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The paper introduces a new sequential text‑to‑scene generation framework that jointly produces scene layouts and objects. It proposes a 3D Autoregressive Diffusion model (3D‑ARD+) that first generates coarse 3D latents conditioned on text and the current scene, then refines them into fine‑grained object geometry and appearance. A large dataset of 230K indoor scenes with paired text instructions is curated for training, and the model demonstrates the ability to place objects according to complex spatial and semantic text prompts.
The paper introduces a post‑training approach that enables a single inference process to transition from text reasoning to image synthesis, eliminating the need for explicit modality switching. Using the 14B BAGEL model, the authors demonstrate that targeted post‑training data and reward‑weighted training improve multimodal image generation across four independent T2I benchmarks. The study highlights the benefits of joint text‑image generation and strategic data selection for enhancing T2I performance.
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
The paper introduces a new learning paradigm for visual generation that separates knowledge acquisition and high-fidelity rendering. Autoregressive pretraining is used to acquire semantic knowledge, while diffusion models are employed afterward for detailed image synthesis. The authors demonstrate that this approach improves learning efficiency and final generation quality, and they provide compute‑optimal scaling laws that enable training a 3B‑parameter text‑to‑image model with only 6,600 H100 GPU‑hours, achieving state‑of‑the‑art scores on GenEval and DPG‑Bench.
Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation.
Intern Lumina U2 is a diffusion large language model that uses multi-codebook visual tokens in a fully discrete space, allowing each visual position to be represented by a group of complementary codes. The model concatenates embeddings at each position and employs a lightweight autoregressive head to predict codes sequentially while the diffusion backbone processes all positions in parallel. Built on the unified 4D representation of AToken, it supports image, video, and 3D inputs, achieves leading performance in visual understanding, image generation, and editing, and includes a cross‑platform training, inference, and evaluation system with a stability‑aware sampling strategy that speeds up image generation by 4.45×.