dora

src/diffusers/loaders/lora_conversion_utils.py

@@ -153,12 +153,9 @@ def _convert_non_diffusers_lora_to_diffusers(state_dict, unet_name="unet", text_
                 "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
             )
 
-    # Iterate over all LoRA weights.
-    all_lora_keys = list(state_dict.keys())
-    for key in all_lora_keys:
-        if not key.endswith("lora_down.weight"):
-            continue
-
+    # every down weight has a corresponding up weight and potentially an alpha weight
+    lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
+    for key in lora_keys:
         # Extract LoRA name.
         lora_name = key.split(".")[0]
 
@@ -177,9 +174,12 @@ def _convert_non_diffusers_lora_to_diffusers(state_dict, unet_name="unet", text_
             # Store DoRA scale if present.
             if dora_present_in_unet:
                 dora_scale_key_to_replace = "_lora.down." if "_lora.down." in diffusers_name else ".lora.down."
-                unet_state_dict[
-                    diffusers_name.replace(dora_scale_key_to_replace, ".lora_magnitude_vector.")
-                ] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                new_key = diffusers_name.replace(dora_scale_key_to_replace, ".lora_magnitude_vector.")
+                # dora_weight = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                dora_weight = state_dict.pop(lora_name + ".dora_scale")
+                if dora_weight.dim() <= 2:
+                    dora_weight = dora_weight.squeeze()
+                unet_state_dict[new_key] = dora_weight
 
         # Handle text encoder LoRAs.
         elif lora_name.startswith(("lora_te_", "lora_te1_", "lora_te2_")):
@@ -194,18 +194,24 @@ def _convert_non_diffusers_lora_to_diffusers(state_dict, unet_name="unet", text_
                 te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
 
             # Store DoRA scale if present.
-            if dora_present_in_te or dora_present_in_te2:
+            if (dora_present_in_te or dora_present_in_te2):
                 dora_scale_key_to_replace_te = (
                     "_lora.down." if "_lora.down." in diffusers_name else ".lora_linear_layer."
                 )
                 if lora_name.startswith(("lora_te_", "lora_te1_")):
-                    te_state_dict[
-                        diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
-                    ] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                    new_key = diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
+                    # dora_weight = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                    dora_weight = state_dict.pop(lora_name + ".dora_scale")
+                    if dora_weight.dim() <= 2:
+                        dora_weight = dora_weight.squeeze()
+                    te_state_dict[new_key] = dora_weight
                 elif lora_name.startswith("lora_te2_"):
-                    te2_state_dict[
-                        diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
-                    ] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                    new_key = diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
+                    # dora_weight = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                    dora_weight = state_dict.pop(lora_name + ".dora_scale")
+                    if dora_weight.dim() <= 2:
+                        dora_weight = dora_weight.squeeze()
+                    te2_state_dict[new_key] = dora_weight
 
         # Store alpha if present.
         if lora_name_alpha in state_dict:
@@ -214,7 +220,8 @@ def _convert_non_diffusers_lora_to_diffusers(state_dict, unet_name="unet", text_
 
     # Check if any keys remain.
     if len(state_dict) > 0:
-        raise ValueError(f"The following keys have not been correctly renamed: \n\n {', '.join(state_dict.keys())}")
+        all_keys_remaining = sorted(list(state_dict.keys()))
+        raise ValueError(f"The following keys have not been correctly renamed: \n\n {', '.join(all_keys_remaining)}")
 
     logger.info("Non-diffusers checkpoint detected.")
 
@@ -285,7 +292,7 @@ def _convert_unet_lora_key(key):
         pass
     else:
         pass
-
+    
     return diffusers_name