nodes/lora_nodes.py

import torch
import comfy.model_management
import comfy.utils
import folder_paths
import os
import logging
from tqdm import tqdm
import numpy as np

device = comfy.model_management.get_torch_device()

CLAMP_QUANTILE = 0.99

def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptive_param=1.0, clamp_quantile=True):
    """
    Extracts LoRA weights from a weight difference tensor using SVD.
    """
    conv2d = (len(diff.shape) == 4)
    kernel_size = None if not conv2d else diff.size()[2:4]
    conv2d_3x3 = conv2d and kernel_size != (1, 1)
    out_dim, in_dim = diff.size()[0:2]

    if conv2d:
        if conv2d_3x3:
            diff = diff.flatten(start_dim=1)
        else:
            diff = diff.squeeze()

    diff_float = diff.float()
    if algorithm == "svd_lowrank":
        U, S, V = torch.svd_lowrank(diff_float, q=min(rank, in_dim, out_dim), niter=lowrank_iters)
        U = U @ torch.diag(S)
        Vh = V.t()
    else:
        #torch.linalg.svdvals() 
        U, S, Vh = torch.linalg.svd(diff_float)
        # Flexible rank selection logic like locon: https://github.com/KohakuBlueleaf/LyCORIS/blob/main/tools/extract_locon.py
        if "adaptive" in lora_type:
            if lora_type == "adaptive_ratio":
                min_s = torch.max(S) * adaptive_param
                lora_rank = torch.sum(S > min_s).item()
            elif lora_type == "adaptive_energy":
                energy = torch.cumsum(S**2, dim=0)
                total_energy = torch.sum(S**2)
                threshold = adaptive_param * total_energy  # e.g., adaptive_param=0.95 for 95%
                lora_rank = torch.sum(energy < threshold).item() + 1
            elif lora_type == "adaptive_quantile":
                s_cum = torch.cumsum(S, dim=0)
                min_cum_sum = adaptive_param * torch.sum(S)
                lora_rank = torch.sum(s_cum < min_cum_sum).item()
            elif lora_type == "adaptive_fro":
                S_squared = S.pow(2)
                S_fro_sq = float(torch.sum(S_squared))
                sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
                lora_rank = int(torch.searchsorted(sum_S_squared, adaptive_param**2)) + 1
                lora_rank = max(1, min(lora_rank, len(S)))
            else:
                pass  # Will print after capping
            
            # Cap adaptive rank by the specified max rank
            lora_rank = min(lora_rank, rank)
            
            # Calculate and print actual fro percentage retained after capping
            if lora_type == "adaptive_fro":
                S_squared = S.pow(2)
                s_fro = torch.sqrt(torch.sum(S_squared))
                s_red_fro = torch.sqrt(torch.sum(S_squared[:lora_rank]))
                fro_percent = float(s_red_fro / s_fro)
                print(f"{key} Extracted LoRA rank: {lora_rank}, Frobenius retained: {fro_percent:.1%}")
            else:
                print(f"{key} Extracted LoRA rank: {lora_rank}")
        else:
            lora_rank = rank

        lora_rank = max(1, lora_rank)
        lora_rank = min(out_dim, in_dim, lora_rank)
        
        U = U[:, :lora_rank]
        S = S[:lora_rank]
        U = U @ torch.diag(S)
        Vh = Vh[:lora_rank, :]

    if clamp_quantile:
        dist = torch.cat([U.flatten(), Vh.flatten()])
        if dist.numel() > 100_000:
            # Sample 100,000 elements for quantile estimation
            idx = torch.randperm(dist.numel(), device=dist.device)[:100_000]
            dist_sample = dist[idx]
            hi_val = torch.quantile(dist_sample, CLAMP_QUANTILE)
        else:
            hi_val = torch.quantile(dist, CLAMP_QUANTILE)
        low_val = -hi_val

        U = U.clamp(low_val, hi_val)
        Vh = Vh.clamp(low_val, hi_val)
    if conv2d:
        U = U.reshape(out_dim, lora_rank, 1, 1)
        Vh = Vh.reshape(lora_rank, in_dim, kernel_size[0], kernel_size[1])
    return (U, Vh)


def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, algorithm, lowrank_iters, out_dtype, bias_diff=False, adaptive_param=1.0, clamp_quantile=True):
    comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
    model_diff.model.diffusion_model.cpu()
    sd = model_diff.model_state_dict(filter_prefix=prefix_model)
    del model_diff
    comfy.model_management.soft_empty_cache()
    for k, v in sd.items():
        if isinstance(v, torch.Tensor):
            sd[k] = v.cpu()

    # Get total number of keys to process for progress bar
    total_keys = len([k for k in sd if k.endswith(".weight") or (bias_diff and k.endswith(".bias"))])
    
    # Create progress bar
    progress_bar = tqdm(total=total_keys, desc=f"Extracting LoRA ({prefix_lora.strip('.')})")
    comfy_pbar = comfy.utils.ProgressBar(total_keys)

    for k in sd:
        if k.endswith(".weight"):
            weight_diff = sd[k]
            if weight_diff.ndim == 5:
                logging.info(f"Skipping 5D tensor for key {k}") #skip patch embed
                progress_bar.update(1)
                comfy_pbar.update(1)
                continue
            if lora_type != "full":
                if weight_diff.ndim < 2:
                    if bias_diff:
                        output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu()
                    progress_bar.update(1)
                    comfy_pbar.update(1)
                    continue
                try:
                    out = extract_lora(weight_diff.to(device), k, rank, algorithm, lora_type, lowrank_iters=lowrank_iters, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
                    output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().to(out_dtype).cpu()
                    output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().to(out_dtype).cpu()
                except Exception as e:
                    logging.warning(f"Could not generate lora weights for key {k}, error {e}")
            else:
                output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu()
            
            progress_bar.update(1)
            comfy_pbar.update(1)

        elif bias_diff and k.endswith(".bias"):
            output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().to(out_dtype).cpu()
            progress_bar.update(1)
            comfy_pbar.update(1)
    progress_bar.close()
    return output_sd

class LoraExtractKJ:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": 
                {
                    "finetuned_model": ("MODEL",),
                    "original_model": ("MODEL",),
                    "filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
                    "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1, "tooltip": "The rank to use for standard LoRA, or maximum rank limit for adaptive methods."}),
                    "lora_type": (["standard", "full", "adaptive_ratio", "adaptive_quantile", "adaptive_energy", "adaptive_fro"],),
                    "algorithm": (["svd_linalg", "svd_lowrank"], {"default": "svd_linalg", "tooltip": "SVD algorithm to use, svd_lowrank is faster but less accurate."}),
                    "lowrank_iters": ("INT", {"default": 7, "min": 1, "max": 100, "step": 1, "tooltip": "The number of subspace iterations for lowrank SVD algorithm."}),
                    "output_dtype": (["fp16", "bf16", "fp32"], {"default": "fp16"}),
                    "bias_diff": ("BOOLEAN", {"default": True}),
                    "adaptive_param": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "For ratio mode, this is the ratio of the maximum singular value. For quantile mode, this is the quantile of the singular values. For fro mode, this is the Frobenius norm retention ratio."}),
                    "clamp_quantile": ("BOOLEAN", {"default": True}),
                },

    }
    RETURN_TYPES = ()
    FUNCTION = "save"
    OUTPUT_NODE = True

    CATEGORY = "KJNodes/lora"

    def save(self, finetuned_model, original_model, filename_prefix, rank, lora_type, algorithm, lowrank_iters, output_dtype, bias_diff, adaptive_param, clamp_quantile):
        if algorithm == "svd_lowrank" and lora_type != "standard":
            raise ValueError("svd_lowrank algorithm is only supported for standard LoRA extraction.")

        dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[output_dtype]
        m = finetuned_model.clone()
        kp = original_model.get_key_patches("diffusion_model.")
        for k in kp:
            m.add_patches({k: kp[k]}, - 1.0, 1.0)
        model_diff = m

        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)

        output_sd = {}
        if model_diff is not None:
            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
        if "adaptive" in lora_type:
            rank_str = f"{lora_type}_{adaptive_param:.2f}"
        else:
            rank_str = rank
        output_checkpoint = f"{filename}_rank_{rank_str}_{output_dtype}_{counter:05}_.safetensors"
        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)

        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
        return {}

NODE_CLASS_MAPPINGS = {
    "LoraExtractKJ": LoraExtractKJ
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "LoraExtractKJ": "LoraExtractKJ"
}

class LoraReduceRank:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": 
                {
                    "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
                    "new_rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1, "tooltip": "The new rank to resize the LoRA. Acts as max rank when using dynamic_method."}),
                    "dynamic_method": (["disabled", "sv_ratio", "sv_cumulative", "sv_fro"], {"default": "disabled", "tooltip": "Method to use for dynamically determining new alphas and dims"}),
                    "dynamic_param": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Method to use for dynamically determining new alphas and dims"}),
                    "output_dtype": (["match_original", "fp16", "bf16", "fp32"], {"default": "match_original", "tooltip": "Data type to save the LoRA as."}),
                    "verbose": ("BOOLEAN", {"default": True}),
                },

    }
    RETURN_TYPES = ()
    FUNCTION = "save"
    OUTPUT_NODE = True
    EXPERIMENTAL = True
    DESCRIPTION = "Resize a LoRA model by reducing it's rank. Based on kohya's sd-scripts: https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py"

    CATEGORY = "KJNodes/lora"

    def save(self, lora_name, new_rank, output_dtype, dynamic_method, dynamic_param, verbose):

        lora_path = folder_paths.get_full_path("loras", lora_name)
        lora_sd, metadata = comfy.utils.load_torch_file(lora_path, return_metadata=True)

        if output_dtype == "fp16":
            save_dtype = torch.float16
        elif output_dtype == "bf16":
            save_dtype = torch.bfloat16
        elif output_dtype == "fp32":
            save_dtype = torch.float32
        elif output_dtype == "match_original":
            first_weight_key = next(k for k in lora_sd if k.endswith(".weight") and isinstance(lora_sd[k], torch.Tensor))
            save_dtype = lora_sd[first_weight_key].dtype

        new_lora_sd = {}
        for k, v in lora_sd.items():
            new_lora_sd[k.replace(".default", "")] = v
        del lora_sd
        print("Resizing Lora...")
        output_sd, old_dim, new_alpha, rank_list = resize_lora_model(new_lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose)

        # update metadata
        if metadata is None:
            metadata = {}

        comment = metadata.get("ss_training_comment", "")

        if dynamic_method == "disabled":
            metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {new_rank}; {comment}"
            metadata["ss_network_dim"] = str(new_rank)
            metadata["ss_network_alpha"] = str(new_alpha)
        else:
            metadata["ss_training_comment"] = f"Dynamic resize with {dynamic_method}: {dynamic_param} from {old_dim}; {comment}"
            metadata["ss_network_dim"] = "Dynamic"
            metadata["ss_network_alpha"] = "Dynamic"

        # cast to save_dtype before calculating hashes
        for key in list(output_sd.keys()):
            value = output_sd[key]
            if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
                output_sd[key] = value.to(save_dtype)

        output_filename_prefix = "loras/" + lora_name

        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(output_filename_prefix, self.output_dir)
        output_dtype_str = f"_{output_dtype}" if output_dtype != "match_original" else ""
        average_rank = str(int(np.mean(rank_list)))
        rank_str = new_rank if dynamic_method == "disabled" else f"dynamic_{average_rank}"
        output_checkpoint = f"{filename.replace('.safetensors', '')}_resized_from_{old_dim}_to_{rank_str}{output_dtype_str}_{counter:05}_.safetensors"
        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
        print(f"Saving resized LoRA to {output_checkpoint}")

        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=metadata)
        return {}

NODE_CLASS_MAPPINGS = {
    "LoraExtractKJ": LoraExtractKJ
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "LoraExtractKJ": "LoraExtractKJ"
}

# Convert LoRA to different rank approximation (should only be used to go to lower rank)
# This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
# Thanks to cloneofsimo

# This version is based on
# https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py

MIN_SV = 1e-6

LORA_DOWN_UP_FORMATS = [
    ("lora_down", "lora_up"),  # sd-scripts LoRA
    ("lora_A", "lora_B"),  # PEFT LoRA
    ("down", "up"),  # ControlLoRA
]

# Indexing functions
def index_sv_cumulative(S, target):
    original_sum = float(torch.sum(S))
    cumulative_sums = torch.cumsum(S, dim=0) / original_sum
    index = int(torch.searchsorted(cumulative_sums, target)) + 1
    index = max(1, min(index, len(S) - 1))

    return index


def index_sv_fro(S, target):
    S_squared = S.pow(2)
    S_fro_sq = float(torch.sum(S_squared))
    sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
    index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
    index = max(1, min(index, len(S) - 1))

    return index


def index_sv_ratio(S, target):
    max_sv = S[0]
    min_sv = max_sv / target
    index = int(torch.sum(S > min_sv).item())
    index = max(1, min(index, len(S) - 1))

    return index


# Modified from Kohaku-blueleaf's extract/merge functions
def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
    out_size, in_size, kernel_size, _ = weight.size()
    if weight.dtype != torch.float32:
        weight = weight.to(torch.float32)
    U, S, Vh = torch.linalg.svd(weight.reshape(out_size, -1).to(device))

    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
    lora_rank = param_dict["new_rank"]

    U = U[:, :lora_rank]
    S = S[:lora_rank]
    U = U @ torch.diag(S)
    Vh = Vh[:lora_rank, :]

    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
    param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
    del U, S, Vh, weight
    return param_dict


def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
    out_size, in_size = weight.size()

    if weight.dtype != torch.float32:
        weight = weight.to(torch.float32)
    U, S, Vh = torch.linalg.svd(weight.to(device))

    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
    lora_rank = param_dict["new_rank"]

    U = U[:, :lora_rank]
    S = S[:lora_rank]
    U = U @ torch.diag(S)
    Vh = Vh[:lora_rank, :]

    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
    param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
    del U, S, Vh, weight
    return param_dict


def merge_conv(lora_down, lora_up, device):
    in_rank, in_size, kernel_size, k_ = lora_down.shape
    out_size, out_rank, _, _ = lora_up.shape
    assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"

    lora_down = lora_down.to(device)
    lora_up = lora_up.to(device)

    merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
    weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
    del lora_up, lora_down
    return weight


def merge_linear(lora_down, lora_up, device):
    in_rank, in_size = lora_down.shape
    out_size, out_rank = lora_up.shape
    assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"

    lora_down = lora_down.to(device)
    lora_up = lora_up.to(device)

    weight = lora_up @ lora_down
    del lora_up, lora_down
    return weight


# Calculate new rank


def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
    param_dict = {}

    if dynamic_method == "sv_ratio":
        # Calculate new dim and alpha based off ratio
        new_rank = index_sv_ratio(S, dynamic_param) + 1
        new_alpha = float(scale * new_rank)

    elif dynamic_method == "sv_cumulative":
        # Calculate new dim and alpha based off cumulative sum
        new_rank = index_sv_cumulative(S, dynamic_param) + 1
        new_alpha = float(scale * new_rank)

    elif dynamic_method == "sv_fro":
        # Calculate new dim and alpha based off sqrt sum of squares
        new_rank = index_sv_fro(S, dynamic_param) + 1
        new_alpha = float(scale * new_rank)
    else:
        new_rank = rank
        new_alpha = float(scale * new_rank)

    if S[0] <= MIN_SV:  # Zero matrix, set dim to 1
        new_rank = 1
        new_alpha = float(scale * new_rank)
    elif new_rank > rank:  # cap max rank at rank
        new_rank = rank
        new_alpha = float(scale * new_rank)

    # Calculate resize info
    s_sum = torch.sum(torch.abs(S))
    s_rank = torch.sum(torch.abs(S[:new_rank]))

    S_squared = S.pow(2)
    s_fro = torch.sqrt(torch.sum(S_squared))
    s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
    fro_percent = float(s_red_fro / s_fro)

    param_dict["new_rank"] = new_rank
    param_dict["new_alpha"] = new_alpha
    param_dict["sum_retained"] = (s_rank) / s_sum
    param_dict["fro_retained"] = fro_percent
    param_dict["max_ratio"] = S[0] / S[new_rank - 1]

    return param_dict


def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
    max_old_rank = None
    new_alpha = None
    verbose_str = "\n"
    fro_list = []
    rank_list = []

    if dynamic_method:
        print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")

    lora_down_weight = None
    lora_up_weight = None

    o_lora_sd = lora_sd.copy()
    block_down_name = None
    block_up_name = None

    total_keys = len([k for k in lora_sd if k.endswith(".weight")])

    pbar = comfy.utils.ProgressBar(total_keys)
    for key, value in tqdm(lora_sd.items(), leave=True, desc="Resizing LoRA weights"):
        key_parts = key.split(".")
        block_down_name = None
        for _format in LORA_DOWN_UP_FORMATS:
            # Currently we only match lora_down_name in the last two parts of key
            # because ("down", "up") are general words and may appear in block_down_name
            if len(key_parts) >= 2 and _format[0] == key_parts[-2]:
                block_down_name = ".".join(key_parts[:-2])
                lora_down_name = "." + _format[0]
                lora_up_name = "." + _format[1]
                weight_name = "." + key_parts[-1]
                break
            if len(key_parts) >= 1 and _format[0] == key_parts[-1]:
                block_down_name = ".".join(key_parts[:-1])
                lora_down_name = "." + _format[0]
                lora_up_name = "." + _format[1]
                weight_name = ""
                break

        if block_down_name is None:
            # This parameter is not lora_down
            continue

        # Now weight_name can be ".weight" or ""
        # Find corresponding lora_up and alpha
        block_up_name = block_down_name
        lora_down_weight = value
        lora_up_weight = lora_sd.get(block_up_name + lora_up_name + weight_name, None)
        lora_alpha = lora_sd.get(block_down_name + ".alpha", None)

        weights_loaded = lora_down_weight is not None and lora_up_weight is not None

        if weights_loaded:

            conv2d = len(lora_down_weight.size()) == 4
            old_rank = lora_down_weight.size()[0]
            max_old_rank = max(max_old_rank or 0, old_rank)
            

            if lora_alpha is None:
                scale = 1.0
            else:
                scale = lora_alpha / old_rank

            if conv2d:
                full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
                param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
            else:
                full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
                param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)

            if verbose:
                max_ratio = param_dict["max_ratio"]
                sum_retained = param_dict["sum_retained"]
                fro_retained = param_dict["fro_retained"]
                if not np.isnan(fro_retained):
                    fro_list.append(float(fro_retained))
                log_str = f"{block_down_name:75} | sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}, new dim: {param_dict['new_rank']}"
                tqdm.write(log_str)
                verbose_str += log_str
            
            if verbose and dynamic_method:
                verbose_str += f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
            else:
                verbose_str += "\n"

            new_alpha = param_dict["new_alpha"]
            o_lora_sd[block_down_name + lora_down_name + weight_name] = param_dict["lora_down"].to(save_dtype).contiguous()
            o_lora_sd[block_up_name + lora_up_name + weight_name] = param_dict["lora_up"].to(save_dtype).contiguous()
            o_lora_sd[block_down_name + ".alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)

            block_down_name = None
            block_up_name = None
            lora_down_weight = None
            lora_up_weight = None
            weights_loaded = False
            rank_list.append(param_dict["new_rank"])
            del param_dict
        pbar.update(1)

    if verbose:
        print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
    return o_lora_sd, max_old_rank, new_alpha, rank_list