LivePortrait/assets/docs/speed.md
Jianzhu Guo bbb2e33599
feat: animals mode, several updates and improvements (#264)
* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* chore: refactor

* chore: refactor

* chore: refactor

* fix: video cropping

* chore: refactor

* chore: remove timm

* merge: animal support (#258)

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

* feat: update

---------

Co-authored-by: zhangdingyun <zhangdingyun@kuaishou.com>

feat: update

feat: update

chore: stage

* chore: stage

* chore: refactor

* chore: refactor

* doc: update readme

* doc: update readme

* doc: update readme

* chore: refactor

* doc: update

* doc: update

* doc: update

* doc: update

* chore: rename

* doc: update

* doc: update

* chore: refactor

* doc: update

* chore: refactor

* chore: refactor

* doc: update

* chore: update clip feature

* chore: add landmark option

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

* doc: update

---------

Co-authored-by: zhangdingyun <zhangdingyun@kuaishou.com>
Co-authored-by: zzzweakman <1819489045@qq.com>
2024-08-02 22:39:05 +08:00

14 lines
903 B
Markdown

### Speed
Below are the results of inferring one frame on an RTX 4090 GPU using the native PyTorch framework with `torch.compile`:
| Model | Parameters(M) | Model Size(MB) | Inference(ms) |
|-----------------------------------|:-------------:|:--------------:|:-------------:|
| Appearance Feature Extractor | 0.84 | 3.3 | 0.82 |
| Motion Extractor | 28.12 | 108 | 0.84 |
| Spade Generator | 55.37 | 212 | 7.59 |
| Warping Module | 45.53 | 174 | 5.21 |
| Stitching and Retargeting Modules | 0.23 | 2.3 | 0.31 |
*Note: The values for the Stitching and Retargeting Modules represent the combined parameter counts and total inference time of three sequential MLP networks.*