chore: clean up project metadata and docs

Author: GACLove

.github/workflows/publish.yml

@@ -6,6 +6,7 @@ on:
       - "v*.*.*"
 
 permissions:
+  contents: read
   issues: write
 
 jobs:

.github/workflows/quality.yml

@@ -0,0 +1,27 @@
+name: Quality checks
+
+on:
+  push:
+  pull_request:
+
+permissions:
+  contents: read
+
+jobs:
+  pre-commit:
+    name: Ruff and repository checks
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install pre-commit
+        run: python -m pip install pre-commit
+
+      - name: Run checks
+        run: pre-commit run --all-files

CONTRIBUTING.md

@@ -0,0 +1,32 @@
+# Contributing
+
+## Development setup
+
+The wrapper follows the Python version supported by the bundled LightX2V
+submodule (Python 3.10 or newer).
+
+```bash
+git clone --recursive https://github.com/ModelTC/ComfyUI-Lightx2vWrapper.git
+cd ComfyUI-Lightx2vWrapper
+python -m pip install -r lightx2v/requirements.txt
+python -m pip install -r requirements.txt pre-commit
+pre-commit install
+```
+
+## Before opening a pull request
+
+Run the repository checks from the project root:
+
+```bash
+pre-commit run --all-files
+```
+
+For workflow or runner changes, validate the affected ComfyUI workflow with a
+local LightX2V installation and include the workflow or reproduction details
+in the pull request description. Keep changes to the `lightx2v` submodule in a
+separate upstream contribution unless the wrapper specifically requires a
+submodule revision update.
+
+Pull requests should explain the user-visible behavior, list validation
+performed, and avoid committing model weights, generated media, credentials,
+or local debugging artifacts.

README.md

@@ -1,202 +1,97 @@
-[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Python](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/downloads/) [![ComfyUI](https://img.shields.io/badge/ComfyUI-Custom%20Node-brightgreen)](https://github.com/comfyanonymous/ComfyUI) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Models-lightx2v-yellow)](https://huggingface.co/lightx2v) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/GacLove/ComfyUI-Lightx2vWrapper)
+[![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/downloads/)
+[![ComfyUI](https://img.shields.io/badge/ComfyUI-Custom%20Node-brightgreen)](https://github.com/comfyanonymous/ComfyUI)
+[![Models](https://img.shields.io/badge/models-LightX2V-yellow)](https://huggingface.co/lightx2v)
 
 # ComfyUI-Lightx2vWrapper
 
-[中文版](./README_CN.md) | English
+ComfyUI custom nodes for [LightX2V](https://github.com/ModelTC/LightX2V). The
+wrapper exposes LightX2V's modular configuration and runner APIs, together
+with ComfyUI-friendly file inputs and output previews.
 
-A ComfyUI custom node wrapper for LightX2V, enabling modular video generation with advanced optimization features.
+[中文文档](README_CN.md)
 
-## Features
+## Supported workflows
 
-- **Modular Configuration System**: Separate nodes for each aspect of video generation
-- **Text-to-Video (T2V) and Image-to-Video (I2V)**: Support for both generation modes
-- **Advanced Optimizations**:
-  - TeaCache acceleration (up to 3x speedup)
-  - Quantization support (int8, fp8)
-  - Memory optimization with CPU offloading
-  - Lightweight VAE options
-- **LoRA Support**: Chain multiple LoRA models for customization
-- **Multiple Model Support**: wan2.1, hunyuan architectures
+The repository includes runnable workflow examples under
+[`example_workflows/`](example_workflows/):
 
-## Installation
+- [Wan 2.2](example_workflows/lighx2v_wan2.2.json) text/image-to-video
+- [SekoTalk](example_workflows/lightx2v_seko_talk.json) and [static
+  SekoTalk](example_workflows/lightx2v_seko_talk_static.json)
+- [Multi-person SekoTalk](example_workflows/lightx2v_seko_talk_multi_talk.json)
+- [SeedVR2](example_workflows/lightx2v_seedvr.json) video restoration
+- [SwiftVR image](example_workflows/lightx2v_swiftvr_image.json) and [SwiftVR
+  video](example_workflows/lightx2v_swiftvr_video.json) restoration
 
-1. Clone this repository with submodules into your ComfyUI's `custom_nodes` directory:
+The examples are versioned with the wrapper and are the best reference for
+current node names and connections.
 
-```bash
-cd ComfyUI/custom_nodes
-git clone --recursive https://github.com/gaclove/ComfyUI-Lightx2vWrapper.git
-```
+## Installation
 
-If you already cloned without submodules, initialize them:
+Clone the repository with its LightX2V submodule into ComfyUI's
+`custom_nodes` directory:
 
 ```bash
+cd ComfyUI/custom_nodes
+git clone --recursive https://github.com/ModelTC/ComfyUI-Lightx2vWrapper.git
 cd ComfyUI-Lightx2vWrapper
-git submodule update --init --recursive
+python -m pip install -r lightx2v/requirements.txt
+python -m pip install -r requirements.txt
 ```
 
-2. Install dependencies:
+For an existing checkout, update the submodule with:
 
 ```bash
-cd ComfyUI-Lightx2vWrapper
-# Install lightx2v submodule dependencies
-pip install -r lightx2v/requirements.txt
-# Install ComfyUI wrapper dependencies
-pip install -r requirements.txt
+git submodule update --init --recursive
 ```
 
-3. Download models and place them in `ComfyUI/models/lightx2v/` directory
-
-## Node Overview
-
-### Configuration Nodes
-
-#### 1. LightX2V Inference Config
-
-Basic inference configuration for video generation.
-
-- **Inputs**: model, task_type, inference_steps, seed, cfg_scale, width, height, video_length, fps
-- **Output**: Base configuration object
-
-#### 2. LightX2V TeaCache
-
-Feature caching acceleration configuration.
-
-- **Inputs**: enable, threshold (0.0-1.0), use_ret_steps
-- **Output**: TeaCache configuration
-- **Note**: Lower threshold = more speedup (0.1 ~2x, 0.2 ~3x)
-
-#### 3. LightX2V Quantization
-
-Model quantization settings for memory efficiency.
-
-- **Inputs**: dit_precision, t5_precision, clip_precision, backend, sensitive_layers_precision
-- **Output**: Quantization configuration
-- **Backends**: Auto-detected (vllm, sgl, q8f)
-
-#### 4. LightX2V Memory Optimization
-
-Memory management strategies.
-
-- **Inputs**: optimization_level, attention_type, enable_rotary_chunking, cpu_offload, unload_after_generate
-- **Output**: Memory optimization configuration
-
-#### 5. LightX2V Lightweight VAE
-
-VAE optimization options.
-
-- **Inputs**: use_tiny_vae, use_tiling_vae
-- **Output**: VAE configuration
-
-#### 6. LightX2V LoRA Loader
-
-Load and chain LoRA models.
+Download the model files from [LightX2V on Hugging Face](https://huggingface.co/lightx2v)
+and place them under `ComfyUI/models/lightx2v/`.
 
-- **Inputs**: lora_name, strength (0.0-2.0), lora_chain (optional)
-- **Output**: LoRA chain configuration
+## Node groups
 
-### Combination Node
+- **Configuration:** `LightX2VInferenceConfig`, `LightX2VTeaCache`,
+  `LightX2VQuantization`, and `LightX2VMemoryOptimization`.
+- **Composition:** `LightX2VConfigCombinerV2` and `LightX2VConfigCombinerV3`.
+- **Inference:** `LightX2VModularInferenceV2`.
+- **Talk inputs:** single-object, file-list, JSON, and multi-object talk
+  combiners.
+- **Video inputs and restoration:** `LightX2VInputVideoPath`, SeedVR2 and
+  SwiftVR loaders/samplers, plus `LightX2VOutputVideoPreview`.
+- **LoRA:** `LightX2VLoRALoader`.
 
-#### 7. LightX2V Config Combiner
+The inference node currently accepts the model classes exposed by
+`model_utils.support_model_cls_list`: Wan 2.1/2.2 variants, SekoTalk,
+Qwen Image, and SeedVR2. The retired `*_distill` values remain selectable as
+compatibility aliases for saved ComfyUI workflows and are normalized before
+LightX2V's runner is built.
 
-Combines all configuration modules into a single configuration.
+## Model files
 
-- **Inputs**: All configuration types (optional)
-- **Output**: Combined configuration object
+Place each model in its own directory under `ComfyUI/models/lightx2v/`. LoRA
+files can be placed in either `ComfyUI/models/lightx2v/loras/` or
+`ComfyUI/models/loras/`.
 
-### Inference Node
-
-#### 8. LightX2V Modular Inference
-
-Main inference node for video generation.
-
-- **Inputs**: combined_config, prompt, negative_prompt, image (optional), audio (optional)
-- **Outputs**: Generated video frames
-
-## Usage Examples
-
-### Basic T2V Workflow
-
-1. Create LightX2V Inference Config (task_type: "t2v")
-2. Use LightX2V Config Combiner
-3. Connect to LightX2V Modular Inference with text prompt
-4. Save video output
-
-### I2V with Optimizations
-
-1. Load input image
-2. Create LightX2V Inference Config (task_type: "i2v")
-3. Add LightX2V TeaCache (threshold: 0.26)
-4. Add LightX2V Memory Optimization
-5. Combine configs with LightX2V Config Combiner
-6. Run LightX2V Modular Inference
-
-### With LoRA
-
-1. Create base configuration
-2. Load LoRA with LightX2V LoRA Loader
-3. Chain multiple LoRAs if needed
-4. Combine all configs
-5. Run inference
-
-## Model Directory Structure
-
-Download models from: <https://huggingface.co/lightx2v>
-
-Models should be placed in:
-
-```txt
+```text
 ComfyUI/models/lightx2v/
-├── Wan2.1-I2V-14B-720P-xxx/     # Main model checkpoints
-├── Wan2.1-I2V-14B-480P-xxx/     # Main model checkpoints
-├── loras/          # LoRA models
+├── Wan2.2-I2V-A14B-distill-fp8/
+├── seedvr2_ema_3b_fp8/
+└── loras/
 ```
 
-## Tips
+The exact directory names depend on the downloaded checkpoint. The model
+picker only lists directories that exist on the local ComfyUI installation.
 
-- Start with default settings and adjust based on your hardware
-- Use TeaCache with threshold 0.1-0.2 for significant speedup
-- Enable memory optimization if running on limited VRAM
-- Quantization can reduce memory usage but may affect quality
-- Chain multiple LoRAs for complex style combinations
+## Development
 
-## Troubleshooting
-
-- **Out of Memory**: Enable memory optimization or use quantization
-- **Slow Generation**: Enable TeaCache or reduce inference steps
-- **Model Not Found**: Check model paths in `ComfyUI/models/lightx2v/`
-
-## Example Workflows
-
-Example workflow JSON files are provided in the `examples/` directory:
-
-- `wan_i2v.json` - Basic image-to-video
-- `wan_i2v_with_distill_lora.json` - I2V with distillation LoRA
-- `wan_t2v_with_distill_lora.json` - T2V with distillation LoRA
-
-## Contributing Guidelines
-
-We welcome community contributions! Before submitting code, please ensure you follow these steps:
-
-### Install Development Dependencies
-
-```bash
-pip install ruff pre-commit
-```
-
-### Code Quality Check
-
-Before committing code, run the following command:
+Install the development hook and run the same checks used by CI:
 
 ```bash
+python -m pip install pre-commit
+pre-commit install
 pre-commit run --all-files
 ```
 
-This will automatically check code formatting, syntax errors, and other code quality issues.
-
-### Contribution Process
-
-1. Fork this repository
-2. Create a feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add some amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Create a Pull Request
+See [`CONTRIBUTING.md`](CONTRIBUTING.md) for pull request and workflow
+validation guidelines.