WIP implementing training

README.md

@@ -10,17 +10,25 @@ a general understanding of python, pip, pytorch, and using virtual environments:
 Linux:
 
 ```bash
+git submodule update --init --recursive
 pythion3 -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt
+cd requirements/sd-scripts
+pip install --no-deps -e .
+cd ../..
 ```
 
 Windows:
 
 ```bash
+git submodule update --init --recursive
 pythion3 -m venv venv
 venv\Scripts\activate
 pip install -r requirements.txt
+cd requirements/sd-scripts
+pip install --no-deps -e .
+cd ../..
 ```
 
 ## Current Tools

config/examples/train.example.json

@@ -5,7 +5,11 @@
     "base_model": "/path/to/base/model",
     "training_folder": "/path/to/output/folder",
     "is_v2": false,
-    "device": "cpu",
+    "device": "cuda",
+    "gradient_accumulation_steps": 1,
+    "mixed_precision": "fp16",
+    "logging_dir": "/path/to/tensorboard/log/folder",
+
     "process": [
       {
         "type": "fine_tune"

jobs/BaseJob.py

@@ -1,3 +1,4 @@
+import importlib
 from collections import OrderedDict
 from typing import List
 
@@ -48,6 +49,8 @@ class BaseJob:
         if len(self.config['process']) == 0:
             raise ValueError('config file is invalid. "config.process" must be a list of processes')
 
+        module = importlib.import_module('jobs.process')
+
         # add the processes
         self.process = []
         for i, process in enumerate(self.config['process']):
@@ -56,7 +59,8 @@ class BaseJob:
 
             # check if dict key is process type
             if process['type'] in process_dict:
-                self.process.append(process_dict[process['type']](i, self, process))
+                ProcessClass = getattr(module, process_dict[process['type']])
+                self.process.append(ProcessClass(i, self, process))
             else:
                 raise ValueError(f'config file is invalid. Unknown process type: {process["type"]}')
 

jobs/ExtractJob.py

@@ -1,19 +1,16 @@
 from toolkit.kohya_model_util import load_models_from_stable_diffusion_checkpoint
-from .BaseJob import BaseJob
 from collections import OrderedDict
 from typing import List
 
 from jobs.process import BaseExtractProcess
-
-from jobs.process import ExtractLoconProcess
+from jobs import BaseJob
 
 process_dict = {
-    'locon': ExtractLoconProcess,
+    'locon': 'ExtractLoconProcess',
 }
 
 
 class ExtractJob(BaseJob):
-    process: List[BaseExtractProcess]
 
     def __init__(self, config: OrderedDict):
         super().__init__(config)

jobs/TrainJob.py

@@ -1,38 +1,85 @@
-from toolkit.kohya_model_util import load_models_from_stable_diffusion_checkpoint
-from .BaseJob import BaseJob
-from collections import OrderedDict
-from typing import List
-
-from jobs.process import BaseExtractProcess, TrainFineTuneProcess
-
-process_dict = {
-    'fine_tine': TrainFineTuneProcess
-}
-
-
-class TrainJob(BaseJob):
-    process: List[BaseExtractProcess]
-
-    def __init__(self, config: OrderedDict):
-        super().__init__(config)
-        self.base_model_path = self.get_conf('base_model', required=True)
-        self.base_model = None
-        self.training_folder = self.get_conf('training_folder', required=True)
-        self.is_v2 = self.get_conf('is_v2', False)
-        self.device = self.get_conf('device', 'cpu')
-
-        # loads the processes from the config
-        self.load_processes(process_dict)
-
-    def run(self):
-        super().run()
-        # load models
-        print(f"Loading base model for training")
-        print(f" - Loading base model: {self.base_model_path}")
-        self.base_model = load_models_from_stable_diffusion_checkpoint(self.is_v2, self.base_model_path)
-
-        print("")
-        print(f"Running  {len(self.process)} process{'' if len(self.process) == 1 else 'es'}")
-
-        for process in self.process:
-            process.run()
+# from jobs import BaseJob
+# from toolkit.kohya_model_util import load_models_from_stable_diffusion_checkpoint
+# from collections import OrderedDict
+# from typing import List
+# from jobs.process import BaseExtractProcess, TrainFineTuneProcess
+# import gc
+# import time
+# import argparse
+# import itertools
+# import math
+# import os
+# from multiprocessing import Value
+#
+# from tqdm import tqdm
+# import torch
+# from accelerate.utils import set_seed
+# from accelerate import Accelerator
+# import diffusers
+# from diffusers import DDPMScheduler
+#
+# from toolkit.paths import SD_SCRIPTS_ROOT
+#
+# import sys
+#
+# sys.path.append(SD_SCRIPTS_ROOT)
+#
+# import library.train_util as train_util
+# import library.config_util as config_util
+# from library.config_util import (
+#     ConfigSanitizer,
+#     BlueprintGenerator,
+# )
+# import toolkit.train_tools as train_tools
+# import library.custom_train_functions as custom_train_functions
+# from library.custom_train_functions import (
+#     apply_snr_weight,
+#     get_weighted_text_embeddings,
+#     prepare_scheduler_for_custom_training,
+#     pyramid_noise_like,
+#     apply_noise_offset,
+#     scale_v_prediction_loss_like_noise_prediction,
+# )
+#
+# process_dict = {
+#     'fine_tine': 'TrainFineTuneProcess'
+# }
+#
+#
+# class TrainJob(BaseJob):
+#     process: List[BaseExtractProcess]
+#
+#     def __init__(self, config: OrderedDict):
+#         super().__init__(config)
+#         self.base_model_path = self.get_conf('base_model', required=True)
+#         self.base_model = None
+#         self.training_folder = self.get_conf('training_folder', required=True)
+#         self.is_v2 = self.get_conf('is_v2', False)
+#         self.device = self.get_conf('device', 'cpu')
+#         self.gradient_accumulation_steps = self.get_conf('gradient_accumulation_steps', 1)
+#         self.mixed_precision = self.get_conf('mixed_precision', False)  # fp16
+#         self.logging_dir = self.get_conf('logging_dir', None)
+#
+#         # loads the processes from the config
+#         self.load_processes(process_dict)
+#
+#         # setup accelerator
+#         self.accelerator = Accelerator(
+#             gradient_accumulation_steps=self.gradient_accumulation_steps,
+#             mixed_precision=self.mixed_precision,
+#             log_with=None if self.logging_dir is None else 'tensorboard',
+#             logging_dir=self.logging_dir,
+#         )
+#
+#     def run(self):
+#         super().run()
+#         # load models
+#         print(f"Loading base model for training")
+#         print(f" - Loading base model: {self.base_model_path}")
+#         self.base_model = load_models_from_stable_diffusion_checkpoint(self.is_v2, self.base_model_path)
+#
+#         print("")
+#         print(f"Running  {len(self.process)} process{'' if len(self.process) == 1 else 'es'}")
+#
+#         for process in self.process:
+#             process.run()

jobs/__init__.py

@@ -1,3 +1,2 @@
 from .BaseJob import BaseJob
 from .ExtractJob import ExtractJob
-from .TrainJob import TrainJob

jobs/process/BaseExtractProcess.py

@@ -3,13 +3,13 @@ from collections import OrderedDict
 
 from safetensors.torch import save_file
 
-from jobs import ExtractJob
 from jobs.process.BaseProcess import BaseProcess
 from toolkit.metadata import get_meta_for_safetensors
 
+from typing import ForwardRef
+
 
 class BaseExtractProcess(BaseProcess):
-    job: ExtractJob
     process_id: int
     config: OrderedDict
     output_folder: str
@@ -19,7 +19,7 @@ class BaseExtractProcess(BaseProcess):
     def __init__(
             self,
             process_id: int,
-            job: ExtractJob,
+            job,
             config: OrderedDict
     ):
         super().__init__(process_id, job, config)

jobs/process/BaseProcess.py

@@ -1,8 +1,7 @@
 import copy
 import json
 from collections import OrderedDict
-
-from jobs import BaseJob
+from typing import ForwardRef
 
 
 class BaseProcess:
@@ -11,7 +10,7 @@ class BaseProcess:
     def __init__(
             self,
             process_id: int,
-            job: BaseJob,
+            job: 'BaseJob',
             config: OrderedDict
     ):
         self.process_id = process_id
@@ -40,3 +39,5 @@ class BaseProcess:
     def add_meta(self, additional_meta: OrderedDict):
         self.meta.update(additional_meta)
 
+
+from jobs import BaseJob

jobs/process/BaseTrainProcess.py

@@ -1,17 +1,15 @@
 from collections import OrderedDict
-from jobs import TrainJob
 from jobs.process.BaseProcess import BaseProcess
 
 
 class BaseTrainProcess(BaseProcess):
-    job: TrainJob
     process_id: int
     config: OrderedDict
 
     def __init__(
             self,
             process_id: int,
-            job: TrainJob,
+            job,
             config: OrderedDict
     ):
         super().__init__(process_id, job, config)

jobs/process/ExtractLoconProcess.py

@@ -1,7 +1,6 @@
 from collections import OrderedDict
 from toolkit.lycoris_utils import extract_diff
 from .BaseExtractProcess import BaseExtractProcess
-from .. import ExtractJob
 
 mode_dict = {
     'fixed': {
@@ -28,7 +27,7 @@ mode_dict = {
 
 
 class ExtractLoconProcess(BaseExtractProcess):
-    def __init__(self, process_id: int, job: ExtractJob, config: OrderedDict):
+    def __init__(self, process_id: int, job, config: OrderedDict):
         super().__init__(process_id, job, config)
         self.mode = self.get_conf('mode', 'fixed')
         self.use_sparse_bias = self.get_conf('use_sparse_bias', False)

requirements.txt

@@ -3,4 +3,5 @@ safetensors
 diffusers
 transformers
 lycoris_lora
-flatten_json
+flatten_json
+accelerator

run.py

@@ -1,9 +1,5 @@
 import os
 import sys
-from collections import OrderedDict
-
-from jobs import BaseJob
-
 sys.path.insert(0, os.getcwd())
 import argparse
 from toolkit.job import get_job
@@ -49,6 +45,8 @@ def main():
     jobs_completed = 0
     jobs_failed = 0
 
+    print(f"Running {len(config_file_list)} job{'' if len(config_file_list) == 1 else 's'}")
+
     for config_file in config_file_list:
         try:
             job = get_job(config_file)

scripts/train_dreambooth.py

@@ -0,0 +1,547 @@
+import gc
+import time
+import argparse
+import itertools
+import math
+import os
+from multiprocessing import Value
+
+from tqdm import tqdm
+import torch
+from accelerate.utils import set_seed
+import diffusers
+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,
+)
+import custom_tools.train_tools as train_tools
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import (
+    apply_snr_weight,
+    get_weighted_text_embeddings,
+    prepare_scheduler_for_custom_training,
+    pyramid_noise_like,
+    apply_noise_offset,
+    scale_v_prediction_loss_like_noise_prediction,
+)
+
+# perlin_noise,
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+SD_SCRIPTS_ROOT = os.path.join(PROJECT_ROOT, "repositories", "sd-scripts")
+
+
+def train(args):
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, False)
+
+    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
+
+    tokenizer = train_util.load_tokenizer(args)
+
+    # データセットを準備する
+    if args.dataset_class is None:
+        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)
+    else:
+        train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer)
+
+    current_epoch = Value("i", 0)
+    current_step = Value("i", 0)
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
+
+    if args.no_token_padding:
+        train_dataset_group.disable_token_padding()
+
+    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"
+
+    # replace captions with names
+    if args.name_replace is not None:
+        print(f"Replacing captions [name] with '{args.name_replace}'")
+
+        train_dataset_group = train_tools.replace_filewords_in_dataset_group(
+            train_dataset_group, args
+        )
+
+    # 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"
+        )
+
+    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)
+
+    # モデルを読み込む
+    text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype, accelerator)
+
+    # 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())
+
+    # 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, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
+        vae.to("cpu")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()
+
+        accelerator.wait_for_everyone()
+
+    # 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=collater,
+        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 * math.ceil(
+            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
+        )
+        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}")
+
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(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, optimizer, accelerator.num_processes)
+
+    # 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)
+
+    # transform DDP after prepare
+    text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
+
+    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
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
+
+    # 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
+    )
+    prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
+
+    if accelerator.is_main_process:
+        accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name)
+
+    if args.sample_first or args.sample_only:
+        # Do initial sample before starting training
+        train_tools.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer,
+                                 text_encoder, unet, force_sample=True)
+
+    if args.sample_only:
+        return
+    loss_list = []
+    loss_total = 0.0
+    for epoch in range(num_train_epochs):
+        print(f"\nepoch {epoch+1}/{num_train_epochs}")
+        current_epoch.value = 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):
+            current_step.value = global_step
+            # 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):
+                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]
+
+                # Sample noise that we'll add to the latents
+                if args.train_noise_seed is not None:
+                    torch.manual_seed(args.train_noise_seed)
+                    torch.cuda.manual_seed(args.train_noise_seed)
+                    # make same seed for each item in the batch by stacking them
+                    single_noise = torch.randn_like(latents[0])
+                    noise = torch.stack([single_noise for _ in range(b_size)])
+                    noise = noise.to(latents.device)
+                elif args.seed_lock:
+                    noise = train_tools.get_noise_from_latents(latents)
+                else:
+                    noise = torch.randn_like(latents, device=latents.device)
+
+                if args.noise_offset:
+                    noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
+                elif args.multires_noise_iterations:
+                    noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
+                # elif args.perlin_noise:
+                #     noise = perlin_noise(noise, latents.device, args.perlin_noise)  # only shape of noise is used currently
+
+                # Get the text embedding for conditioning
+                with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
+                    if args.weighted_captions:
+                        encoder_hidden_states = get_weighted_text_embeddings(
+                            tokenizer,
+                            text_encoder,
+                            batch["captions"],
+                            accelerator.device,
+                            args.max_token_length // 75 if args.max_token_length else 1,
+                            clip_skip=args.clip_skip,
+                        )
+                    else:
+                        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
+                with accelerator.autocast():
+                    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
+
+                if args.min_snr_gamma:
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+                if args.scale_v_pred_loss_like_noise_pred:
+                    loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
+
+                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
+                )
+
+                # 指定ステップごとにモデルを保存
+                if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
+                    accelerator.wait_for_everyone()
+                    if accelerator.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_epoch_end_or_stepwise(
+                            args,
+                            False,
+                            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,
+                        )
+
+            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().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".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:
+            if accelerator.is_main_process:
+                # checking for saving is in util
+                src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
+                train_util.save_sd_model_on_epoch_end_or_stepwise(
+                    args,
+                    True,
+                    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 and is_main_process:
+        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.")
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    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)
+    custom_train_functions.add_custom_train_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(
+        "--sample_first",
+        action="store_true",
+        help="Sample first interval before training",
+        default=False
+    )
+
+    parser.add_argument(
+        "--name_replace",
+        type=str,
+        help="Replaces [name] in prompts. Used is sampling, training, and regs",
+        default=None
+    )
+
+    parser.add_argument(
+        "--train_noise_seed",
+        type=int,
+        help="Use custom seed for training noise",
+        default=None
+    )
+
+    parser.add_argument(
+        "--sample_only",
+        action="store_true",
+        help="Only generate samples. Used for generating training data with specific seeds to alter during training",
+        default=False
+    )
+
+    parser.add_argument(
+        "--seed_lock",
+        action="store_true",
+        help="Locks the seed to the latent images so the same latent will always have the same noise",
+        default=False
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    args = train_util.read_config_from_file(args, parser)
+
+    train(args)

toolkit/job.py

@@ -1,8 +1,7 @@
-from jobs import BaseJob
 from toolkit.config import get_config
 
 
-def get_job(config_path) -> BaseJob:
+def get_job(config_path):
     config = get_config(config_path)
     if not config['job']:
         raise ValueError('config file is invalid. Missing "job" key')
@@ -11,8 +10,8 @@ def get_job(config_path) -> BaseJob:
     if job == 'extract':
         from jobs import ExtractJob
         return ExtractJob(config)
-    elif job == 'train':
-        from jobs import TrainJob
-        return TrainJob(config)
+    # elif job == 'train':
+    #     from jobs import TrainJob
+    #     return TrainJob(config)
     else:
         raise ValueError(f'Unknown job type {job}')

toolkit/paths.py

@@ -2,3 +2,4 @@ import os
 
 TOOLKIT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 CONFIG_ROOT = os.path.join(TOOLKIT_ROOT, 'config')
+SD_SCRIPTS_ROOT = os.path.join(TOOLKIT_ROOT, "repositories", "sd-scripts")

toolkit/train_tools.py

@@ -0,0 +1,361 @@
+import argparse
+import json
+import os
+import time
+
+from diffusers import (
+    StableDiffusionPipeline,
+    DDPMScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDIMScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    KDPM2DiscreteScheduler,
+    KDPM2AncestralDiscreteScheduler,
+)
+from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline
+import torch
+import re
+
+SCHEDULER_LINEAR_START = 0.00085
+SCHEDULER_LINEAR_END = 0.0120
+SCHEDULER_TIMESTEPS = 1000
+SCHEDLER_SCHEDULE = "scaled_linear"
+
+
+def replace_filewords_prompt(prompt, args: argparse.Namespace):
+    # if name_replace attr in args (may not be)
+    if hasattr(args, "name_replace") and args.name_replace is not None:
+        # replace [name] to args.name_replace
+        prompt = prompt.replace("[name]", args.name_replace)
+    if hasattr(args, "prepend") and args.prepend is not None:
+        # prepend to every item in prompt file
+        prompt = args.prepend + ' ' + prompt
+    if hasattr(args, "append") and args.append is not None:
+        # append to every item in prompt file
+        prompt = prompt + ' ' + args.append
+    return prompt
+
+
+def replace_filewords_in_dataset_group(dataset_group, args: argparse.Namespace):
+    # if name_replace attr in args (may not be)
+    if hasattr(args, "name_replace") and args.name_replace is not None:
+        if not len(dataset_group.image_data) > 0:
+            # throw error
+            raise ValueError("dataset_group.image_data is empty")
+        for key in dataset_group.image_data:
+            dataset_group.image_data[key].caption = dataset_group.image_data[key].caption.replace(
+                "[name]", args.name_replace)
+
+    return dataset_group
+
+
+def get_seeds_from_latents(latents):
+    # latents shape = (batch_size, 4, height, width)
+    # for speed we only use 8x8 slice of the first channel
+    seeds = []
+
+    # split batch up
+    for i in range(latents.shape[0]):
+        # use only first channel, multiply by 255 and convert to int
+        tensor = latents[i, 0, :, :] * 255.0  # shape = (height, width)
+        # slice 8x8
+        tensor = tensor[:8, :8]
+        # clip to 0-255
+        tensor = torch.clamp(tensor, 0, 255)
+        # convert to 8bit int
+        tensor = tensor.to(torch.uint8)
+        # convert to bytes
+        tensor_bytes = tensor.cpu().numpy().tobytes()
+        # hash
+        hash_object = hashlib.sha256(tensor_bytes)
+        # get hex
+        hex_dig = hash_object.hexdigest()
+        # convert to int
+        seed = int(hex_dig, 16) % (2 ** 32)
+        # append
+        seeds.append(seed)
+    return seeds
+
+
+def get_noise_from_latents(latents):
+    seed_list = get_seeds_from_latents(latents)
+    noise = []
+    for seed in seed_list:
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        noise.append(torch.randn_like(latents[0]))
+    return torch.stack(noise)
+
+
+# mix 0 is completely noise mean, mix 1 is completely target mean
+
+def match_noise_to_target_mean_offset(noise, target, mix=0.5, dim=None):
+    dim = dim or (1, 2, 3)
+    # reduce mean of noise on dim 2, 3, keeping 0 and 1 intact
+    noise_mean = noise.mean(dim=dim, keepdim=True)
+    target_mean = target.mean(dim=dim, keepdim=True)
+
+    new_noise_mean = mix * target_mean + (1 - mix) * noise_mean
+
+    noise = noise - noise_mean + new_noise_mean
+    return noise
+
+
+def sample_images(
+        accelerator,
+        args: argparse.Namespace,
+        epoch,
+        steps,
+        device,
+        vae,
+        tokenizer,
+        text_encoder,
+        unet,
+        prompt_replacement=None,
+        force_sample=False
+):
+    """
+    StableDiffusionLongPromptWeightingPipelineの改造版を使うようにしたので、clip skipおよびプロンプトの重みづけに対応した
+    """
+    if not force_sample:
+        if args.sample_every_n_steps is None and args.sample_every_n_epochs is None:
+            return
+        if args.sample_every_n_epochs is not None:
+            # sample_every_n_steps は無視する
+            if epoch is None or epoch % args.sample_every_n_epochs != 0:
+                return
+        else:
+            if steps % args.sample_every_n_steps != 0 or epoch is not None:  # steps is not divisible or end of epoch
+                return
+
+    is_sample_only = args.sample_only
+    is_generating_only = hasattr(args, "is_generating_only") and args.is_generating_only
+
+    print(f"\ngenerating sample images at step / サンプル画像生成 ステップ: {steps}")
+    if not os.path.isfile(args.sample_prompts):
+        print(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}")
+        return
+
+    org_vae_device = vae.device  # CPUにいるはず
+    vae.to(device)
+
+    # read prompts
+
+    # with open(args.sample_prompts, "rt", encoding="utf-8") as f:
+    #     prompts = f.readlines()
+
+    if args.sample_prompts.endswith(".txt"):
+        with open(args.sample_prompts, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+        prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"]
+    elif args.sample_prompts.endswith(".json"):
+        with open(args.sample_prompts, "r", encoding="utf-8") as f:
+            prompts = json.load(f)
+
+    # schedulerを用意する
+    sched_init_args = {}
+    if args.sample_sampler == "ddim":
+        scheduler_cls = DDIMScheduler
+    elif args.sample_sampler == "ddpm":  # ddpmはおかしくなるのでoptionから外してある
+        scheduler_cls = DDPMScheduler
+    elif args.sample_sampler == "pndm":
+        scheduler_cls = PNDMScheduler
+    elif args.sample_sampler == "lms" or args.sample_sampler == "k_lms":
+        scheduler_cls = LMSDiscreteScheduler
+    elif args.sample_sampler == "euler" or args.sample_sampler == "k_euler":
+        scheduler_cls = EulerDiscreteScheduler
+    elif args.sample_sampler == "euler_a" or args.sample_sampler == "k_euler_a":
+        scheduler_cls = EulerAncestralDiscreteScheduler
+    elif args.sample_sampler == "dpmsolver" or args.sample_sampler == "dpmsolver++":
+        scheduler_cls = DPMSolverMultistepScheduler
+        sched_init_args["algorithm_type"] = args.sample_sampler
+    elif args.sample_sampler == "dpmsingle":
+        scheduler_cls = DPMSolverSinglestepScheduler
+    elif args.sample_sampler == "heun":
+        scheduler_cls = HeunDiscreteScheduler
+    elif args.sample_sampler == "dpm_2" or args.sample_sampler == "k_dpm_2":
+        scheduler_cls = KDPM2DiscreteScheduler
+    elif args.sample_sampler == "dpm_2_a" or args.sample_sampler == "k_dpm_2_a":
+        scheduler_cls = KDPM2AncestralDiscreteScheduler
+    else:
+        scheduler_cls = DDIMScheduler
+
+    if args.v_parameterization:
+        sched_init_args["prediction_type"] = "v_prediction"
+
+    scheduler = scheduler_cls(
+        num_train_timesteps=SCHEDULER_TIMESTEPS,
+        beta_start=SCHEDULER_LINEAR_START,
+        beta_end=SCHEDULER_LINEAR_END,
+        beta_schedule=SCHEDLER_SCHEDULE,
+        **sched_init_args,
+    )
+
+    # clip_sample=Trueにする
+    if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
+        # print("set clip_sample to True")
+        scheduler.config.clip_sample = True
+
+    pipeline = StableDiffusionLongPromptWeightingPipeline(
+        text_encoder=text_encoder,
+        vae=vae,
+        unet=unet,
+        tokenizer=tokenizer,
+        scheduler=scheduler,
+        clip_skip=args.clip_skip,
+        safety_checker=None,
+        feature_extractor=None,
+        requires_safety_checker=False,
+    )
+    pipeline.to(device)
+
+    if is_generating_only:
+        save_dir = args.output_dir
+    else:
+        save_dir = args.output_dir + "/sample"
+    os.makedirs(save_dir, exist_ok=True)
+
+    rng_state = torch.get_rng_state()
+    cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
+
+    with torch.no_grad():
+        with accelerator.autocast():
+            for i, prompt in enumerate(prompts):
+                if not accelerator.is_main_process:
+                    continue
+
+                if isinstance(prompt, dict):
+                    negative_prompt = prompt.get("negative_prompt")
+                    sample_steps = prompt.get("sample_steps", 30)
+                    width = prompt.get("width", 512)
+                    height = prompt.get("height", 512)
+                    scale = prompt.get("scale", 7.5)
+                    seed = prompt.get("seed")
+                    prompt = prompt.get("prompt")
+
+                    prompt = replace_filewords_prompt(prompt, args)
+                    negative_prompt = replace_filewords_prompt(negative_prompt, args)
+                else:
+                    prompt = replace_filewords_prompt(prompt, args)
+                    # prompt = prompt.strip()
+                    # if len(prompt) == 0 or prompt[0] == "#":
+                    #     continue
+
+                    # subset of gen_img_diffusers
+                    prompt_args = prompt.split(" --")
+                    prompt = prompt_args[0]
+                    negative_prompt = None
+                    sample_steps = 30
+                    width = height = 512
+                    scale = 7.5
+                    seed = None
+                    for parg in prompt_args:
+                        try:
+                            m = re.match(r"w (\d+)", parg, re.IGNORECASE)
+                            if m:
+                                width = int(m.group(1))
+                                continue
+
+                            m = re.match(r"h (\d+)", parg, re.IGNORECASE)
+                            if m:
+                                height = int(m.group(1))
+                                continue
+
+                            m = re.match(r"d (\d+)", parg, re.IGNORECASE)
+                            if m:
+                                seed = int(m.group(1))
+                                continue
+
+                            m = re.match(r"s (\d+)", parg, re.IGNORECASE)
+                            if m:  # steps
+                                sample_steps = max(1, min(1000, int(m.group(1))))
+                                continue
+
+                            m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
+                            if m:  # scale
+                                scale = float(m.group(1))
+                                continue
+
+                            m = re.match(r"n (.+)", parg, re.IGNORECASE)
+                            if m:  # negative prompt
+                                negative_prompt = m.group(1)
+                                continue
+
+                        except ValueError as ex:
+                            print(f"Exception in parsing / 解析エラー: {parg}")
+                            print(ex)
+
+                if seed is not None:
+                    torch.manual_seed(seed)
+                    torch.cuda.manual_seed(seed)
+
+                if prompt_replacement is not None:
+                    prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
+                    if negative_prompt is not None:
+                        negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
+
+                height = max(64, height - height % 8)  # round to divisible by 8
+                width = max(64, width - width % 8)  # round to divisible by 8
+                print(f"prompt: {prompt}")
+                print(f"negative_prompt: {negative_prompt}")
+                print(f"height: {height}")
+                print(f"width: {width}")
+                print(f"sample_steps: {sample_steps}")
+                print(f"scale: {scale}")
+                image = pipeline(
+                    prompt=prompt,
+                    height=height,
+                    width=width,
+                    num_inference_steps=sample_steps,
+                    guidance_scale=scale,
+                    negative_prompt=negative_prompt,
+                ).images[0]
+
+                ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
+                num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
+                seed_suffix = "" if seed is None else f"_{seed}"
+
+                if is_generating_only:
+                    img_filename = (
+                        f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{num_suffix}_{i:02d}{seed_suffix}.png"
+                    )
+                else:
+                    img_filename = (
+                        f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{i:04d}{seed_suffix}.png"
+                    )
+                if is_sample_only:
+                    # make prompt txt file
+                    img_path_no_ext = os.path.join(save_dir, img_filename[:-4])
+                    with open(img_path_no_ext + ".txt", "w") as f:
+                        # put prompt in txt file
+                        f.write(prompt)
+                        # close file
+                        f.close()
+
+                image.save(os.path.join(save_dir, img_filename))
+
+                # wandb有効時のみログを送信
+                try:
+                    wandb_tracker = accelerator.get_tracker("wandb")
+                    try:
+                        import wandb
+                    except ImportError:  # 事前に一度確認するのでここはエラー出ないはず
+                        raise ImportError("No wandb / wandb がインストールされていないようです")
+
+                    wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
+                except:  # wandb 無効時
+                    pass
+
+    # clear pipeline and cache to reduce vram usage
+    del pipeline
+    torch.cuda.empty_cache()
+
+    torch.set_rng_state(rng_state)
+    if cuda_rng_state is not None:
+        torch.cuda.set_rng_state(cuda_rng_state)
+    vae.to(org_vae_device)