ForensicsTok turns image manipulation localization into autoregressive token generation with a smoothing decoder and multi-scale forensic feature fusion, showing gains over MLLM baselines and slight gains over expert models on six benchmarks.
An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 37:128940–128966
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InsightTok improves text and face fidelity in discrete image tokenization via content-aware perceptual losses, with gains transferring to autoregressive generation.
Lens is a 3.8B-parameter text-to-image model that reaches competitive or superior performance to >6B-parameter systems using 19.3% of the training compute of Z-Image through a densely captioned 800M dataset, multi-resolution batching, semantic VAE, strong language encoder, RL fine-tuning, and 4-step
citing papers explorer
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ForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localization
ForensicsTok turns image manipulation localization into autoregressive token generation with a smoothing decoder and multi-scale forensic feature fusion, showing gains over MLLM baselines and slight gains over expert models on six benchmarks.
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InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation
InsightTok improves text and face fidelity in discrete image tokenization via content-aware perceptual losses, with gains transferring to autoregressive generation.
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Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models
Lens is a 3.8B-parameter text-to-image model that reaches competitive or superior performance to >6B-parameter systems using 19.3% of the training compute of Z-Image through a densely captioned 800M dataset, multi-resolution batching, semantic VAE, strong language encoder, RL fine-tuning, and 4-step