VideoCoF is a unified video editing framework based on diffusion transformers (Wan2.1). It implements a 'See → Reason → Edit' pipeline using chain-of-frames (CoT) reasoning, enabling object removal, addition, swap, and local style transfer. The architecture includes a modular pipeline (`WanPipeline`) with flow matching solvers, VAE, text encoder, and transformer backbone. Inference can be done via a fast inference script (`fast_infer.py`) using 4-step DMD, batch inference via JSON, or through a Gradio UI. Training supports LoRA fine-tuning with DeepSpeed ZeRO-2/3 for 1.3B and 14B models, with custom dataset classes (including CoT and joint image-video). External dependencies include Diffusers, Transformers, Gradio, OpenCV, DeepSpeed, and optional paifuser for optimizations. While primarily an application for video editing, the repo has strong research character with extensive evaluation metrics (CLIP, GPT), configuration files, and experiment scripts. The code is modular in parts but contains experimental features, hardcoded paths, and reliance on an internal optimization package (paifuser) that may limit external reuse. The core utility modules (LoRA, caching, attention, schedulers) are well-crafted and potentially reusable across other diffusion model projects.
Scal3R is a research codebase for CVPR 2026 Highlight paper on large-scale 3D reconstruction from unordered images using test-time training. The core contribution is a novel bidirectional attention mechanism with fast weight updates (TTT layers) that adapts during inference to improve reconstruction quality for large scenes. The repository provides an end-to-end inference pipeline: given a folder of images, it estimates camera poses, depth maps, and produces a colored point cloud. The architecture builds on a DINOv2 backbone and VGGT-based aggregator with custom attention heads, loop detection via SALAD, and pose graph optimization with submap management. The codebase is well-structured with separate modules for data loading, model definition, inference orchestration, and visualization tools. It includes fallback implementations for key components (e.g., Python-only loop solver, simple correlation-based loop detector) to improve portability, though GPU and CUDA are assumed. The repository is primarily a research artifact with clean utilities that could be extracted for other 3D vision projects. Evaluation code and dataset scripts are explicitly marked as not yet released, limiting reproducibility until those are provided.