--- base_model: - Qwen/Qwen2.5-7B-Instruct language: - en license: apache-2.0 pipeline_tag: image-text-to-text library_name: hpsv3 ---
# 🎯 HPSv3: Towards Wide-Spectrum Human Preference Score (ICCV 2025) [![Project Website](https://img.shields.io/badge/🌐-Project%20Website-deepgray)](https://mizzenai.github.io/HPSv3.project/) [![arXiv](https://img.shields.io/badge/arXiv-2508.03789-b31b1b.svg)](https://arxiv.org/abs/2508.03789) [![ICCV 2025](https://img.shields.io/badge/ICCV-2025-blue.svg)]() [![Code](https://img.shields.io/badge/Code-black?logo=github)](https://github.com/MizzenAI/HPSv3) [![Model](https://img.shields.io/badge/πŸ€—-Model-yellow)](https://huggingface.co/MizzenAI/HPSv3) [![Dataset](https://img.shields.io/badge/πŸ€—-Dataset-green)](https://huggingface.co/datasets/MizzenAI/HPDv3) **Yuhang Ma**1,3*  **Yunhao Shui**1,4*  **Xiaoshi Wu**2  **Keqiang Sun**1,2†  **Hongsheng Li**2,5,6† 1Mizzen AI   2CUHK MMLab   3King’s College London   4Shanghai Jiaotong University   5Shanghai AI Laboratory   6CPII, InnoHK   *Equal Contribution  †Equal Advising
## πŸ“– Introduction This is the official implementation for the paper: [HPSv3: Towards Wide-Spectrum Human Preference Score](https://huggingface.co/papers/2508.03789). First, we introduce a VLM-based preference model **HPSv3**, trained on a "wide spectrum" preference dataset **HPDv3** with 1.08M text-image pairs and 1.17M annotated pairwise comparisons, covering both state-of-the-art and earlier generative models, as well as high- and low-quality real-world images. Second, we propose a novel reasoning approach for iterative image refinement, **CoHP(Chain-of-Human-Preference)**, which efficiently improves image quality without requiring additional training data.

Teaser

## ✨ Updates - **[2025-08-08]** πŸŽ‰ We release [HPDv3](https://huggingface.co/datasets/MizzenAI/HPDv3) dataset!. - **[2025-08-06]** πŸŽ‰ We release HPSv3: inference code, training code, cohp code and [HPSv3 model weights](https://huggingface.co/MizzenAI/HPSv3). And [PyPI Package](https://pypi.org/project/hpsv3/). ## πŸ“‘ Table of Contents 1. [πŸš€ Quick Start](#πŸš€-quick-start) 2. [🌐 Gradio Demo](#🌐-gradio-demo) 3. [πŸ‹οΈ Training](#πŸ‹οΈ-training) 4. [πŸ“Š Benchmark](#πŸ“Š-benchmark) 5. [🎯 CoHP (Chain-of-Human-Preference)](#🎯-cohp-chain-of-human-preference) --- ## πŸš€ Quick Start HPSv3 is a state-of-the-art human preference score model for evaluating image quality and prompt alignment. It builds upon the Qwen2-VL architecture to provide accurate assessments of generated images. ### πŸ’» Installation ```bash # Install locally for development or training. git clone https://github.com/MizzenAI/HPSv3.git cd HPSv3 conda env create -f environment.yaml conda activate hpsv3 # Recommend: Install flash-attn pip install flash-attn==2.7.4.post1 pip install -e . ``` ### πŸ› οΈ Basic Usage #### Simple Inference Example ```python from hpsv3 import HPSv3RewardInferencer # Initialize the model inferencer = HPSv3RewardInferencer(device='cuda') # Evaluate images image_paths = ["assets/example1.png", "assets/example2.png"] prompts = [ "cute chibi anime cartoon fox, smiling wagging tail with a small cartoon heart above sticker", "cute chibi anime cartoon fox, smiling wagging tail with a small cartoon heart above sticker" ] # Get preference scores rewards = inferencer.reward(image_paths, prompts) scores = [reward[0].item() for reward in rewards] # Extract mu values print(f"Image scores: {scores}") ``` --- ## 🌐 Gradio Demo Launch an interactive web interface to test HPSv3: ```bash python gradio_demo/demo.py ``` The demo will be available at `http://localhost:7860` and provides:

Gradio Demo

## πŸ“ Dataset ### Human Preference Dataset v3 Human Preference Dataset v3 (HPD v3) comprises 1.08M text-image pairs and 1.17M annotated pairwise data. To modeling the wide spectrum of human preference, we introduce newest state-of-the-art generative models and high quality real photographs while maintaining old models and lower quality real images.
Detail information of HPD v3 | Image Source | Type | Num Image | Prompt Source | Split | |--------------|------|-----------|---------------|-------| | High Quality Image (HQI) | Real Image | 57759 | VLM Caption | Train & Test | | MidJourney | - | 331955 | User | Train | | CogView4 | DiT | 400 | HQI+HPDv2+JourneyDB | Test | | FLUX.1 dev | DiT | 48927 | HQI+HPDv2+JourneyDB | Train & Test | | Infinity | Autoregressive | 27061 | HQI+HPDv2+JourneyDB | Train & Test | | Kolors | DiT | 49705 | HQI+HPDv2+JourneyDB | Train & Test | | HunyuanDiT | DiT | 46133 | HQI+HPDv2+JourneyDB | Train & Test | | Stable Diffusion 3 Medium | DiT | 49266 | HQI+HPDv2+JourneyDB | Train & Test | | Stable Diffusion XL | Diffusion | 49025 | HQI+HPDv2+JourneyDB | Train & Test | | Pixart Sigma | Diffusion | 400 | HQI+HPDv2+JourneyDB | Test | | Stable Diffusion 2 | Diffusion | 19124 | HQI+JourneyDB | Train & Test | | CogView2 | Autoregressive | 3823 | HQI+JourneyDB | Train & Test | | FuseDream | Diffusion | 468 | HQI+JourneyDB | Train & Test | | VQ-Diffusion | Diffusion | 18837 | HQI+JourneyDB | Train & Test | | Glide | Diffusion | 19989 | HQI+JourneyDB | Train & Test | | Stable Diffusion 1.4 | Diffusion | 18596 | HQI+JourneyDB | Train & Test | | Stable Diffusion 1.1 | Diffusion | 19043 | HQI+JourneyDB | Train & Test | | Curated HPDv2 | - | 327763 | - | Train |
### Download HPDv3 ```bash huggingface-cli download --repo-type dataset MizzenAI/HPDv3 --local-dir /your-local-dataset-path ``` ### Pairwise Training Data Format **Important Note: For simplicity, path1's image is always the prefered one** #### All Annotated Pairs (`all.json`) **Important Notes: In HPDv3, we simply put the preferred sample at the first place (path1)** `all.json` contains **all** annotated pairs except for test. ```bash [ # samples from HPDv3 annotation pipeline { "prompt": "Description of the visual content or the generation prompt.", "choice_dist": [12, 7], # Distribution of votes from annotators (12 votes for image1, 7 votes for image2) "confidence": 0.9999907, # Confidence score reflecting preference reliability, based on annotators' capabilities (independent of choice_dist) "path1": "images/uuid1.jpg", # File path to the preferred image "path2": "images/uuid2.jpg", # File path to the non-preferred image "model1": "flux", # Model used to generate the preferred image (path1) "model2": "infinity" # Model used to generate the non-preferred image (path2) }, # samples from Midjourney { "prompt": "Description of the visual content or the generation prompt.", "choice_dist": null, # No distribution of votes Information from Discord "confidence": null, # No Confidence Information from Discord "path1": "images/uuid1.jpg", # File path to the preferred image. "path2": "images/uuid2.jpg", # File path to the non-preferred image. "model1": "midjourney", # Comparsion between images generated from midjourney "model2": "midjourney" # Comparsion between images generated from midjourney }, # samples from Curated HPDv2 { "prompt": "Description of the visual content or the generation prompt.", "choice_dist": null, # No distribution of votes Information from the original HPDv2 traindataset "confidence": null, # No Confidence Information from the original HPDv2 traindataset "path1": "images/uuid1.jpg", # File path to the preferred image. "path2": "images/uuid2.jpg", # File path to the non-preferred image. "model1": "hpdv2", # No specific model name in the original HPDv2 traindataset, set to hpdv2 "model2": "hpdv2" # No specific model name in the original HPDv2 traindataset, set to hpdv2 }, ] ``` #### Train set (`train.json`) We sample part of training data from `all.json` to build training dataset `train.json`. Moreover, to improve robustness, we integrate random sampled part of data from [Pick-a-pic](https://huggingface.co/datasets/pickapic-anonymous/pickapic_v1) and [ImageRewardDB](https://huggingface.co/datasets/zai-org/ImageRewardDB), which is `pickapic.json` and `imagereward.json`. For these two datasets, we only provide the pair infomation, and its corresponding image can be found in their official dataset repository. #### Test Set (`test.json`) ```bash [ { "prompt": "Description of the visual content", "path1": "images/uuid1.jpg", # Preferred sample "path2": "images/uuid2.jpg", # Unpreferred sample "model1": "flux", # Model used to generate the preferred sample (path1). "model2": "infinity", # Model used to generate the non-preferred sample (path2). } ] ``` ## πŸ‹οΈ Training ### πŸš€ Training Command ```bash # Use Method 2 to install locally git clone https://github.com/MizzenAI/HPSv3.git cd HPSv3 conda env create -f environment.yaml conda activate hpsv3 # Recommend: Install flash-attn pip install flash-attn==2.7.4.post1 pip install -e . # Train with 7B model deepspeed hpsv3/train.py --config hpsv3/config/HPSv3_7B.yaml ```
Important Config Argument | Configuration Section | Parameter | Value | Description | |----------------------|-----------|-------|-------------| | **Model Configuration** | `rm_head_type` | `"ranknet"` | Type of reward model head architecture | | | `lora_enable` | `False` | Enable LoRA (Low-Rank Adaptation) for efficient fine-tuning. If `False`, language tower is fully trainable| | | `vision_lora` | `False` | Apply LoRA specifically to vision components. If `False`, vision tower is fully trainable| | | `model_name_or_path` | `"Qwen/Qwen2-VL-7B-Instruct"` | Path to the base model checkpoint | | **Data Configuration** | `confidence_threshold` | `0.95` | Minimum confidence score for training data | | | `train_json_list` | `[example_train.json]` | List of training data files | | | `test_json_list` | `[validation_sets]` | List of validation datasets with names | | | `output_dim` | `2` | Output dimension of the reward head for $\mu$ and $\sigma$| | | `loss_type` | `"uncertainty"` | Loss function type for training |
--- ## πŸ“Š Benchmark To evaluate **HPSv3 preference accuracy** or **human preference score of image generation model**, follow the detail instruction is in [Evaluate Insctruction](evaluate/README.md)
Preference Accuracy of HPSv3 | Model | ImageReward | Pickscore | HPDv2 | HPDv3 | |------|-------------|-----------|-------|-------| | [CLIP ViT-H/14](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) | 57.1 | 60.8 | 65.1 | 48.6 | | [Aesthetic Score Predictor](https://github.com/christophschuhmann/improved-aesthetic-predictor) | 57.4 | 56.8 | 76.8 | 59.9 | | [ImageReward](https://github.com/THUDM/ImageReward) | 65.1 | 61.1 | 74.0 | 58.6 | | [PickScore](https://github.com/yuvalkirstain/PickScore) | 61.6 | 70.5 | 79.8 | 65.6 | | [HPS](https://github.com/tgxs002/align_sd) | 61.2 | 66.7 | 77.6 | 63.8 | | [HPSv2](https://github.com/tgxs002/HPSv2) | 65.7 | 63.8 | 83.3 | 65.3 | | [MPS](https://github.com/Kwai-Kolors/MPS) | **67.5** | 63.1 | 83.5 | 64.3 | | HPSv3 | 66.8 | **72.8** | **85.4** | **76.9** |
Image Generation Benchmark of HPSv3 | Model | Overall | Characters | Arts | Design | Architecture | Animals | Natural Scenery | Transportation | Products | Others | Plants | Food | Science | |------|---------|------------|------|--------|--------------|---------|-----------------|----------------|----------|--------|--------|------|---------| | Kolors | **10.55** | **11.79** | **10.47** | **9.87** | 10.82 | **10.60** | 9.89 | 10.68 | 10.93 | **10.50** | **10.63** | 11.06 | 9.51 | | Flux-dev | 10.43 | 11.70 | 10.32 | 9.39 | **10.93** | 10.38 | 10.01 | **10.84** | **11.24** | 10.21 | 10.38 | **11.24** | 9.16 | | Playgroundv2.5 | 10.27 | 11.07 | 9.84 | 9.64 | 10.45 | 10.38 | 9.94 | 10.51 | 10.62 | 10.15 | 10.62 | 10.84 | 9.39 | | Infinity | 10.26 | 11.17 | 9.95 | 9.43 | 10.36 | 9.27 | **10.11** | 10.36 | 10.59 | 10.08 | 10.30 | 10.59 | **9.62** | | CogView4 | 9.61 | 10.72 | 9.86 | 9.33 | 9.88 | 9.16 | 9.45 | 9.69 | 9.86 | 9.45 | 9.49 | 10.16 | 8.97 | | PixArt-Ξ£ | 9.37 | 10.08 | 9.07 | 8.41 | 9.83 | 8.86 | 8.87 | 9.44 | 9.57 | 9.52 | 9.73 | 10.35 | 8.58 | | Gemini 2.0 Flash | 9.21 | 9.98 | 8.44 | 7.64 | 10.11 | 9.42 | 9.01 | 9.74 | 9.64 | 9.55 | 10.16 | 7.61 | 9.23 | | SDXL | 8.20 | 8.67 | 7.63 | 7.53 | 8.57 | 8.18 | 7.76 | 8.65 | 8.85 | 8.32 | 8.43 | 8.78 | 7.29 | | HunyuanDiT | 8.19 | 7.96 | 8.11 | 8.28 | 8.71 | 7.24 | 7.86 | 8.33 | 8.55 | 8.28 | 8.31 | 8.48 | 8.20 | | Stable Diffusion 3 Medium | 5.31 | 6.70 | 5.98 | 5.15 | 5.25 | 4.09 | 5.24 | 4.25 | 5.71 | 5.84 | 6.01 | 5.71 | 4.58 | | SD2 | -0.24 | -0.34 | -0.56 | -1.35 | -0.24 | -0.54 | -0.32 | 1.00 | 1.11 | -0.01 | -0.38 | -0.38 | -0.84 |
--- ## 🎯 CoHP (Chain-of-Human-Preference) COHP is our novel reasoning approach for iterative image refinement that efficiently improves image quality without requiring additional training data. It works by generating images with multiple diffusion models, selecting the best one using reward models, and then iteratively refining it through image-to-image generation.

cohp

### πŸš€ Usage #### Basic Command ```bash python hpsv3/cohp/run_cohp.py \ --prompt "A beautiful sunset over mountains" \ --index "sample_001" \ --device "cuda:0" \ --reward_model "hpsv3" ``` #### Parameters - `--prompt`: Text prompt for image generation (required) - `--index`: Unique identifier for saving results (required) - `--device`: GPU device to use (default: 'cuda:1') - `--reward_model`: Reward model for scoring images - `hpsv3`: HPSv3 model (default, recommended) - `hpsv2`: HPSv2 model - `imagereward`: ImageReward model - `pickscore`: PickScore model #### Supported Generation Models COHP uses multiple state-of-the-art diffusion models for initial generation: **FLUX.1 dev**, **Kolors**, **Stable Diffusion 3 Medium**, **Playground v2.5** #### How COHP Works 1. **Multi-Model Generation**: Generates images using all supported models 2. **Reward Scoring**: Evaluates each image using the specified reward model 3. **Best Model Selection**: Chooses the model that produced the highest-scoring image 4. **Iterative Refinement**: Performs 4 rounds of image-to-image generation to improve quality 5. **Adaptive Strength**: Uses strength=0.8 for rounds 1-2, then 0.5 for rounds 3-4 --- ## 🦾 Results as Reward Model We perform [DanceGRPO](https://github.com/XueZeyue/DanceGRPO) as the reinforcement learning method. Here are some results. All experiments using the same setting and we use **Stable Diffusion 1.4** as our backbone.

### More Results of HPsv3 as Reward Model (Stable Diffusion 1.4)

cohp

## πŸ“š Citation If you find HPSv3 useful in your research, please cite our work: ```bibtex @inproceedings{hpsv3, title={HPSv3: Towards Wide-Spectrum Human Preference Score}, author={Ma, Yuhang and Wu, Xiaoshi and Sun, Keqiang and Li, Hongsheng}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2025} } ``` --- ## πŸ™ Acknowledgements We would like to thank the [VideoAlign](https://github.com/KwaiVGI/VideoAlign) codebase for providing valuable references. --- ## πŸ’¬ Support For questions and support: - **Issues**: [GitHub Issues](https://github.com/MizzenAI/HPSv3/issues) - **Email**: yhshui@mizzen.ai & yhma@mizzen.ai