[LoRA] support more comyui loras for Flux 🚨

src/diffusers/loaders/lora_conversion_utils.py

@@ -13,15 +13,22 @@
 # limitations under the License.
 
 import re
+from typing import List
 
 import torch
 
-from ..utils import is_peft_version, logging
+from ..utils import is_peft_version, logging, state_dict_all_zero
 
 
 logger = logging.get_logger(__name__)
 
 
+def swap_scale_shift(weight):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
+
 def _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config, delimiter="_", block_slice_pos=5):
     # 1. get all state_dict_keys
     all_keys = list(state_dict.keys())
@@ -313,6 +320,7 @@ def _convert_text_encoder_lora_key(key, lora_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.")
+
     return diffusers_name
 
 
@@ -331,8 +339,7 @@ def _get_alpha_name(lora_name_alpha, diffusers_name, alpha):
 
 
 # The utilities under `_convert_kohya_flux_lora_to_diffusers()`
-# are taken from https://github.com/kohya-ss/sd-scripts/blob/a61cf73a5cb5209c3f4d1a3688dd276a4dfd1ecb/networks/convert_flux_lora.py
-# All credits go to `kohya-ss`.
+# are adapted from https://github.com/kohya-ss/sd-scripts/blob/a61cf73a5cb5209c3f4d1a3688dd276a4dfd1ecb/networks/convert_flux_lora.py
 def _convert_kohya_flux_lora_to_diffusers(state_dict):
     def _convert_to_ai_toolkit(sds_sd, ait_sd, sds_key, ait_key):
         if sds_key + ".lora_down.weight" not in sds_sd:
@@ -341,7 +348,8 @@ def _convert_to_ai_toolkit(sds_sd, ait_sd, sds_key, ait_key):
 
         # scale weight by alpha and dim
         rank = down_weight.shape[0]
-        alpha = sds_sd.pop(sds_key + ".alpha").item()  # alpha is scalar
+        default_alpha = torch.tensor(rank, dtype=down_weight.dtype, device=down_weight.device, requires_grad=False)
+        alpha = sds_sd.pop(sds_key + ".alpha", default_alpha).item()  # alpha is scalar
         scale = alpha / rank  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
 
         # calculate scale_down and scale_up to keep the same value. if scale is 4, scale_down is 2 and scale_up is 2
@@ -362,7 +370,10 @@ def _convert_to_ai_toolkit_cat(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
         sd_lora_rank = down_weight.shape[0]
 
         # scale weight by alpha and dim
-        alpha = sds_sd.pop(sds_key + ".alpha")
+        default_alpha = torch.tensor(
+            sd_lora_rank, dtype=down_weight.dtype, device=down_weight.device, requires_grad=False
+        )
+        alpha = sds_sd.pop(sds_key + ".alpha", default_alpha)
         scale = alpha / sd_lora_rank
 
         # calculate scale_down and scale_up
@@ -516,10 +527,103 @@ def _convert_sd_scripts_to_ai_toolkit(sds_sd):
                 f"transformer.single_transformer_blocks.{i}.norm.linear",
             )
 
+        # TODO: alphas.
+        def assign_remaining_weights(assignments, source):
+            for lora_key in ["lora_A", "lora_B"]:
+                orig_lora_key = "lora_down" if lora_key == "lora_A" else "lora_up"
+                for target_fmt, source_fmt, transform in assignments:
+                    target_key = target_fmt.format(lora_key=lora_key)
+                    source_key = source_fmt.format(orig_lora_key=orig_lora_key)
+                    value = source.pop(source_key)
+                    if transform:
+                        value = transform(value)
+                    ait_sd[target_key] = value
+
+        if any("guidance_in" in k for k in sds_sd):
+            assign_remaining_weights(
+                [
+                    (
+                        "time_text_embed.guidance_embedder.linear_1.{lora_key}.weight",
+                        "lora_unet_guidance_in_in_layer.{orig_lora_key}.weight",
+                        None,
+                    ),
+                    (
+                        "time_text_embed.guidance_embedder.linear_2.{lora_key}.weight",
+                        "lora_unet_guidance_in_out_layer.{orig_lora_key}.weight",
+                        None,
+                    ),
+                ],
+                sds_sd,
+            )
+
+        if any("img_in" in k for k in sds_sd):
+            assign_remaining_weights(
+                [
+                    ("x_embedder.{lora_key}.weight", "lora_unet_img_in.{orig_lora_key}.weight", None),
+                ],
+                sds_sd,
+            )
+
+        if any("txt_in" in k for k in sds_sd):
+            assign_remaining_weights(
+                [
+                    ("context_embedder.{lora_key}.weight", "lora_unet_txt_in.{orig_lora_key}.weight", None),
+                ],
+                sds_sd,
+            )
+
+        if any("time_in" in k for k in sds_sd):
+            assign_remaining_weights(
+                [
+                    (
+                        "time_text_embed.timestep_embedder.linear_1.{lora_key}.weight",
+                        "lora_unet_time_in_in_layer.{orig_lora_key}.weight",
+                        None,
+                    ),
+                    (
+                        "time_text_embed.timestep_embedder.linear_2.{lora_key}.weight",
+                        "lora_unet_time_in_out_layer.{orig_lora_key}.weight",
+                        None,
+                    ),
+                ],
+                sds_sd,
+            )
+
+        if any("vector_in" in k for k in sds_sd):
+            assign_remaining_weights(
+                [
+                    (
+                        "time_text_embed.text_embedder.linear_1.{lora_key}.weight",
+                        "lora_unet_vector_in_in_layer.{orig_lora_key}.weight",
+                        None,
+                    ),
+                    (
+                        "time_text_embed.text_embedder.linear_2.{lora_key}.weight",
+                        "lora_unet_vector_in_out_layer.{orig_lora_key}.weight",
+                        None,
+                    ),
+                ],
+                sds_sd,
+            )
+
+        if any("final_layer" in k for k in sds_sd):
+            # Notice the swap in processing for "final_layer".
+            assign_remaining_weights(
+                [
+                    (
+                        "norm_out.linear.{lora_key}.weight",
+                        "lora_unet_final_layer_adaLN_modulation_1.{orig_lora_key}.weight",
+                        swap_scale_shift,
+                    ),
+                    ("proj_out.{lora_key}.weight", "lora_unet_final_layer_linear.{orig_lora_key}.weight", None),
+                ],
+                sds_sd,
+            )
+
         remaining_keys = list(sds_sd.keys())
         te_state_dict = {}
         if remaining_keys:
-            if not all(k.startswith("lora_te") for k in remaining_keys):
+            if not all(k.startswith(("lora_te", "lora_te1")) for k in remaining_keys):
                 raise ValueError(f"Incompatible keys detected: \n\n {', '.join(remaining_keys)}")
             for key in remaining_keys:
                 if not key.endswith("lora_down.weight"):
@@ -680,10 +784,97 @@ def _convert(original_key, diffusers_key, state_dict, new_state_dict):
     if has_peft_state_dict:
         state_dict = {k: v for k, v in state_dict.items() if k.startswith("transformer.")}
         return state_dict
+
     # Another weird one.
     has_mixture = any(
         k.startswith("lora_transformer_") and ("lora_down" in k or "lora_up" in k or "alpha" in k) for k in state_dict
     )
+
+    # ComfyUI.
+    state_dict = {k.replace("diffusion_model.", "lora_unet_"): v for k, v in state_dict.items()}
+    state_dict = {k.replace("text_encoders.clip_l.transformer.", "lora_te_"): v for k, v in state_dict.items()}
+
+    has_position_embedding = any("position_embedding" in k for k in state_dict)
+    if has_position_embedding:
+        zero_status_pe = state_dict_all_zero(state_dict, "position_embedding")
+        if zero_status_pe:
+            logger.info(
+                "The `position_embedding` LoRA params are all zeros which make them ineffective. "
+                "So, we will purge them out of the curret state dict to make loading possible."
+            )
+
+        else:
+            logger.info(
+                "The state_dict has position_embedding LoRA params and we currently do not support them. "
+                "Open an issue if you need this supported - https://github.com/huggingface/diffusers/issues/new."
+            )
+        state_dict = {k: v for k, v in state_dict.items() if "position_embedding" not in k}
+
+    has_t5xxl = any(k.startswith("text_encoders.t5xxl.transformer.") for k in state_dict)
+    if has_t5xxl:
+        zero_status_t5 = state_dict_all_zero(state_dict, "text_encoders.t5xxl")
+        if zero_status_t5:
+            logger.info(
+                "The `t5xxl` LoRA params are all zeros which make them ineffective. "
+                "So, we will purge them out of the curret state dict to make loading possible."
+            )
+        else:
+            logger.info(
+                "T5-xxl keys found in the state dict, which are currently unsupported. We will filter them out."
+                "Open an issue if this is a problem - https://github.com/huggingface/diffusers/issues/new."
+            )
+        state_dict = {k: v for k, v in state_dict.items() if not k.startswith("text_encoders.t5xxl.transformer.")}
+
+    has_diffb = any("diff_b" in k and k.startswith(("lora_unet_", "lora_te_")) for k in state_dict)
+    if has_diffb:
+        zero_status_diff_b = state_dict_all_zero(state_dict, "diff_b")
+        if zero_status_diff_b:
+            logger.info(
+                "The `diff_b` LoRA params are all zeros which make them ineffective. "
+                "So, we will purge them out of the curret state dict to make loading possible."
+            )
+        else:
+            logger.info(
+                "`diff_b` keys found in the state dict which are currently unsupported. "
+                "So, we will filter out those keys. Open an issue if this is a problem - "
+                "https://github.com/huggingface/diffusers/issues/new."
+            )
+        state_dict = {k: v for k, v in state_dict.items() if "diff_b" not in k}
+
+    has_norm_diff = any("norm" in k and "diff" in k for k in state_dict)
+    if has_norm_diff:
+        zero_status_diff = state_dict_all_zero(state_dict, "diff")
+        if zero_status_diff:
+            logger.info(
+                "The `diff` LoRA params are all zeros which make them ineffective. "
+                "So, we will purge them out of the curret state dict to make loading possible."
+            )
+        else:
+            logger.info(
+                "Normalization diff keys found in the state dict which are currently unsupported. "
+                "So, we will filter out those keys. Open an issue if this is a problem - "
+                "https://github.com/huggingface/diffusers/issues/new."
+            )
+        state_dict = {k: v for k, v in state_dict.items() if "norm" not in k and "diff" not in k}
+
+    limit_substrings = ["lora_down", "lora_up"]
+    if any("alpha" in k for k in state_dict):
+        limit_substrings.append("alpha")
+
+    state_dict = {
+        _custom_replace(k, limit_substrings): v
+        for k, v in state_dict.items()
+        if k.startswith(("lora_unet_", "lora_te_"))
+    }
+
+    if any("text_projection" in k for k in state_dict):
+        logger.info(
+            "`text_projection` keys found in the `state_dict` which are unexpected. "
+            "So, we will filter out those keys. Open an issue if this is a problem - "
+            "https://github.com/huggingface/diffusers/issues/new."
+        )
+        state_dict = {k: v for k, v in state_dict.items() if "text_projection" not in k}
+
     if has_mixture:
         return _convert_mixture_state_dict_to_diffusers(state_dict)
 
@@ -798,6 +989,23 @@ def handle_qkv(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
     return new_state_dict
 
 
+def _custom_replace(key: str, substrings: List[str]) -> str:
+    pattern = "(" + "|".join(re.escape(sub) for sub in substrings) + ")"
+
+    match = re.search(pattern, key)
+    if match:
+        start_sub = match.start()
+        if start_sub > 0 and key[start_sub - 1] == ".":
+            boundary = start_sub - 1
+        else:
+            boundary = start_sub
+        left = key[:boundary].replace(".", "_")
+        right = key[boundary:]
+        return left + right
+    else:
+        return key.replace(".", "_")
+
+
 def _convert_bfl_flux_control_lora_to_diffusers(original_state_dict):
     converted_state_dict = {}
     original_state_dict_keys = list(original_state_dict.keys())
@@ -806,11 +1014,6 @@ def _convert_bfl_flux_control_lora_to_diffusers(original_state_dict):
     inner_dim = 3072
     mlp_ratio = 4.0
 
-    def swap_scale_shift(weight):
-        shift, scale = weight.chunk(2, dim=0)
-        new_weight = torch.cat([scale, shift], dim=0)
-        return new_weight
-
     for lora_key in ["lora_A", "lora_B"]:
         ## time_text_embed.timestep_embedder <-  time_in
         converted_state_dict[

src/diffusers/loaders/peft.py

@@ -58,59 +58,24 @@
 }
 
 
-def _maybe_adjust_config(config):
-    """
-    We may run into some ambiguous configuration values when a model has module names, sharing a common prefix
-    (`proj_out.weight` and `blocks.transformer.proj_out.weight`, for example) and they have different LoRA ranks. This
-    method removes the ambiguity by following what is described here:
-    https://github.com/huggingface/diffusers/pull/9985#issuecomment-2493840028.
-    """
-    # Track keys that have been explicitly removed to prevent re-adding them.
-    deleted_keys = set()
-
+def _maybe_raise_error_for_ambiguity(config):
     rank_pattern = config["rank_pattern"].copy()
     target_modules = config["target_modules"]
-    original_r = config["r"]
 
     for key in list(rank_pattern.keys()):
-        key_rank = rank_pattern[key]
-
         # try to detect ambiguity
         # `target_modules` can also be a str, in which case this loop would loop
         # over the chars of the str. The technically correct way to match LoRA keys
         # in PEFT is to use LoraModel._check_target_module_exists (lora_config, key).
         # But this cuts it for now.
         exact_matches = [mod for mod in target_modules if mod == key]
         substring_matches = [mod for mod in target_modules if key in mod and mod != key]
-        ambiguous_key = key
 
         if exact_matches and substring_matches:
-            # if ambiguous, update the rank associated with the ambiguous key (`proj_out`, for example)
-            config["r"] = key_rank
-            # remove the ambiguous key from `rank_pattern` and record it as deleted
-            del config["rank_pattern"][key]
-            deleted_keys.add(key)
-            # For substring matches, add them with the original rank only if they haven't been assigned already
-            for mod in substring_matches:
-                if mod not in config["rank_pattern"] and mod not in deleted_keys:
-                    config["rank_pattern"][mod] = original_r
-
-            # Update the rest of the target modules with the original rank if not already set and not deleted
-            for mod in target_modules:
-                if mod != ambiguous_key and mod not in config["rank_pattern"] and mod not in deleted_keys:
-                    config["rank_pattern"][mod] = original_r
-
-    # Handle alphas to deal with cases like:
-    # https://github.com/huggingface/diffusers/pull/9999#issuecomment-2516180777
-    has_different_ranks = len(config["rank_pattern"]) > 1 and list(config["rank_pattern"])[0] != config["r"]
-    if has_different_ranks:
-        config["lora_alpha"] = config["r"]
-        alpha_pattern = {}
-        for module_name, rank in config["rank_pattern"].items():
-            alpha_pattern[module_name] = rank
-        config["alpha_pattern"] = alpha_pattern
-
-    return config
+            if is_peft_version("<", "0.14.1"):
+                raise ValueError(
+                    "There are ambiguous keys present in this LoRA. To load it, please update your `peft` installation - `pip install -U peft`."
+                )
 
 
 class PeftAdapterMixin:
@@ -254,16 +219,18 @@ def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="trans
                 # Cannot figure out rank from lora layers that don't have atleast 2 dimensions.
                 # Bias layers in LoRA only have a single dimension
                 if "lora_B" in key and val.ndim > 1:
-                    # TODO: revisit this after https://github.com/huggingface/peft/pull/2382 is merged.
-                    rank[key] = val.shape[1]
+                    # Check out https://github.com/huggingface/peft/pull/2419 for the `^` symbol.
+                    # We may run into some ambiguous configuration values when a model has module
+                    # names, sharing a common prefix (`proj_out.weight` and `blocks.transformer.proj_out.weight`,
+                    # for example) and they have different LoRA ranks.
+                    rank[f"^{key}"] = val.shape[1]
 
             if network_alphas is not None and len(network_alphas) >= 1:
                 alpha_keys = [k for k in network_alphas.keys() if k.startswith(f"{prefix}.")]
                 network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
 
             lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict)
-            # TODO: revisit this after https://github.com/huggingface/peft/pull/2382 is merged.
-            lora_config_kwargs = _maybe_adjust_config(lora_config_kwargs)
+            _maybe_raise_error_for_ambiguity(lora_config_kwargs)
 
             if "use_dora" in lora_config_kwargs:
                 if lora_config_kwargs["use_dora"]:

src/diffusers/utils/__init__.py

@@ -126,6 +126,7 @@
     convert_state_dict_to_kohya,
     convert_state_dict_to_peft,
     convert_unet_state_dict_to_peft,
+    state_dict_all_zero,
 )
 from .typing_utils import _get_detailed_type, _is_valid_type