Attempting to intergrate IP Adapter for DiT / Next-DiT based architectures
Certainly! Here's a comprehensive README.md for your GitHub repository focused on creating an IP-Adapter implementation for DiT models, starting with Lumina-Next-SFT:
# IP-Adapter for DiT Models
## Overview
This project aims to implement and adapt the IP-Adapter (Image Prompt Adapter) technique for Diffusion Transformer (DiT) models, with a particular focus on Lumina-Next-SFT. Our goal is to enhance text-to-image generation capabilities by incorporating image prompts into DiT-based architectures.
## Table of Contents
1. [Introduction](#introduction)
2. [Features](#features)
3. [Getting Started](#getting-started)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Model Architecture](#model-architecture)
7. [Training](#training)
8. [Evaluation](#evaluation)
9. [Results](#results)
10. [Contributing](#contributing)
11. [License](#license)
12. [Acknowledgements](#acknowledgements)
## Introduction
IP-Adapter is a technique that allows the integration of image prompts into text-to-image diffusion models. This project extends this concept to DiT models, starting with Lumina-Next-SFT. By doing so, we aim to improve the control and quality of generated images in DiT-based systems.
## Features
- IP-Adapter implementation compatible with DiT architectures
- Support for Lumina-Next-SFT as the initial target model
- Flexible design to accommodate various DiT-based models
- Training pipeline for fine-tuning IP-Adapter on custom datasets
- Evaluation tools to assess the quality and fidelity of generated images
## Getting Started
### Prerequisites
- Python 3.8+
- PyTorch 1.9+
- Transformers library
- Diffusers library
- Accelerate library
### Installation
1. Clone the repository:git clone https://github.com/yourusername/ip-adapter-dit.git cd ip-adapter-dit
2. Install the required packages:
pip install -r requirements.txt
## Usage
To train the IP-Adapter for Lumina-Next-SFT:
python train_ip_adapter.py --pretrained_model_name_or_path /path/to/lumina-next-sft
--data_json_file /path/to/your/dataset.json
--data_root_path /path/to/your/images
--image_encoder_path /path/to/clip/image/encoder
--output_dir ./output
For inference:
python inference.py --model_path ./output/best_model
--prompt "A scenic landscape"
--image_prompt /path/to/reference/image.jpg
## Model Architecture
Our IP-Adapter implementation for DiT models consists of:
1. A DiT-based backbone (e.g., Lumina-Next-SFT)
2. An image encoder (e.g., CLIP vision model)
3. A text encoder (e.g., Gemma-2b)
4. An IP-Adapter module that injects image information into the DiT's attention layers
[Include a diagram of the architecture if possible]
## Training
The training process involves:
1. Encoding input images and text prompts
2. Generating noisy latents
3. Predicting the noise using the IP-Adapter enhanced DiT model
4. Computing the loss and updating the model parameters
Detailed training configurations and hyperparameters can be found in `config.yaml`.
## Evaluation
We evaluate our model using:
- FID (Fréchet Inception Distance) for image quality
- CLIP score for text-image alignment
- Human evaluation for subjective quality assessment