Search Results for "2-plan"
Sort By:
Showing 10 open source projects for "2-plan"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
LTX-2
Python inference and LoRA trainer package for the LTX-2 audio–video
LTX-2 is a powerful, open-source toolkit developed by Lightricks that provides a modular, high-performance base for building real-time graphics and visual effects applications. It is architected to give developers low-level control over rendering pipelines, GPU resource management, shader orchestration, and cross-platform abstractions so they can craft visually compelling experiences without starting from scratch. -
2
Wan2.2
Wan2.2: Open and Advanced Large-Scale Video Generative Model
Wan2.2 is a major upgrade to the Wan series of open and advanced large-scale video generative models, incorporating cutting-edge innovations to boost video generation quality and efficiency. It introduces a Mixture-of-Experts (MoE) architecture that splits the denoising process across specialized expert models, increasing total model capacity without raising computational costs. Wan2.2 integrates meticulously curated cinematic aesthetic data, enabling precise control over lighting,... -
3
LTX-2.3
Official Python inference and LoRA trainer package
LTX-2 is designed for both research and production workflows and can generate high-resolution video clips with precise control over structure, motion, and camera behavior. -
4
Phenaki - Pytorch
Implementation of Phenaki Video, which uses Mask GIT
Implementation of Phenaki Video, which uses Mask GIT to produce text-guided videos of up to 2 minutes in length, in Pytorch. It will also combine another technique involving a token critic for potentially even better generations. A new paper suggests that instead of relying on the predicted probabilities of each token as a measure of confidence, one can train an extra critic to decide what to iteratively mask during sampling. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
5
AI YouTube Shorts Generator
A python tool that uses GPT-4, FFmpeg, and OpenCV
AI-YouTube-Shorts-Generator is a Python-based tool that automates the creation of short-form vertical video clips (“shorts”) from longer source videos — ideal for adapting content for platforms like YouTube Shorts, Instagram Reels, or TikTok. It analyzes input video (whether a local file or a YouTube URL), transcribes audio (with optional GPU-accelerated speech-to-text), uses an AI model to identify the most compelling or engaging segments, and then crops/resizes the video and applies... -
6
Video Diffusion - Pytorch
Implementation of Video Diffusion Models
...For conditioning on text, they derived text embeddings by first passing the tokenized text through BERT-large. You can also directly pass in the descriptions of the video as strings, if you plan on using BERT-base for text conditioning. This repository also contains a handy Trainer class for training on a folder of gifs. Each gif must be of the correct dimensions image_size and num_frames. -
7
Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning... -
8
Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN)
...Additionally, we will try adding an extra linear attention on the main branch as well as self-conditioning in the pixel space. The insight of being able to self-condition on any hidden state of the network as well as the newly proposed sigmoid noise schedule are the two main findings. -
9
Text2Video
Software tool that converts text to video for more engaging experience
...So I imagined, "What if there was a tool that turns text into something more engaging such as a video, wouldn't it improve my learning experience?" I created a prototype web application that takes text as an input and generates a video as an output. I plan to further work on the project targeting young college students who are aged between 18 to 23 because they tend to prefer learning through videos over books based on the survey I found. The technologies I used for the project are HTML, CSS, Javascript, Node.js, CCapture.js, ffmpegserver.js, Amazon Polly, Python, Flask, gevent, spaCy, and Pixabay API. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
...The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical information. To add appropriate color to the geometrical information of the scene, the domain translation from depth to color is performed for each image. This model has three networks in the generator. In addition, the model has two discriminators.
- Previous
- You're on page 1
- Next