Haiper vs LivePortrait
Haiper
haiper.aiLivePortrait
liveportrait.github.ioSide by side
| Figure | Haiper | LivePortrait |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| API | Yes | — |
| Self-hosted | — | Yes |
| Plans published | 10 | 0 |
| Spec score · Image-to-Video Generators | 8.2 (#9) | 7.9 (#11) |
| Privacy | Users can mark generations private so only they can see them; unmarked videos may appear in Haiper's public area. | The repository says generated motion template files can speed inference and protect privacy. |
| Support | Haiper directs users to its Discord Support channel to report generation errors, glitches, and bugs. | The maintainers invite bug reports and suggestions through repository issues and pull requests and provide a contact email for project inquiries. |
| Video editing | The repaint mode lets users select regions in a video's first frame to regenerate using a text prompt. | The project supports portrait video editing using a source video and a driving video. |
Plans
Haiper
- Haiper Video 2.0 API — image to video, 540p$0.03, paid once
- Haiper Image 2.0 API — 720p$0.04, paid once
- Haiper Video 1.5 API — extend duration$0.05, paid once
- Haiper Video 1.5 API — image to video$0.05, paid once
- Haiper Video 1.5 API — text to video$0.05, paid once
- Haiper Video 2.0 API — image to video, 720p$0.05, paid once
- Haiper Video 2.0 API — text to video, 720p$0.05, paid once
- Haiper Video Upscaler API — 1080p$0.05, paid once
- Haiper Image 2.0 API — 1080p$0.08, paid once
- Haiper Image 2.0 API — 4k$0.12, paid once
In their own words
LivePortrait
LivePortrait creates portrait video from a single image, using motion from video, audio, text, or generation. It also supports editing portrait video using a source video and a driving video. Stitching and eye and lip retargeting controls provide ways to adjust animation. The project supplies inference code, pretrained models, and a Gradio interface for local use, as well as a demo hosted on Hugging Face Spaces. Its PyTorch implementation is released under the MIT License, and the project reports generation speed of 12.8 ms on an RTX 4090 GPU with PyTorch. Human-mode instructions cover Linux, Windows, and Apple Silicon macOS; Intel macOS is untested. Animal models can animate cats, dogs, and pandas using human-driven motion, but animal mode is tested only on Linux and Windows with an NVIDIA GPU and is unsupported on macOS. Local setup requires git, conda, FFmpeg, Python 3.10, and downloaded pretrained weights. Commercial users must replace the included InsightFace detection models. Maintainers warn of possible deepfake misuse and visual artifacts.