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Bingchen Liu

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6 papers

2 author rows

ICML Conference 2025 Conference Paper

SANA 1. 5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Enze Xie
Junsong Chen
Yuyang Zhao
Jincheng Yu
Ligeng Zhu
Yujun Lin 0001
Zhekai Zhang
Muyang Li

This paper presents SANA-1. 5, a linear Diffusion Transformer for efficient scaling in text-to-image generation. Building upon SANA-1. 0, we introduce three key innovations: (1) Efficient Training Scaling: A depth-growth paradigm that enables scaling from 1. 6B to 4. 8B parameters with significantly reduced computational resources, combined with a memory-efficient 8-bit optimizer. (2) Model Depth Pruning: A block importance analysis technique for efficient model compression to arbitrary sizes with minimal quality loss. (3) Inference-time Scaling: A repeated sampling strategy that trades computation for model capacity, enabling smaller models to match larger model quality at inference time. Through these strategies, SANA-1. 5 achieves a text-image alignment score of 0. 72 on GenEval, which can be further improved to 0. 80 through inference scaling, establishing a new SoTA on GenEval benchmark. These innovations enable efficient model scaling across different compute budgets while maintaining high quality, making high-quality image generation more accessible.

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AAAI Conference 2021 Conference Paper

Self-Supervised Sketch-to-Image Synthesis

Bingchen Liu
Yizhe Zhu
Kunpeng Song
Ahmed Elgammal

Imagining a colored realistic image from an arbitrary drawn sketch is one of human capabilities that we eager machines to mimic. Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. To this end, we first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. With the synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images both content-faithful to the sketches and style-consistent to the RGB-images. While prior works employ either the cycleconsistence loss or dedicated attentional modules to enforce the content/style fidelity, we show AE’s superior performance with pure self-supervisions. To further improve the synthesis quality in high resolution, we also leverage an adversarial network to refine the details of synthetic images. Extensive experiments on 10242 resolution demonstrate a new state-ofart-art performance of the proposed model on CelebA-HQ and Wiki-Art datasets. Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images being both style-consistent and semantically meaningful.

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AAAI Conference 2021 Conference Paper

TIME: Text and Image Mutual-Translation Adversarial Networks

Bingchen Liu
Kunpeng Song
Yizhe Zhu
Gerard de Melo
Ahmed Elgammal

Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. While previous methods tackle the T2I problem as a uni-directional task and use pre-trained language models to enforce the image–text consistency, TIME requires neither extra modules nor pretraining. We show that the performance of G can be boosted substantially by training it jointly with D as a language model. Specifically, we adopt Transformers to model the cross-modal connections between the image features and word embeddings, and design an annealing conditional hinge loss that dynamically balances the adversarial learning. In our experiments, TIME achieves state-of-the-art (SOTA) performance on the CUB dataset (Inception Score of 4. 91 and Fréchet Inception Distance of 14. 3 on CUB), and shows promising performance on MS-COCO dataset on image captioning and downstream vision-language tasks.

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ICLR Conference 2021 Conference Paper

Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis

Bingchen Liu
Yizhe Zhu
Kunpeng Song
Ahmed M. Elgammal

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for GAN with minimum computing cost. We propose a light-weight GAN structure that gains superior quality on 1024^2 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples. Two technique designs constitute our work, a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder. With thirteen datasets covering a wide variety of image domains (The datasets and code are available at https://github.com/odegeasslbc/FastGAN-pytorch), we show our model's superior performance compared to the state-of-the-art StyleGAN2, when data and computing budget are limited.

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AAAI Conference 2020 Conference Paper

OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization

Bingchen Liu
Yizhe Zhu
Zuohui Fu
Gerard de Melo
Ahmed Elgammal

Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN). While previous works mostly attempt to tackle disentanglement learning through VAE and seek to implicitly minimize the Total Correlation (TC) objective with various sorts of approximation methods, we show that GANs have a natural advantage in disentangling with an alternating latent variable (noise) sampling method that is straightforward and robust. Furthermore, we provide a brand-new perspective on designing the structure of the generator and discriminator, demonstrating that a minor structural change and an orthogonal regularization on model weights entails an improved disentanglement. Instead of experimenting on simple toy datasets, we conduct experiments on higher-resolution images and show that OOGAN greatly pushes the boundary of unsupervised disentanglement.

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AAAI Conference 2018 Conference Paper

The Shape of Art History in the Eyes of the Machine

Ahmed Elgammal
Bingchen Liu
Diana Kim
Mohamed Elhoseiny
Marian Mazzone

How does the machine classify styles in art? And how does it relate to art historians’ methods for analyzing style? Several studies showed the ability of the machine to learn and predict styles, such as Renaissance, Baroque, Impressionism, etc. , from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classiﬁcation on 67K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are a few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich Wölfﬂin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively conﬁrms art historian observations.

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