[LoRA PEFT] fix LoRA loading so that correct alphas are parsed

examples/dreambooth/train_dreambooth_lora.py

@@ -827,6 +827,7 @@ def main(args):
     # now we will add new LoRA weights to the attention layers
     unet_lora_config = LoraConfig(
         r=args.rank,
+        lora_alpha=args.rank,
         init_lora_weights="gaussian",
         target_modules=["to_k", "to_q", "to_v", "to_out.0", "add_k_proj", "add_v_proj"],
     )
@@ -835,7 +836,10 @@ def main(args):
     # The text encoder comes from 🤗 transformers, we will also attach adapters to it.
     if args.train_text_encoder:
         text_lora_config = LoraConfig(
-            r=args.rank, init_lora_weights="gaussian", target_modules=["q_proj", "k_proj", "v_proj", "out_proj"]
+            r=args.rank,
+            lora_alpha=args.rank,
+            init_lora_weights="gaussian",
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )
         text_encoder.add_adapter(text_lora_config)
 

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -978,15 +978,21 @@ def main(args):
 
     # now we will add new LoRA weights to the attention layers
     unet_lora_config = LoraConfig(
-        r=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"]
+        r=args.rank,
+        lora_alpha=args.rank,
+        init_lora_weights="gaussian",
+        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
     )
     unet.add_adapter(unet_lora_config)
 
     # The text encoder comes from 🤗 transformers, so we cannot directly modify it.
     # So, instead, we monkey-patch the forward calls of its attention-blocks.
     if args.train_text_encoder:
         text_lora_config = LoraConfig(
-            r=args.rank, init_lora_weights="gaussian", target_modules=["q_proj", "k_proj", "v_proj", "out_proj"]
+            r=args.rank,
+            lora_alpha=args.rank,
+            init_lora_weights="gaussian",
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )
         text_encoder_one.add_adapter(text_lora_config)
         text_encoder_two.add_adapter(text_lora_config)

examples/text_to_image/train_text_to_image_lora.py

@@ -452,7 +452,10 @@ def main():
         param.requires_grad_(False)
 
     unet_lora_config = LoraConfig(
-        r=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"]
+        r=args.rank,
+        lora_alpha=args.rank,
+        init_lora_weights="gaussian",
+        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
     )
 
     # Move unet, vae and text_encoder to device and cast to weight_dtype

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -609,7 +609,10 @@ def main(args):
     # now we will add new LoRA weights to the attention layers
     # Set correct lora layers
     unet_lora_config = LoraConfig(
-        r=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"]
+        r=args.rank,
+        lora_alpha=args.rank,
+        init_lora_weights="gaussian",
+        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
     )
 
     unet.add_adapter(unet_lora_config)
@@ -618,7 +621,10 @@ def main(args):
     if args.train_text_encoder:
         # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
         text_lora_config = LoraConfig(
-            r=args.rank, init_lora_weights="gaussian", target_modules=["q_proj", "k_proj", "v_proj", "out_proj"]
+            r=args.rank,
+            lora_alpha=args.rank,
+            init_lora_weights="gaussian",
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )
         text_encoder_one.add_adapter(text_lora_config)
         text_encoder_two.add_adapter(text_lora_config)

tests/lora/test_lora_layers_peft.py

@@ -111,6 +111,7 @@ class PeftLoraLoaderMixinTests:
 
     def get_dummy_components(self, scheduler_cls=None):
         scheduler_cls = self.scheduler_cls if scheduler_cls is None else LCMScheduler
+        rank = 4
 
         torch.manual_seed(0)
         unet = UNet2DConditionModel(**self.unet_kwargs)
@@ -125,11 +126,14 @@ def get_dummy_components(self, scheduler_cls=None):
             tokenizer_2 = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")
 
         text_lora_config = LoraConfig(
-            r=4, lora_alpha=4, target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], init_lora_weights=False
+            r=rank,
+            lora_alpha=rank,
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
+            init_lora_weights=False,
         )
 
         unet_lora_config = LoraConfig(
-            r=4, lora_alpha=4, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
+            r=rank, lora_alpha=rank, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
         )
 
         unet_lora_attn_procs, unet_lora_layers = create_unet_lora_layers(unet)