[LoRA] Handle DoRA better (#9547)

* handle dora.

* print test

* debug

* fix

* fix-copies

* update logits

* add warning in the test.

* make is_dora check consistent.

* fix-copies

src/diffusers/loaders/lora_pipeline.py

@@ -99,7 +99,7 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
         state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
 
-        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+        is_correct_format = all("lora" in key for key in state_dict.keys())
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
@@ -211,6 +211,11 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
             user_agent=user_agent,
             allow_pickle=allow_pickle,
         )
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
 
         network_alphas = None
         # TODO: replace it with a method from `state_dict_utils`
@@ -562,7 +567,8 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
             unet_config=self.unet.config,
             **kwargs,
         )
-        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+
+        is_correct_format = all("lora" in key for key in state_dict.keys())
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
@@ -684,6 +690,11 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
             user_agent=user_agent,
             allow_pickle=allow_pickle,
         )
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
 
         network_alphas = None
         # TODO: replace it with a method from `state_dict_utils`
@@ -1089,6 +1100,12 @@ class SD3LoraLoaderMixin(LoraBaseMixin):
             allow_pickle=allow_pickle,
         )
 
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
+
         return state_dict
 
     def load_lora_weights(
@@ -1125,7 +1142,7 @@ class SD3LoraLoaderMixin(LoraBaseMixin):
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
         state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
 
-        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+        is_correct_format = all("lora" in key for key in state_dict.keys())
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
@@ -1587,9 +1604,13 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             user_agent=user_agent,
             allow_pickle=allow_pickle,
         )
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
 
         # TODO (sayakpaul): to a follow-up to clean and try to unify the conditions.
-
         is_kohya = any(".lora_down.weight" in k for k in state_dict)
         if is_kohya:
             state_dict = _convert_kohya_flux_lora_to_diffusers(state_dict)
@@ -1659,7 +1680,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             pretrained_model_name_or_path_or_dict, return_alphas=True, **kwargs
         )
 
-        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+        is_correct_format = all("lora" in key for key in state_dict.keys())
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
@@ -2374,6 +2395,12 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
             allow_pickle=allow_pickle,
         )
 
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
+
         return state_dict
 
     def load_lora_weights(
@@ -2405,7 +2432,7 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
         state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
 
-        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+        is_correct_format = all("lora" in key for key in state_dict.keys())
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 

tests/lora/test_lora_layers_sdxl.py

@@ -33,8 +33,10 @@ from diffusers import (
     StableDiffusionXLPipeline,
     T2IAdapter,
 )
+from diffusers.utils import logging
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    CaptureLogger,
     load_image,
     nightly,
     numpy_cosine_similarity_distance,
@@ -620,14 +622,18 @@ class LoraSDXLIntegrationTests(unittest.TestCase):
         pipeline.load_lora_weights("hf-internal-testing/dora-trained-on-kohya")
         pipeline.enable_model_cpu_offload()
 
-        images = pipeline(
-            "photo of ohwx dog",
-            num_inference_steps=10,
-            generator=torch.manual_seed(0),
-            output_type="np",
-        ).images
+        logger = logging.get_logger("diffusers.loaders.lora_pipeline")
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            images = pipeline(
+                "photo of ohwx dog",
+                num_inference_steps=10,
+                generator=torch.manual_seed(0),
+                output_type="np",
+            ).images
+        assert "It seems like you are using a DoRA checkpoint" in cap_logger.out
 
         predicted_slice = images[0, -3:, -3:, -1].flatten()
-        expected_slice_scale = np.array([0.3932, 0.3742, 0.4429, 0.3737, 0.3504, 0.433, 0.3948, 0.3769, 0.4516])
+        expected_slice_scale = np.array([0.1817, 0.0697, 0.2346, 0.0900, 0.1261, 0.2279, 0.1767, 0.1991, 0.2886])
         max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)
         assert max_diff < 1e-3