When Do Diffusion Models learn to Generate Multiple Objects?
arXiv:2603.18532v1 Announce Type: cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar
arXiv:2603.18767v1 Announce Type: new Abstract: Concept unlearning has emerged as a promising direction for reducing the risks of harmful content generation in text-to-image
arXiv:2607.03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques
arXiv:2610.21539v1 Announce Type: new Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse
arXiv:2607.21606v1 Announce Type: new Abstract: Recent advances in powerful text-to-image generation models have made it increasingly important to develop test-time methods that
arXiv:2610.14136v1 Announce Type: new Abstract: Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images
arXiv:2606.27732v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive
arXiv:2605.13450v1 Announce Type: new Abstract: Measuring the creativity of large language models (LLMs) is essential for designing methods that can improve creativity and for
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