lora_diffusion/cli_lora_add.py

from typing import Literal, Union, Dict

import fire
from diffusers import StableDiffusionPipeline

import torch
from .lora import tune_lora_scale, weight_apply_lora


def add(
    path_1: str,
    path_2: str,
    output_path: str = "./merged_lora.pt",
    alpha: float = 0.5,
    mode: Literal["lpl", "upl"] = "lpl",
):
    if mode == "lpl":
        out_list = []
        l1 = torch.load(path_1)
        l2 = torch.load(path_2)

        l1pairs = zip(l1[::2], l1[1::2])
        l2pairs = zip(l2[::2], l2[1::2])

        for (x1, y1), (x2, y2) in zip(l1pairs, l2pairs):
            x1.data = alpha * x1.data + (1 - alpha) * x2.data
            y1.data = alpha * y1.data + (1 - alpha) * y2.data

            out_list.append(x1)
            out_list.append(y1)

        torch.save(out_list, output_path)

    elif mode == "upl":

        loaded_pipeline = StableDiffusionPipeline.from_pretrained(
            path_1,
        ).to("cpu")

        weight_apply_lora(loaded_pipeline.unet, torch.load(path_2), alpha=alpha)

        if output_path.endswith(".pt"):
            output_path = output_path[:-3]

        loaded_pipeline.save_pretrained(output_path)


def main():
    fire.Fire(add)