Jasna

FixAnything is an open-source video-refinement tool that repairs rendering artifacts from 3D representations, including 3D Gaussian splats, NeRFs, meshes, and sparse point clouds. It repurposes the pretrained Wan2.1 video diffusion model with minimal modification and fine-tuning, using a FixAnything LoRA to turn a rendered camera-path video into a refined video. The inference pipeline accepts a video file or folder of frames; its documented workflow uses 61-frame renderings resized to 832×480 and writes a generated video, the resized input, and a side-by-side comparison. An optional pipeline reconstructs scenes from a small set of photographs with MapAnything, renders the reconstruction along an interpolated camera path, and then refines that rendering. The repository contains inference code and downloads the underlying Wan2.1 model and FixAnything weights; the code and weights are released under the Apache 2.0 License.

JoyAI-Video-Edit is an open-source, instruction-guided system for editing live camera streams or uploaded videos as frames arrive. It processes frames causally without waiting for the complete video, requiring a predefined sequence length, or revisiting future frames, and supports subject and local edits, background replacement, style and motion changes, and reference-guided editing. Its autoregressive diffusion architecture combines an MLLM-based condition encoder, a causal video VAE, and a 16-billion-parameter multimodal diffusion transformer. The deployment uses aligned autoregressive distribution-matching distillation, long-horizon optimization, bounded KV-state inference, and deployment-oriented scheduling to reduce train–inference mismatch and temporal drift during streaming generation. The repository reports 30 FPS end-to-end throughput at 720 × 1248 in its deployment benchmark. The repository provides deployment code and model checkpoints, a local server with a browser interface, and instructions for CUDA-based inference. It is licensed under Apache 2.0.

Kandinsky 6.0 Video is a family of diffusion models from Kandinsky Lab for synchronized text-to-audio-video and image-to-audio-video generation. Its Lite and Pro models produce five-second video clips with synchronized 44 kHz audio, including lip synchronization. A separate super-resolution model can raise the output to Full HD. The project supports NVIDIA GPUs and provides integrations for Diffusers, ComfyUI, vLLM-Omni, SGLang, and FastVideo, with command-line and API-based generation workflows.

Spirula Studio is a cross-vendor 3D Gaussian Splatting trainer that processes photos or video into Gaussian splats and textured meshes in a self-contained binary. It combines video frame extraction, AI masking, structure-from-motion, Gaussian Splatting training, meshing, depth and normal generation, and skybox processing, with native support for fisheye and 360-degree equirectangular datasets. Its Vulkan backend runs on NVIDIA, AMD, Intel, and Apple GPUs, while a CUDA backend supports CUDA-capable NVIDIA GPUs; the application provides GUI and CLI workflows for Windows, Linux, and macOS. Quantized training is designed to support up to 10 million SH3 Gaussians in 8 GB of VRAM, and masking requires a separately downloaded SAM checkpoint.

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for numerical computing and large-scale machine learning. It transforms Python and NumPy-style functions through automatic differentiation with `jax.grad`, automatic batching and vectorization with `jax.vmap`, and just-in-time compilation with `jax.jit`. It uses XLA to compile computations for GPUs, TPUs, and other accelerators. JAX supports compiler-driven multi-device execution: `Mesh`, `PartitionSpec`, and `NamedSharding` declare array placement across device meshes, allowing distributed training code to include automatically managed parallel work such as gradient synchronization. Its compilation tools also include ahead-of-time compilation, portable artifact export with `jax.export`, and conversion to TensorFlow SavedModels through `jax2tf` for serving integrations. The project describes itself as a research project rather than an official Google product.