ImagenHub: Standardizing the evaluation of conditional image generation models

ImagenHub
♠️Max Ku, ♠️Tianle Li, †Kai Zhang, ♣Yujie Lu, ♥Xingyu Fu, ♢Wenwen Zhuang, ♠️Wenhu Chen
♠️University of Waterloo, †Ohio State University, ♣University of California, Santa Barbara, ♥University of Pensylvania, ♢Central South University m3ku@uwaterloo.ca, t29li@uwaterloo.ca, zhang.13253@osu.edu, yujielu@ucsb.edu, xingyuf2@seas.upenn.edu, wwzhuang01@gmail.com, wenhuchen@uwaterloo.ca

ICLR 2024

🤗 Dataset Code 📘 Docs arXiv 🖼️🏛️ Visualization ⚔️ GenAI-Arena 👨‍💻 List of Supported Models 📘 Contribution Guide 📘 HumanEval Guidelines ICLR Slides and Poster
ImagenHub

Figure 1: The overview of ImagenHub framework, which consists of the newly curated ImagenHub dataset, ImagenHub library, and ImagenHub evaluation platform and standard.

Abstract

We introduce ImagenHub, a one-stop library to standardize the inference and evaluation of all the conditional image generation models. Firstly, we define seven prominent tasks and curate high-quality evaluation datasets for them. Secondly, we built a unified inference pipeline to ensure fair comparison. Thirdly, we design two human evaluation scores, i.e. Semantic Consistency and Perceptual Quality, along with comprehensive guidelines to evaluate generated images. We train expert raters to evaluate the model outputs based on the proposed metrics. Our human evaluation achieves a high inter-worker agreement of Krippendorff’s alpha on 76% models with a value higher than 0.4. We comprehensively evaluated a total of around 30 models and observed three key takeaways: (1) the existing models' performance is generally unsatisfying except for Text-guided Image Generation and Subject-driven Image Generation, with 74% models achieving an overall score lower than 0.5. (2) we examined the claims from published papers and found 83% of them hold with a few exceptions. (3) None of the existing automatic metrics has a Spearman’s correlation higher than 0.2 except subject-driven image generation. Moving forward, we will continue our efforts to evaluate newly published models and update our leaderboard to keep track of the progress in conditional image generation.

Overall Results

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Figure 2: The best and the average model performance in each task.

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Figure 3: Model performance and standard deviation in each task.

How images are rated?

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Figure 4: The visualization of all the conditional image generation tasks. Here we consider tasks with 1-3 conditions, where ∅ means empty. The special token [V] and [M] are special identifiers.

To standardize the conduction of a rigorous human evaluation, we stipulate the criteria for each measurement as follows:

  • Semantic Consistency (SC), score in range [0, 0.5, 1]. It measures the level that the generated image is coherent in terms of the condition provided (i.e. Prompts, Subject Token, etc.).
  • Perceptual Quality (PQ), score in range [0, 0.5, 1]. It measures the level at which the generated image is visually convincing and gives off a natural sense.
  • Overall Human Score (O), equal to sqrt(SC x PQ).

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Comprehensive Results

Model SCAvg PQAvg Overall(O) FleissŌ KdŌ
Text-guided Image Generation Model
Dalle-3 - Unknown 0.79±0.02 0.79±0.14 0.76±0.08 0.19 - 0.34 -
Midjourney - Unknown 0.67±0.06 0.92±0.06 0.73±0.07 0.34 - 0.51 -
DeepFloydIF - Unknown 0.65±0.02 0.62±0.06 0.59±0.02 0.32 - 0.51 -
Stable Diffusion XL - Unknown 0.62±0.03 0.64±0.05 0.59±0.03 0.37 - 0.61 -
Dalle-2 - Unknown 0.58±0.04 0.62±0.06 0.54±0.04 0.27 - 0.40 -
OpenJourney - Unknown 0.53±0.02 0.59±0.05 0.50±0.02 0.30 - 0.47 -
Stable Diffusion 2.1 - Unknown 0.56±0.02 0.53±0.05 0.50±0.03 0.38 - 0.50 -
Mask-guided Image Editing Model
SDXL-Inpainting - Unknown 0.49±0.05 0.51±0.02 0.37±0.05 0.50 - 0.72 -
SD-Inpainting - Unknown 0.28±0.04 0.27±0.10 0.17±0.07 0.31 - 0.49 -
GLIDE - Unknown 0.20±0.05 0.48±0.06 0.16±0.05 0.33 - 0.56 -
BlendedDiffusion - Unknown 0.12±0.03 0.11±0.03 0.05±0.02 0.36 - 0.44 -
Text-guided Image Editing Model
MagicBrush - Unknown 0.51±0.01 0.65±0.06 0.47±0.02 0.44 - 0.67 -
InstructPix2Pix - Unknown 0.29±0.01 0.70±0.06 0.27±0.02 0.55 - 0.74 -
Prompt-to-prompt - Unknown 0.17±0.05 0.55±0.09 0.15±0.06 0.36 - 0.53 -
CycleDiffusion - Unknown 0.17±0.03 0.56±0.11 0.14±0.04 0.41 - 0.63 -
SDEdit - Unknown 0.04±0.03 0.56±0.12 0.04±0.03 0.13 - 0.13 -
Text2Live - Unknown 0.02±0.01 0.82±0.04 0.02±0.02 0.10 - 0.17 -
DiffEdit - Unknown 0.02±0.01 0.23±0.04 0.01±0.01 0.24 - 0.24 -
Pix2PixZero - Unknown 0.01±0.00 0.48±0.09 0.01±0.01 0.37 - 0.37 -
Subject-driven Image Generation Model
SuTI - Unknown 0.64±0.11 0.68±0.08 0.58±0.12 0.20 - 0.39 -
DreamBooth - Unknown 0.51±0.08 0.93±0.02 0.55±0.11 0.37 - 0.60 -
BLIP-Diffusion - Unknown 0.29±0.04 0.93±0.04 0.35±0.06 0.22 - 0.39 -
TextualInversion - Unknown 0.21±0.04 0.74±0.08 0.21±0.05 0.35 - 0.52 -
DreamBooth-Lora - Unknown 0.07±0.01 0.82±0.07 0.09±0.01 0.29 - 0.37 -
Subject-driven Image Editing Model
PhotoSwap - Unknown 0.34±0.02 0.65±0.04 0.36±0.02 0.35 - 0.46 -
DreamEdit - Unknown 0.31±0.03 0.61±0.03 0.32±0.03 0.33 - 0.52 -
BLIP-Diffusion - Unknown 0.09±0.03 0.70±0.02 0.09±0.03 0.41 - 0.47 -
Multi-concept Image Composition Model
CustomDiffusion - Unknown 0.26±0.01 0.86±0.05 0.29±0.01 0.73 - 0.88 -
DreamBooth - Unknown 0.11±0.02 0.78±0.02 0.13±0.02 0.61 - 0.71 -
TextualInversion - Unknown 0.04±0.01 0.74±0.05 0.05±0.01 0.62 - 0.77 -
Control-guided Image Generation Model
ControlNet - Unknown 0.42±0.05 0.19±0.04 0.23±0.04 0.37 - 0.57 -
UniControl - Unknown 0.38±0.07 0.20±0.06 0.23±0.07 0.36 - 0.58 -

Citation

Please kindly cite our paper if you use our code, data, models or results: 

@inproceedings{
            ku2024imagenhub,
            title={ImagenHub: Standardizing the evaluation of conditional image generation models},
            author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
            booktitle={The Twelfth International Conference on Learning Representations},
            year={2024},
            url={https://openreview.net/forum?id=OuV9ZrkQlc}
            }