FIX [PEFT / Core] Copy the state dict when passing it to `load_lo…

…ra_weights` (huggingface#7058)

* copy the state dict in load lora weights

* fixup

src/diffusers/loaders/lora.py

@@ -106,6 +106,10 @@ def load_lora_weights(
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
 
+        # if a dict is passed, copy it instead of modifying it inplace
+        if isinstance(pretrained_model_name_or_path_or_dict, dict):
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
         # 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)
 
@@ -1229,6 +1233,10 @@ def load_lora_weights(
         # it here explicitly to be able to tell that it's coming from an SDXL
         # pipeline.
 
+        # if a dict is passed, copy it instead of modifying it inplace
+        if isinstance(pretrained_model_name_or_path_or_dict, dict):
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
         # 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,

tests/lora/test_lora_layers_peft.py

@@ -26,11 +26,13 @@
 from huggingface_hub import hf_hub_download
 from huggingface_hub.repocard import RepoCard
 from packaging import version
+from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
     AutoPipelineForImage2Image,
+    AutoPipelineForText2Image,
     ControlNetModel,
     DDIMScheduler,
     DiffusionPipeline,
@@ -1745,6 +1747,40 @@ def test_load_unload_load_kohya_lora(self):
         self.assertTrue(np.allclose(lora_images, lora_images_again, atol=1e-3))
         release_memory(pipe)
 
+    def test_not_empty_state_dict(self):
+        # Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+        ).to("cuda")
+        pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+        cached_file = hf_hub_download("hf-internal-testing/lcm-lora-test-sd-v1-5", "test_lora.safetensors")
+        lcm_lora = load_file(cached_file)
+
+        pipe.load_lora_weights(lcm_lora, adapter_name="lcm")
+        self.assertTrue(lcm_lora != {})
+        release_memory(pipe)
+
+    def test_load_unload_load_state_dict(self):
+        # Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+        ).to("cuda")
+        pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+        cached_file = hf_hub_download("hf-internal-testing/lcm-lora-test-sd-v1-5", "test_lora.safetensors")
+        lcm_lora = load_file(cached_file)
+        previous_state_dict = lcm_lora.copy()
+
+        pipe.load_lora_weights(lcm_lora, adapter_name="lcm")
+        self.assertDictEqual(lcm_lora, previous_state_dict)
+
+        pipe.unload_lora_weights()
+        pipe.load_lora_weights(lcm_lora, adapter_name="lcm")
+        self.assertDictEqual(lcm_lora, previous_state_dict)
+
+        release_memory(pipe)
+
 
 @slow
 @require_torch_gpu