change

lora_gui.py

@@ -904,7 +904,6 @@ def train_model(
 
     run_cmd += run_cmd_advanced_training(
         adaptive_noise_scale=adaptive_noise_scale,
-        additional_parameters=additional_parameters,
         bucket_no_upscale=bucket_no_upscale,
         bucket_reso_steps=bucket_reso_steps,
         cache_latents=cache_latents,
@@ -961,7 +960,6 @@ def train_model(
         noise_offset=noise_offset,
         noise_offset_type=noise_offset_type,
         optimizer=optimizer,
-        optimizer_args=optimizer_args,
         output_dir=output_dir,
         output_name=output_name,
         persistent_data_loader_workers=persistent_data_loader_workers,
@@ -1006,6 +1004,11 @@ def train_model(
         output_dir,
     )
 
+    run_cmd += run_cmd_advanced_training(
+        optimizer_args=optimizer_args,
+        additional_parameters=additional_parameters,
+    )
+
     if print_only_bool:
         log.warning(
             "Here is the trainer command as a reference. It will not be executed:\n"

requirements.txt

@@ -25,11 +25,9 @@
 # this is for onnx:
 # this is for onnx: 
 # timm==0.6.12ge
-accelerate==0.25.0
 aiofiles==23.2.1
 altair==4.2.2
 beartype
-bitsandbytes==0.43.0
 dadaptation==3.1
 diffusers[torch]==0.25.0
 easygui==0.98.3
@@ -41,7 +39,7 @@ huggingface-hub==0.20.1
 imagesize==1.4.1
 invisible-watermark==0.2.0
 lion-pytorch==0.0.6
-lycoris_lora==2.3.0.dev5
+lycoris_lora==2.3.0.dev4
 omegaconf==2.3.0
 onnx==1.14.1
 onnxruntime-gpu==1.16.0

train_network.py

@@ -540,7 +540,7 @@ def train(self, args):
                     for e in emas:
                         e.to(device=accelerator.device)
 
-            if args.optimizer_type.lower().endswith("scheduleFree"):
+            if args.optimizer_type.lower().endswith("schedulefree"):
                 network, optimizer, train_dataloader = accelerator.prepare(
                     network, optimizer, train_dataloader
                 )  
@@ -1026,7 +1026,7 @@ def remove_model(old_ckpt_name):
                             accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                     optimizer.step()
-                    if not args.optimizer_type.lower().endswith("scheduleFree"):
+                    if not args.optimizer_type.lower().endswith("schedulefree"):
                         lr_scheduler.step()
                     optimizer.zero_grad(set_to_none=True)