merge_lora.py

import os
from safetensors.torch import load_file, save_file
from transformers import AutoTokenizer, AutoModel
from diffusers import StableDiffusionPipeline
import torch

# Paths to your LoRA files
file1_path = "hansika motwaniF1.safetensors"
file2_path = "lora.safetensors"
file3_path = "NSFW_master.safetensors"
merged_file_path = "merged_lora.safetensors"

def load_tensors(file_path):
    return load_file(file_path)

try:
    tensors1 = load_tensors(file1_path)
    tensors2 = load_tensors(file2_path)
    tensors3 = load_tensors(file3_path)

    # Merge tensors
    merged_tensors = {**tensors1, **tensors2, **tensors3}

    # Save merged tensors
    save_file(merged_tensors, merged_file_path)

    print(f"Merged file saved at: {merged_file_path}")

    # Validate the merged file
    merged_load = load_file(merged_file_path)
    print(merged_load.keys())

    try:
        # Load the tokenizer and model
        tokenizer = AutoTokenizer.from_pretrained("Tjay143/HijabGirls")
        model = AutoModel.from_pretrained("Tjay143/HijabGirls", from_tf=False, from_safetensors=True)
        pipeline = StableDiffusionPipeline.from_pretrained("Tjay143/HijabGirls", torch_dtype=torch.float16)
        print("It is successfully loaded!")
    except Exception as e:
        print(f"Error: {e}")
except Exception as e:
    print(f"An error occurred: {e}")