style

Author: linoytsaban

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -662,7 +662,8 @@ def parse_args(input_args=None):
         type=str,
         default=None,
         help=(
-            'The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. E.g. - "q_proj,k_proj,v_proj,out_proj" will result in lora training of attention layers only'),
+            'The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. E.g. - "q_proj,k_proj,v_proj,out_proj" will result in lora training of attention layers only'
+        ),
     )
     parser.add_argument(
         "--adam_epsilon",
@@ -1591,8 +1592,20 @@ def main(args):
     if args.lora_blocks is not None:
         target_modules = [block.strip() for block in args.lora_blocks.split(",")]
     else:
-        target_modules = ["to_k", "to_q", "to_v", "to_out.0",
-                        "add_k_proj", "add_q_proj", "add_v_proj", "to_add_out", "ff.net.0.proj","ff.net.2", "ff_context.net.0.proj","ff_context.net.2"]
+        target_modules = [
+            "to_k",
+            "to_q",
+            "to_v",
+            "to_out.0",
+            "add_k_proj",
+            "add_q_proj",
+            "add_v_proj",
+            "to_add_out",
+            "ff.net.0.proj",
+            "ff.net.2",
+            "ff_context.net.0.proj",
+            "ff_context.net.2",
+        ]
     # now we will add new LoRA weights to the attention layers
     transformer_lora_config = LoraConfig(
         r=args.rank,