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CogView: Mastering Text-to-Image Generation via Transformers

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arxiv 2105.13290 v3 pith:ZJWEOS4K submitted 2021-05-26 cs.CV cs.LG

Ming Ding , Zhuoyi Yang , Wenyi Hong , Wendi Zheng , Chang Zhou , Da Yin , Junyang Lin , Xu Zou
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Zhou Shao Hongxia Yang Jie Tang
This is my paper · ORCID
classification cs.CVcs.LG
keywords cogviewgenerationproblemtext-to-imageachievesadvancebeenbillion-parameter
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem. We also demonstrate the finetuning strategies for various downstream tasks, e.g. style learning, super-resolution, text-image ranking and fashion design, and methods to stabilize pretraining, e.g. eliminating NaN losses. CogView achieves the state-of-the-art FID on the blurred MS COCO dataset, outperforming previous GAN-based models and a recent similar work DALL-E.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    Splitting weight matrices into a fixed-norm direction and learnable per-row/column magnitudes improves LLM training over AdamW/Muon, removes weight decay and warmup, and transfers the optimal LR across width.

  2. Transition Matching: Scalable and Flexible Generative Modeling

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Transition Matching unifies flow matching and continuous autoregressive generation as discrete-time Markov processes, with three variants that improve text-to-image quality and speed.

  3. JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    JuZhou 1.0 is a 0.387B-parameter T2I diffusion model with 4-step inference achieving 0.69 GenEval, trained on 9M Chinese pairs using Sugon K100 accelerators and deployable on Android/iOS devices.

  4. Learning to Skip the Middle Layers of Transformers

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A gated 'skip the middle' Transformer fails to beat fewer-layer dense baselines on the cross-entropy/FLOPs trade-off at 12 layers and 10B tokens.

  5. Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models

    cs.CV 2026-07 conditional novelty 4.0 of 10

    Diff-ID trades a bit of ArcFace identity score for much lower FID, yielding the best FS/FID trade-off among tested face generators, plus qualitative morphing without per-identity fine-tuning.

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