[LoRA] attempt at fixing onetrainer lora. (#8242)

* attempt at fixing onetrainer lora.

* fix

src/diffusers/loaders/lora_conversion_utils.py

@@ -226,6 +226,8 @@ def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_
             diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
             diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
             diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
+            diffusers_name = diffusers_name.replace("text.projection", "text_projection")
+
             if "self_attn" in diffusers_name:
                 if lora_name.startswith(("lora_te_", "lora_te1_")):
                     te_state_dict[diffusers_name] = state_dict.pop(key)
@@ -243,6 +245,10 @@ def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_
                 else:
                     te2_state_dict[diffusers_name] = state_dict.pop(key)
                     te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+            # OneTrainer specificity
+            elif "text_projection" in diffusers_name and lora_name.startswith("lora_te2_"):
+                te2_state_dict[diffusers_name] = state_dict.pop(key)
+                te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
 
             if (is_te_dora_lora or is_te2_dora_lora) and lora_name.startswith(("lora_te_", "lora_te1_", "lora_te2_")):
                 dora_scale_key_to_replace_te = (
@@ -270,7 +276,7 @@ def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_
             network_alphas.update({new_name: alpha})
 
     if len(state_dict) > 0:
-        raise ValueError(f"The following keys have not been correctly be renamed: \n\n {', '.join(state_dict.keys())}")
+        raise ValueError(f"The following keys have not been correctly renamed: \n\n {', '.join(state_dict.keys())}")
 
     logger.info("Kohya-style checkpoint detected.")
     unet_state_dict = {f"{unet_name}.{module_name}": params for module_name, params in unet_state_dict.items()}

src/diffusers/utils/state_dict_utils.py

@@ -62,6 +62,8 @@ DIFFUSERS_TO_PEFT = {
     ".out_proj.lora_linear_layer.down": ".out_proj.lora_A",
     ".lora_linear_layer.up": ".lora_B",
     ".lora_linear_layer.down": ".lora_A",
+    "text_projection.lora.down.weight": "text_projection.lora_A.weight",
+    "text_projection.lora.up.weight": "text_projection.lora_B.weight",
 }
 
 DIFFUSERS_OLD_TO_PEFT = {