REVIEW 7 cited by
Human Preference Score: Better Aligning Text-to-Image Models with Human Preference
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, 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. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .
Forward citations
Cited by 7 Pith papers
-
Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search
Bootstrap Flow-Map Trees construct complete DDPM-like trajectories with a single NFE and dynamic steps, enabling efficient online feedback-driven search and alignment that beats prior tree and SMC samplers.
-
Multi-Turn On-Policy Distillation with Prefix Replay
ReOPD offline-distills multi-turn agentic LLMs via teacher-prefix replay plus step-decay sampling, matching online OPD accuracy at ≥4× speed with zero tool calls.
-
Self-Improving Diffusion Classifiers with Minority Preference Optimization
Fine-tuning a diffusion model with a reconstruction-error minority reward via LoRA+GRPO improves zero-shot diffusion classification by expanding low-density coverage.
-
Symbolic Graphics Programming with Large Language Models
Qwen-2.5-7B trained with reinforcement learning against SigLIP visual similarity scores writes SVG drawings that match text captions about as well as frontier models on several automated metrics.
-
VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment
A VGGT-based pointwise reprojection reward with geometry-aware sampling improves video geometric consistency via SFT/DPO and causal test-time search.
-
RewardDance: Reward Scaling in Visual Generation
RewardDance reframes visual reward modeling as a yes/no judgment task in a VLM and reports consistent gains in text-to-image, text-to-video, and image-to-video generation as the reward model scales from 1B to 26B.
-
Instant Preference Alignment for Text-to-Image Diffusion Models
An MLLM-driven, training-free pipeline extracts preference keywords from a reference image and modulates diffusion cross-attention at global and regional levels for instant, multi-round preference-aligned image generation.
Discussion (0). Sign in to comment.