This repository provides a set of custom nodes for ComfyUI that enable 'broadcast' or 'virtual link' functionality. Instead of manually connecting every node input, users can connect data to an 'Anything Everywhere' node, which then automatically routes that data to any unconnected input of the same type across the workflow. This dramatically reduces visual clutter and simplifies complex workflows. The system supports advanced filtering via regex, group/color restrictions, priority resolution, and subgraph compatibility. It also includes a 'Combo Clone' node for broadcasting dropdown values and a 'Seed Everywhere' node for seed synchronization. The architecture is split between Python backend node definitions (in `use_everywhere.py`) and a rich JavaScript frontend that handles graph analysis, virtual link rendering, and user interface. The core logic resides in the JS modules: `use_everywhere_graph_analysis.js` analyzes the graph to determine which inputs can be connected, `use_everywhere_classes.js` models the broadcast links, and `use_everywhere_ui.js` renders the virtual connections with animations and highlights. The system integrates deeply with ComfyUI's graph lifecycle, intercepting prompt submission to temporarily convert virtual links into real links for execution. Key entry points include the Python node classes (`AnythingEverywhere`, `ComboClone`, etc.) and the JavaScript extension registration in `use_everywhere.js`. The primary data flow is: user connects data to a UE node → graph analysis identifies matching unconnected inputs → virtual links are stored in graph metadata → on prompt, virtual links are materialized as real links → after execution, links are removed. External dependencies are minimal: it relies on ComfyUI's frontend package and standard web APIs.
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.
This repository is a ComfyUI custom node pack (ComfyUI-dapaoAPI) that provides a comprehensive suite of nodes for AI-powered image, video, audio, and text generation. It acts as a unified relay client to multiple commercial AI services (dapaoAI, RunningHub, Gemini, Seedance, Wan, etc.), abstracting API details, handling media preprocessing, error diagnostics, task queue persistence, and concurrent batch execution. The architecture is a classic ComfyUI extension: each node type is defined as a Python class with input/output specifications, and shared utility modules (e.g., `dreamshark_runtime` for job submission, `image_input_utils` for tensor-to-PNG conversion, `node_error_utils` for formatted error handling) are used across all generation nodes. A front-end JavaScript layer adds dynamic widget behavior, price badges, and rich prompt editors within ComfyUI's existing UI framework. The pack notably includes a hybrid nature: it bundles a `skills/` directory containing detailed markdown-based AI agent instructions (for short drama writing, music caption rewriter, article formatting, GPT image templating) that are injected into LLM prompts during node execution or used as standalone agent guides. These skills are not data pipelines but rather structured knowledge assets for prompt engineering, giving the repository a dual character as both a custom node collection and an LLM orchestration framework.