Added experimental concept replacer, replicate converter, bucket maker, and other goodies

extensions_built_in/concept_replacer/ConceptReplacer.py

@@ -0,0 +1,159 @@
+import random
+from collections import OrderedDict
+from torch.utils.data import DataLoader
+from toolkit.prompt_utils import concat_prompt_embeds, split_prompt_embeds
+from toolkit.stable_diffusion_model import StableDiffusion, BlankNetwork
+from toolkit.train_tools import get_torch_dtype, apply_snr_weight
+import gc
+import torch
+from jobs.process import BaseSDTrainProcess
+
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+
+
+class ConceptReplacementConfig:
+    def __init__(self, **kwargs):
+        self.concept: str = kwargs.get('concept', '')
+        self.replacement: str = kwargs.get('replacement', '')
+
+
+class ConceptReplacer(BaseSDTrainProcess):
+
+    def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
+        super().__init__(process_id, job, config, **kwargs)
+        replacement_list = self.config.get('replacements', [])
+        self.replacement_list = [ConceptReplacementConfig(**x) for x in replacement_list]
+
+    def before_model_load(self):
+        pass
+
+    def hook_before_train_loop(self):
+        self.sd.vae.eval()
+        self.sd.vae.to(self.device_torch)
+
+        # textual inversion
+        if self.embedding is not None:
+            # keep original embeddings as reference
+            self.orig_embeds_params = self.sd.text_encoder.get_input_embeddings().weight.data.clone()
+            # set text encoder to train. Not sure if this is necessary but diffusers example did it
+            self.sd.text_encoder.train()
+
+    def hook_train_loop(self, batch):
+        with torch.no_grad():
+            dtype = get_torch_dtype(self.train_config.dtype)
+            noisy_latents, noise, timesteps, conditioned_prompts, imgs = self.process_general_training_batch(batch)
+            network_weight_list = batch.get_network_weight_list()
+
+            # have a blank network so we can wrap it in a context and set multipliers without checking every time
+            if self.network is not None:
+                network = self.network
+            else:
+                network = BlankNetwork()
+
+            batch_replacement_list = []
+            # get a random replacement for each prompt
+            for prompt in conditioned_prompts:
+                replacement = random.choice(self.replacement_list)
+                batch_replacement_list.append(replacement)
+
+            # build out prompts
+            concept_prompts = []
+            replacement_prompts = []
+            for idx, replacement in enumerate(batch_replacement_list):
+                prompt = conditioned_prompts[idx]
+
+                # insert shuffled concept at beginning and end of prompt
+                shuffled_concept = [x.strip() for x in replacement.concept.split(',')]
+                random.shuffle(shuffled_concept)
+                shuffled_concept = ', '.join(shuffled_concept)
+                concept_prompts.append(f"{shuffled_concept}, {prompt}, {shuffled_concept}")
+
+                # insert replacement at beginning and end of prompt
+                shuffled_replacement = [x.strip() for x in replacement.replacement.split(',')]
+                random.shuffle(shuffled_replacement)
+                shuffled_replacement = ', '.join(shuffled_replacement)
+                replacement_prompts.append(f"{shuffled_replacement}, {prompt}, {shuffled_replacement}")
+
+            # predict the replacement without network
+            conditional_embeds = self.sd.encode_prompt(replacement_prompts).to(self.device_torch, dtype=dtype)
+
+            replacement_pred = self.sd.predict_noise(
+                latents=noisy_latents.to(self.device_torch, dtype=dtype),
+                conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
+                timestep=timesteps,
+                guidance_scale=1.0,
+            )
+
+            del conditional_embeds
+            replacement_pred = replacement_pred.detach()
+
+        self.optimizer.zero_grad()
+        flush()
+
+        # text encoding
+        grad_on_text_encoder = False
+        if self.train_config.train_text_encoder:
+            grad_on_text_encoder = True
+
+        if self.embedding:
+            grad_on_text_encoder = True
+
+        # set the weights
+        network.multiplier = network_weight_list
+
+        # activate network if it exits
+        with network:
+            with torch.set_grad_enabled(grad_on_text_encoder):
+                # embed the prompts
+                conditional_embeds = self.sd.encode_prompt(concept_prompts).to(self.device_torch, dtype=dtype)
+            if not grad_on_text_encoder:
+                # detach the embeddings
+                conditional_embeds = conditional_embeds.detach()
+                self.optimizer.zero_grad()
+                flush()
+
+            noise_pred = self.sd.predict_noise(
+                latents=noisy_latents.to(self.device_torch, dtype=dtype),
+                conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
+                timestep=timesteps,
+                guidance_scale=1.0,
+            )
+
+        loss = torch.nn.functional.mse_loss(noise_pred.float(), replacement_pred.float(), reduction="none")
+        loss = loss.mean([1, 2, 3])
+
+        if self.train_config.min_snr_gamma is not None and self.train_config.min_snr_gamma > 0.000001:
+            # add min_snr_gamma
+            loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.min_snr_gamma)
+
+        loss = loss.mean()
+
+        # back propagate loss to free ram
+        loss.backward()
+        flush()
+
+        # apply gradients
+        self.optimizer.step()
+        self.optimizer.zero_grad()
+        self.lr_scheduler.step()
+
+        if self.embedding is not None:
+            # Let's make sure we don't update any embedding weights besides the newly added token
+            index_no_updates = torch.ones((len(self.sd.tokenizer),), dtype=torch.bool)
+            index_no_updates[
+            min(self.embedding.placeholder_token_ids): max(self.embedding.placeholder_token_ids) + 1] = False
+            with torch.no_grad():
+                self.sd.text_encoder.get_input_embeddings().weight[
+                    index_no_updates
+                ] = self.orig_embeds_params[index_no_updates]
+
+        loss_dict = OrderedDict(
+            {'loss': loss.item()}
+        )
+        # reset network multiplier
+        network.multiplier = 1.0
+
+        return loss_dict

extensions_built_in/concept_replacer/__init__.py

@@ -0,0 +1,26 @@
+# This is an example extension for custom training. It is great for experimenting with new ideas.
+from toolkit.extension import Extension
+
+
+# This is for generic training (LoRA, Dreambooth, FineTuning)
+class ConceptReplacerExtension(Extension):
+    # uid must be unique, it is how the extension is identified
+    uid = "concept_replacer"
+
+    # name is the name of the extension for printing
+    name = "Concept Replacer"
+
+    # This is where your process class is loaded
+    # keep your imports in here so they don't slow down the rest of the program
+    @classmethod
+    def get_process(cls):
+        # import your process class here so it is only loaded when needed and return it
+        from .ConceptReplacer import ConceptReplacer
+        return ConceptReplacer
+
+
+
+AI_TOOLKIT_EXTENSIONS = [
+    # you can put a list of extensions here
+    ConceptReplacerExtension,
+]

extensions_built_in/concept_replacer/config/train.example.yaml

@@ -0,0 +1,91 @@
+---
+job: extension
+config:
+  name: test_v1
+  process:
+    - type: 'textual_inversion_trainer'
+      training_folder: "out/TI"
+      device: cuda:0
+      # for tensorboard logging
+      log_dir: "out/.tensorboard"
+      embedding:
+        trigger: "your_trigger_here"
+        tokens: 12
+        init_words: "man with short brown hair"
+        save_format: "safetensors"  # 'safetensors' or 'pt'
+      save:
+        dtype: float16 # precision to save
+        save_every: 100 # save every this many steps
+        max_step_saves_to_keep: 5 # only affects step counts
+      datasets:
+        - folder_path: "/path/to/dataset"
+          caption_ext: "txt"
+          default_caption: "[trigger]"
+          buckets: true
+          resolution: 512
+      train:
+        noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
+        steps: 3000
+        weight_jitter: 0.0
+        lr: 5e-5
+        train_unet: false
+        gradient_checkpointing: true
+        train_text_encoder: false
+        optimizer: "adamw"
+#        optimizer: "prodigy"
+        optimizer_params:
+          weight_decay: 1e-2
+        lr_scheduler: "constant"
+        max_denoising_steps: 1000
+        batch_size: 4
+        dtype: bf16
+        xformers: true
+        min_snr_gamma: 5.0
+#        skip_first_sample: true
+        noise_offset: 0.0 # not needed for this
+      model:
+        # objective reality v2
+        name_or_path: "https://civitai.com/models/128453?modelVersionId=142465"
+        is_v2: false  # for v2 models
+        is_xl: false  # for SDXL models
+        is_v_pred: false # for v-prediction models (most v2 models)
+      sample:
+        sampler: "ddpm" # must match train.noise_scheduler
+        sample_every: 100 # sample every this many steps
+        width: 512
+        height: 512
+        prompts:
+          - "photo of [trigger] laughing"
+          - "photo of [trigger] smiling"
+          - "[trigger] close up"
+          - "dark scene [trigger] frozen"
+          - "[trigger] nighttime"
+          - "a painting of [trigger]"
+          - "a drawing of [trigger]"
+          - "a cartoon of [trigger]"
+          - "[trigger] pixar style"
+          - "[trigger] costume"
+        neg: ""
+        seed: 42
+        walk_seed: false
+        guidance_scale: 7
+        sample_steps: 20
+        network_multiplier: 1.0
+
+      logging:
+        log_every: 10 # log every this many steps
+        use_wandb: false # not supported yet
+        verbose: false
+
+# You can put any information you want here, and it will be saved in the model.
+# The below is an example, but you can put your grocery list in it if you want.
+# It is saved in the model so be aware of that. The software will include this
+# plus some other information for you automatically
+meta:
+  # [name] gets replaced with the name above
+  name: "[name]"
+#  version: '1.0'
+#  creator:
+#    name: Your Name
+#    email: your@gmail.com
+#    website: https://your.website

scripts/convert_cog.py

@@ -0,0 +1,128 @@
+import json
+from collections import OrderedDict
+import os
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+device = torch.device('cpu')
+
+# [diffusers] -> kohya
+embedding_mapping = {
+    'text_encoders_0': 'clip_l',
+    'text_encoders_1': 'clip_g'
+}
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+KEYMAP_ROOT = os.path.join(PROJECT_ROOT, 'toolkit', 'keymaps')
+sdxl_keymap_path = os.path.join(KEYMAP_ROOT, 'stable_diffusion_locon_sdxl.json')
+
+# load keymap
+with open(sdxl_keymap_path, 'r') as f:
+    ldm_diffusers_keymap = json.load(f)['ldm_diffusers_keymap']
+
+# invert the item / key pairs
+diffusers_ldm_keymap = {v: k for k, v in ldm_diffusers_keymap.items()}
+
+
+def get_ldm_key(diffuser_key):
+    diffuser_key = f"lora_unet_{diffuser_key.replace('.', '_')}"
+    diffuser_key = diffuser_key.replace('_lora_down_weight', '.lora_down.weight')
+    diffuser_key = diffuser_key.replace('_lora_up_weight', '.lora_up.weight')
+    diffuser_key = diffuser_key.replace('_alpha', '.alpha')
+    diffuser_key = diffuser_key.replace('_processor_to_', '_to_')
+    diffuser_key = diffuser_key.replace('_to_out.', '_to_out_0.')
+    if diffuser_key in diffusers_ldm_keymap:
+        return diffusers_ldm_keymap[diffuser_key]
+    else:
+        raise KeyError(f"Key {diffuser_key} not found in keymap")
+
+
+def convert_cog(lora_path, embedding_path):
+    embedding_state_dict = OrderedDict()
+    lora_state_dict = OrderedDict()
+
+    # # normal dict
+    # normal_dict = OrderedDict()
+    # example_path = "/mnt/Models/stable-diffusion/models/LoRA/sdxl/LogoRedmond_LogoRedAF.safetensors"
+    # with safe_open(example_path, framework="pt", device='cpu') as f:
+    #     keys = list(f.keys())
+    #     for key in keys:
+    #         normal_dict[key] = f.get_tensor(key)
+
+    with safe_open(embedding_path, framework="pt", device='cpu') as f:
+        keys = list(f.keys())
+        for key in keys:
+            new_key = embedding_mapping[key]
+            embedding_state_dict[new_key] = f.get_tensor(key)
+
+    with safe_open(lora_path, framework="pt", device='cpu') as f:
+        keys = list(f.keys())
+        lora_rank = None
+
+        # get the lora dim first. Check first 3 linear layers just to be safe
+        for key in keys:
+            new_key = get_ldm_key(key)
+            tensor = f.get_tensor(key)
+            num_checked = 0
+            if len(tensor.shape) == 2:
+                this_dim = min(tensor.shape)
+                if lora_rank is None:
+                    lora_rank = this_dim
+                elif lora_rank != this_dim:
+                    raise ValueError(f"lora rank is not consistent, got {tensor.shape}")
+                else:
+                    num_checked += 1
+            if num_checked >= 3:
+                break
+
+        for key in keys:
+            new_key = get_ldm_key(key)
+            tensor = f.get_tensor(key)
+            if new_key.endswith('.lora_down.weight'):
+                alpha_key = new_key.replace('.lora_down.weight', '.alpha')
+                # diffusers does not have alpha, they usa an alpha multiplier of 1 which is a tensor weight of the dims
+                # assume first smallest dim is the lora rank if shape is 2
+                lora_state_dict[alpha_key] = torch.ones(1).to(tensor.device, tensor.dtype) * lora_rank
+
+            lora_state_dict[new_key] = tensor
+
+    return lora_state_dict, embedding_state_dict
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        'lora_path',
+        type=str,
+        help='Path to lora file'
+    )
+    parser.add_argument(
+        'embedding_path',
+        type=str,
+        help='Path to embedding file'
+    )
+
+    parser.add_argument(
+        '--lora_output',
+        type=str,
+        default="lora_output",
+    )
+
+    parser.add_argument(
+        '--embedding_output',
+        type=str,
+        default="embedding_output",
+    )
+
+    args = parser.parse_args()
+
+    lora_state_dict, embedding_state_dict = convert_cog(args.lora_path, args.embedding_path)
+
+    # save them
+    save_file(lora_state_dict, args.lora_output)
+    save_file(embedding_state_dict, args.embedding_output)
+    print(f"Saved lora to {args.lora_output}")
+    print(f"Saved embedding to {args.embedding_output}")

scripts/train_dreambooth.py

@@ -1,547 +0,0 @@
-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/buckets.py

@@ -0,0 +1,110 @@
+from typing import Type, List, Union
+
+BucketResolution = Type[{"width": int, "height": int}]
+
+# resolutions SDXL was trained on with a 1024x1024 base resolution
+resolutions_1024: List[BucketResolution] = [
+    # SDXL Base resolution
+    {"width": 1024, "height": 1024},
+    # SDXL Resolutions, widescreen
+    {"width": 2048, "height": 512},
+    {"width": 1984, "height": 512},
+    {"width": 1920, "height": 512},
+    {"width": 1856, "height": 512},
+    {"width": 1792, "height": 576},
+    {"width": 1728, "height": 576},
+    {"width": 1664, "height": 576},
+    {"width": 1600, "height": 640},
+    {"width": 1536, "height": 640},
+    {"width": 1472, "height": 704},
+    {"width": 1408, "height": 704},
+    {"width": 1344, "height": 704},
+    {"width": 1344, "height": 768},
+    {"width": 1280, "height": 768},
+    {"width": 1216, "height": 832},
+    {"width": 1152, "height": 832},
+    {"width": 1152, "height": 896},
+    {"width": 1088, "height": 896},
+    {"width": 1088, "height": 960},
+    {"width": 1024, "height": 960},
+    # SDXL Resolutions, portrait
+    {"width": 960, "height": 1024},
+    {"width": 960, "height": 1088},
+    {"width": 896, "height": 1088},
+    {"width": 896, "height": 1152},
+    {"width": 832, "height": 1152},
+    {"width": 832, "height": 1216},
+    {"width": 768, "height": 1280},
+    {"width": 768, "height": 1344},
+    {"width": 704, "height": 1408},
+    {"width": 704, "height": 1472},
+    {"width": 640, "height": 1536},
+    {"width": 640, "height": 1600},
+    {"width": 576, "height": 1664},
+    {"width": 576, "height": 1728},
+    {"width": 576, "height": 1792},
+    {"width": 512, "height": 1856},
+    {"width": 512, "height": 1920},
+    {"width": 512, "height": 1984},
+    {"width": 512, "height": 2048},
+]
+
+
+def get_bucket_sizes(resolution: int = 512, divisibility: int = 8) -> List[BucketResolution]:
+    # determine scaler form 1024 to resolution
+    scaler = resolution / 1024
+
+    bucket_size_list = []
+    for bucket in resolutions_1024:
+        # must be divisible by 8
+        width = int(bucket["width"] * scaler)
+        height = int(bucket["height"] * scaler)
+        if width % divisibility != 0:
+            width = width - (width % divisibility)
+        if height % divisibility != 0:
+            height = height - (height % divisibility)
+        bucket_size_list.append({"width": width, "height": height})
+
+    return bucket_size_list
+
+
+def get_bucket_for_image_size(
+        width: int,
+        height: int,
+        bucket_size_list: List[BucketResolution] = None,
+        resolution: Union[int, None] = None
+) -> BucketResolution:
+    if bucket_size_list is None and resolution is None:
+        raise ValueError("Must provide either bucket_size_list or resolution")
+    if bucket_size_list is None:
+        bucket_size_list = get_bucket_sizes(resolution=resolution)
+
+    # Check for exact match first
+    for bucket in bucket_size_list:
+        if bucket["width"] == width and bucket["height"] == height:
+            return bucket
+
+    # If exact match not found, find the closest bucket
+    closest_bucket = None
+    min_removed_pixels = float("inf")
+
+    for bucket in bucket_size_list:
+        scale_w = bucket["width"] / width
+        scale_h = bucket["height"] / height
+
+        # To minimize pixels, we use the larger scale factor to minimize the amount that has to be cropped.
+        scale = max(scale_w, scale_h)
+
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+
+        removed_pixels = (new_width - bucket["width"]) * new_height + (new_height - bucket["height"]) * new_width
+
+        if removed_pixels < min_removed_pixels:
+            min_removed_pixels = removed_pixels
+            closest_bucket = bucket
+
+    if closest_bucket is None:
+        raise ValueError("No suitable bucket found")
+
+    return closest_bucket

toolkit/lora_special.py

@@ -52,7 +52,7 @@ class LoRAModule(ToolkitModuleMixin, torch.nn.Module):
         self.lora_name = lora_name
         self.scalar = torch.tensor(1.0)
 
-        if org_module.__class__.__name__ == "Conv2d":
+        if org_module.__class__.__name__ in CONV_MODULES:
             in_dim = org_module.in_channels
             out_dim = org_module.out_channels
         else:
@@ -66,7 +66,7 @@ class LoRAModule(ToolkitModuleMixin, torch.nn.Module):
         # else:
         self.lora_dim = lora_dim
 
-        if org_module.__class__.__name__ == "Conv2d":
+        if org_module.__class__.__name__ in CONV_MODULES:
             kernel_size = org_module.kernel_size
             stride = org_module.stride
             padding = org_module.padding

toolkit/stable_diffusion_model.py

@@ -134,18 +134,7 @@ class StableDiffusion:
         # TODO handle other schedulers
         # sch = KDPM2DiscreteScheduler
         if self.noise_scheduler is None:
-            sch = DDPMScheduler
+            scheduler = get_sampler('ddpm')
-            # do our own scheduler
-            prediction_type = "v_prediction" if self.model_config.is_v_pred else "epsilon"
-            scheduler = sch(
-                num_train_timesteps=1000,
-                beta_start=0.00085,
-                beta_end=0.0120,
-                beta_schedule="scaled_linear",
-                clip_sample=False,
-                prediction_type=prediction_type,
-                steps_offset=0
-            )
             self.noise_scheduler = scheduler
 
         # move the betas alphas and  alphas_cumprod to device. Sometimed they get stuck on cpu, not sure why