Text To Video Synthesis Colab
Las características principales de camenduru/text-to-video-synthesis-colab son: Foundation Models.
Las alternativas de código abierto para camenduru/text-to-video-synthesis-colab incluyen: abenechehab/adapts — Our paper has been accepted at ICML 2025! 🎉📄🎓 👉 AdaPTS: Adapting Univariate Foundation Models to Probabilistic… alembics/disco-diffusion — This project is a diffusion-based AI art generator and animation framework used to create digital images and motion… baaivision/emu — Emu Series: Generative Multimodal Models from BAAI. baaivision/emu3 — Next-Token Prediction is All You Need. baichuan-inc/baichuan-13b — A 13B large language model developed by Baichuan Intelligent Technology. 01-ai/yi — Yi is a bilingual language model and foundation model designed for natural language processing, reasoning, and reading…
Our paper has been accepted at ICML 2025! 🎉📄🎓 👉 AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
This project is a diffusion-based AI art generator and animation framework used to create digital images and motion graphics from text prompts. It functions as a system for producing stylized videos and AI art through iterative diffusion sampling and neural network models. The framework distinguishes itself through specialized tools for 3D depth animation, using depth-map transformations to create spatial movement. It also includes neural style transfer capabilities to apply specific artistic looks, such as watercolor or pixel art, and utilizes optical flow frame blending to reduce flickering
Emu Series: Generative Multimodal Models from BAAI
Yi is a bilingual language model and foundation model designed for natural language processing, reasoning, and reading comprehension in both English and Chinese. It is built as a transformer-based architecture capable of general purpose text generation and conversational tasks. The model is distinguished by its ability to function as a long context system, processing and analyzing extended input sequences up to 200k tokens. It also supports quantized versions that use low-bit precision to reduce memory footprints, enabling execution on consumer-grade hardware. The project covers a broad rang