Search Results for "input-leap"

...According to actual measurements, arm64the single-architecture performance is better than universal2the dual- architecture Mac on the Apple chip, so Apple chip users are advised to pack arm64single-architecture applications by themselves. Real-ESRGAN can only enlarge the input image with a fixed 2-4x magnification (related to the selected model). This functionality is achieved by downsampling using a conventional scaling algorithm after multiple calls to Real-ESRGAN. Split each frame of the GIF and record the duration, zoom in one by one and then merge. Drag an image file or directory to any position in the window, and its path can be automatically set as the input.

Editly is a tool and framework for declarative NLE (non-linear video editing) using Node.js and FFmpeg. Editly allows you to easily and programmatically create a video from a set of clips, images, audio and titles, with smooth transitions and music overlaid. Editly has a simple CLI for quickly assembling a video from a set of clips or images, or you can use its more flexible JavaScript API. Inspired by ffmpeg-concat, Editly is much faster and doesn't require much storage because it uses...

Shotcut is a free, open source, cross-platform video editor for Windows, Mac and Linux. Major features include support for a wide range of formats; no import required meaning native timeline editing; Blackmagic Design support for input and preview monitoring; and resolution support to 4k. Copyright © 2011-2023 by Meltytech, LLC Shotcut is a trademark of Meltytech, LLC.

...This means stitched files must have the same audio/video codecs, and codec properties (width, height, sample rate, etc...). That said... Aeyae Remux currently doesn't check or enforce this restriction, so beware -- garbage input will likely produce garbage output. Linux binaries are provided as AppImage, built on Ubuntu 14.04. 8 GB of RAM (but more is better) is highly recommended if working with source files longer than 2 hours.

Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! 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. ...

...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.

...This time could possibly be reduced with further optimization. Images are processed in four phases that are executed on a pixel- per- pixel basis. Each phase takes a input image and renders it to a output image. This makes it easy to port the algorithm (back) to GLSL fragment shaders. As bloc97 described in his pseudo-preprint, the Anime4K algorithm is actually quite simple.