Merge branch 'sd3' into val-loss-improvement

library/model_util.py

@@ -643,16 +643,15 @@ def convert_ldm_clip_checkpoint_v2(checkpoint, max_length):
             new_sd[key_pfx + "k_proj" + key_suffix] = values[1]
             new_sd[key_pfx + "v_proj" + key_suffix] = values[2]
 
-    # rename or add position_ids
+    # remove position_ids for newer transformer, which causes error :(
     ANOTHER_POSITION_IDS_KEY = "text_model.encoder.text_model.embeddings.position_ids"
     if ANOTHER_POSITION_IDS_KEY in new_sd:
         # waifu diffusion v1.4
-        position_ids = new_sd[ANOTHER_POSITION_IDS_KEY]
         del new_sd[ANOTHER_POSITION_IDS_KEY]
-    else:
-        position_ids = torch.Tensor([list(range(max_length))]).to(torch.int64)
 
-    new_sd["text_model.embeddings.position_ids"] = position_ids
+    if "text_model.embeddings.position_ids" in new_sd:
+        del new_sd["text_model.embeddings.position_ids"]
+
     return new_sd
 
 

library/sdxl_train_util.py

@@ -344,8 +344,6 @@ def add_sdxl_training_arguments(parser: argparse.ArgumentParser, support_text_en
 
 def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCaching: bool = True):
     assert not args.v2, "v2 cannot be enabled in SDXL training / SDXL学習ではv2を有効にすることはできません"
-    if args.v_parameterization:
-        logger.warning("v_parameterization will be unexpected / SDXL学習ではv_parameterizationは想定外の動作になります")
 
     if args.clip_skip is not None:
         logger.warning("clip_skip will be unexpected / SDXL学習ではclip_skipは動作しません")

train_network.py

@@ -1207,10 +1207,6 @@ class NetworkTrainer:
                 args.max_train_steps > initial_step
             ), f"max_train_steps should be greater than initial step / max_train_stepsは初期ステップより大きい必要があります: {args.max_train_steps} vs {initial_step}"
 
-        progress_bar = tqdm(
-            range(args.max_train_steps - initial_step), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps"
-        )
-
         epoch_to_start = 0
         if initial_step > 0:
             if args.skip_until_initial_step:
@@ -1309,6 +1305,10 @@ class NetworkTrainer:
 
         clean_memory_on_device(accelerator.device)
 
+        progress_bar = tqdm(
+            range(args.max_train_steps - initial_step), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps"
+        )
+
         validation_steps = (
             min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
         )