refactor non-diffusers lora conversion utility.

src/diffusers/loaders/lora.py

@@ -45,7 +45,7 @@ from ..utils import (
     set_adapter_layers,
     set_weights_and_activate_adapters,
 )
-from .lora_conversion_utils import _convert_kohya_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers
+from .lora_conversion_utils import _convert_non_diffusers_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers
 
 
 if is_transformers_available():
@@ -302,7 +302,7 @@ class LoraLoaderMixin:
             if unet_config is not None:
                 # use unet config to remap block numbers
                 state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
-            state_dict, network_alphas = _convert_kohya_lora_to_diffusers(state_dict)
+            state_dict, network_alphas = _convert_non_diffusers_lora_to_diffusers(state_dict)
 
         return state_dict, network_alphas
 

src/diffusers/loaders/lora_conversion_utils.py

@@ -13,6 +13,9 @@
 # limitations under the License.
 
 import re
+from typing import Any, Dict, Tuple
+
+import torch
 
 from ..utils import is_peft_version, logging
 
@@ -123,164 +126,163 @@ def _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config, delimiter="_", b
     return new_state_dict
 
 
-def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_name="text_encoder"):
+def _convert_non_diffusers_lora_to_diffusers(
-    unet_state_dict = {}
+    state_dict: Dict[str, torch.Tensor], unet_name: str = "unet", text_encoder_name: str = "text_encoder"
-    te_state_dict = {}
+) -> Tuple[Dict[str, Any], Dict[str, float]]:
-    te2_state_dict = {}
+    def detect_dora_lora(state_dict: Dict[str, torch.Tensor]) -> Tuple[bool, bool, bool]:
-    network_alphas = {}
         is_unet_dora_lora = any("dora_scale" in k and "lora_unet_" in k for k in state_dict)
         is_te_dora_lora = any("dora_scale" in k and ("lora_te_" in k or "lora_te1_" in k) for k in state_dict)
         is_te2_dora_lora = any("dora_scale" in k and "lora_te2_" in k for k in state_dict)
+        return is_unet_dora_lora, is_te_dora_lora, is_te2_dora_lora
 
+    def check_peft_version(is_unet_dora_lora: bool, is_te_dora_lora: bool, is_te2_dora_lora: bool):
         if is_unet_dora_lora or is_te_dora_lora or is_te2_dora_lora:
             if is_peft_version("<", "0.9.0"):
                 raise ValueError(
                     "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
                 )
 
-    # every down weight has a corresponding up weight and potentially an alpha weight
+    def rename_keys(
-    lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
+        state_dict: Dict[str, torch.Tensor],
-    for key in lora_keys:
+        key: str,
+        unet_state_dict: Dict[str, torch.Tensor],
+        te_state_dict: Dict[str, torch.Tensor],
+        te2_state_dict: Dict[str, torch.Tensor],
+        is_unet_dora_lora: bool,
+        is_te_dora_lora: bool,
+        is_te2_dora_lora: bool,
+    ):
         lora_name = key.split(".")[0]
         lora_name_up = lora_name + ".lora_up.weight"
-        lora_name_alpha = lora_name + ".alpha"
+        diffusers_name = key.replace(lora_name + ".", "").replace("_", ".")
+        lora_type = lora_name.split("_")[1]
 
-        if lora_name.startswith("lora_unet_"):
+        if lora_type == "unet":
-            diffusers_name = key.replace("lora_unet_", "").replace("_", ".")
+            diffusers_name = _adjust_unet_names(diffusers_name)
-
+            unet_state_dict = _populate_state_dict(
-            if "input.blocks" in diffusers_name:
+                unet_state_dict, state_dict, key, lora_name_up, diffusers_name, is_unet_dora_lora
-                diffusers_name = diffusers_name.replace("input.blocks", "down_blocks")
+            )
-            else:
+        else:
-                diffusers_name = diffusers_name.replace("down.blocks", "down_blocks")
+            diffusers_name = _adjust_text_encoder_names(diffusers_name)
-
+            if lora_type in ["te", "te1"]:
-            if "middle.block" in diffusers_name:
+                te_state_dict = _populate_state_dict(
-                diffusers_name = diffusers_name.replace("middle.block", "mid_block")
+                    te_state_dict, state_dict, key, lora_name_up, diffusers_name, is_te_dora_lora
-            else:
+                )
-                diffusers_name = diffusers_name.replace("mid.block", "mid_block")
+            else:
-            if "output.blocks" in diffusers_name:
+                te2_state_dict = _populate_state_dict(
-                diffusers_name = diffusers_name.replace("output.blocks", "up_blocks")
+                    te2_state_dict, state_dict, key, lora_name_up, diffusers_name, is_te2_dora_lora
-            else:
-                diffusers_name = diffusers_name.replace("up.blocks", "up_blocks")
-
-            diffusers_name = diffusers_name.replace("transformer.blocks", "transformer_blocks")
-            diffusers_name = diffusers_name.replace("to.q.lora", "to_q_lora")
-            diffusers_name = diffusers_name.replace("to.k.lora", "to_k_lora")
-            diffusers_name = diffusers_name.replace("to.v.lora", "to_v_lora")
-            diffusers_name = diffusers_name.replace("to.out.0.lora", "to_out_lora")
-            diffusers_name = diffusers_name.replace("proj.in", "proj_in")
-            diffusers_name = diffusers_name.replace("proj.out", "proj_out")
-            diffusers_name = diffusers_name.replace("emb.layers", "time_emb_proj")
-
-            # SDXL specificity.
-            if "emb" in diffusers_name and "time.emb.proj" not in diffusers_name:
-                pattern = r"\.\d+(?=\D*$)"
-                diffusers_name = re.sub(pattern, "", diffusers_name, count=1)
-            if ".in." in diffusers_name:
-                diffusers_name = diffusers_name.replace("in.layers.2", "conv1")
-            if ".out." in diffusers_name:
-                diffusers_name = diffusers_name.replace("out.layers.3", "conv2")
-            if "downsamplers" in diffusers_name or "upsamplers" in diffusers_name:
-                diffusers_name = diffusers_name.replace("op", "conv")
-            if "skip" in diffusers_name:
-                diffusers_name = diffusers_name.replace("skip.connection", "conv_shortcut")
-
-            # LyCORIS specificity.
-            if "time.emb.proj" in diffusers_name:
-                diffusers_name = diffusers_name.replace("time.emb.proj", "time_emb_proj")
-            if "conv.shortcut" in diffusers_name:
-                diffusers_name = diffusers_name.replace("conv.shortcut", "conv_shortcut")
-
-            # General coverage.
-            if "transformer_blocks" in diffusers_name:
-                if "attn1" in diffusers_name or "attn2" in diffusers_name:
-                    diffusers_name = diffusers_name.replace("attn1", "attn1.processor")
-                    diffusers_name = diffusers_name.replace("attn2", "attn2.processor")
-                    unet_state_dict[diffusers_name] = state_dict.pop(key)
-                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-                elif "ff" in diffusers_name:
-                    unet_state_dict[diffusers_name] = state_dict.pop(key)
-                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            elif any(key in diffusers_name for key in ("proj_in", "proj_out")):
-                unet_state_dict[diffusers_name] = state_dict.pop(key)
-                unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            else:
-                unet_state_dict[diffusers_name] = state_dict.pop(key)
-                unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-
-            if is_unet_dora_lora:
-                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"))
-
-        elif lora_name.startswith(("lora_te_", "lora_te1_", "lora_te2_")):
-            if lora_name.startswith(("lora_te_", "lora_te1_")):
-                key_to_replace = "lora_te_" if lora_name.startswith("lora_te_") else "lora_te1_"
-            else:
-                key_to_replace = "lora_te2_"
-
-            diffusers_name = key.replace(key_to_replace, "").replace("_", ".")
-            diffusers_name = diffusers_name.replace("text.model", "text_model")
-            diffusers_name = diffusers_name.replace("self.attn", "self_attn")
-            diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
-            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")
-            if "self_attn" in diffusers_name:
-                if lora_name.startswith(("lora_te_", "lora_te1_")):
-                    te_state_dict[diffusers_name] = state_dict.pop(key)
-                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-                else:
-                    te2_state_dict[diffusers_name] = state_dict.pop(key)
-                    te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            elif "mlp" in diffusers_name:
-                # Be aware that this is the new diffusers convention and the rest of the code might
-                # not utilize it yet.
-                diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
-                if lora_name.startswith(("lora_te_", "lora_te1_")):
-                    te_state_dict[diffusers_name] = state_dict.pop(key)
-                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-                else:
-                    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 = (
-                    "_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"))
-                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"))
 
-        # Rename the alphas so that they can be mapped appropriately.
+        return unet_state_dict, te_state_dict, te2_state_dict
+
+    def _adjust_unet_names(name: str) -> str:
+        replacements = [
+            ("input.blocks", "down_blocks"),
+            ("down.blocks", "down_blocks"),
+            ("middle.block", "mid_block"),
+            ("mid.block", "mid_block"),
+            ("output.blocks", "up_blocks"),
+            ("up.blocks", "up_blocks"),
+            ("transformer.blocks", "transformer_blocks"),
+            ("to.q.lora", "to_q_lora"),
+            ("to.k.lora", "to_k_lora"),
+            ("to.v.lora", "to_v_lora"),
+            ("to.out.0.lora", "to_out_lora"),
+            ("proj.in", "proj_in"),
+            ("proj.out", "proj_out"),
+            ("emb.layers", "time_emb_proj"),
+            ("time.emb.proj", "time_emb_proj"),
+            ("conv.shortcut", "conv_shortcut"),
+            ("skip.connection", "conv_shortcut"),
+        ]
+        for old, new in replacements:
+            name = name.replace(old, new)
+        if "emb" in name and "time.emb.proj" not in name:
+            pattern = r"\.\d+(?=\D*$)"
+            name = re.sub(pattern, "", name, count=1)
+        if ".in." in name:
+            name = name.replace("in.layers.2", "conv1")
+        if ".out." in name:
+            name = name.replace("out.layers.3", "conv2")
+        if "downsamplers" in name or "upsamplers" in name:
+            name = name.replace("op", "conv")
+        return name
+
+    def _adjust_text_encoder_names(name: str) -> str:
+        replacements = [
+            ("text.model", "text_model"),
+            ("self.attn", "self_attn"),
+            ("q.proj.lora", "to_q_lora"),
+            ("k.proj.lora", "to_k_lora"),
+            ("v.proj.lora", "to_v_lora"),
+            ("out.proj.lora", "to_out_lora"),
+            ("text.projection", "text_projection"),
+        ]
+        for old, new in replacements:
+            name = name.replace(old, new)
+        return name
+
+    def _populate_state_dict(state_dict, main_dict, down_key, up_key, name, is_dora_lora):
+        state_dict[name] = main_dict.pop(down_key)
+        state_dict[name.replace(".down.", ".up.")] = main_dict.pop(up_key)
+        if is_dora_lora:
+            dora_key = down_key.replace("lora_down.weight", "dora_scale")
+            scale_key = "_lora.down." if "_lora.down." in name else ".lora.down."
+            state_dict[name.replace(scale_key, ".lora_magnitude_vector.")] = main_dict.pop(dora_key)
+        return state_dict
+
+    def update_network_alphas(
+        state_dict: Dict[str, torch.Tensor],
+        network_alphas: Dict[str, float],
+        diffusers_name: str,
+        lora_name_alpha: str,
+    ):
         if lora_name_alpha in state_dict:
             alpha = state_dict.pop(lora_name_alpha).item()
-            if lora_name_alpha.startswith("lora_unet_"):
+            prefix = (
-                prefix = "unet."
+                "unet."
-            elif lora_name_alpha.startswith(("lora_te_", "lora_te1_")):
+                if "unet" in lora_name_alpha
-                prefix = "text_encoder."
+                else "text_encoder."
-            else:
+                if "te1" in lora_name_alpha
-                prefix = "text_encoder_2."
+                else "text_encoder_2."
+            )
             new_name = prefix + diffusers_name.split(".lora.")[0] + ".alpha"
             network_alphas.update({new_name: alpha})
 
-    if len(state_dict) > 0:
+    unet_state_dict = {}
-        raise ValueError(f"The following keys have not been correctly be renamed: \n\n {', '.join(state_dict.keys())}")
+    te_state_dict = {}
+    te2_state_dict = {}
+    network_alphas = {}
 
-    logger.info("Kohya-style checkpoint detected.")
+    is_unet_dora_lora, is_te_dora_lora, is_te2_dora_lora = detect_dora_lora(state_dict)
+    check_peft_version(is_unet_dora_lora, is_te_dora_lora, is_te2_dora_lora)
+
+    lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
+    for key in lora_keys:
+        unet_state_dict, te_state_dict, te2_state_dict = rename_keys(
+            state_dict,
+            key,
+            unet_state_dict,
+            te_state_dict,
+            te2_state_dict,
+            is_unet_dora_lora,
+            is_te_dora_lora,
+            is_te2_dora_lora,
+        )
+        lora_name = key.split(".")[0]
+        lora_name_alpha = lora_name + ".alpha"
+        diffusers_name = key.replace(lora_name + ".", "").replace("_", ".")
+        update_network_alphas(state_dict, network_alphas, diffusers_name, lora_name_alpha)
+
+    if state_dict:
+        raise ValueError(f"The following keys have not been correctly renamed: \n\n {', '.join(state_dict.keys())}")
+
+    logger.info("Non-diffusers LoRA checkpoint detected.")
     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()}
-    te2_state_dict = (
+
-        {f"text_encoder_2.{module_name}": params for module_name, params in te2_state_dict.items()}
+    if te2_state_dict:
-        if len(te2_state_dict) > 0
+        te2_state_dict = {f"text_encoder_2.{module_name}": params for module_name, params in te2_state_dict.items()}
-        else None
-    )
-    if te2_state_dict is not None:
         te_state_dict.update(te2_state_dict)
 
     new_state_dict = {**unet_state_dict, **te_state_dict}