Merge branch 'main' into restrict-config-update-peft

Author: sayakpaul

src/diffusers/loaders/lora_conversion_utils.py

@@ -588,18 +588,23 @@ def _convert(original_key, diffusers_key, state_dict, new_state_dict):
             new_state_dict[diffusers_down_key.replace(".lora_A.", ".lora_B.")] = up_weight
 
         all_unique_keys = {
-            k.replace(".lora_down.weight", "").replace(".lora_up.weight", "").replace(".alpha", "") for k in state_dict
+            k.replace(".lora_down.weight", "").replace(".lora_up.weight", "").replace(".alpha", "")
+            for k in state_dict
+            if not k.startswith(("lora_unet_"))
         }
-        all_unique_keys = sorted(all_unique_keys)
-        assert all("lora_transformer_" in k for k in all_unique_keys), f"{all_unique_keys=}"
+        assert all(k.startswith(("lora_transformer_", "lora_te1_")) for k in all_unique_keys), f"{all_unique_keys=}"
 
+        has_te_keys = False
         for k in all_unique_keys:
             if k.startswith("lora_transformer_single_transformer_blocks_"):
                 i = int(k.split("lora_transformer_single_transformer_blocks_")[-1].split("_")[0])
                 diffusers_key = f"single_transformer_blocks.{i}"
             elif k.startswith("lora_transformer_transformer_blocks_"):
                 i = int(k.split("lora_transformer_transformer_blocks_")[-1].split("_")[0])
                 diffusers_key = f"transformer_blocks.{i}"
+            elif k.startswith("lora_te1_"):
+                has_te_keys = True
+                continue
             else:
                 raise NotImplementedError
 
@@ -615,17 +620,57 @@ def _convert(original_key, diffusers_key, state_dict, new_state_dict):
                     remaining = k.split("attn_")[-1]
                     diffusers_key += f".attn.{remaining}"
 
-            if diffusers_key == f"transformer_blocks.{i}":
-                print(k, diffusers_key)
             _convert(k, diffusers_key, state_dict, new_state_dict)
 
+        if has_te_keys:
+            layer_pattern = re.compile(r"lora_te1_text_model_encoder_layers_(\d+)")
+            attn_mapping = {
+                "q_proj": ".self_attn.q_proj",
+                "k_proj": ".self_attn.k_proj",
+                "v_proj": ".self_attn.v_proj",
+                "out_proj": ".self_attn.out_proj",
+            }
+            mlp_mapping = {"fc1": ".mlp.fc1", "fc2": ".mlp.fc2"}
+            for k in all_unique_keys:
+                if not k.startswith("lora_te1_"):
+                    continue
+
+                match = layer_pattern.search(k)
+                if not match:
+                    continue
+                i = int(match.group(1))
+                diffusers_key = f"text_model.encoder.layers.{i}"
+
+                if "attn" in k:
+                    for key_fragment, suffix in attn_mapping.items():
+                        if key_fragment in k:
+                            diffusers_key += suffix
+                            break
+                elif "mlp" in k:
+                    for key_fragment, suffix in mlp_mapping.items():
+                        if key_fragment in k:
+                            diffusers_key += suffix
+                            break
+
+                _convert(k, diffusers_key, state_dict, new_state_dict)
+
+        if state_dict:
+            remaining_all_unet = all(k.startswith("lora_unet_") for k in state_dict)
+        if remaining_all_unet:
+            keys = list(state_dict.keys())
+            for k in keys:
+                state_dict.pop(k)
+
         if len(state_dict) > 0:
             raise ValueError(
                 f"Expected an empty state dict at this point but its has these keys which couldn't be parsed: {list(state_dict.keys())}."
             )
 
-        new_state_dict = {f"transformer.{k}": v for k, v in new_state_dict.items()}
-        return new_state_dict
+        transformer_state_dict = {
+            f"transformer.{k}": v for k, v in new_state_dict.items() if not k.startswith("text_model.")
+        }
+        te_state_dict = {f"text_encoder.{k}": v for k, v in new_state_dict.items() if k.startswith("text_model.")}
+        return {**transformer_state_dict, **te_state_dict}
 
     # This is  weird.
     # https://huggingface.co/sayakpaul/different-lora-from-civitai/tree/main?show_file_info=sharp_detailed_foot.safetensors
@@ -640,6 +685,7 @@ def _convert(original_key, diffusers_key, state_dict, new_state_dict):
     )
     if has_mixture:
         return _convert_mixture_state_dict_to_diffusers(state_dict)
+
     return _convert_sd_scripts_to_ai_toolkit(state_dict)