Fix bug in LCM Distillation Scripts when args.unet_time_cond_proj_dim is used (#6523)

* Fix bug where unet's time_cond_proj_dim is not set correctly if using args.unet_time_cond_proj_dim.

* make style

examples/consistency_distillation/train_lcm_distill_sd_wds.py

@@ -921,10 +921,12 @@ def main(args):
 
     # 7. Create online student U-Net. This will be updated by the optimizer (e.g. via backpropagation.)
     # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None
-    if teacher_unet.config.time_cond_proj_dim is None:
-        teacher_unet.config["time_cond_proj_dim"] = args.unet_time_cond_proj_dim
-    time_cond_proj_dim = teacher_unet.config.time_cond_proj_dim
-    unet = UNet2DConditionModel(**teacher_unet.config)
+    time_cond_proj_dim = (
+        teacher_unet.config.time_cond_proj_dim
+        if teacher_unet.config.time_cond_proj_dim is not None
+        else args.unet_time_cond_proj_dim
+    )
+    unet = UNet2DConditionModel.from_config(teacher_unet.config, time_cond_proj_dim=time_cond_proj_dim)
     # load teacher_unet weights into unet
     unet.load_state_dict(teacher_unet.state_dict(), strict=False)
     unet.train()

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

@@ -980,10 +980,12 @@ def main(args):
 
     # 7. Create online student U-Net. This will be updated by the optimizer (e.g. via backpropagation.)
     # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None
-    if teacher_unet.config.time_cond_proj_dim is None:
-        teacher_unet.config["time_cond_proj_dim"] = args.unet_time_cond_proj_dim
-    time_cond_proj_dim = teacher_unet.config.time_cond_proj_dim
-    unet = UNet2DConditionModel(**teacher_unet.config)
+    time_cond_proj_dim = (
+        teacher_unet.config.time_cond_proj_dim
+        if teacher_unet.config.time_cond_proj_dim is not None
+        else args.unet_time_cond_proj_dim
+    )
+    unet = UNet2DConditionModel.from_config(teacher_unet.config, time_cond_proj_dim=time_cond_proj_dim)
     # load teacher_unet weights into unet
     unet.load_state_dict(teacher_unet.state_dict(), strict=False)
     unet.train()