fix cond image normlization, add independent LR for control

library/sdxl_train_util.py

@@ -12,7 +12,6 @@ from accelerate import init_empty_weights
 from tqdm import tqdm
 from transformers import CLIPTokenizer
 from library import model_util, sdxl_model_util, train_util, sdxl_original_unet
-from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline
 from .utils import setup_logging
 
 setup_logging()
@@ -378,4 +377,6 @@ def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCachin
 
 
 def sample_images(*args, **kwargs):
+    from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline
+
     return train_util.sample_images_common(SdxlStableDiffusionLongPromptWeightingPipeline, *args, **kwargs)

library/train_util.py

@@ -31,6 +31,7 @@ import hashlib
 import subprocess
 from io import BytesIO
 import toml
+# from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from tqdm import tqdm
 
@@ -912,6 +913,23 @@ class BaseDataset(torch.utils.data.Dataset):
             if info.image_size is None:
                 info.image_size = self.get_image_size(info.absolute_path)
 
+        # # run in parallel
+        # max_workers = min(os.cpu_count(), len(self.image_data))  # TODO consider multi-gpu (processes)
+        # with ThreadPoolExecutor(max_workers) as executor:
+        #     futures = []
+        #     for info in tqdm(self.image_data.values(), desc="loading image sizes"):
+        #         if info.image_size is None:
+        #             def get_and_set_image_size(info):
+        #                 info.image_size = self.get_image_size(info.absolute_path)
+        #             futures.append(executor.submit(get_and_set_image_size, info))
+        #             # consume futures to reduce memory usage and prevent Ctrl-C hang
+        #             if len(futures) >= max_workers:
+        #                 for future in futures:
+        #                     future.result()
+        #                 futures = []
+        #     for future in futures:
+        #         future.result()
+
         if self.enable_bucket:
             logger.info("make buckets")
         else:
@@ -1826,7 +1844,7 @@ class DreamBoothDataset(BaseDataset):
                 # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う
                 captions = []
                 missing_captions = []
-                for img_path in img_paths:
+                for img_path in tqdm(img_paths, desc="read caption"):
                     cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard)
                     if cap_for_img is None and subset.class_tokens is None:
                         logger.warning(