Upload scripts/training/train_flux_lora.py with huggingface_hub

scripts/training/train_flux_lora.py

@@ -303,10 +303,18 @@ def main():
         num_training_steps=args.max_train_steps,
     )
 
-    # Fast-forward scheduler if resuming
-    if resume_step > 0:
-        for _ in range(resume_step):
-            lr_scheduler.step()
+    # Restore optimizer + scheduler state if resuming
+    if resume_step > 0 and resume_path:
+        training_state_path = resume_path / "training_state.pt"
+        if training_state_path.exists():
+            state = torch.load(str(training_state_path), map_location="cpu")
+            optimizer.load_state_dict(state["optimizer"])
+            lr_scheduler.load_state_dict(state["lr_scheduler"])
+            print(f"  Restored optimizer + scheduler state from checkpoint")
+        else:
+            print(f"  No training_state.pt found, fast-forwarding scheduler...")
+            for _ in range(resume_step):
+                lr_scheduler.step()
 
     # Dataset
     print(f"  Loading dataset from {args.data_dir}")