$ show NevermindNilas/TheAnimeScripter
TheAnimeScripter (TAS) is a production-grade AI video enhancement toolkit specialized for anime content. It performs frame interpolation (RIFE 4.15-4.25 variants, GMFSS, DistilDRBA), super-resolution (30+ architectures via a vendored spandrel fork), restoration, frame deduplication, monocular depth estimation (Depth Anything V2/V3, Video Depth Anything, Limbo), scene-change detection, stabilization, segmentation, and object detection. It ships as a CLI tool (main.py), an Adobe After Effects plugin (src/server/aeComms.py via Socket.IO), and a standalone desktop build (build.py + tools/build_support/). The pipeline is: decode video via FFmpeg or the custom nelux library, stream frames through a FrameWindow sliding buffer, apply dedup → restore → upscale → interpolate stages, then re-encode with configurable codecs, bit depths, and BT.709/BT.2020 color spaces.
The LLM identified 6 files worth highlighting for reuse:
Provides calculatePadding and smallestValidMultiple helpers that determine valid input dimensions for upscaling models. Deliberately torch-free so it can be imported in lightweight CI contexts. Isolated and reusable for any image/video pipeline that must pad tensors to model-required multiples.
Maps encoder names to FFmpeg flag lists, selects pixel formats based on bit depth/codec/alpha, and builds BT.709/BT.2020 colorspace conversion filters from probed metadata. Isolated from torch/cv2/nelux specifically so it can be unit-tested without heavy dependencies. Reusable for any FFmpeg-based transcoding tool.
CliConfig validates CLI choices, coerces JSON config values to argparse-compatible types, auto-enables parent flags, and warns on unknown keys. The validateChoiceForKey/coerceValueForKey functions are generic and could be extracted for any argparse application needing JSON config merging with precedence rules.
Implements a zero-overhead HTTP preview sink: PreviewSink holds the latest frame thread-safely, PreviewSampler throttles JPEG encoding on a worker thread with wall-clock gating, and PreviewHTTPHandler serves frames only when a viewer is connected. A clean, reusable pattern for live preview in any video processing tool.
CudaChecker centralizes CUDA/ROCm/MPS/CPU device selection using NVML, nvidia-smi, and registry scans, with queryGpus and detectGPUArchitecture helpers. Used across nearly every backend module; the device-detection logic is generic and extractable into any ML application.
resolveWeightPath/resolveWeightDir/downloadModels implement model weight resolution with version-isolated caches, retry/404 handling, and integrity checks. The pattern of mapping model names to CDN URLs with local caching and fallback is reusable for any ML application distributing weights.