OpenAI Blog
Jul 18, 2022
Reducing bias and improving safety in DALL·E 2
Today, we are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population.
DALL·E 2 pre-training mitigations
In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy.
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OpenAI Blog
May 18, 2022
DALL·E 2 research preview update
Early users have created over 3 million images to date and helped us improve our safety processes. We’re excited to begin adding up to 1,000 new users from our waitlist each week.
OpenAI Blog
Aug 31, 2022
DALL·E: Introducing outpainting
Extend creativity and tell a bigger story with DALL·E images of any size.
OpenAI Blog
Jan 5, 2021
DALL·E: Creating images from text
We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
arXiv AI
Jul 8
PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails
arXiv:2607. 05910v1 Announce Type: cross Abstract: Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image.
arXiv AI
22h ago
DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization
arXiv:2608. 17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks.
arXiv Machine Learning
1d ago
IP Protection in the Era of Visual Generative AI: A Survey
arXiv:2608. 14730v1 Announce Type: cross Abstract: The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and redistribution of protected data and model assets.
arXiv AI
Jun 26
Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks
arXiv:2606. 27147v1 Announce Type: cross Abstract: Unlike diffusion-based models that operate in continuous latent spaces, autoregressive unified multimodal models produce images by sequentially predicting discretized visual tokens.
arXiv Machine Learning
Jun 17
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
arXiv:2403. 18957v3 Announce Type: replace-cross Abstract: Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.
arXiv AI
Jun 12
Reconstructing Template-Memorized Images from Natural Prompts
arXiv:2507. 07947v4 Announce Type: replace-cross Abstract: Recent advances in generative models, such as diffusion models, have raised concerns related to privacy, copyright infringement, and data stewardship.
Hugging Face Trending Papers
Jun 25
Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks
Unlike diffusion-based models that operate in continuous latent spaces, autoregressive unified multimodal models produce images by sequentially predicting discretized visual tokens. These tokens are derived from a codebook that maps embeddings to quantized visual patterns.
arXiv AI
2d ago
Concept Guidance: Precise, Training-Free Latent Control for Text-to-Image Generation
arXiv:2608. 14172v1 Announce Type: cross Abstract: Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.