Diff-Instruct*: Towards Human-Preferred One-step Text-to-image Generative Models

• URL: http://arxiv.org/abs/2410.20898v2
• Date: Tue, 24 Dec 2024 05:22:40 GMT
• Title: Diff-Instruct*: Towards Human-Preferred One-step Text-to-image Generative Models
• Authors: Weijian Luo, Colin Zhang, Debing Zhang, Zhengyang Geng,
• Abstract summary: We introduce Diff-Instruct* (DI*), an image data-free approach for building one-step text-to-image generative models. We frame human preference alignment as online reinforcement learning using human feedback. Unlike traditional RLHF approaches, which rely on the KL divergence for regularization, we introduce a novel score-based divergence regularization.
• Score: 8.352666876052616
• License:
• Abstract: In this paper, we introduce the Diff-Instruct* (DI*), an image data-free approach for building one-step text-to-image generative models that align with human preference while maintaining the ability to generate highly realistic images. We frame human preference alignment as online reinforcement learning using human feedback (RLHF), where the goal is to maximize the reward function while regularizing the generator distribution to remain close to a reference diffusion process. Unlike traditional RLHF approaches, which rely on the KL divergence for regularization, we introduce a novel score-based divergence regularization, which leads to significantly better performances. Although the direct calculation of this preference alignment objective remains intractable, we demonstrate that we can efficiently compute its gradient by deriving an equivalent yet tractable loss function. Remarkably, we used Diff-Instruct* to train a Stable Diffusion-XL-based 1-step model, the 2.6B DI*-SDXL-1step text-to-image model, which can generate images of a resolution of 1024x1024 with only 1 generation step. DI*-SDXL-1step model uses only 1.88% inference time and 29.30% GPU memory cost to outperform 12B FLUX-dev-50step significantly in PickScore, ImageReward, and CLIPScore on Parti prompt benchmark and HPSv2.1 on Human Preference Score benchmark, establishing a new state-of-the-art benchmark of human-preferred 1-step text-to-image generative models. Besides the strong quantitative performances, extensive qualitative comparisons also confirm the advantages of DI* in terms of maintaining diversity, improving image layouts, and enhancing aesthetic colors. We have released our industry-ready model on the homepage: \url{https://github.com/pkulwj1994/diff_instruct_star}.

Related papers

• When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization [92.17160980120404]
  We introduce Causally Regularized Tokenization (CRT), which uses knowledge of the stage 2 generation modeling procedure to embed useful inductive biases in stage 1 latents. CRT makes stage 1 reconstruction performance worse, but makes stage 2 generation performance better by making the tokens easier to model. We match state-of-the-art discrete autoregressive ImageNet generation (2.18 FID) with less than half the tokens per image.
  arXiv  Detail & Related papers  (2024-12-20T20:32:02Z)
• Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences [0.0]
  We introduce Diff-Instruct++ (DI++), the first, fast-converging and image data-free human preference alignment method for one-step text-to-image generators. In the experiment sections, we align both UNet-based and DiT-based one-step generators using DI++, which use the Stable Diffusion 1.5 and the PixelArt-$alpha$ as the reference diffusion processes. The resulting DiT-based one-step text-to-image model achieves a strong Aesthetic Score of 6.19 and an Image Reward of 1.24 on the validation prompt dataset
  arXiv  Detail & Related papers  (2024-10-24T16:17:18Z)
• Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective [52.778766190479374]
  Latent-based image generative models have achieved notable success in image generation tasks. Despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. We propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling.
  arXiv  Detail & Related papers  (2024-10-16T12:13:17Z)
• Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization [97.35427957922714]
  We present an algorithm named pairwise sample optimization (PSO), which enables the direct fine-tuning of an arbitrary timestep-distilled diffusion model. PSO introduces additional reference images sampled from the current time-step distilled model, and increases the relative likelihood margin between the training images and reference images. We show that PSO can directly adapt distilled models to human-preferred generation with both offline and online-generated pairwise preference image data.
  arXiv  Detail & Related papers  (2024-10-04T07:05:16Z)
• Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding [84.3224556294803]
  Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. We aim to optimize downstream reward functions while preserving the naturalness of these design spaces. Our algorithm integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future.
  arXiv  Detail & Related papers  (2024-08-15T16:47:59Z)
• Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models [42.28905346604424]
  Deep Reward Tuning (DRTune) is an algorithm that supervises the final output image of a text-to-image diffusion model. DRTune consistently outperforms other algorithms, particularly for low-level control signals.
  arXiv  Detail & Related papers  (2024-05-01T15:26:14Z)
• Reinforcement Learning from Diffusion Feedback: Q* for Image Search [2.5835347022640254]
  We present two models for image generation using model-agnostic learning. RLDF is a singular approach for visual imitation through prior-preserving reward function guidance. It generates high-quality images over varied domains showcasing class-consistency and strong visual diversity.
  arXiv  Detail & Related papers  (2023-11-27T09:20:12Z)
• Diffusion Model Alignment Using Direct Preference Optimization [103.2238655827797]
  Diffusion-DPO is a method to align diffusion models to human preferences by directly optimizing on human comparison data. We fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO. We also develop a variant that uses AI feedback and has comparable performance to training on human preferences.
  arXiv  Detail & Related papers  (2023-11-21T15:24:05Z)
• Human Preference Score: Better Aligning Text-to-Image Models with Human Preference [41.270068272447055]
  We collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. We propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences.
  arXiv  Detail & Related papers  (2023-03-25T10:09:03Z)
• On Distillation of Guided Diffusion Models [94.95228078141626]
  We propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from. For standard diffusion models trained on the pixelspace, our approach is able to generate images visually comparable to that of the original model. For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps.
  arXiv  Detail & Related papers  (2022-10-06T18:03:56Z)

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.