support SD3 LoRA

train_network.py

@@ -129,6 +129,7 @@ class NetworkTrainer:
     def get_models_for_text_encoding(self, args, accelerator, text_encoders):
         """
         Returns a list of models that will be used for text encoding. SDXL uses wrapped and unwrapped models.
+        FLUX.1 and SD3 may cache some outputs of the text encoder, so return the models that will be used for encoding (not cached).
         """
         return text_encoders
 
@@ -591,6 +592,7 @@ class NetworkTrainer:
             # unet.to(accelerator.device)  # this makes faster `to(dtype)` below, but consumes 23 GB VRAM
             # unet.to(dtype=unet_weight_dtype)  # without moving to gpu, this takes a lot of time and main memory
 
+            logger.info(f"set U-Net weight dtype to {unet_weight_dtype}, device to {accelerator.device}")
             unet.to(accelerator.device, dtype=unet_weight_dtype)  # this seems to be safer than above
 
         unet.requires_grad_(False)