Image Generators
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1-Bit Bonsai Image 4B Image Generation for Local Devices
Introducing 1-bit and Ternary Bonsai Image 4B: Image Generation for Local Devices Today we’re releasing Bonsai Image 4B, a family of compact image-generation models designed to run high-quality diffusion inference on local hardware: from laptops to phones. Bonsai Image 4B comes in two variants: - 1-bit Bonsai Image 4B uses binary {−1, +1} transformer weights with an FP16 group-wise scaling factor, giving 1.125 effective bits per weight. It targets maximum compression and is the right fit...
Pinterest Canvas: Large-Scale Image Generation at Pinterest
arXiv:2603.06453v2 Announce Type: replace Abstract: While recent image generation models demonstrate a remarkable ability to handle a wide variety of image generation tasks, this flexibility makes them hard to control via prompting or simple inference adaptation alone, rendering them unsuitable for use cases with strict product requirements. In this paper, we introduce Pinterest Canvas, our large-scale image generation system built to support image editing and enhancement use cases at...
STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation
arXiv:2606.07036v1 Announce Type: new Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for large-scale training data for foundation models. Latent diffusion models have dominated the image generation domain, with recent works emphasizing that the choice of latent space is critical to the quality of generated images. Existing state-of-the-art generative models in histopathology use...
TextFake: Benchmarking AI-Generated Image Detection on Text-Rich Images
arXiv:2606.01050v1 Announce Type: new Abstract: Recent AI-generated image (AIGI) detectors perform well on natural-image benchmarks, but their behavior on text-rich forgeries, such as fabricated screenshots, documents, and news pages prevalent in misinformation, remains untested. We introduce TextFake, a 20,000-image benchmark for text-rich AIGI detection spanning 28 languages, 4 topic categories, and 2 scene modalities.
DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection
arXiv:2606.06918v1 Announce Type: new Abstract: The rapid evolution of generative image models challenges existing AI-generated image detectors, particularly in open-world settings with unseen generators. Recent training-free approaches measure robustness gaps in frozen vision foundation models (VFMs), detecting fakes via perturbation-induced embedding drift. However, these methods rely on fixed invariance geometry inherited from pretraining and lack principled adaptation to the detection task.
Safeguarding Text-to-Image Generation via Inference-Time Prompt-Noise Optimization
arXiv:2412.03876v2 Announce Type: replace Abstract: Text-to-Image (T2I) diffusion models are widely recognized for their ability to generate high-quality and diverse images based on text prompts. However, despite recent advances, these models are still prone to generating unsafe images containing sensitive or inappropriate content, which can be harmful to users. Current efforts to prevent inappropriate image generation for diffusion models are easy to bypass and vulnerable to adversarial...
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
arXiv:2606.04457v1 Announce Type: new Abstract: Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and images, thereby improving generation quality. Recent works such as X-Omni and BLIP3o-Next have explored this direction, but they typically use a two-stage external pipeline: a separate autoregressive model first generates semantic tokens, which are then fed as conditioning to an independent diffusion...
Should politicians be using AI-generated images?
The article discusses the use of AI-generated images by politicians, specifically Roma Britnell, who has been using them to promote her campaign. The article raises questions about the ethics and implications of using AI-generated images in politics, and whether it is a legitimate way to engage with voters. The article also highlights the potential risks and consequences of using AI-generated images, including the potential for misinformation and manipulation.
GenClaw: Code-Driven Agentic Image Generation
arXiv:2605.30248v2 Announce Type: replace Abstract: Image generation models have evolved from text-conditioned pixel synthesis toward multimodal agents endowed with visual comprehension and tool invocation capabilities. Yet, existing agents remain at the mercy of underlying black-box image models. Their workflow is trapped in a repetitive cycle of prompt rewriting for generation refinement, leaving them with no mechanism to directly manipulate the canvas.
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
Announce Type: replace Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on image realism and shallow text-image alignment, lacking a comprehensive assessment of complex semantic understanding and world knowledge integration in text-to-image generation. To address this challenge, we propose \textbf{WISE}, the first benchmark specifically designed for...