[QoL] Small fixes

src/diffusers/loaders.py

@@ -62,6 +62,7 @@
 
 LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
 LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
+TOTAL_EXAMPLE_KEYS = 5
 
 TEXT_INVERSION_NAME = "learned_embeds.bin"
 TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors"
@@ -187,6 +188,7 @@ def map_from(module, state_dict, *args, **kwargs):
 class UNet2DConditionLoadersMixin:
     text_encoder_name = TEXT_ENCODER_NAME
     unet_name = UNET_NAME
+    aux_state_dict_populated = None
 
     def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
         r"""
@@ -1062,6 +1064,7 @@ def load_lora_into_unet(cls, state_dict, network_alpha, unet, state_dict_aux=Non
 
         if state_dict_aux:
             unet._load_lora_aux(state_dict_aux, network_alpha=network_alpha)
+            unet.aux_state_dict_populated = True
 
     @classmethod
     def load_lora_into_text_encoder(cls, state_dict, network_alpha, text_encoder, lora_scale=1.0, state_dict_aux=None):
@@ -1314,9 +1317,12 @@ def _convert_kohya_lora_to_diffusers(cls, state_dict):
         unet_state_dict_aux = {}
         te_state_dict_aux = {}
         network_alpha = None
+        unloaded_keys = []
 
         for key, value in state_dict.items():
-            if "lora_down" in key:
+            if "hada" in key or "skip" in key:
+                unloaded_keys.append(key)
+            elif "lora_down" in key:
                 lora_name = key.split(".")[0]
                 lora_name_up = lora_name + ".lora_up.weight"
                 lora_name_alpha = lora_name + ".alpha"
@@ -1351,6 +1357,7 @@ def _convert_kohya_lora_to_diffusers(cls, state_dict):
                     elif any(key in diffusers_name for key in ("proj_in", "proj_out")):
                         unet_state_dict_aux[diffusers_name] = value
                         unet_state_dict_aux[diffusers_name.replace(".down.", ".up.")] = state_dict[lora_name_up]
+
                 elif lora_name.startswith("lora_te_"):
                     diffusers_name = key.replace("lora_te_", "").replace("_", ".")
                     diffusers_name = diffusers_name.replace("text.model", "text_model")
@@ -1366,6 +1373,13 @@ def _convert_kohya_lora_to_diffusers(cls, state_dict):
                         te_state_dict_aux[diffusers_name] = value
                         te_state_dict_aux[diffusers_name.replace(".down.", ".up.")] = state_dict[lora_name_up]
 
+        logger.info("Kohya-style checkpoint detected.")
+        if len(unloaded_keys) > 0:
+            example_unloaded_keys = ", ".join(x for x in unloaded_keys[:TOTAL_EXAMPLE_KEYS])
+            logger.warning(
+                f"There are some keys (such as: {example_unloaded_keys}) in the checkpoints we don't provide support for."
+            )
+
         unet_state_dict = {f"{UNET_NAME}.{module_name}": params for module_name, params in unet_state_dict.items()}
         te_state_dict = {f"{TEXT_ENCODER_NAME}.{module_name}": params for module_name, params in te_state_dict.items()}
         new_state_dict = {**unet_state_dict, **te_state_dict}
@@ -1400,6 +1414,11 @@ def unload_lora_weights(self):
             else:
                 self.unet.set_default_attn_processor()
 
+            if self.unet.aux_state_dict_populated:
+                for _, module in self.unet.named_modules():
+                    if hasattr(module, "old_forward") and module.old_forward is not None:
+                        module.forward = module.old_forward
+
         # Safe to call the following regardless of LoRA.
         self._remove_text_encoder_monkey_patch()
 

src/diffusers/models/lora.py

@@ -87,11 +87,13 @@ class Conv2dWithLoRA(nn.Conv2d):
     def __init__(self, *args, lora_layer: Optional[LoRAConv2dLayer] = None, **kwargs):
         super().__init__(*args, **kwargs)
         self.lora_layer = lora_layer
+        self.old_forward = None
 
     def forward(self, x):
         if self.lora_layer is None:
             return super().forward(x)
         else:
+            self.old_forward = super().forward
             return super().forward(x) + self.lora_layer(x)
 
 
@@ -103,9 +105,11 @@ class LinearWithLoRA(nn.Linear):
     def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
         super().__init__(*args, **kwargs)
         self.lora_layer = lora_layer
+        self.old_forward = None
 
     def forward(self, x):
         if self.lora_layer is None:
             return super().forward(x)
         else:
+            self.old_forward = super().forward
             return super().forward(x) + self.lora_layer(x)

tests/models/test_lora_layers.py

@@ -554,7 +554,7 @@ def test_a1111(self):
 
         images = images[0, -3:, -3:, -1].flatten()
 
-        expected = np.array([0.3743, 0.3893, 0.3835, 0.3891, 0.3949, 0.3649, 0.3858, 0.3802, 0.3245])
+        expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])
 
         self.assertTrue(np.allclose(images, expected, atol=1e-4))
 
@@ -594,6 +594,7 @@ def test_unload_lora(self):
         lora_filename = "Colored_Icons_by_vizsumit.safetensors"
 
         pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        generator = torch.manual_seed(0)
         lora_images = pipe(
             prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
         ).images