Pith. sign in

REVIEW 5 cited by

Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2410.08261 v4 pith:GS2IW2E6 submitted 2024-10-10 cs.CV

Jinbin Bai , Tian Ye , Wei Chow , Enxin Song , Xiangtai Li , Zhen Dong , Lei Zhu , Shuicheng Yan This is my paper
classification cs.CV
keywords meissonictext-to-imagehigh-qualityhigh-resolutionimageimageslikemasked
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL. By incorporating a comprehensive suite of architectural innovations, advanced positional encoding strategies, and optimized sampling conditions, Meissonic substantially improves MIM's performance and efficiency. Additionally, we leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers to further enhance image fidelity and resolution. Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images. Extensive experiments validate Meissonic's capabilities, demonstrating its potential as a new standard in text-to-image synthesis. We release a model checkpoint capable of producing $1024 \times 1024$ resolution images.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

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

  1. Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    Masked discrete diffusion with token editing and grouped cross-entropy reaches strong text-to-image generation scores in an 8B decoder-only model, reporting GenEval 0.90, DPG 86.9, HPSv3 10.76.

  2. IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction

    cs.CV 2025-10 conditional novelty 6.0 of 10

    IAR2 achieves state-of-the-art ImageNet 256×256 image generation (FID 1.50 with rejection sampling) by splitting visual tokens into semantic and detail codes and predicting them hierarchically with a local-context-awa...

  3. Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation

    cs.CV 2025-09 conditional novelty 6.0 of 10

    Lavida-O introduces an elastic mixture-of-transformers architecture that brings high-resolution text-to-image generation, object grounding, and image editing into a single masked diffusion model, using planning and se...

  4. DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer

    cs.CV 2025-07 conditional novelty 5.0 of 10

    DC-AR generates 512x512 images in 12 masked autoregressive steps plus 20 diffusion refinement steps, using a 32x compressed 2D tokenizer, and reports gFID 5.49 on MJHQ-30K.

  5. Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline

    cs.CV 2025-06 conditional novelty 5.0 of 10

    TAO pipelines object-centric anomaly scores into SAM2 prompts with a temporal consistency filter to obtain pixel-level anomaly segmentation and tracking.

Pith tools