How to Merge a Lora to Checkpoint in Flux

How to Merge Flux LoRA into a Checkpoint: A Step-by-Step Tutorial

Merging a Flux LoRA model into a base checkpoint is a great way to enhance the capabilities of your pre-trained models. Whether you’re working with AI models like Stable Diffusion or any custom-trained model, using LoRA allows you to introduce fine-tuned control over the features and style of your model. In this tutorial, I'll walk you through how to merge a Flux LoRA model into your checkpoint using a Python script.

Prerequisites

Before we start, make sure you have the following installed on your system:

• Python 3.6+

• Git (for version control, if needed)

• Necessary Python libraries, which you can install using:

  pip install torch safetensors tqdm

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File Setup

Here’s how you should organize your files:

1. LoRA Model: Download and place your Flux LoRA model in a folder called lora_models.
2. Base Checkpoint Model: Place your pre-trained base model in a folder called checkpoints/input.

You should also create an output folder to store your merged models, called checkpoints/output.

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Folder Structure

Make sure your project structure looks like this:

/lora_models                  # Folder for your LoRA model
/checkpoints/input            # Base checkpoint model
/checkpoints/output           # Merged model output

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The Python Script: LoRA to Checkpoint Merger

Below is the Python script you’ll use to merge your Flux LoRA model into the base checkpoint. This script has been designed for flexibility — you can either fully blend the models or mix them using different weights.

import os
import torch
from tqdm import tqdm
from safetensors.torch import load_file, save_file

# Main entry point
def merge_lora_with_checkpoint(config):
    print(f"\nStarting the merge process with configuration: {config}")

    # Load LoRA and checkpoint
    lora_dir = "lora_models"
    checkpoint_dir = "checkpoints/input"
    output_dir = "checkpoints/output"

    lora_path = os.path.join(lora_dir, config['lora_file'])
    checkpoint_path = os.path.join(checkpoint_dir, config['checkpoint_file'])

    # Ensure output directory exists
    os.makedirs(output_dir, exist_ok=True)

    lora_data = load_file(lora_path)
    checkpoint_data = load_file(checkpoint_path)

    # Merge based on the selected strategy
    if config['merge_type'] == 'blend':
        merged_model = full_merge(lora_data, checkpoint_data, config['merge_ratio'])
    else:
        merged_model = selective_merge(lora_data, checkpoint_data, config['merge_weights'])

    save_merged_model(merged_model, output_dir, config['lora_file'], config['checkpoint_file'], config['merge_ratio'])

    print("Merge completed successfully!")

# Full model merging with a specific ratio
def full_merge(lora_data, checkpoint_data, ratio):
    merged = {}
    total_layers = set(checkpoint_data.keys()).union(lora_data.keys())

    for layer in tqdm(total_layers, desc="Merging Layers", unit="layer"):
        if layer in checkpoint_data and layer in lora_data:
            merged[layer] = checkpoint_data[layer] + (ratio * lora_data[layer])
        elif layer in checkpoint_data:
            merged[layer] = checkpoint_data[layer]
        else:
            merged[layer] = ratio * lora_data[layer]
    
    return merged

# Selective merge with different ratios per layer
def selective_merge(lora_data, checkpoint_data, merge_weights):
    merged = {}
    total_layers = set(checkpoint_data.keys()).union(lora_data.keys())

    for layer in tqdm(total_layers, desc="Selective Merging", unit="layer"):
        if layer in merge_weights:
            ratio = merge_weights[layer]
            merged[layer] = checkpoint_data.get(layer, 0) + (ratio * lora_data.get(layer, 0))
        else:
            merged[layer] = checkpoint_data.get(layer, lora_data.get(layer))

    return merged

# Save the merged model to disk
def save_merged_model(merged_data, output_dir, lora_file, checkpoint_file, ratio):
    lora_name = os.path.splitext(lora_file)[0]
    checkpoint_name = os.path.splitext(checkpoint_file)[0]
    output_file = f"{checkpoint_name}_merged_with_{lora_name}_r{int(ratio * 100)}.safetensors"
    output_path = os.path.join(output_dir, output_file)

    save_file(merged_data, output_path)
    print(f"Model saved as: {output_file}")

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Running the Script

Once you have set up your files, simply run the Python script:

python main.py

You will be prompted for the following input:

• LoRA Model File: Enter the name of your LoRA model (e.g., flux_lora.safetensors).
• Checkpoint Model File: Enter the name of your base checkpoint (e.g., base_checkpoint.safetensors).
• Merge Type: Choose between blend (to fully merge the models with a given ratio) or selective (to apply different weights for different layers).
• Merge Ratio: If blending, specify how much influence the LoRA model should have (e.g., 0.3 for 30%).

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Testing the Merged Model

After the merge process, the merged model will be saved in the checkpoints/output folder. You can load this merged checkpoint into your favorite AI application, like Stable Diffusion, to start generating images.

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FAQ: Common Questions About Merging LoRA and Checkpoints

Q1: What is a LoRA model?
A: LoRA (Low-Rank Adaptation) models are used to fine-tune large pre-trained models efficiently. They adapt the weights of specific layers without retraining the entire model.

Q2: What happens if the sizes of the layers in my LoRA and checkpoint models don't match?
A: The script handles this by padding the smaller tensor so that both tensors match in size before merging. This ensures compatibility between different models.

Q3: Can I merge multiple LoRA models into one checkpoint?
A: Yes! You can run the script multiple times, each time merging a new LoRA model into the previously merged checkpoint.

Q4: What’s the ideal merge ratio?
A: This depends on your use case. If you want the LoRA model to have a significant influence, try a merge ratio of around 30-40%. For subtler effects, 10-20% may be sufficient.

Q5: Is there a limit to how many LoRA models I can merge?
A: There is no strict limit, but merging too many models could lead to unintended behavior or loss of clarity in your model’s outputs.

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