目前在图像和视频生成上,目前主流的模型基本都是diffusion体系(链接),而基于LM的架构在生成的质量上要低于diffusion系列不少(FID得分高40%左右)。在本文中,作者认为LM为生成Visual的质量不高,主要是以前的Visual Tokenizer(将图片连续表达表示离散的表达)效果不佳,提出了一种新的Visual Tokenizer,从而可以大幅的提高基于...
论文链接:[2310.05737] Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation (arxiv.org) 代码链接:lucidrains/magvit2-pytorch: Implementation of MagViT2 Tokenizer in Pytorch (github.com)(非官方) 这篇论文出自谷歌,提出了一种在视频生成任务上,让语言模型超过Diffusion模型的方法。这篇...
LANGUAGE MODEL BEATS DIFFUSION- TOKENIZER IS KEY TO VISUAL GENERATION(Google & CMU 2024) - mardinff于20240828发布在抖音,已经收获了248个喜欢,来抖音,记录美好生活!
[CV]《Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation》L Yu, J Lezama, N B. Gundavarapu, L Versari, K Sohn, D Minnen, Y Cheng, A Gupta, X Gu, A G. Hauptmann, B Gong, M Yang, I Essa, D A. Ross, L Jiang [Google & CMU] (2023) O网页链接 #机器...
LANGUAGE MODEL BEATS DIFFUSION- TOKENIZER IS KEY TO VISUAL GENERATION(Google & CMU 2024), 视频播放量 486、弹幕量 3、点赞数 23、投硬币枚数 14、收藏人数 35、转发人数 9, 视频作者 mardinff, 作者简介 ,相关视频:Black-Box Prompt Optimization- Aligning L
@misc{yu2023language,title={Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation},author={Lijun Yu and José Lezama and Nitesh B. Gundavarapu and Luca Versari and Kihyuk Sohn and David Minnen and Yong Cheng and Agrim Gupta and Xiuye Gu and Alexander G. Hauptmann and ...
Language Model Beats Diffusion – Tokenizer is Key to Visual Generation https://arxiv.org/pdf/2310.05737.pdf InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationhttps://arxiv.org/pdf/2309.06380.pdf Perceptual Losses for Real-Time Style Transfer and Super-Resolu...
TextDiffuser/TextDiffuser-2 (NEW): Diffusion Models as Text Painters Speech WavLM: speech pre-training for full stack tasks VALL-E: a neural codec language model for TTS Multimodal (X + Language) LayoutLM/LayoutLMv2/LayoutLMv3: multimodal (text + layout/format + image) Document Foundation...
Poster girl on the Anime Kawaii Diffusion Model Card Give me the code already!! fromdiffusersimportDiffusionPipeline diffusion_pipe=DiffusionPipeline.from_pretrained("Ojimi/anime-kawai-diffusion")diffusion_pipe=diffusion_pipe.to("cuda")defcreate_image(text,outputfile,prompt):image=diffusion_pipe(text....
“With this tokenizer we train a Masked Language Model following Yu et al. (2023a), using the token factorization described in Section 3.2.” 也确实不应该是冻结的,这里的codebook是独立提出的,和LLM的embedding层无关。其他的用LLM进行多模态的模型要么将codebook和LLM的embedding对齐,要么用映射层进行映射...