FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models (2407.19453v1)

Abstract: In recent years, large-scale pre-trained diffusion models have demonstrated their outstanding capabilities in image and video generation tasks. However, existing models tend to produce visual objects commonly found in the training dataset, which diverges from user input prompts. The underlying reason behind the inaccurate generated results lies in the model's difficulty in sampling from specific intervals of the initial noise distribution corresponding to the prompt. Moreover, it is challenging to directly optimize the initial distribution, given that the diffusion process involves multiple denoising steps. In this paper, we introduce a Fine-tuning Initial Noise Distribution (FIND) framework with policy optimization, which unleashes the powerful potential of pre-trained diffusion networks by directly optimizing the initial distribution to align the generated contents with user-input prompts. To this end, we first reformulate the diffusion denoising procedure as a one-step Markov decision process and employ policy optimization to directly optimize the initial distribution. In addition, a dynamic reward calibration module is proposed to ensure training stability during optimization. Furthermore, we introduce a ratio clipping algorithm to utilize historical data for network training and prevent the optimized distribution from deviating too far from the original policy to restrain excessive optimization magnitudes. Extensive experiments demonstrate the effectiveness of our method in both text-to-image and text-to-video tasks, surpassing SOTA methods in achieving consistency between prompts and the generated content. Our method achieves 10 times faster than the SOTA approach. Our homepage is available at \url{https://github.com/vpx-ecnu/FIND-website}.

• The paper introduces the FIND framework, fine-tuning initial noise distribution via policy optimization to improve semantic alignment with prompts.
• It reformulates the denoising process as a one-step Markov decision process with dynamic reward calibration and ratio clipping.
• Experimental results show a tenfold speed increase and enhanced performance in both text-to-image and text-to-video tasks.

Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models

The paper "FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models" presents a novel approach to enhance the consistency between generated content and user prompts in pre-trained diffusion models. This research addresses a notable limitation in existing diffusion models, which often struggle to produce artifacts that align closely with user input prompts despite their recognized prowess in image and video generation.

Summary of Contributions

The core contribution of this paper is the introduction of the Fine-tuning Initial Noise Distribution (FIND) framework, which leverages policy optimization to directly adjust the initial noise distribution. The paper posits that inaccuracies in generated content originate partly from a bias in sampling the initial noise distribution. By optimizing this distribution, the authors aim to improve the semantic alignment with prompts without altering the structure of the baseline diffusion model.

Key innovations and components introduced in FIND include:

1. Reformulation of the Denoising Process: The authors reformulate the diffusion denoising procedure as a one-step Markov decision process (MDP). This reformulation facilitates the use of policy optimization strategies, typically employed in reinforcement learning, to optimize the initial noise distributions effectively.
2. Dynamic Reward Calibration Module: To maintain stability during training, the paper introduces a dynamic reward calibration module. This module adjusts consistently with the evolving reward landscape, ensuring stable and effective training processes during the optimization of the noise distribution.
3. Ratio Clipping Algorithm: This algorithm leverages historical data to contain the optimization trajectory, thereby preventing the modified distribution from substantially deviating from the original policy. This approach preserves the robustness of the model while optimizing its performance according to the prompt alignment objectives.

Experimental Evaluation

The experimental results demonstrate that the proposed FIND framework outperforms state-of-the-art methods in ensuring consistency between prompts and generated images or videos across various tasks. Noteworthy outcomes of the experiments include:

• The method achieved a tenfold speed increase compared to the existing state-of-the-art approaches, indicating its computational efficiency and practicality for real-world applications.
• Enhanced semantic alignment in both text-to-image and text-to-video tasks, showcasing its generalizability across different domains.

Implications and Future Directions

The implications of this research are significant, particularly in the field of AI-driven content generation where precise user control is often desired. By enhancing the fidelity between input prompts and generated outputs without demanding extensive retraining of models, FIND offers a more resource-efficient pathway to model optimization.

Looking ahead, the findings suggest several future research directions. One potential avenue is exploring the application of FIND in broader multimodal generative tasks beyond image and video, such as 3D object and audio generation. Additionally, the integration of advanced reward functions that incorporate finer-grained semantic feedback could further enhance the precision of prompt alignment.

In conclusion, this paper provides a substantial contribution to refining the control mechanisms in diffusion models through innovative optimization techniques. The proposed framework not only demonstrates technical proficiency but also offers a strategic direction for further advancements in the domain of generative modeling.