ComfyUI/comfy/lora.py

"""
    This file is part of ComfyUI.
    Copyright (C) 2024 Comfy

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
"""

from __future__ import annotations
import comfy.utils
import comfy.model_management
import comfy.model_base
import logging
import torch

LORA_CLIP_MAP = {
    "mlp.fc1": "mlp_fc1",
    "mlp.fc2": "mlp_fc2",
    "self_attn.k_proj": "self_attn_k_proj",
    "self_attn.q_proj": "self_attn_q_proj",
    "self_attn.v_proj": "self_attn_v_proj",
    "self_attn.out_proj": "self_attn_out_proj",
}


def load_lora(lora, to_load, log_missing=True):
    patch_dict = {}
    loaded_keys = set()
    for x in to_load:
        alpha_name = "{}.alpha".format(x)
        alpha = None
        if alpha_name in lora.keys():
            alpha = lora[alpha_name].item()
            loaded_keys.add(alpha_name)

        dora_scale_name = "{}.dora_scale".format(x)
        dora_scale = None
        if dora_scale_name in lora.keys():
            dora_scale = lora[dora_scale_name]
            loaded_keys.add(dora_scale_name)

        regular_lora = "{}.lora_up.weight".format(x)
        diffusers_lora = "{}_lora.up.weight".format(x)
        diffusers2_lora = "{}.lora_B.weight".format(x)
        diffusers3_lora = "{}.lora.up.weight".format(x)
        transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
        A_name = None

        if regular_lora in lora.keys():
            A_name = regular_lora
            B_name = "{}.lora_down.weight".format(x)
            mid_name = "{}.lora_mid.weight".format(x)
        elif diffusers_lora in lora.keys():
            A_name = diffusers_lora
            B_name = "{}_lora.down.weight".format(x)
            mid_name = None
        elif diffusers2_lora in lora.keys():
            A_name = diffusers2_lora
            B_name = "{}.lora_A.weight".format(x)
            mid_name = None
        elif diffusers3_lora in lora.keys():
            A_name = diffusers3_lora
            B_name = "{}.lora.down.weight".format(x)
            mid_name = None
        elif transformers_lora in lora.keys():
            A_name = transformers_lora
            B_name ="{}.lora_linear_layer.down.weight".format(x)
            mid_name = None

        if A_name is not None:
            mid = None
            if mid_name is not None and mid_name in lora.keys():
                mid = lora[mid_name]
                loaded_keys.add(mid_name)
            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
            loaded_keys.add(A_name)
            loaded_keys.add(B_name)


        ######## loha
        hada_w1_a_name = "{}.hada_w1_a".format(x)
        hada_w1_b_name = "{}.hada_w1_b".format(x)
        hada_w2_a_name = "{}.hada_w2_a".format(x)
        hada_w2_b_name = "{}.hada_w2_b".format(x)
        hada_t1_name = "{}.hada_t1".format(x)
        hada_t2_name = "{}.hada_t2".format(x)
        if hada_w1_a_name in lora.keys():
            hada_t1 = None
            hada_t2 = None
            if hada_t1_name in lora.keys():
                hada_t1 = lora[hada_t1_name]
                hada_t2 = lora[hada_t2_name]
                loaded_keys.add(hada_t1_name)
                loaded_keys.add(hada_t2_name)

            patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale))
            loaded_keys.add(hada_w1_a_name)
            loaded_keys.add(hada_w1_b_name)
            loaded_keys.add(hada_w2_a_name)
            loaded_keys.add(hada_w2_b_name)


        ######## lokr
        lokr_w1_name = "{}.lokr_w1".format(x)
        lokr_w2_name = "{}.lokr_w2".format(x)
        lokr_w1_a_name = "{}.lokr_w1_a".format(x)
        lokr_w1_b_name = "{}.lokr_w1_b".format(x)
        lokr_t2_name = "{}.lokr_t2".format(x)
        lokr_w2_a_name = "{}.lokr_w2_a".format(x)
        lokr_w2_b_name = "{}.lokr_w2_b".format(x)

        lokr_w1 = None
        if lokr_w1_name in lora.keys():
            lokr_w1 = lora[lokr_w1_name]
            loaded_keys.add(lokr_w1_name)

        lokr_w2 = None
        if lokr_w2_name in lora.keys():
            lokr_w2 = lora[lokr_w2_name]
            loaded_keys.add(lokr_w2_name)

        lokr_w1_a = None
        if lokr_w1_a_name in lora.keys():
            lokr_w1_a = lora[lokr_w1_a_name]
            loaded_keys.add(lokr_w1_a_name)

        lokr_w1_b = None
        if lokr_w1_b_name in lora.keys():
            lokr_w1_b = lora[lokr_w1_b_name]
            loaded_keys.add(lokr_w1_b_name)

        lokr_w2_a = None
        if lokr_w2_a_name in lora.keys():
            lokr_w2_a = lora[lokr_w2_a_name]
            loaded_keys.add(lokr_w2_a_name)

        lokr_w2_b = None
        if lokr_w2_b_name in lora.keys():
            lokr_w2_b = lora[lokr_w2_b_name]
            loaded_keys.add(lokr_w2_b_name)

        lokr_t2 = None
        if lokr_t2_name in lora.keys():
            lokr_t2 = lora[lokr_t2_name]
            loaded_keys.add(lokr_t2_name)

        if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
            patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale))

        #glora
        a1_name = "{}.a1.weight".format(x)
        a2_name = "{}.a2.weight".format(x)
        b1_name = "{}.b1.weight".format(x)
        b2_name = "{}.b2.weight".format(x)
        if a1_name in lora:
            patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
            loaded_keys.add(a1_name)
            loaded_keys.add(a2_name)
            loaded_keys.add(b1_name)
            loaded_keys.add(b2_name)

        w_norm_name = "{}.w_norm".format(x)
        b_norm_name = "{}.b_norm".format(x)
        w_norm = lora.get(w_norm_name, None)
        b_norm = lora.get(b_norm_name, None)

        if w_norm is not None:
            loaded_keys.add(w_norm_name)
            patch_dict[to_load[x]] = ("diff", (w_norm,))
            if b_norm is not None:
                loaded_keys.add(b_norm_name)
                patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,))

        diff_name = "{}.diff".format(x)
        diff_weight = lora.get(diff_name, None)
        if diff_weight is not None:
            patch_dict[to_load[x]] = ("diff", (diff_weight,))
            loaded_keys.add(diff_name)

        diff_bias_name = "{}.diff_b".format(x)
        diff_bias = lora.get(diff_bias_name, None)
        if diff_bias is not None:
            patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,))
            loaded_keys.add(diff_bias_name)

    if log_missing:
        for x in lora.keys():
            if x not in loaded_keys:
                logging.warning("lora key not loaded: {}".format(x))

    return patch_dict

def model_lora_keys_clip(model, key_map={}):
    sdk = model.state_dict().keys()
    for k in sdk:
        if k.endswith(".weight"):
            key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names

    text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
    clip_l_present = False
    clip_g_present = False
    for b in range(32): #TODO: clean up
        for c in LORA_CLIP_MAP:
            k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
            if k in sdk:
                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
                key_map[lora_key] = k
                lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
                key_map[lora_key] = k
                lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
                key_map[lora_key] = k

            k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
            if k in sdk:
                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
                key_map[lora_key] = k
                lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
                key_map[lora_key] = k
                clip_l_present = True
                lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
                key_map[lora_key] = k

            k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
            if k in sdk:
                clip_g_present = True
                if clip_l_present:
                    lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
                    key_map[lora_key] = k
                    lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
                    key_map[lora_key] = k
                else:
                    lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
                    key_map[lora_key] = k
                    lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
                    key_map[lora_key] = k
                    lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
                    key_map[lora_key] = k

    for k in sdk:
        if k.endswith(".weight"):
            if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora
                l_key = k[len("t5xxl.transformer."):-len(".weight")]
                t5_index = 1
                if clip_g_present:
                    t5_index += 1
                if clip_l_present:
                    t5_index += 1
                    if t5_index == 2:
                        key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux
                        t5_index += 1

                key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k
            elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
                l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
                lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))
                key_map[lora_key] = k


    k = "clip_g.transformer.text_projection.weight"
    if k in sdk:
        key_map["lora_prior_te_text_projection"] = k #cascade lora?
        # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
        key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora

    k = "clip_l.transformer.text_projection.weight"
    if k in sdk:
        key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning

    return key_map

def model_lora_keys_unet(model, key_map={}):
    sd = model.state_dict()
    sdk = sd.keys()

    for k in sdk:
        if k.startswith("diffusion_model.") and k.endswith(".weight"):
            key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
            key_map["lora_unet_{}".format(key_lora)] = k
            key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
            key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names

    diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
    for k in diffusers_keys:
        if k.endswith(".weight"):
            unet_key = "diffusion_model.{}".format(diffusers_keys[k])
            key_lora = k[:-len(".weight")].replace(".", "_")
            key_map["lora_unet_{}".format(key_lora)] = unet_key
            key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format

            diffusers_lora_prefix = ["", "unet."]
            for p in diffusers_lora_prefix:
                diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
                if diffusers_lora_key.endswith(".to_out.0"):
                    diffusers_lora_key = diffusers_lora_key[:-2]
                key_map[diffusers_lora_key] = unet_key

    if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
        diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
        for k in diffusers_keys:
            if k.endswith(".weight"):
                to = diffusers_keys[k]
                key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format
                key_map[key_lora] = to

                key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
                key_map[key_lora] = to

                key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
                key_map[key_lora] = to

    if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow
        diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
        for k in diffusers_keys:
            if k.endswith(".weight"):
                to = diffusers_keys[k]
                key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format
                key_map[key_lora] = to

    if isinstance(model, comfy.model_base.HunyuanDiT):
        for k in sdk:
            if k.startswith("diffusion_model.") and k.endswith(".weight"):
                key_lora = k[len("diffusion_model."):-len(".weight")]
                key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format

    if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux
        diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
        for k in diffusers_keys:
            if k.endswith(".weight"):
                to = diffusers_keys[k]
                key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
                key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer

    return key_map


def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
    lora_diff *= alpha
    weight_calc = weight + lora_diff.type(weight.dtype)
    weight_norm = (
        weight_calc.transpose(0, 1)
        .reshape(weight_calc.shape[1], -1)
        .norm(dim=1, keepdim=True)
        .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
        .transpose(0, 1)
    )

    weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
    if strength != 1.0:
        weight_calc -= weight
        weight += strength * (weight_calc)
    else:
        weight[:] = weight_calc
    return weight

def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor:
    """
    Pad a tensor to a new shape with zeros.

    Args:
        tensor (torch.Tensor): The original tensor to be padded.
        new_shape (List[int]): The desired shape of the padded tensor.

    Returns:
        torch.Tensor: A new tensor padded with zeros to the specified shape.

    Note:
        If the new shape is smaller than the original tensor in any dimension,
        the original tensor will be truncated in that dimension.
    """
    if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]):
        raise ValueError("The new shape must be larger than the original tensor in all dimensions")

    if len(new_shape) != len(tensor.shape):
        raise ValueError("The new shape must have the same number of dimensions as the original tensor")

    # Create a new tensor filled with zeros
    padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device)

    # Create slicing tuples for both tensors
    orig_slices = tuple(slice(0, dim) for dim in tensor.shape)
    new_slices = tuple(slice(0, dim) for dim in tensor.shape)

    # Copy the original tensor into the new tensor
    padded_tensor[new_slices] = tensor[orig_slices]

    return padded_tensor

def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, original_weights=None):
    for p in patches:
        strength = p[0]
        v = p[1]
        strength_model = p[2]
        offset = p[3]
        function = p[4]
        if function is None:
            function = lambda a: a

        old_weight = None
        if offset is not None:
            old_weight = weight
            weight = weight.narrow(offset[0], offset[1], offset[2])

        if strength_model != 1.0:
            weight *= strength_model

        if isinstance(v, list):
            v = (calculate_weight(v[1:], comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype, copy=True), key, intermediate_dtype=intermediate_dtype), )

        if len(v) == 1:
            patch_type = "diff"
        elif len(v) == 2:
            patch_type = v[0]
            v = v[1]

        if patch_type == "diff":
            diff: torch.Tensor = v[0]
            # An extra flag to pad the weight if the diff's shape is larger than the weight
            do_pad_weight = len(v) > 1 and v[1]['pad_weight']
            if do_pad_weight and diff.shape != weight.shape:
                logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape))
                weight = pad_tensor_to_shape(weight, diff.shape)

            if strength != 0.0:
                if diff.shape != weight.shape:
                    logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape))
                else:
                    weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype))
        elif patch_type == "model_as_lora":
            target_weight: torch.Tensor = v[0]
            diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \
                          comfy.model_management.cast_to_device(original_weights[key][0], weight.device, intermediate_dtype)
            weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype))
        elif patch_type == "lora": #lora/locon
            mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype)
            mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
            dora_scale = v[4]
            if v[2] is not None:
                alpha = v[2] / mat2.shape[0]
            else:
                alpha = 1.0

            if v[3] is not None:
                #locon mid weights, hopefully the math is fine because I didn't properly test it
                mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype)
                final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
                mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
            try:
                lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
                if dora_scale is not None:
                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
                else:
                    weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
            except Exception as e:
                logging.error("ERROR {} {} {}".format(patch_type, key, e))
        elif patch_type == "lokr":
            w1 = v[0]
            w2 = v[1]
            w1_a = v[3]
            w1_b = v[4]
            w2_a = v[5]
            w2_b = v[6]
            t2 = v[7]
            dora_scale = v[8]
            dim = None

            if w1 is None:
                dim = w1_b.shape[0]
                w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype),
                                comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype))
            else:
                w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype)

            if w2 is None:
                dim = w2_b.shape[0]
                if t2 is None:
                    w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype),
                                    comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype))
                else:
                    w2 = torch.einsum('i j k l, j r, i p -> p r k l',
                                        comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
                                        comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype),
                                        comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype))
            else:
                w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype)

            if len(w2.shape) == 4:
                w1 = w1.unsqueeze(2).unsqueeze(2)
            if v[2] is not None and dim is not None:
                alpha = v[2] / dim
            else:
                alpha = 1.0

            try:
                lora_diff = torch.kron(w1, w2).reshape(weight.shape)
                if dora_scale is not None:
                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
                else:
                    weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
            except Exception as e:
                logging.error("ERROR {} {} {}".format(patch_type, key, e))
        elif patch_type == "loha":
            w1a = v[0]
            w1b = v[1]
            if v[2] is not None:
                alpha = v[2] / w1b.shape[0]
            else:
                alpha = 1.0

            w2a = v[3]
            w2b = v[4]
            dora_scale = v[7]
            if v[5] is not None: #cp decomposition
                t1 = v[5]
                t2 = v[6]
                m1 = torch.einsum('i j k l, j r, i p -> p r k l',
                                    comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype),
                                    comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype),
                                    comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype))

                m2 = torch.einsum('i j k l, j r, i p -> p r k l',
                                    comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
                                    comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype),
                                    comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype))
            else:
                m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype),
                                comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype))
                m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype),
                                comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype))

            try:
                lora_diff = (m1 * m2).reshape(weight.shape)
                if dora_scale is not None:
                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
                else:
                    weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
            except Exception as e:
                logging.error("ERROR {} {} {}".format(patch_type, key, e))
        elif patch_type == "glora":
            dora_scale = v[5]

            old_glora = False
            if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
                rank = v[0].shape[0]
                old_glora = True

            if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
                if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]:
                    pass
                else:
                    old_glora = False
                    rank = v[1].shape[0]

            a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
            a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
            b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
            b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)

            if v[4] is not None:
                alpha = v[4] / rank
            else:
                alpha = 1.0

            try:
                if old_glora:
                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora
                else:
                    if weight.dim() > 2:
                        lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
                    else:
                        lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
                    lora_diff += torch.mm(b1, b2).reshape(weight.shape)

                if dora_scale is not None:
                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
                else:
                    weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
            except Exception as e:
                logging.error("ERROR {} {} {}".format(patch_type, key, e))
        else:
            logging.warning("patch type not recognized {} {}".format(patch_type, key))

        if old_weight is not None:
            weight = old_weight

    return weight