Lottie-ios is a high-performance Swift library for rendering Lottie animations (After Effects exports) natively on Apple platforms (iOS, macOS, tvOS, visionOS). It provides two rendering engines: a full-featured Main Thread engine and a GPU-accelerated Core Animation engine, with automatic fallback based on feature compatibility. The library offers UIKit (LottieAnimationView) and SwiftUI (LottieView) wrappers, interactive controls (AnimatedButton, AnimatedSwitch), dynamic property overrides via a value provider system, and support for the .lottie compressed archive format. It includes embedded, reusable libraries for ZIP archive handling (ZipFoundation), declarative UI model configuration (EpoxyCore), and LRU caching. The codebase is well-structured with extensive test coverage including snapshot tests, keypath logging, and performance benchmarks, and is actively maintained by Airbnb.
This repository is a ComfyUI custom node pack that extends the standard ControlNet functionality with advanced scheduling, weighting, and masking capabilities. It provides nodes for timestep and latent keyframes, custom weight curves, attention masks, effect masks, and sliding context windows for video generation (AnimateDiff integration). The system supports multiple control types: ControlNet, T2IAdapter, ControlLoRA, ControlLLLite, SparseCtrl, SVD-ControlNet, Reference, ControlNet++, and CtrLoRA. It also replicates features from Auto1111 like 'My prompt is more important' via soft weights and uncond_multiplier. The architecture is modular, with separate files for each control family (control.py, control_reference.py, control_ctrlora.py, etc.), a shared utilities module (utils.py) providing base classes like AdvancedControlBase and ControlWeights, and a node registration layer (nodes.py, nodes_main.py, etc.). The system integrates deeply with ComfyUI's internal APIs (comfy.controlnet, comfy.model_patcher, comfy.hooks) and uses monkey-patching to inject advanced behavior into standard control objects. A custom inter-node communication protocol (DinkLink) enables coordination with AnimateDiff-Evolved. Key entry points are the node classes registered in __init__.py and nodes.py, which are loaded by ComfyUI's extension system. The primary data flow involves loading a control model, optionally converting it to an advanced version, applying timestep/latent keyframes and weights, and then applying the control during sampling with optional reference injection or sliding context. The system is actively maintained with recent commits, CI publishing, and example workflows demonstrating advanced features like Qwen Image Inpainting and Anima LLLite v2.