we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE. This SDE can be reversed for sample generation if we know the score of the marginal distributions at each intermediate time step, which can be estimated with score matching. The ...
We have extended the code to support multi speed/sde diffusion. Multi speed diffusion opens the avenue for further research in conditional generation and hierarchical represenation learning using the score-based diffusion framework. In this paper, we use multi speed diffusion to derive the CMDE and...
西交|深度学习研讨班-6|从VAE到Diffusion和ScoreMatching再统一为SDE 6050 -- 2:14:17 App 西交|深度学习研讨班-4|从Attention到Transformer再到Mamba 25.1万 1103 45:34:17 App 【教材+源码】深度学习必看圣经!李沐大神《动手学习深度学习》最新版视频教程分享,比追剧还爽!(深度学习/神经网络/计算机视觉) 14....
扩散模型是一种生成模型,在过去的几年里忽然火了起来,这其中也是有一定原因的。 单看2020前后的几篇开创性的文章我们就可以知道扩散模型的性能了,比如在图像生成方面打败了GANs。最近,研究人员想必也都看到了OpenAI上个月发布的图像生成模型DALL-E 2中也是用到了扩散模型。 DALL-E 2使用同一个caption生成的不同...
【研2基本功 Score-based Diffusion 2】手搓Diffusion SDE,CCF-A向你招手 2.8万播放 《从图形计算到世界模型》北京大学 陈宝权教授 - GAMES 2024 特邀报告 1.3万播放 不会引导动作?保姆级万能摆姿引导话术来了|动漫风篇|附实拍案例讲解+话术思维导图 64.1万播放 概率论与数理统计知识详解【小元老师】【考研数学...
score_sde_vp Score-Based Generative Modeling through Stochastic Differential Equations Unconditional Image Generation semantic_stable_diffusion Semantic Guidance Text-Guided Generation stable_diffusion_text2img Stable Diffusion Text-to-Image Generation stable_diffusion_img2img Stable Diffusion Image-to-Image Tex...
Reference Materials Score-Based Generative Modeling through Stochastic Differential Equations score_sde_pytorch denoising-diffusion-pytorch Authors Heyang Xue(https://github.com/WelkinYang) and Qicong Xie(https://github.com/QicongXie)
40 45 ScoreModel.add_argparse_args( 41 46 parser.add_argument_group("ScoreModel", description=ScoreModel.__name__)) 42 47 sde_class.add_argparse_args( @@ -63,28 +68,31 @@ def get_argparse_groups(parser): 63 68 ) 64 69 65 70 # Set up logger configuration 66 - if arg...
cont_kl_anneal --sde_type vpsde \ --iw_sample_p ll_iw --num_process_per_node 4 --use_se \ --vae_checkpoint$EXPR_ID/vae/checkpoint.pt --dae_arch ncsnpp --embedding_scale 1000 --mixing_logit_init -6 \ --warmup_epochs 20 --drop_inactive_var --skip_final_eval --fid_dir$...
The script has various arguments to adjust sampler configurations (ODE & SDE), sampling steps, change the classifier-free guidance scale, etc. For example, to sample from our 256x256 SiT-XL model with default ODE setting, you can use: python sample.py ODE --image-size 256 --seed 1 For...