In this paper, we introduce PixArt-\(\Sigma\), a Diffusion Transformer model (DiT) capable of
directly generating images at 4K resolution. PixArt-\(\Sigma\) represents a significant …

The diffusion model has been proven a powerful generative model in recent years, yet it
remains a challenge in generating visual text. Although existing work has endeavored to …

We present Lumina-mGPT, a family of multimodal autoregressive models capable of various
vision and language tasks, particularly excelling in generating flexible photorealistic images …

Text-to-image diffusion models have recently received increasing interest for their
astonishing ability to produce high-fidelity images from solely text inputs. Subsequent …

Recent studies have shown that the denoising process in (generative) diffusion models can
induce meaningful (discriminative) representations inside the model, though the quality of …

Video generation requires modeling a vast spatiotemporal space, which demands
significant computational resources and data usage. To reduce the complexity, the …

Abstract Text-to-image (T2I) generation with Stable Diffusion models (SDMs) involves high
computing demands due to billion-scale parameters. To enhance efficiency, recent studies …

Abstract Text-to-image (T2I) diffusion models notably the unCLIP models (eg DALL-E-2)
achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at …

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start
the process over again once new data becomes available. A much more efficient solution is …

We address the challenges of precise image inversion and disentangled image editing in
the context of few-step diffusion models. We introduce an encoder based iterative inversion …