr.Dropdown(
|
||||
label='Device',
|
||||
choices=['cpu', 'cuda',],
|
||||
choices=[
|
||||
'cpu',
|
||||
'cuda',
|
||||
],
|
||||
value='cuda',
|
||||
interactive=True,
|
||||
)
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -5,7 +5,9 @@ from .common_gui import get_folder_path
|
|||
import os
|
||||
|
||||
|
||||
def caption_images(train_data_dir, caption_extension, batch_size, thresh, replace_underscores):
|
||||
def caption_images(
|
||||
train_data_dir, caption_extension, batch_size, thresh, replace_underscores
|
||||
):
|
||||
# Check for caption_text_input
|
||||
# if caption_text_input == "":
|
||||
# msgbox("Caption text is missing...")
|
||||
|
|
@ -76,7 +78,7 @@ def gradio_wd14_caption_gui_tab():
|
|||
batch_size = gr.Number(
|
||||
value=1, label='Batch size', interactive=True
|
||||
)
|
||||
|
||||
|
||||
replace_underscores = gr.Checkbox(
|
||||
label='Replace underscores in filenames with spaces',
|
||||
value=False,
|
||||
|
|
@ -87,6 +89,12 @@ def gradio_wd14_caption_gui_tab():
|
|||
|
||||
caption_button.click(
|
||||
caption_images,
|
||||
inputs=[train_data_dir, caption_extension, batch_size, thresh, replace_underscores],
|
||||
inputs=[
|
||||
train_data_dir,
|
||||
caption_extension,
|
||||
batch_size,
|
||||
thresh,
|
||||
replace_underscores,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
|
|
|||
61
lora_gui.py
61
lora_gui.py
|
|
@ -4,6 +4,7 @@
|
|||
# v3.1: Adding captionning of images to utilities
|
||||
|
||||
import gradio as gr
|
||||
import easygui
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
|
|
@ -26,6 +27,7 @@ from library.common_gui import (
|
|||
run_cmd_training,
|
||||
# set_legacy_8bitadam,
|
||||
update_my_data,
|
||||
check_if_model_exist,
|
||||
)
|
||||
from library.dreambooth_folder_creation_gui import (
|
||||
gradio_dreambooth_folder_creation_tab,
|
||||
|
|
@ -120,7 +122,8 @@ def save_configuration(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
|
@ -236,15 +239,16 @@ def open_configuration(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
|
||||
ask_for_file = True if ask_for_file.get('label') == 'True' else False
|
||||
|
||||
original_file_path = file_path
|
||||
|
||||
|
||||
if ask_for_file:
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
|
|
@ -342,10 +346,11 @@ def train_model(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
print_only_bool = True if print_only.get('label') == 'True' else False
|
||||
|
||||
|
||||
if pretrained_model_name_or_path == '':
|
||||
msgbox('Source model information is missing')
|
||||
return
|
||||
|
|
@ -380,6 +385,9 @@ def train_model(
|
|||
)
|
||||
stop_text_encoder_training_pct = 0
|
||||
|
||||
if check_if_model_exist(output_name, output_dir, save_model_as):
|
||||
return
|
||||
|
||||
# If string is empty set string to 0.
|
||||
if text_encoder_lr == '':
|
||||
text_encoder_lr = 0
|
||||
|
|
@ -417,7 +425,7 @@ def train_model(
|
|||
or f.endswith('.webp')
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
print(f'Folder {folder}: {num_images} images found')
|
||||
|
||||
# Calculate the total number of steps for this folder
|
||||
|
|
@ -425,7 +433,7 @@ def train_model(
|
|||
|
||||
# Print the result
|
||||
print(f'Folder {folder}: {steps} steps')
|
||||
|
||||
|
||||
total_steps += steps
|
||||
|
||||
# calculate max_train_steps
|
||||
|
|
@ -492,9 +500,7 @@ def train_model(
|
|||
)
|
||||
return
|
||||
run_cmd += f' --network_module=lycoris.kohya'
|
||||
run_cmd += (
|
||||
f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=lora"'
|
||||
)
|
||||
run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=lora"'
|
||||
if LoRA_type == 'LyCORIS/LoHa':
|
||||
try:
|
||||
import lycoris
|
||||
|
|
@ -504,9 +510,7 @@ def train_model(
|
|||
)
|
||||
return
|
||||
run_cmd += f' --network_module=lycoris.kohya'
|
||||
run_cmd += (
|
||||
f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=loha"'
|
||||
)
|
||||
run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=loha"'
|
||||
if LoRA_type == 'Kohya LoCon':
|
||||
run_cmd += f' --network_module=networks.lora'
|
||||
run_cmd += (
|
||||
|
|
@ -595,8 +599,10 @@ def train_model(
|
|||
output_dir,
|
||||
)
|
||||
|
||||
if print_only_bool:
|
||||
print('\033[93m\nHere is the trainer command as a reference. It will not be executed:\033[0m\n')
|
||||
if print_only_bool:
|
||||
print(
|
||||
'\033[93m\nHere is the trainer command as a reference. It will not be executed:\033[0m\n'
|
||||
)
|
||||
print('\033[96m' + run_cmd + '\033[0m\n')
|
||||
else:
|
||||
print(run_cmd)
|
||||
|
|
@ -611,7 +617,9 @@ def train_model(
|
|||
|
||||
if not last_dir.is_dir():
|
||||
# Copy inference model for v2 if required
|
||||
save_inference_file(output_dir, v2, v_parameterization, output_name)
|
||||
save_inference_file(
|
||||
output_dir, v2, v_parameterization, output_name
|
||||
)
|
||||
|
||||
|
||||
def lora_tab(
|
||||
|
|
@ -811,7 +819,12 @@ def lora_tab(
|
|||
# Show of hide LoCon conv settings depending on LoRA type selection
|
||||
def LoRA_type_change(LoRA_type):
|
||||
print('LoRA type changed...')
|
||||
if LoRA_type == 'LoCon' or LoRA_type == 'Kohya LoCon' or LoRA_type == 'LyCORIS/LoHa' or LoRA_type == 'LyCORIS/LoCon':
|
||||
if (
|
||||
LoRA_type == 'LoCon'
|
||||
or LoRA_type == 'Kohya LoCon'
|
||||
or LoRA_type == 'LyCORIS/LoHa'
|
||||
or LoRA_type == 'LyCORIS/LoCon'
|
||||
):
|
||||
return gr.Group.update(visible=True)
|
||||
else:
|
||||
return gr.Group.update(visible=False)
|
||||
|
|
@ -876,7 +889,8 @@ def lora_tab(
|
|||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
noise_offset,additional_parameters,
|
||||
noise_offset,
|
||||
additional_parameters,
|
||||
) = gradio_advanced_training()
|
||||
color_aug.change(
|
||||
color_aug_changed,
|
||||
|
|
@ -908,7 +922,7 @@ def lora_tab(
|
|||
gradio_verify_lora_tab()
|
||||
|
||||
button_run = gr.Button('Train model', variant='primary')
|
||||
|
||||
|
||||
button_print = gr.Button('Print training command')
|
||||
|
||||
# Setup gradio tensorboard buttons
|
||||
|
|
@ -992,7 +1006,8 @@ def lora_tab(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
]
|
||||
|
||||
button_open_config.click(
|
||||
|
|
@ -1001,7 +1016,7 @@ def lora_tab(
|
|||
outputs=[config_file_name] + settings_list + [LoCon_row],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
|
||||
button_load_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
|
|
@ -1028,7 +1043,7 @@ def lora_tab(
|
|||
inputs=[dummy_db_false] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
|
||||
button_print.click(
|
||||
train_model,
|
||||
inputs=[dummy_db_true] + settings_list,
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ from library.common_gui import (
|
|||
gradio_source_model,
|
||||
# set_legacy_8bitadam,
|
||||
update_my_data,
|
||||
check_if_model_exist,
|
||||
)
|
||||
from library.tensorboard_gui import (
|
||||
gradio_tensorboard,
|
||||
|
|
@ -110,7 +111,8 @@ def save_configuration(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
|
@ -222,15 +224,16 @@ def open_configuration(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
|
||||
ask_for_file = True if ask_for_file.get('label') == 'True' else False
|
||||
|
||||
original_file_path = file_path
|
||||
|
||||
|
||||
if ask_for_file:
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
|
|
@ -316,7 +319,8 @@ def train_model(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
):
|
||||
if pretrained_model_name_or_path == '':
|
||||
msgbox('Source model information is missing')
|
||||
|
|
@ -350,6 +354,9 @@ def train_model(
|
|||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
if check_if_model_exist(output_name, output_dir, save_model_as):
|
||||
return
|
||||
|
||||
# Get a list of all subfolders in train_data_dir
|
||||
subfolders = [
|
||||
f
|
||||
|
|
@ -761,7 +768,8 @@ def ti_tab(
|
|||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
noise_offset,additional_parameters,
|
||||
noise_offset,
|
||||
additional_parameters,
|
||||
) = gradio_advanced_training()
|
||||
color_aug.change(
|
||||
color_aug_changed,
|
||||
|
|
@ -866,7 +874,8 @@ def ti_tab(
|
|||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,additional_parameters,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
]
|
||||
|
||||
button_open_config.click(
|
||||
|
|
@ -875,7 +884,7 @@ def ti_tab(
|
|||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
|
||||
button_load_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
|
|
|
|||
625
train_db.py
625
train_db.py
|
|
@ -7,6 +7,7 @@ import argparse
|
|||
import itertools
|
||||
import math
|
||||
import os
|
||||
import toml
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
|
|
@ -17,348 +18,392 @@ from diffusers import DDPMScheduler
|
|||
import library.train_util as train_util
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
|
||||
|
||||
def collate_fn(examples):
|
||||
return examples[0]
|
||||
return examples[0]
|
||||
|
||||
|
||||
def train(args):
|
||||
train_util.verify_training_args(args)
|
||||
train_util.prepare_dataset_args(args, False)
|
||||
train_util.verify_training_args(args)
|
||||
train_util.prepare_dataset_args(args, False)
|
||||
|
||||
cache_latents = args.cache_latents
|
||||
cache_latents = args.cache_latents
|
||||
|
||||
if args.seed is not None:
|
||||
set_seed(args.seed) # 乱数系列を初期化する
|
||||
if args.seed is not None:
|
||||
set_seed(args.seed) # 乱数系列を初期化する
|
||||
|
||||
tokenizer = train_util.load_tokenizer(args)
|
||||
tokenizer = train_util.load_tokenizer(args)
|
||||
|
||||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, True))
|
||||
if args.dataset_config is not None:
|
||||
print(f"Load dataset config from {args.dataset_config}")
|
||||
user_config = config_util.load_user_config(args.dataset_config)
|
||||
ignored = ["train_data_dir", "reg_data_dir"]
|
||||
if any(getattr(args, attr) is not None for attr in ignored):
|
||||
print("ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(', '.join(ignored)))
|
||||
else:
|
||||
user_config = {
|
||||
"datasets": [{
|
||||
"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)
|
||||
}]
|
||||
}
|
||||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, True))
|
||||
if args.dataset_config is not None:
|
||||
print(f"Load dataset config from {args.dataset_config}")
|
||||
user_config = config_util.load_user_config(args.dataset_config)
|
||||
ignored = ["train_data_dir", "reg_data_dir"]
|
||||
if any(getattr(args, attr) is not None for attr in ignored):
|
||||
print(
|
||||
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
|
||||
", ".join(ignored)
|
||||
)
|
||||
)
|
||||
else:
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
|
||||
]
|
||||
}
|
||||
|
||||
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
|
||||
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
||||
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
|
||||
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
||||
|
||||
if args.no_token_padding:
|
||||
train_dataset_group.disable_token_padding()
|
||||
if args.no_token_padding:
|
||||
train_dataset_group.disable_token_padding()
|
||||
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group)
|
||||
return
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group)
|
||||
return
|
||||
|
||||
if cache_latents:
|
||||
assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
|
||||
if cache_latents:
|
||||
assert (
|
||||
train_dataset_group.is_latent_cacheable()
|
||||
), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
|
||||
|
||||
# acceleratorを準備する
|
||||
print("prepare accelerator")
|
||||
# acceleratorを準備する
|
||||
print("prepare accelerator")
|
||||
|
||||
if args.gradient_accumulation_steps > 1:
|
||||
print(f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong")
|
||||
print(
|
||||
f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル（U-NetおよびText Encoder）の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です")
|
||||
if args.gradient_accumulation_steps > 1:
|
||||
print(
|
||||
f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong"
|
||||
)
|
||||
print(
|
||||
f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル（U-NetおよびText Encoder）の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です"
|
||||
)
|
||||
|
||||
accelerator, unwrap_model = train_util.prepare_accelerator(args)
|
||||
accelerator, unwrap_model = train_util.prepare_accelerator(args)
|
||||
|
||||
# mixed precisionに対応した型を用意しておき適宜castする
|
||||
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
||||
# mixed precisionに対応した型を用意しておき適宜castする
|
||||
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
||||
|
||||
# モデルを読み込む
|
||||
text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
|
||||
# モデルを読み込む
|
||||
text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
|
||||
|
||||
# verify load/save model formats
|
||||
if load_stable_diffusion_format:
|
||||
src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
|
||||
src_diffusers_model_path = None
|
||||
else:
|
||||
src_stable_diffusion_ckpt = None
|
||||
src_diffusers_model_path = args.pretrained_model_name_or_path
|
||||
# verify load/save model formats
|
||||
if load_stable_diffusion_format:
|
||||
src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
|
||||
src_diffusers_model_path = None
|
||||
else:
|
||||
src_stable_diffusion_ckpt = None
|
||||
src_diffusers_model_path = args.pretrained_model_name_or_path
|
||||
|
||||
if args.save_model_as is None:
|
||||
save_stable_diffusion_format = load_stable_diffusion_format
|
||||
use_safetensors = args.use_safetensors
|
||||
else:
|
||||
save_stable_diffusion_format = args.save_model_as.lower() == 'ckpt' or args.save_model_as.lower() == 'safetensors'
|
||||
use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
|
||||
if args.save_model_as is None:
|
||||
save_stable_diffusion_format = load_stable_diffusion_format
|
||||
use_safetensors = args.use_safetensors
|
||||
else:
|
||||
save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
|
||||
use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
|
||||
|
||||
# モデルに xformers とか memory efficient attention を組み込む
|
||||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||||
# モデルに xformers とか memory efficient attention を組み込む
|
||||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||||
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
train_dataset_group.cache_latents(vae)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
# 学習を準備する：モデルを適切な状態にする
|
||||
train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
|
||||
unet.requires_grad_(True) # 念のため追加
|
||||
text_encoder.requires_grad_(train_text_encoder)
|
||||
if not train_text_encoder:
|
||||
print("Text Encoder is not trained.")
|
||||
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
|
||||
if not cache_latents:
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
if train_text_encoder:
|
||||
trainable_params = (itertools.chain(unet.parameters(), text_encoder.parameters()))
|
||||
else:
|
||||
trainable_params = unet.parameters()
|
||||
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
||||
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset_group, batch_size=1, shuffle=True, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
|
||||
|
||||
# 学習ステップ数を計算する
|
||||
if args.max_train_epochs is not None:
|
||||
args.max_train_steps = args.max_train_epochs * len(train_dataloader)
|
||||
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
|
||||
|
||||
if args.stop_text_encoder_training is None:
|
||||
args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end
|
||||
|
||||
# lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
|
||||
lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
|
||||
num_training_steps=args.max_train_steps,
|
||||
num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
|
||||
if args.full_fp16:
|
||||
assert args.mixed_precision == "fp16", "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
||||
print("enable full fp16 training.")
|
||||
unet.to(weight_dtype)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
if train_text_encoder:
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler)
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
if not train_text_encoder:
|
||||
text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f"resume training from state: {args.resume}")
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||||
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
|
||||
print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
|
||||
num_train_timesteps=1000, clip_sample=False)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("dreambooth")
|
||||
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
train_dataset_group.set_current_epoch(epoch + 1)
|
||||
|
||||
# 指定したステップ数までText Encoderを学習する：epoch最初の状態
|
||||
unet.train()
|
||||
# train==True is required to enable gradient_checkpointing
|
||||
if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
|
||||
text_encoder.train()
|
||||
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
# 指定したステップ数でText Encoderの学習を止める
|
||||
if global_step == args.stop_text_encoder_training:
|
||||
print(f"stop text encoder training at step {global_step}")
|
||||
if not args.gradient_checkpointing:
|
||||
text_encoder.train(False)
|
||||
text_encoder.requires_grad_(False)
|
||||
|
||||
with accelerator.accumulate(unet):
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
# latentに変換
|
||||
if cache_latents:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
train_dataset_group.cache_latents(vae)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||||
# 学習を準備する：モデルを適切な状態にする
|
||||
train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
|
||||
unet.requires_grad_(True) # 念のため追加
|
||||
text_encoder.requires_grad_(train_text_encoder)
|
||||
if not train_text_encoder:
|
||||
print("Text Encoder is not trained.")
|
||||
|
||||
# Get the text embedding for conditioning
|
||||
with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
encoder_hidden_states = train_util.get_hidden_states(
|
||||
args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype)
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
if not cache_latents:
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
if train_text_encoder:
|
||||
trainable_params = itertools.chain(unet.parameters(), text_encoder.parameters())
|
||||
else:
|
||||
trainable_params = unet.parameters()
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
else:
|
||||
target = noise
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collate_fn,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||||
loss = loss.mean([1, 2, 3])
|
||||
# 学習ステップ数を計算する
|
||||
if args.max_train_epochs is not None:
|
||||
args.max_train_steps = args.max_train_epochs * len(train_dataloader)
|
||||
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
|
||||
|
||||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||||
loss = loss * loss_weights
|
||||
if args.stop_text_encoder_training is None:
|
||||
args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
# lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
|
||||
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
if train_text_encoder:
|
||||
params_to_clip = (itertools.chain(unet.parameters(), text_encoder.parameters()))
|
||||
else:
|
||||
params_to_clip = unet.parameters()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
|
||||
if args.full_fp16:
|
||||
assert (
|
||||
args.mixed_precision == "fp16"
|
||||
), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
||||
print("enable full fp16 training.")
|
||||
unet.to(weight_dtype)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
if train_text_encoder:
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
if not train_text_encoder:
|
||||
text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error
|
||||
|
||||
train_util.sample_images(accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
|
||||
accelerator.log(logs, step=global_step)
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f"resume training from state: {args.resume}")
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
|
||||
print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch+1)
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
noise_scheduler = DDPMScheduler(
|
||||
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
|
||||
)
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_epoch_end(args, accelerator, src_path, save_stable_diffusion_format, use_safetensors,
|
||||
save_dtype, epoch, num_train_epochs, global_step, unwrap_model(text_encoder), unwrap_model(unet), vae)
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("dreambooth")
|
||||
|
||||
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
train_dataset_group.set_current_epoch(epoch + 1)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
unet = unwrap_model(unet)
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
# 指定したステップ数までText Encoderを学習する：epoch最初の状態
|
||||
unet.train()
|
||||
# train==True is required to enable gradient_checkpointing
|
||||
if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
|
||||
text_encoder.train()
|
||||
|
||||
accelerator.end_training()
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
# 指定したステップ数でText Encoderの学習を止める
|
||||
if global_step == args.stop_text_encoder_training:
|
||||
print(f"stop text encoder training at step {global_step}")
|
||||
if not args.gradient_checkpointing:
|
||||
text_encoder.train(False)
|
||||
text_encoder.requires_grad_(False)
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
with accelerator.accumulate(unet):
|
||||
with torch.no_grad():
|
||||
# latentに変換
|
||||
if cache_latents:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||||
|
||||
if is_main_process:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_train_end(args, src_path, save_stable_diffusion_format, use_safetensors,
|
||||
save_dtype, epoch, global_step, text_encoder, unet, vae)
|
||||
print("model saved.")
|
||||
# Get the text embedding for conditioning
|
||||
with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
encoder_hidden_states = train_util.get_hidden_states(
|
||||
args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype
|
||||
)
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
else:
|
||||
target = noise
|
||||
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||||
loss = loss.mean([1, 2, 3])
|
||||
|
||||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||||
loss = loss * loss_weights
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
if train_text_encoder:
|
||||
params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
|
||||
else:
|
||||
params_to_clip = unet.parameters()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
|
||||
train_util.sample_images(
|
||||
accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_epoch_end(
|
||||
args,
|
||||
accelerator,
|
||||
src_path,
|
||||
save_stable_diffusion_format,
|
||||
use_safetensors,
|
||||
save_dtype,
|
||||
epoch,
|
||||
num_train_epochs,
|
||||
global_step,
|
||||
unwrap_model(text_encoder),
|
||||
unwrap_model(unet),
|
||||
vae,
|
||||
)
|
||||
|
||||
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
unet = unwrap_model(unet)
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
|
||||
if is_main_process:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_train_end(
|
||||
args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae
|
||||
)
|
||||
print("model saved.")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, True, False, True)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, True, False, True)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
|
||||
parser.add_argument("--no_token_padding", action="store_true",
|
||||
help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）")
|
||||
parser.add_argument("--stop_text_encoder_training", type=int, default=None,
|
||||
help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない")
|
||||
parser.add_argument(
|
||||
"--no_token_padding",
|
||||
action="store_true",
|
||||
help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--stop_text_encoder_training",
|
||||
type=int,
|
||||
default=None,
|
||||
help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
train(args)
|
||||
args = parser.parse_args()
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
|
|
|
|||
1186
train_network.py
1186
train_network.py
File diff suppressed because it is too large
Load Diff
|
|
@ -3,6 +3,7 @@ import argparse
|
|||
import gc
|
||||
import math
|
||||
import os
|
||||
import toml
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
|
|
@ -13,8 +14,8 @@ from diffusers import DDPMScheduler
|
|||
import library.train_util as train_util
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
|
||||
imagenet_templates_small = [
|
||||
|
|
@ -71,456 +72,500 @@ imagenet_style_templates_small = [
|
|||
|
||||
|
||||
def collate_fn(examples):
|
||||
return examples[0]
|
||||
return examples[0]
|
||||
|
||||
|
||||
def train(args):
|
||||
if args.output_name is None:
|
||||
args.output_name = args.token_string
|
||||
use_template = args.use_object_template or args.use_style_template
|
||||
if args.output_name is None:
|
||||
args.output_name = args.token_string
|
||||
use_template = args.use_object_template or args.use_style_template
|
||||
|
||||
train_util.verify_training_args(args)
|
||||
train_util.prepare_dataset_args(args, True)
|
||||
train_util.verify_training_args(args)
|
||||
train_util.prepare_dataset_args(args, True)
|
||||
|
||||
cache_latents = args.cache_latents
|
||||
cache_latents = args.cache_latents
|
||||
|
||||
if args.seed is not None:
|
||||
set_seed(args.seed)
|
||||
if args.seed is not None:
|
||||
set_seed(args.seed)
|
||||
|
||||
tokenizer = train_util.load_tokenizer(args)
|
||||
tokenizer = train_util.load_tokenizer(args)
|
||||
|
||||
# acceleratorを準備する
|
||||
print("prepare accelerator")
|
||||
accelerator, unwrap_model = train_util.prepare_accelerator(args)
|
||||
# acceleratorを準備する
|
||||
print("prepare accelerator")
|
||||
accelerator, unwrap_model = train_util.prepare_accelerator(args)
|
||||
|
||||
# mixed precisionに対応した型を用意しておき適宜castする
|
||||
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
||||
# mixed precisionに対応した型を用意しておき適宜castする
|
||||
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
||||
|
||||
# モデルを読み込む
|
||||
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
|
||||
# モデルを読み込む
|
||||
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
|
||||
|
||||
# Convert the init_word to token_id
|
||||
if args.init_word is not None:
|
||||
init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
|
||||
if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
|
||||
print(
|
||||
f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}")
|
||||
else:
|
||||
init_token_ids = None
|
||||
|
||||
# add new word to tokenizer, count is num_vectors_per_token
|
||||
token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
|
||||
num_added_tokens = tokenizer.add_tokens(token_strings)
|
||||
assert num_added_tokens == args.num_vectors_per_token, f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
|
||||
|
||||
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
|
||||
print(f"tokens are added: {token_ids}")
|
||||
assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
|
||||
assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
|
||||
|
||||
# Resize the token embeddings as we are adding new special tokens to the tokenizer
|
||||
text_encoder.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Initialise the newly added placeholder token with the embeddings of the initializer token
|
||||
token_embeds = text_encoder.get_input_embeddings().weight.data
|
||||
if init_token_ids is not None:
|
||||
for i, token_id in enumerate(token_ids):
|
||||
token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]]
|
||||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||||
|
||||
# load weights
|
||||
if args.weights is not None:
|
||||
embeddings = load_weights(args.weights)
|
||||
assert len(token_ids) == len(
|
||||
embeddings), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
|
||||
# print(token_ids, embeddings.size())
|
||||
for token_id, embedding in zip(token_ids, embeddings):
|
||||
token_embeds[token_id] = embedding
|
||||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||||
print(f"weighs loaded")
|
||||
|
||||
print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
|
||||
|
||||
# データセットを準備する
|
||||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
|
||||
if args.dataset_config is not None:
|
||||
print(f"Load dataset config from {args.dataset_config}")
|
||||
user_config = config_util.load_user_config(args.dataset_config)
|
||||
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
|
||||
if any(getattr(args, attr) is not None for attr in ignored):
|
||||
print("ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(', '.join(ignored)))
|
||||
else:
|
||||
use_dreambooth_method = args.in_json is None
|
||||
if use_dreambooth_method:
|
||||
print("Use DreamBooth method.")
|
||||
user_config = {
|
||||
"datasets": [{
|
||||
"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)
|
||||
}]
|
||||
}
|
||||
# Convert the init_word to token_id
|
||||
if args.init_word is not None:
|
||||
init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
|
||||
if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
|
||||
print(
|
||||
f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
|
||||
)
|
||||
else:
|
||||
print("Train with captions.")
|
||||
user_config = {
|
||||
"datasets": [{
|
||||
"subsets": [{
|
||||
"image_dir": args.train_data_dir,
|
||||
"metadata_file": args.in_json,
|
||||
}]
|
||||
}]
|
||||
}
|
||||
init_token_ids = None
|
||||
|
||||
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
|
||||
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
||||
# add new word to tokenizer, count is num_vectors_per_token
|
||||
token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
|
||||
num_added_tokens = tokenizer.add_tokens(token_strings)
|
||||
assert (
|
||||
num_added_tokens == args.num_vectors_per_token
|
||||
), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
|
||||
|
||||
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
|
||||
if use_template:
|
||||
print("use template for training captions. is object: {args.use_object_template}")
|
||||
templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
|
||||
replace_to = " ".join(token_strings)
|
||||
captions = []
|
||||
for tmpl in templates:
|
||||
captions.append(tmpl.format(replace_to))
|
||||
train_dataset_group.add_replacement("", captions)
|
||||
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
|
||||
print(f"tokens are added: {token_ids}")
|
||||
assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
|
||||
assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
|
||||
|
||||
if args.num_vectors_per_token > 1:
|
||||
prompt_replacement = (args.token_string, replace_to)
|
||||
# Resize the token embeddings as we are adding new special tokens to the tokenizer
|
||||
text_encoder.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Initialise the newly added placeholder token with the embeddings of the initializer token
|
||||
token_embeds = text_encoder.get_input_embeddings().weight.data
|
||||
if init_token_ids is not None:
|
||||
for i, token_id in enumerate(token_ids):
|
||||
token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]]
|
||||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||||
|
||||
# load weights
|
||||
if args.weights is not None:
|
||||
embeddings = load_weights(args.weights)
|
||||
assert len(token_ids) == len(
|
||||
embeddings
|
||||
), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
|
||||
# print(token_ids, embeddings.size())
|
||||
for token_id, embedding in zip(token_ids, embeddings):
|
||||
token_embeds[token_id] = embedding
|
||||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||||
print(f"weighs loaded")
|
||||
|
||||
print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
|
||||
|
||||
# データセットを準備する
|
||||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
|
||||
if args.dataset_config is not None:
|
||||
print(f"Load dataset config from {args.dataset_config}")
|
||||
user_config = config_util.load_user_config(args.dataset_config)
|
||||
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
|
||||
if any(getattr(args, attr) is not None for attr in ignored):
|
||||
print(
|
||||
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
|
||||
", ".join(ignored)
|
||||
)
|
||||
)
|
||||
else:
|
||||
prompt_replacement = None
|
||||
else:
|
||||
if args.num_vectors_per_token > 1:
|
||||
replace_to = " ".join(token_strings)
|
||||
train_dataset_group.add_replacement(args.token_string, replace_to)
|
||||
prompt_replacement = (args.token_string, replace_to)
|
||||
else:
|
||||
prompt_replacement = None
|
||||
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group, show_input_ids=True)
|
||||
return
|
||||
if len(train_dataset_group) == 0:
|
||||
print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
|
||||
return
|
||||
|
||||
if cache_latents:
|
||||
assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
|
||||
|
||||
# モデルに xformers とか memory efficient attention を組み込む
|
||||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||||
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
train_dataset_group.cache_latents(vae)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
trainable_params = text_encoder.get_input_embeddings().parameters()
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
||||
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset_group, batch_size=1, shuffle=True, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
|
||||
|
||||
# 学習ステップ数を計算する
|
||||
if args.max_train_epochs is not None:
|
||||
args.max_train_steps = args.max_train_epochs * len(train_dataloader)
|
||||
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
|
||||
|
||||
# lr schedulerを用意する
|
||||
lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
|
||||
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
||||
num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
|
||||
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
|
||||
# print(len(index_no_updates), torch.sum(index_no_updates))
|
||||
orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
|
||||
|
||||
# Freeze all parameters except for the token embeddings in text encoder
|
||||
text_encoder.requires_grad_(True)
|
||||
text_encoder.text_model.encoder.requires_grad_(False)
|
||||
text_encoder.text_model.final_layer_norm.requires_grad_(False)
|
||||
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
|
||||
# text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
|
||||
|
||||
unet.requires_grad_(False)
|
||||
unet.to(accelerator.device, dtype=weight_dtype)
|
||||
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
|
||||
unet.train()
|
||||
else:
|
||||
unet.eval()
|
||||
|
||||
if not cache_latents:
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f"resume training from state: {args.resume}")
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||||
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
|
||||
print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
|
||||
num_train_timesteps=1000, clip_sample=False)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("textual_inversion")
|
||||
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
train_dataset_group.set_current_epoch(epoch + 1)
|
||||
|
||||
text_encoder.train()
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(text_encoder):
|
||||
with torch.no_grad():
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
# latentに変換
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
# weight_dtype) use float instead of fp16/bf16 because text encoder is float
|
||||
encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
use_dreambooth_method = args.in_json is None
|
||||
if use_dreambooth_method:
|
||||
print("Use DreamBooth method.")
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
|
||||
]
|
||||
}
|
||||
else:
|
||||
target = noise
|
||||
print("Train with captions.")
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{
|
||||
"subsets": [
|
||||
{
|
||||
"image_dir": args.train_data_dir,
|
||||
"metadata_file": args.in_json,
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||||
loss = loss.mean([1, 2, 3])
|
||||
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
|
||||
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
||||
|
||||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||||
loss = loss * loss_weights
|
||||
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
|
||||
if use_template:
|
||||
print("use template for training captions. is object: {args.use_object_template}")
|
||||
templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
|
||||
replace_to = " ".join(token_strings)
|
||||
captions = []
|
||||
for tmpl in templates:
|
||||
captions.append(tmpl.format(replace_to))
|
||||
train_dataset_group.add_replacement("", captions)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
if args.num_vectors_per_token > 1:
|
||||
prompt_replacement = (args.token_string, replace_to)
|
||||
else:
|
||||
prompt_replacement = None
|
||||
else:
|
||||
if args.num_vectors_per_token > 1:
|
||||
replace_to = " ".join(token_strings)
|
||||
train_dataset_group.add_replacement(args.token_string, replace_to)
|
||||
prompt_replacement = (args.token_string, replace_to)
|
||||
else:
|
||||
prompt_replacement = None
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = text_encoder.get_input_embeddings().parameters()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group, show_input_ids=True)
|
||||
return
|
||||
if len(train_dataset_group) == 0:
|
||||
print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
|
||||
return
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
if cache_latents:
|
||||
assert (
|
||||
train_dataset_group.is_latent_cacheable()
|
||||
), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
|
||||
|
||||
# Let's make sure we don't update any embedding weights besides the newly added token
|
||||
# モデルに xformers とか memory efficient attention を組み込む
|
||||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||||
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[index_no_updates]
|
||||
train_dataset_group.cache_latents(vae)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
|
||||
train_util.sample_images(accelerator, args, None, global_step, accelerator.device,
|
||||
vae, tokenizer, text_encoder, unet, prompt_replacement)
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
trainable_params = text_encoder.get_input_embeddings().parameters()
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
|
||||
accelerator.log(logs, step=global_step)
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collate_fn,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step+1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
# 学習ステップ数を計算する
|
||||
if args.max_train_epochs is not None:
|
||||
args.max_train_steps = args.max_train_epochs * len(train_dataloader)
|
||||
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
# lr schedulerを用意する
|
||||
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch+1)
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
|
||||
# print(len(index_no_updates), torch.sum(index_no_updates))
|
||||
orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
|
||||
|
||||
updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
|
||||
# Freeze all parameters except for the token embeddings in text encoder
|
||||
text_encoder.requires_grad_(True)
|
||||
text_encoder.text_model.encoder.requires_grad_(False)
|
||||
text_encoder.text_model.final_layer_norm.requires_grad_(False)
|
||||
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
|
||||
# text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
|
||||
unet.requires_grad_(False)
|
||||
unet.to(accelerator.device, dtype=weight_dtype)
|
||||
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
|
||||
unet.train()
|
||||
else:
|
||||
unet.eval()
|
||||
|
||||
def save_func():
|
||||
ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + '.' + args.save_model_as
|
||||
if not cache_latents:
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f"resume training from state: {args.resume}")
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||||
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
|
||||
print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
noise_scheduler = DDPMScheduler(
|
||||
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
|
||||
)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("textual_inversion")
|
||||
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
train_dataset_group.set_current_epoch(epoch + 1)
|
||||
|
||||
text_encoder.train()
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(text_encoder):
|
||||
with torch.no_grad():
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
# latentに変換
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
# weight_dtype) use float instead of fp16/bf16 because text encoder is float
|
||||
encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
else:
|
||||
target = noise
|
||||
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||||
loss = loss.mean([1, 2, 3])
|
||||
|
||||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||||
loss = loss * loss_weights
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = text_encoder.get_input_embeddings().parameters()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
# Let's make sure we don't update any embedding weights besides the newly added token
|
||||
with torch.no_grad():
|
||||
unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[
|
||||
index_no_updates
|
||||
]
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
|
||||
train_util.sample_images(
|
||||
accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step + 1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
|
||||
|
||||
def save_func():
|
||||
ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as
|
||||
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
||||
print(f"saving checkpoint: {ckpt_file}")
|
||||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||||
|
||||
def remove_old_func(old_epoch_no):
|
||||
old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as
|
||||
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
|
||||
if os.path.exists(old_ckpt_file):
|
||||
print(f"removing old checkpoint: {old_ckpt_file}")
|
||||
os.remove(old_ckpt_file)
|
||||
|
||||
saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
|
||||
if saving and args.save_state:
|
||||
train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
|
||||
|
||||
train_util.sample_images(
|
||||
accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
|
||||
)
|
||||
|
||||
# end of epoch
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone()
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
|
||||
if is_main_process:
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
|
||||
ckpt_name = model_name + "." + args.save_model_as
|
||||
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
||||
print(f"saving checkpoint: {ckpt_file}")
|
||||
|
||||
print(f"save trained model to {ckpt_file}")
|
||||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||||
|
||||
def remove_old_func(old_epoch_no):
|
||||
old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + '.' + args.save_model_as
|
||||
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
|
||||
if os.path.exists(old_ckpt_file):
|
||||
print(f"removing old checkpoint: {old_ckpt_file}")
|
||||
os.remove(old_ckpt_file)
|
||||
|
||||
saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
|
||||
if saving and args.save_state:
|
||||
train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
|
||||
|
||||
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device,
|
||||
vae, tokenizer, text_encoder, unet, prompt_replacement)
|
||||
|
||||
# end of epoch
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone()
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
|
||||
if is_main_process:
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
|
||||
ckpt_name = model_name + '.' + args.save_model_as
|
||||
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
||||
|
||||
print(f"save trained model to {ckpt_file}")
|
||||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||||
print("model saved.")
|
||||
print("model saved.")
|
||||
|
||||
|
||||
def save_weights(file, updated_embs, save_dtype):
|
||||
state_dict = {"emb_params": updated_embs}
|
||||
state_dict = {"emb_params": updated_embs}
|
||||
|
||||
if save_dtype is not None:
|
||||
for key in list(state_dict.keys()):
|
||||
v = state_dict[key]
|
||||
v = v.detach().clone().to("cpu").to(save_dtype)
|
||||
state_dict[key] = v
|
||||
if save_dtype is not None:
|
||||
for key in list(state_dict.keys()):
|
||||
v = state_dict[key]
|
||||
v = v.detach().clone().to("cpu").to(save_dtype)
|
||||
state_dict[key] = v
|
||||
|
||||
if os.path.splitext(file)[1] == '.safetensors':
|
||||
from safetensors.torch import save_file
|
||||
save_file(state_dict, file)
|
||||
else:
|
||||
torch.save(state_dict, file) # can be loaded in Web UI
|
||||
if os.path.splitext(file)[1] == ".safetensors":
|
||||
from safetensors.torch import save_file
|
||||
|
||||
save_file(state_dict, file)
|
||||
else:
|
||||
torch.save(state_dict, file) # can be loaded in Web UI
|
||||
|
||||
|
||||
def load_weights(file):
|
||||
if os.path.splitext(file)[1] == '.safetensors':
|
||||
from safetensors.torch import load_file
|
||||
data = load_file(file)
|
||||
else:
|
||||
# compatible to Web UI's file format
|
||||
data = torch.load(file, map_location='cpu')
|
||||
if type(data) != dict:
|
||||
raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}")
|
||||
if os.path.splitext(file)[1] == ".safetensors":
|
||||
from safetensors.torch import load_file
|
||||
|
||||
if 'string_to_param' in data: # textual inversion embeddings
|
||||
data = data['string_to_param']
|
||||
if hasattr(data, '_parameters'): # support old PyTorch?
|
||||
data = getattr(data, '_parameters')
|
||||
data = load_file(file)
|
||||
else:
|
||||
# compatible to Web UI's file format
|
||||
data = torch.load(file, map_location="cpu")
|
||||
if type(data) != dict:
|
||||
raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}")
|
||||
|
||||
emb = next(iter(data.values()))
|
||||
if type(emb) != torch.Tensor:
|
||||
raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}")
|
||||
if "string_to_param" in data: # textual inversion embeddings
|
||||
data = data["string_to_param"]
|
||||
if hasattr(data, "_parameters"): # support old PyTorch?
|
||||
data = getattr(data, "_parameters")
|
||||
|
||||
if len(emb.size()) == 1:
|
||||
emb = emb.unsqueeze(0)
|
||||
emb = next(iter(data.values()))
|
||||
if type(emb) != torch.Tensor:
|
||||
raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}")
|
||||
|
||||
return emb
|
||||
if len(emb.size()) == 1:
|
||||
emb = emb.unsqueeze(0)
|
||||
|
||||
return emb
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, True, True, False)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, True, True, False)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
|
||||
parser.add_argument("--save_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"],
|
||||
help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）")
|
||||
parser.add_argument(
|
||||
"--save_model_as",
|
||||
type=str,
|
||||
default="pt",
|
||||
choices=[None, "ckpt", "pt", "safetensors"],
|
||||
help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）",
|
||||
)
|
||||
|
||||
parser.add_argument("--weights", type=str, default=None,
|
||||
help="embedding weights to initialize / 学習するネットワークの初期重み")
|
||||
parser.add_argument("--num_vectors_per_token", type=int, default=1,
|
||||
help='number of vectors per token / トークンに割り当てるembeddingsの要素数')
|
||||
parser.add_argument("--token_string", type=str, default=None,
|
||||
help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること")
|
||||
parser.add_argument("--init_word", type=str, default=None,
|
||||
help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
|
||||
parser.add_argument("--use_object_template", action='store_true',
|
||||
help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する")
|
||||
parser.add_argument("--use_style_template", action='store_true',
|
||||
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する")
|
||||
parser.add_argument("--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み")
|
||||
parser.add_argument(
|
||||
"--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--token_string",
|
||||
type=str,
|
||||
default=None,
|
||||
help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
|
||||
)
|
||||
parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
|
||||
parser.add_argument(
|
||||
"--use_object_template",
|
||||
action="store_true",
|
||||
help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_style_template",
|
||||
action="store_true",
|
||||
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
train(args)
|
||||
args = parser.parse_args()
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
|
|
|
|||
Loading…
Reference in New Issue