Merge branch 'sd3' of https://github.com/kohya-ss/sd-scripts into sd3

# Conflicts:
#	sd3_train.py

.github/workflows/typos.yml

@@ -18,4 +18,4 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: typos-action
-        uses: crate-ci/typos@v1.21.0
+        uses: crate-ci/typos@v1.24.3

fine_tune.py

@@ -337,6 +337,9 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
     train_util.sample_images(
         accelerator, args, 0, global_step, accelerator.device, vae, tokenize_strategy.tokenizer, text_encoder, unet
     )
+    if len(accelerator.trackers) > 0:
+        # log empty object to commit the sample images to wandb
+        accelerator.log({}, step=0)
 
     loss_recorder = train_util.LossRecorder()
     for epoch in range(num_train_epochs):
@@ -456,7 +459,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
                         )
 
             current_loss = loss.detach().item()  # 平均なのでbatch sizeは関係ないはず
-            if args.logging_dir is not None:
+            if len(accelerator.trackers) > 0:
                 logs = {"loss": current_loss}
                 train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True)
                 accelerator.log(logs, step=global_step)
@@ -469,7 +472,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             if global_step >= args.max_train_steps:
                 break
 
-        if args.logging_dir is not None:
+        if len(accelerator.trackers) > 0:
             logs = {"loss/epoch": loss_recorder.moving_average}
             accelerator.log(logs, step=epoch + 1)
 

finetune/tag_images_by_wd14_tagger.py

@@ -11,7 +11,7 @@
 from tqdm import tqdm
 
 import library.train_util as train_util
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 
 setup_logging()
 import logging
@@ -42,8 +42,10 @@ def preprocess_image(image):
     pad_t = pad_y // 2
     image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)
 
-    interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
-    image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
+    if size > IMAGE_SIZE:
+        image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA)
+    else:
+        image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE))
 
     image = image.astype(np.float32)
     return image