A Superposição de Modelos de Difusão Usando o Estimador de Densidade de Itô
The Superposition of Diffusion Models Using the Itô Density Estimator
December 23, 2024
Autores: Marta Skreta, Lazar Atanackovic, Avishek Joey Bose, Alexander Tong, Kirill Neklyudov
cs.AI
Resumo
A explosão cambriana de modelos de difusão pré-treinados facilmente acessíveis sugere uma demanda por métodos que combinem vários modelos de difusão pré-treinados diferentes sem incorrer no significativo ônus computacional de re-treinar um modelo combinado maior. Neste artigo, formulamos o problema de combinar múltiplos modelos de difusão pré-treinados na etapa de geração sob um novo framework proposto denominado superposição. Teoricamente, derivamos a superposição a partir de princípios rigorosos derivados da célebre equação de continuidade e projetamos dois novos algoritmos feitos sob medida para combinar modelos de difusão no SuperDiff. O SuperDiff aproveita um novo estimador de densidade de Itô escalável para a log-verossimilhança da EDS de difusão, o que não gera nenhum custo adicional em comparação com o estimador bem conhecido de Hutchinson necessário para cálculos de divergência. Demonstramos que o SuperDiff é escalável para grandes modelos de difusão pré-treinados, pois a superposição é realizada exclusivamente por meio de composição durante a inferência, e também desfruta de uma implementação sem complicações, pois combina diferentes campos vetoriais pré-treinados por meio de um esquema automatizado de reponderação. Notavelmente, mostramos que o SuperDiff é eficiente durante o tempo de inferência e imita operadores de composição tradicionais, como o OR lógico e o AND lógico. Demonstramos empiricamente a utilidade do uso do SuperDiff para gerar imagens mais diversas no CIFAR-10, edição de imagem condicionada por prompt mais fiel usando Diffusion Estável e melhoria no design de estruturas de proteínas incondicionalmente de novo. https://github.com/necludov/super-diffusion
English
The Cambrian explosion of easily accessible pre-trained diffusion models
suggests a demand for methods that combine multiple different pre-trained
diffusion models without incurring the significant computational burden of
re-training a larger combined model. In this paper, we cast the problem of
combining multiple pre-trained diffusion models at the generation stage under a
novel proposed framework termed superposition. Theoretically, we derive
superposition from rigorous first principles stemming from the celebrated
continuity equation and design two novel algorithms tailor-made for combining
diffusion models in SuperDiff. SuperDiff leverages a new scalable It\^o density
estimator for the log likelihood of the diffusion SDE which incurs no
additional overhead compared to the well-known Hutchinson's estimator needed
for divergence calculations. We demonstrate that SuperDiff is scalable to large
pre-trained diffusion models as superposition is performed solely through
composition during inference, and also enjoys painless implementation as it
combines different pre-trained vector fields through an automated re-weighting
scheme. Notably, we show that SuperDiff is efficient during inference time, and
mimics traditional composition operators such as the logical OR and the logical
AND. We empirically demonstrate the utility of using SuperDiff for generating
more diverse images on CIFAR-10, more faithful prompt conditioned image editing
using Stable Diffusion, and improved unconditional de novo structure design of
proteins. https://github.com/necludov/super-diffusionSummary
AI-Generated Summary
DeepSeek-R1: Incentivizando a Capacidade de Raciocínio em LLMs via
Aprendizado por ReforçoDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via
Reinforcement Learning
DeepSeek-R1: Incentivizando a Capacidade de Raciocínio em LLMs via
Aprendizado por Reforço
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via
Reinforcement Learning
DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li, Y. Q. Wang, Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, Zhen Zhang•Jan 22, 2025•3735
Relatório Técnico do Qwen2.5Qwen2.5 Technical Report
Relatório Técnico do Qwen2.5
Qwen2.5 Technical Report
Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zihan Qiu•Dec 19, 2024•36311
MiniMax-01: Dimensionamento de Modelos de Fundação com Atenção RelâmpagoMiniMax-01: Scaling Foundation Models with Lightning Attention
MiniMax-01: Dimensionamento de Modelos de Fundação com Atenção Relâmpago
MiniMax-01: Scaling Foundation Models with Lightning Attention
MiniMax, Aonian Li, Bangwei Gong, Bo Yang, Boji Shan, Chang Liu, Cheng Zhu, Chunhao Zhang, Congchao Guo, Da Chen, Dong Li, Enwei Jiao, Gengxin Li, Guojun Zhang, Haohai Sun, Houze Dong, Jiadai Zhu, Jiaqi Zhuang, Jiayuan Song, Jin Zhu, Jingtao Han, Jingyang Li, Junbin Xie, Junhao Xu, Junjie Yan, Kaishun Zhang, Kecheng Xiao, Kexi Kang, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Zheng, Linbo Chai, Long Xing, Meizhi Ju, Mingyuan Chi, Mozhi Zhang, Peikai Huang, Pengcheng Niu, Pengfei Li, Pengyu Zhao, Qi Yang, Qidi Xu, Qiexiang Wang, Qin Wang, Qiuhui Li, Ruitao Leng, Shengmin Shi, Shuqi Yu, Sichen Li, Songquan Zhu, Tao Huang, Tianrun Liang, Weigao Sun, Weixuan Sun, Weiyu Cheng, Wenkai Li, Xiangjun Song, Xiao Su, Xiaodong Han, Xinjie Zhang, Xinzhu Hou, Xu Min, Xun Zou, Xuyang Shen, Yan Gong, Yingjie Zhu, Yipeng Zhou, Yiran Zhong, Yongyi Hu, Yuanxiang Fan, Yue Yu, Yufeng Yang, Yuhao Li, Yunan Huang, Yunji Li, Yunpeng Huang, Yunzhi Xu, Yuxin Mao, Zehan Li, Zekang Li, Zewei Tao, Zewen Ying, Zhaoyang Cong, Zhen Qin, Zhenhua Fan, Zhihang Yu, Zhuo Jiang, Zijia Wu•Jan 14, 2025•2836