click here to download the project base paper supervised learning project

ABSTRACT

Learning from human feedback has been shown to improve text-to-image models of general projects. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function. Even though relatively simple approaches have been investigated, fine-tuning text-to-image models with the reward function remains challenging. In this work, we propose using online reinforcement learning (RL) to fine-tune text-to-image models. We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward. Our approach, coined DPOK, integrates policy optimization with KL regularization. In our experiments, we show that DPOK is generally superior to supervised fine-tuning concerning both image-text alignment and image quality.

INTRODUCTION
Recent advances in diffusion models, together with pre-trained of text encoders have led to impressive results in text-to-image generation. Large-scale text-to-image models, generate high-quality, creative images given novel text prompts. However, despite these advances, current models have systematic weaknesses. For example, current models have a limited ability to compose multiple objects.
Learning from human feedback (LHF) has proven to be an effective means to overcome these limitations demonstrating that certain properties, such as generating objects with specific colours, counts, and backgrounds, can be improved by learning a reward function from human feedback, followed by fine-tuning the text-to-image model using supervised learning.

Title: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Abstract:
This postgraduate project focuses on enhancing Text-to-Image Diffusion Models through the application of Reinforcement Learning (RL). The goal is to refine the generation of realistic images from textual descriptions by employing RL techniques to fine-tune existing diffusion models. The project explores how RL can improve the quality, diversity, and coherence of generated images in response to textual prompts.

Existing System:
Current Text-to-Image models often face challenges in generating high-quality and diverse images that faithfully represent textual input. The existing systems may produce artifacts, lack coherence, or struggle with intricate details in complex descriptions.

Proposed System:
The proposed system integrates Reinforcement Learning into Text-to-Image Diffusion Models, allowing for targeted fine-tuning. By incorporating RL, the system aims to address the limitations of existing models, offering more precise control over the image generation process and improving the overall quality of generated images.

Module-wise Explanation:

1. Text Embedding Module: Converts textual descriptions into a format suitable for processing by the model.
2. Diffusion Model Module: Utilizes diffusion models to generate initial images from text.
3. Reinforcement Learning Module: Fine-tunes generated images using RL techniques, optimizing for improved quality, diversity, and coherence.
4. Evaluation Module: Assesses the quality of generated images through metrics such as Inception Score, Fréchet Inception Distance, and user feedback.

System Requirements:

• GPU-accelerated hardware for efficient model training.
• Python runtime environment.
• Deep learning libraries (e.g., TensorFlow, PyTorch).
• Reinforcement learning libraries (e.g., OpenAI Gym).

Algorithms:

• Text Embedding: Word embeddings (e.g., Word2Vec, GloVe).
• Diffusion Model: Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs).
• Reinforcement Learning: Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO).

Hardware and Software Requirements:

• Hardware: GPU (NVIDIA CUDA-compatible) for accelerated model training.
• Software: Operating system (e.g., Linux), Python interpreter, deep learning frameworks, RL libraries.

Architecture:
The system adopts a modular architecture, with components for text processing, image generation, RL fine-tuning, and evaluation. These components interact seamlessly to refine the image generation process.

Technologies Used:

• Deep learning frameworks: TensorFlow or PyTorch.
• Reinforcement learning libraries: OpenAI Gym.
• Text processing tools: NLTK or SpaCy.

User Interface:
While the primary focus is on model development, a simple web-based interface may be designed for users to input textual prompts and view generated images. The interface could provide options for users to fine-tune specific aspects of the generated images through RL-based controls.

Input:

• Textual descriptions or prompts for image generation.

Output:

• High-quality, diverse, and coherent images generated in response to textual prompts. Evaluation metrics assessing the quality of generated images.

UML DIAGRAMS

Collaboration Diagram

Architecture diagram

class diagram

sequence diagram

use case diagram

activity diagram

component diagram

Deployment Diagram

Flow chart Diagram