ComfyUI-VoxCPM is a ComfyUI custom node package that provides expressive text-to-speech (TTS) using the VoxCPM model family (v1.x and v2). It supports zero-shot TTS, voice cloning (prompt or reference audio), voice design via text descriptions, LoRA inference, and LoRA fine-tuning. The architecture is a ComfyUI extension with heavy JavaScript frontend, HTTP API routes, and vendored model inference code. The core functionality is implemented in the `src/voxcpm/` directory, which contains model architectures (MiniCPM4, AudioVAE, local DiT, etc.), a standalone CLI, and training utilities. External dependencies include ComfyUI, PyTorch, Hugging Face Hub, and several optional libraries (argbind, datasets, modelscope, etc.). The codebase demonstrates good software engineering practices with clean abstractions, reusable components, and graceful degradation for optional features. However, it is tightly coupled to ComfyUI's ecosystem and vendors upstream model code, which may drift from upstream. The repository also includes a standalone `hf_progress` library for HuggingFace transfer progress tracking, which is a well-isolated, reusable component.
This repository implements a single ComfyUI custom node, `RTXVideoSuperResolution`, which leverages NVIDIA's RTX Video Super Resolution technology via the `nvidia-vfx` library to upscale images and video frames. The node supports scaling by a multiplier or to target dimensions, and offers quality levels (LOW, MEDIUM, HIGH, ULTRA). It uses batching for efficient GPU memory usage and proper tensor manipulation. The codebase is minimal but well-structured, with a clean schema definition, enum-based resize type selection, and correct integration with the ComfyUI extension API. However, it is tightly coupled to ComfyUI's internal API and the NVIDIA RTX ecosystem, limiting direct reuse outside of that context. No tests, CI/CD beyond publishing, or additional functionality are present.