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Changelog
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
Last updated over 1 year ago
realesr-general-x4v3 and realesr-general-wdn-x4v3 models — lightweight models optimized for general scenes-dn flag (0–1 range) for realesr-general-x4v3inference_video — no manual model download requiredinference_realesrgan_video
The major update: 🎉
✅ We update the RealESRGAN AnimeVideo-v3 model, which can achieve better results with a faster inference speed. The improvements are:
Have a nice day 😸 and happy everyday 😃
I am happy to add a simple logo to Real-ESRGAN 😋 (designed by myself! and inspired by the 4K logo) So I release a new version~
You can see it on ReadMe
Long long time no see😺
We have added small models for anime videos: 🎉
You can find demos and usages in https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_video_model.md
:white_check_mark: We add small models that are optimized for anime videos :-)
Now we have added two models.
| Models...
See you again 😺
The major update: :tada:
finetune_realesrgan_x4plus.ymlgenerate_multiscale_DF2K.pygenerate_meta_info.pyFile list:
RealESRGAN_x4plus_netD.pthRealESRGAN_x2plus_netD.pth--outscaleFile list:
RealESRGAN_x2plus.pthThis release is mainly for updating realesrgan-ncnn-vulkan executable files.
We have added Linux/MacOS executable files
We add back the tta option
We add the function: when the destination folder does not exist, create it!
File list:
realesrgan-ncnn-vulkan-20210801-windows.ziprealesrgan-ncnn-vulkan-20210801-macos.ziprealesrgan-ncnn-vulkan-20210801-ubuntu.zipThis release is mainly for storing pre-trained models and executable files.
RealESRGAN-ncnn-vulkan-20210725-windows.zip
RealESRNet_x4plus.pth
ESRGAN_SRx4_DF2KOST_official-ff704c30.pth
Note that: RealESRGAN_x4plus.pth can be found in https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth
This release is mainly for storing pre-trained models and executable files.
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Stable Diffusion web UI
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
A feature-rich command-line audio/video downloader