Search Results for "image edit batch"

v5.1.1. Warlock-Studio is a Windows application that uses Real-ESRGAN, BSRGAN, IRCNN, GFPGAN, RealESRNet, RealESRAnime and RIFE Artificial Intelligence models to upscale, restore faces, interpolate frames and reduce noise in images and videos. the application supports GPU acceleration (including multi-GPU setups) and offers batch processing for large workloads. It includes drag-and-drop handling for single or multiple files, optional pre-resize functions, and an automatic tiling system...

RenderTune is a powerful and versatile video rendering tool that allows you to effortlessly create stunning videos from your audio and image files. With RenderTune, you can combine single or multiple audio files into one video and even choose the order in which they appear. Customize your output video location, resolution, and padding options to achieve the perfect look for your video. Our latest version (v1.1.3) includes important bug fixes and improvements, such as better support for...

I am sorry. This app currently supports only Japanese. (English support will be available soon) This app is a video editing application that runs on Windows. You can easily trim or combine unnecessary parts of MP4 files shot with smartphones or digital cameras, synthesize audio files and image files, and synthesize text characters. The completed project can be output as an MP4 file, so it can be posted on video sites such as YouTube. Since it is still under development, the...

...The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g., RGBS) inputs. Using VapourSynth you can pass a Video directly to VSGAN, without any frame extraction needed. Any edit you make in the VapourSynth script with or without VSGAN can be re-used for any other video. VSGAN is released under the MIT License, ensuring it will stay free, with the ability to be used commercially.

...This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders.