Video-to-Video
SeedVR
Iceclear commited on
Commit
40d518e
·
verified ·
1 Parent(s): 9f7bb1a

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +13 -1
README.md CHANGED
@@ -25,6 +25,18 @@ library_name: seedvr
25
  alt="SeedVR Paper on ArXiv"
26
  />
27
  </a>
 
 
 
 
 
 
 
 
 
 
 
 
28
  <a href="https://www.youtube.com/watch?v=aPpBs_B2iCY" target='_blank'>
29
  <img
30
  src="https://img.shields.io/badge/Demo%20Video-%23FF0000.svg?logo=YouTube&logoColor=white"
@@ -41,7 +53,7 @@ library_name: seedvr
41
 
42
 
43
  ## 📮 Notice
44
- **Limitations:** These are the prototype models and the performance may not be perfectly align with the paper. Our methods are sometimes not robust to heavy degradations and very large motions, and shares some failure cases with existing methods, e.g., fail to fully remove the degradation or simply generate unpleasing details. Moreover, due to the strong generation ability, Our methods tend to overly generate details on inputs with very light degradations, e.g., 720p AIGC videos, leading to oversharpened results occasionally.
45
 
46
 
47
  ## ✍️ Citation
 
25
  alt="SeedVR Paper on ArXiv"
26
  />
27
  </a>
28
+ <a href="https://github.com/ByteDance-Seed/SeedVR">
29
+ <img
30
+ alt="Github" src="https://img.shields.io/badge/SeedVR2-Codebase-536af5?color=536af5&logo=github"
31
+ alt="SeedVR Codebase"
32
+ />
33
+ </a>
34
+ <a href="https://huggingface.co/models?other=seedvr">
35
+ <img
36
+ src="https://img.shields.io/badge/SeedVR2-Models-yellow?logo=huggingface&logoColor=yellow"
37
+ alt="SeedVR Models"
38
+ />
39
+ </a>
40
  <a href="https://www.youtube.com/watch?v=aPpBs_B2iCY" target='_blank'>
41
  <img
42
  src="https://img.shields.io/badge/Demo%20Video-%23FF0000.svg?logo=YouTube&logoColor=white"
 
53
 
54
 
55
  ## 📮 Notice
56
+ **Limitations:** These are the prototype models and the performance may not perfectly align with the paper. Our methods are sometimes not robust to heavy degradations and very large motions, and shares some failure cases with existing methods, e.g., fail to fully remove the degradation or simply generate unpleasing details. Moreover, due to the strong generation ability, Our methods tend to overly generate details on inputs with very light degradations, e.g., 720p AIGC videos, leading to oversharpened results occasionally.
57
 
58
 
59
  ## ✍️ Citation