7 papers · 1 filter
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models
Xu Ma, Peize Sun, Haoyu Ma +22
Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models.…
PixelFlow: Pixel-Space Generative Models with Flow
Shoufa Chen, Chongjian Ge, Shilong Zhang +2
We present PixelFlow, a family of image generation models that operate directly in the raw pixel space, in contrast to the predominant latent-space models. This approach simplifies…
Goku: Flow Based Video Generative Foundation Models
Shoufa Chen, Chongjian Ge, Yuqi Zhang +19
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. We…
FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation
Shilong Zhang, Wenbo Li, Shoufa Chen +7
DiT models have achieved great success in text-to-video generation, leveraging their scalability in model capacity and data scale. High content and motion fidelity aligned with tex…
Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
Yatai Ji, Jiacheng Zhang, Jie Wu +9
Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality o…
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment
Huayu Chen, Hang Su, Peize Sun +1
Classifier-Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models. However, in autoregressive (AR) multi-modal generation, CFG int…