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

• URL: http://arxiv.org/abs/2407.19453v1
• Date: Sun, 28 Jul 2024 10:07:55 GMT
• Title: FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models
• Authors: Changgu Chen, Libing Yang, Xiaoyan Yang, Lianggangxu Chen, Gaoqi He, CHangbo Wang, Yang Li,
• Abstract summary: We introduce a Fine-tuning Initial Noise Distribution (FIND) framework with policy optimization. Our method achieves 10 times faster than the SOTA approach.
• Score: 10.969811500333755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}.

Related papers

• Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning [53.60934432718044]
  Cross-domain few-shot learning methods face challenges with large-scale pre-trained models due to inaccessible source data and training strategies. This paper introduces Step-wise Distribution Alignment Guided Style Prompt Tuning (StepSPT) StepSPT implicitly narrows domain gaps through prediction distribution optimization.
  arXiv  Detail & Related papers  (2024-11-15T09:34:07Z)
• Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization [55.14484317645865]
  We develop a conditional diffusion model to produce exceptional quality prompts for offline reinforcement learning tasks. We show that the Prompt diffuser is a robust and effective tool for the prompt-tuning process, demonstrating strong performance in the meta-RL tasks.
  arXiv  Detail & Related papers  (2024-11-02T07:38:02Z)
• DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization [22.546989373687655]
  We propose a novel pruning method that derives an efficient diffusion model via a more intelligent and differentiable pruner. Our approach achieves 4.4 x speedup for SD-1.5 without any loss of accuracy, significantly outperforming the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-22T12:18:24Z)
• Training-free Diffusion Model Alignment with Sampling Demons [15.400553977713914]
  We propose an optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining. Our approach works by controlling noise distribution in denoising steps to concentrate density on regions corresponding to high rewards through optimization. To the best of our knowledge, the proposed approach is the first inference-time, backpropagation-free preference alignment method for diffusion models.
  arXiv  Detail & Related papers  (2024-10-08T07:33:49Z)
• Inference-Time Alignment of Diffusion Models with Direct Noise Optimization [45.77751895345154]
  We propose a novel alignment approach, named Direct Noise Optimization (DNO), that optimize the injected noise during the sampling process of diffusion models. By design, DNO operates at inference-time, and thus is tuning-free and prompt-agnostic, with the alignment occurring in an online fashion during generation. We conduct extensive experiments on several important reward functions and demonstrate that the proposed DNO approach can achieve state-of-the-art reward scores within a reasonable time budget for generation.
  arXiv  Detail & Related papers  (2024-05-29T08:39:39Z)
• Align Your Steps: Optimizing Sampling Schedules in Diffusion Models [63.927438959502226]
  Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed, relying on many sequential function evaluations through large neural networks. We propose a general and principled approach to optimizing the sampling schedules of DMs for high-quality outputs.
  arXiv  Detail & Related papers  (2024-04-22T18:18:41Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• Implicit Diffusion: Efficient Optimization through Stochastic Sampling [46.049117719591635]
  We present a new algorithm to optimize distributions defined implicitly by parameterized diffusions. We introduce a general framework for first-order optimization of these processes, that performs jointly. We apply it to training energy-based models and finetuning denoising diffusions.
  arXiv  Detail & Related papers  (2024-02-08T08:00:11Z)
• Diffusion Models with Deterministic Normalizing Flow Priors [23.212848643552395]
  We propose DiNof ($textbfDi$ffusion with $textbfNo$rmalizing $textbff$low priors), a technique that makes use of normalizing flows and diffusion models. Experiments on standard image generation datasets demonstrate the advantage of the proposed method over existing approaches.
  arXiv  Detail & Related papers  (2023-09-03T21:26:56Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior [103.00403682863427]
  We propose PriorGrad to improve the efficiency of the conditional diffusion model. We show that PriorGrad achieves a faster convergence leading to data and parameter efficiency and improved quality.
  arXiv  Detail & Related papers  (2021-06-11T14:04:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.