Dev

.github/workflows/typos.yml

@@ -18,4 +18,4 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: typos-action
-        uses: crate-ci/typos@v1.19.0
+        uses: crate-ci/typos@v1.21.0

README.md

@@ -178,6 +178,12 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 - The ControlNet training script `train_controlnet.py` for SD1.5/2.x was not working, but it has been fixed. PR [#1284](https://github.com/kohya-ss/sd-scripts/pull/1284) Thanks to sdbds!
 
+- `train_network.py` and `sdxl_train_network.py` now restore the order/position of data loading from DataSet when resuming training. PR [#1353](https://github.com/kohya-ss/sd-scripts/pull/1353) [#1359](https://github.com/kohya-ss/sd-scripts/pull/1359) Thanks to KohakuBlueleaf!
+  - This resolves the issue where the order of data loading from DataSet changes when resuming training.
+  - Specify the `--skip_until_initial_step` option to skip data loading until the specified step. If not specified, data loading starts from the beginning of the DataSet (same as before).
+  - If `--resume` is specified, the step saved in the state is used.
+  - Specify the `--initial_step` or `--initial_epoch` option to skip data loading until the specified step or epoch. Use these options in conjunction with `--skip_until_initial_step`. These options can be used without `--resume` (use them when resuming training with `--network_weights`).
+
 - An option `--disable_mmap_load_safetensors` is added to disable memory mapping when loading the model's .safetensors in SDXL. PR [#1266](https://github.com/kohya-ss/sd-scripts/pull/1266) Thanks to Zovjsra!
   - It seems that the model file loading is faster in the WSL environment etc.
   - Available in `sdxl_train.py`, `sdxl_train_network.py`, `sdxl_train_textual_inversion.py`, and `sdxl_train_control_net_lllite.py`.
@@ -235,6 +241,12 @@ https://github.com/kohya-ss/sd-scripts/pull/1290) Thanks to frodo821!
 
 - SD1.5/2.x 用の ControlNet 学習スクリプト `train_controlnet.py` が動作しなくなっていたのが修正されました。PR [#1284](https://github.com/kohya-ss/sd-scripts/pull/1284) sdbds 氏に感謝します。
 
+- `train_network.py` および `sdxl_train_network.py` で、学習再開時に DataSet の読み込み順についても復元できるようになりました。PR [#1353](https://github.com/kohya-ss/sd-scripts/pull/1353) [#1359](https://github.com/kohya-ss/sd-scripts/pull/1359) KohakuBlueleaf 氏に感謝します。
+  - これにより、学習再開時に DataSet の読み込み順が変わってしまう問題が解消されます。
+  - `--skip_until_initial_step` オプションを指定すると、指定したステップまで DataSet 読み込みをスキップします。指定しない場合の動作は変わりません（DataSet の最初から読み込みます）
+  - `--resume` オプションを指定すると、state に保存されたステップ数が使用されます。
+  - `--initial_step` または `--initial_epoch` オプションを指定すると、指定したステップまたはエポックまで DataSet 読み込みをスキップします。これらのオプションは `--skip_until_initial_step` と併用してください。またこれらのオプションは `--resume` と併用しなくても使えます（`--network_weights` を用いた学習再開時などにお使いください ）。
+
 - SDXL でモデルの .safetensors を読み込む際にメモリマッピングを無効化するオプション `--disable_mmap_load_safetensors` が追加されました。PR [#1266](https://github.com/kohya-ss/sd-scripts/pull/1266) Zovjsra 氏に感謝します。
   - WSL 環境等でモデルファイルの読み込みが高速化されるようです。
   - `sdxl_train.py`、`sdxl_train_network.py`、`sdxl_train_textual_inversion.py`、`sdxl_train_control_net_lllite.py` で使用可能です。
@@ -253,6 +265,12 @@ https://github.com/kohya-ss/sd-scripts/pull/1290) frodo821 氏に感謝します
 
 - `gen_imgs.py` のプロンプトオプションに、保存時のファイル名を指定する `--f` オプションを追加しました。また同スクリプトで Diffusers ベースのキーを持つ LoRA の重みに対応しました。
 
+### Jun 23, 2024 / 2024-06-23: 
+
+- Fixed `cache_latents.py` and `cache_text_encoder_outputs.py` not working. (Will be included in the next release.)
+
+- `cache_latents.py` および `cache_text_encoder_outputs.py` が動作しなくなっていたのを修正しました。（次回リリースに含まれます。）
+
 ### Apr 7, 2024 / 2024-04-07: v0.8.7
 
 - The default value of `huber_schedule` in Scheduled Huber Loss is changed from `exponential` to `snr`, which is expected to give better results.

_typos.toml

@@ -2,6 +2,7 @@
 # Instruction:  https://github.com/marketplace/actions/typos-action#getting-started
 
 [default.extend-identifiers]
+ddPn08="ddPn08"
 
 [default.extend-words]
 NIN="NIN"
@@ -27,6 +28,7 @@ rik="rik"
 koo="koo"
 yos="yos"
 wn="wn"
+hime="hime"
 
 
 [files]

library/ipex/attention.py

@@ -5,7 +5,7 @@
 
 # pylint: disable=protected-access, missing-function-docstring, line-too-long
 
-# ARC GPUs can't allocate more than 4GB to a single block so we slice the attetion layers
+# ARC GPUs can't allocate more than 4GB to a single block so we slice the attention layers
 
 sdpa_slice_trigger_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 4))
 attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 4))

library/train_util.py

@@ -657,8 +657,16 @@ def set_caching_mode(self, mode):
 
     def set_current_epoch(self, epoch):
         if not self.current_epoch == epoch:  # epochが切り替わったらバケツをシャッフルする
-            self.shuffle_buckets()
-        self.current_epoch = epoch
+            if epoch > self.current_epoch:
+                logger.info("epoch is incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch))
+                num_epochs = epoch - self.current_epoch
+                for _ in range(num_epochs):
+                    self.current_epoch += 1
+                    self.shuffle_buckets()
+                # self.current_epoch seem to be set to 0 again in the next epoch. it may be caused by skipped_dataloader?
+            else:
+                logger.warning("epoch is not incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch))
+                self.current_epoch = epoch
 
     def set_current_step(self, step):
         self.current_step = step
@@ -1265,7 +1273,8 @@ def __getitem__(self, index):
                 if subset.alpha_mask:
                     if img.shape[2] == 4:
                         alpha_mask = img[:, :, 3]  # [H,W]
-                        alpha_mask = transforms.ToTensor()(alpha_mask)  # 0-255 -> 0-1
+                        alpha_mask = alpha_mask.astype(np.float32) / 255.0  # 0.0~1.0
+                        alpha_mask = torch.FloatTensor(alpha_mask)
                     else:
                         alpha_mask = torch.ones((img.shape[0], img.shape[1]), dtype=torch.float32)
                 else:
@@ -2211,7 +2220,7 @@ def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool, alph
 # 戻り値は、latents_tensor, (original_size width, original_size height), (crop left, crop top)
 def load_latents_from_disk(
     npz_path,
-) -> Tuple[Optional[torch.Tensor], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
+) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
     npz = np.load(npz_path)
     if "latents" not in npz:
         raise ValueError(f"error: npz is old format. please re-generate {npz_path}")
@@ -2229,7 +2238,7 @@ def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_ltrb, fli
     if flipped_latents_tensor is not None:
         kwargs["latents_flipped"] = flipped_latents_tensor.float().cpu().numpy()
     if alpha_mask is not None:
-        kwargs["alpha_mask"] = alpha_mask  # ndarray
+        kwargs["alpha_mask"] = alpha_mask.float().cpu().numpy()
     np.savez(
         npz_path,
         latents=latents_tensor.float().cpu().numpy(),
@@ -2425,16 +2434,20 @@ def load_arbitrary_dataset(args, tokenizer) -> MinimalDataset:
     return train_dataset_group
 
 
-def load_image(image_path, alpha=False):
-    image = Image.open(image_path)
-    if alpha:
-        if not image.mode == "RGBA":
-            image = image.convert("RGBA")
-    else:
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-    img = np.array(image, np.uint8)
-    return img
+def load_image(image_path, alpha=False):    
+    try:
+        with Image.open(image_path) as image:
+            if alpha:
+                if not image.mode == "RGBA":
+                    image = image.convert("RGBA")
+            else:
+                if not image.mode == "RGB":
+                    image = image.convert("RGB")
+            img = np.array(image, np.uint8)
+            return img
+    except (IOError, OSError) as e:
+        logger.error(f"Error loading file: {image_path}")
+        raise e
 
 
 # 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top, crop right, crop bottom)
@@ -2496,8 +2509,9 @@ def cache_batch_latents(
             if image.shape[2] == 4:
                 alpha_mask = image[:, :, 3]  # [H,W]
                 alpha_mask = alpha_mask.astype(np.float32) / 255.0
+                alpha_mask = torch.FloatTensor(alpha_mask)  # [H,W]
             else:
-                alpha_mask = np.ones_like(image[:, :, 0], dtype=np.float32)
+                alpha_mask = torch.ones_like(image[:, :, 0], dtype=torch.float32)  # [H,W]
         else:
             alpha_mask = None
         alpha_masks.append(alpha_mask)
@@ -5551,6 +5565,8 @@ def add(self, *, epoch: int, step: int, loss: float) -> None:
         if epoch == 0:
             self.loss_list.append(loss)
         else:
+            while len(self.loss_list) <= step:
+                self.loss_list.append(0.0)
             self.loss_total -= self.loss_list[step]
             self.loss_list[step] = loss
         self.loss_total += loss

networks/control_net_lllite_for_train.py

@@ -7,8 +7,10 @@
 import torch
 from library import sdxl_original_unet
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 # input_blocksに適用するかどうか / if True, input_blocks are not applied
@@ -103,19 +105,15 @@ def set_lllite(self, depth, cond_emb_dim, name, mlp_dim, dropout=None, multiplie
         add_lllite_modules(self, in_dim, depth, cond_emb_dim, mlp_dim)
 
         self.cond_image = None
-        self.cond_emb = None
 
     def set_cond_image(self, cond_image):
         self.cond_image = cond_image
-        self.cond_emb = None
 
     def forward(self, x):
         if not self.enabled:
             return super().forward(x)
 
-        if self.cond_emb is None:
-            self.cond_emb = self.lllite_conditioning1(self.cond_image)
-        cx = self.cond_emb
+        cx = self.lllite_conditioning1(self.cond_image)  # make forward and backward compatible
 
         # reshape / b,c,h,w -> b,h*w,c
         n, c, h, w = cx.shape
@@ -159,9 +157,7 @@ def forward(self, x):  # , cond_image=None):
         if not self.enabled:
             return super().forward(x)
 
-        if self.cond_emb is None:
-            self.cond_emb = self.lllite_conditioning1(self.cond_image)
-        cx = self.cond_emb
+        cx = self.lllite_conditioning1(self.cond_image)
 
         cx = torch.cat([cx, self.down(x)], dim=1)
         cx = self.mid(cx)

sdxl_train.py

@@ -481,6 +481,26 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             text_encoder2 = accelerator.prepare(text_encoder2)
         optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
 
+    # TextEncoderの出力をキャッシュするときにはCPUへ移動する
+    if args.cache_text_encoder_outputs:
+        # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
+        text_encoder1.to("cpu", dtype=torch.float32)
+        text_encoder2.to("cpu", dtype=torch.float32)
+        clean_memory_on_device(accelerator.device)
+    else:
+        # make sure Text Encoders are on GPU
+        text_encoder1.to(accelerator.device)
+        text_encoder2.to(accelerator.device)
+
+    # 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:
+        # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
+        # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+
+    # resumeする
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
+
     if args.fused_backward_pass:
         # use fused optimizer for backward pass: other optimizers will be supported in the future
         import library.adafactor_fused
@@ -532,26 +552,6 @@ def optimizer_hook(parameter: torch.Tensor):
                         parameter_optimizer_map[parameter] = opt_idx
                         num_parameters_per_group[opt_idx] += 1
 
-    # TextEncoderの出力をキャッシュするときにはCPUへ移動する
-    if args.cache_text_encoder_outputs:
-        # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
-        text_encoder1.to("cpu", dtype=torch.float32)
-        text_encoder2.to("cpu", dtype=torch.float32)
-        clean_memory_on_device(accelerator.device)
-    else:
-        # make sure Text Encoders are on GPU
-        text_encoder1.to(accelerator.device)
-        text_encoder2.to(accelerator.device)
-
-    # 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:
-        # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
-        # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
-        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)
@@ -589,7 +589,11 @@ def optimizer_hook(parameter: torch.Tensor):
             init_kwargs["wandb"] = {"name": args.wandb_run_name}
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
-        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs)
+        accelerator.init_trackers(
+            "finetuning" if args.log_tracker_name is None else args.log_tracker_name,
+            config=train_util.get_sanitized_config_or_none(args),
+            init_kwargs=init_kwargs,
+        )
 
     # For --sample_at_first
     sdxl_train_util.sample_images(

sdxl_train_control_net_lllite.py

@@ -289,6 +289,9 @@ def train(args):
     # acceleratorがなんかよろしくやってくれるらしい
     unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
 
+    if isinstance(unet, DDP):
+        unet._set_static_graph() # avoid error for multiple use of the parameter
+
     if args.gradient_checkpointing:
         unet.train()  # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる
     else:

tools/cache_latents.py

@@ -16,15 +16,15 @@
     ConfigSanitizer,
     BlueprintGenerator,
 )
-from library.utils import setup_logging
-
+from library.utils import setup_logging, add_logging_arguments
 setup_logging()
 import logging
 
 logger = logging.getLogger(__name__)
 
 
 def cache_to_disk(args: argparse.Namespace) -> None:
+    setup_logging(args, reset=True)
     train_util.prepare_dataset_args(args, True)
 
     # check cache latents arg
@@ -97,6 +97,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
 
     # acceleratorを準備する
     logger.info("prepare accelerator")
+    args.deepspeed = False
     accelerator = 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
@@ -176,6 +177,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
 def setup_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
 
+    add_logging_arguments(parser)
     train_util.add_sd_models_arguments(parser)
     train_util.add_training_arguments(parser, True)
     train_util.add_dataset_arguments(parser, True, True, True)

tools/cache_text_encoder_outputs.py

@@ -16,12 +16,13 @@
     ConfigSanitizer,
     BlueprintGenerator,
 )
-from library.utils import setup_logging
+from library.utils import setup_logging, add_logging_arguments
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
 
 def cache_to_disk(args: argparse.Namespace) -> None:
+    setup_logging(args, reset=True)
     train_util.prepare_dataset_args(args, True)
 
     # check cache arg
@@ -99,6 +100,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
 
     # acceleratorを準備する
     logger.info("prepare accelerator")
+    args.deepspeed = False
     accelerator = 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
@@ -171,6 +173,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
 def setup_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
 
+    add_logging_arguments(parser)
     train_util.add_sd_models_arguments(parser)
     train_util.add_training_arguments(parser, True)
     train_util.add_dataset_arguments(parser, True, True, True)