From 2df4f7b7ff306391a8bb2fc4c9d88b7369d34714 Mon Sep 17 00:00:00 2001 From: gaclove Date: Fri, 23 Oct 2026 15:27:11 +0800 Subject: [PATCH] chore: clean up project metadata and docs --- .github/workflows/publish.yml | 1 + .github/workflows/quality.yml | 27 ++++ CONTRIBUTING.md | 32 +++++ README.md | 229 +++++++++------------------------- README_CN.md | 223 ++++++++------------------------- file_handlers.py | 28 ----- pyproject.toml | 6 +- 7 files changed, 180 insertions(+), 366 deletions(-) create mode 100644 .github/workflows/quality.yml create mode 100644 CONTRIBUTING.md diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 68f039a..3edf6e0 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -6,6 +6,7 @@ on: - "v*.*.*" permissions: + contents: read issues: write jobs: diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml new file mode 100644 index 0000000..ca8a76c --- /dev/null +++ b/.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 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..c501ca0 --- /dev/null +++ b/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. diff --git a/README.md b/README.md index 54e524d..869c336 100644 --- a/README.md +++ b/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/): + +- [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 + +The examples are versioned with the wrapper and are the best reference for +current node names and connections. ## Installation -1. Clone this repository with submodules into your ComfyUI's `custom_nodes` directory: +Clone the repository with its LightX2V submodule into ComfyUI's +`custom_nodes` directory: ```bash cd ComfyUI/custom_nodes -git clone --recursive https://github.com/gaclove/ComfyUI-Lightx2vWrapper.git +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 ``` -If you already cloned without submodules, initialize them: +For an existing checkout, update the submodule with: ```bash -cd ComfyUI-Lightx2vWrapper git submodule update --init --recursive ``` -2. Install dependencies: +Download the model files from [LightX2V on Hugging Face](https://huggingface.co/lightx2v) +and place them under `ComfyUI/models/lightx2v/`. -```bash -cd ComfyUI-Lightx2vWrapper -# Install lightx2v submodule dependencies -pip install -r lightx2v/requirements.txt -# Install ComfyUI wrapper dependencies -pip install -r requirements.txt -``` +## Node groups -3. Download models and place them in `ComfyUI/models/lightx2v/` directory +- **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`. -## Node Overview +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. -### Configuration Nodes +## Model files -#### 1. LightX2V Inference Config +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/`. -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. - -- **Inputs**: lora_name, strength (0.0-2.0), lora_chain (optional) -- **Output**: LoRA chain configuration - -### Combination Node - -#### 7. LightX2V Config Combiner - -Combines all configuration modules into a single configuration. - -- **Inputs**: All configuration types (optional) -- **Output**: Combined configuration object - -### 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: - -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. diff --git a/README_CN.md b/README_CN.md index cec5be9..0ec281c 100644 --- a/README_CN.md +++ b/README_CN.md @@ -1,202 +1,89 @@ -[![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-自定义节点-brightgreen)](https://github.com/comfyanonymous/ComfyUI) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20模型-lightx2v-yellow)](https://huggingface.co/lightx2v) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/GacLove/ComfyUI-Lightx2vWrapper) +[![许可证](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-自定义节点-brightgreen)](https://github.com/comfyanonymous/ComfyUI) +[![模型](https://img.shields.io/badge/models-LightX2V-yellow)](https://huggingface.co/lightx2v) # ComfyUI-Lightx2vWrapper -[English](./README.md) | 中文版 +[English](README.md) | 中文 -LightX2V 的 ComfyUI 自定义节点封装，通过模块化配置实现视频生成和高级优化功能。 +[LightX2V](https://github.com/ModelTC/LightX2V) 的 ComfyUI 自定义节点封装， +提供模块化配置、ComfyUI 文件输入以及视频输出预览。 -## 功能特性 +## 已提供的工作流 -- **模块化配置系统**：为视频生成的各个方面提供独立的配置节点 -- **文本生成视频（T2V）和图像生成视频（I2V）**：支持两种生成模式 -- **高级优化功能**： - - TeaCache 加速（最高可达 3 倍速度提升） - - 量化支持（int8、fp8） - - 内存优化与 CPU 卸载 - - 轻量级 VAE 选项 -- **LoRA 支持**：可串联多个 LoRA 模型进行自定义 -- **多模型支持**：wan2.1、hunyuan 架构 +仓库在 [`example_workflows/`](example_workflows/) 中提供了与当前节点注册表 +同步的示例： -## 安装说明 +- [Wan 2.2](example_workflows/lighx2v_wan2.2.json) 文生视频/图生视频 +- [SekoTalk](example_workflows/lightx2v_seko_talk.json) 和[静态 + SekoTalk](example_workflows/lightx2v_seko_talk_static.json) +- [多人 SekoTalk](example_workflows/lightx2v_seko_talk_multi_talk.json) +- [SeedVR2](example_workflows/lightx2v_seedvr.json) 视频修复 +- [SwiftVR 图像](example_workflows/lightx2v_swiftvr_image.json) 和 [SwiftVR + 视频](example_workflows/lightx2v_swiftvr_video.json) 修复 -1. 将此仓库克隆到 ComfyUI 的 `custom_nodes` 目录，包含子模块： +示例工作流会随封装器一起更新，可作为当前节点名称和连线方式的参考。 + +## 安装 + +将仓库和 LightX2V 子模块一起克隆到 ComfyUI 的 `custom_nodes` 目录： ```bash cd ComfyUI/custom_nodes -git clone --recursive https://github.com/gaclove/ComfyUI-Lightx2vWrapper.git +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 ``` -如果已经克隆但未包含子模块，请初始化子模块： +如果已有仓库但还没有子模块： ```bash -cd ComfyUI-Lightx2vWrapper git submodule update --init --recursive ``` -2. 安装依赖： +从 [Hugging Face 的 LightX2V 页面](https://huggingface.co/lightx2v) 下载模型， +放到 `ComfyUI/models/lightx2v/` 下。 -```bash -cd ComfyUI-Lightx2vWrapper -# 安装 lightx2v 子模块依赖 -pip install -r lightx2v/requirements.txt -# 安装 ComfyUI 封装器依赖 -pip install -r requirements.txt -``` +## 节点分组 -3. 下载模型并放置在 `ComfyUI/models/lightx2v/` 目录 +- **配置：** `LightX2VInferenceConfig`、`LightX2VTeaCache`、 + `LightX2VQuantization`、`LightX2VMemoryOptimization` +- **组合：** `LightX2VConfigCombinerV2`、`LightX2VConfigCombinerV3` +- **推理：** `LightX2VModularInferenceV2` +- **Talk 输入：** 单对象、文件列表、JSON 和多人 Talk 组合节点 +- **视频输入与修复：** `LightX2VInputVideoPath`、SeedVR2/SwiftVR 加载器与 + 采样器、`LightX2VOutputVideoPreview` +- **LoRA：** `LightX2VLoRALoader` -## 节点概览 +推理节点当前支持 `model_utils.support_model_cls_list` 中的 Wan 2.1/2.2 +变体、SekoTalk、Qwen Image 和 SeedVR2。旧 workflow 使用的 `*_distill` +值仍作为兼容别名保留，构建 LightX2V runner 前会自动归一化。 -### 配置节点 +## 模型目录 -#### 1. LightX2V Inference Config（推理配置） +每个模型放在 `ComfyUI/models/lightx2v/` 下的独立目录中。LoRA 可以放在 +`ComfyUI/models/lightx2v/loras/` 或 `ComfyUI/models/loras/`： -视频生成的基础推理配置。 - -- **输入**：model（模型）、task_type（任务类型）、inference_steps（推理步数）、seed（种子）、cfg_scale（CFG 缩放）、width（宽度）、height（高度）、video_length（视频长度）、fps（帧率） -- **输出**：基础配置对象 - -#### 2. LightX2V TeaCache（TeaCache 配置） - -特征缓存加速配置。 - -- **输入**：enable（启用）、threshold（阈值 0.0-1.0）、use_ret_steps（使用返回步数） -- **输出**：TeaCache 配置 -- **注意**：阈值越低 = 速度提升越大（0.1 约 2 倍，0.2 约 3 倍） - -#### 3. LightX2V Quantization（量化配置） - -模型量化设置，提升内存效率。 - -- **输入**：dit_precision（DIT 精度）、t5_precision（T5 精度）、clip_precision（CLIP 精度）、backend（后端）、sensitive_layers_precision（敏感层精度） -- **输出**：量化配置 -- **后端**：自动检测（vllm、sgl、q8f） - -#### 4. LightX2V Memory Optimization（内存优化） - -内存管理策略。 - -- **输入**：optimization_level（优化级别）、attention_type（注意力类型）、enable_rotary_chunking（启用旋转分块）、cpu_offload（CPU 卸载）、unload_after_generate（生成后卸载） -- **输出**：内存优化配置 - -#### 5. LightX2V Lightweight VAE（轻量级 VAE） - -VAE 优化选项。 - -- **输入**：use_tiny_vae（使用微型 VAE）、use_tiling_vae（使用平铺 VAE） -- **输出**：VAE 配置 - -#### 6. LightX2V LoRA Loader（LoRA 加载器） - -加载并串联 LoRA 模型。 - -- **输入**：lora_name（LoRA 名称）、strength（强度 0.0-2.0）、lora_chain（LoRA 链，可选） -- **输出**：LoRA 链配置 - -### 组合节点 - -#### 7. LightX2V Config Combiner（配置组合器） - -将所有配置模块组合成单一配置。 - -- **输入**：所有配置类型（可选） -- **输出**：组合后的配置对象 - -### 推理节点 - -#### 8. LightX2V Modular Inference（模块化推理） - -视频生成的主推理节点。 - -- **输入**：combined_config（组合配置）、prompt（提示词）、negative_prompt（负面提示词）、image（图像，可选）、audio（音频，可选） -- **输出**：生成的视频帧 - -## 使用示例 - -### 基础 T2V 工作流 - -1. 创建 LightX2V Inference Config（task_type: "t2v"） -2. 使用 LightX2V Config Combiner -3. 连接到 LightX2V Modular Inference，输入文本提示词 -4. 保存视频输出 - -### 带优化的 I2V - -1. 加载输入图像 -2. 创建 LightX2V Inference Config（task_type: "i2v"） -3. 添加 LightX2V TeaCache（threshold: 0.26） -4. 添加 LightX2V Memory Optimization -5. 使用 LightX2V Config Combiner 组合配置 -6. 运行 LightX2V Modular Inference - -### 使用 LoRA - -1. 创建基础配置 -2. 使用 LightX2V LoRA Loader 加载 LoRA -3. 如需要可串联多个 LoRA -4. 组合所有配置 -5. 运行推理 - -## 模型目录结构 - -从以下地址下载模型： - -模型应放置在： - -```txt +```text ComfyUI/models/lightx2v/ -├── Wan2.1-I2V-14B-720P-xxx/ # 主模型检查点 -├── Wan2.1-I2V-14B-480P-xxx/ # 主模型检查点 -├── loras/ # LoRA 模型 +├── Wan2.2-I2V-A14B-distill-fp8/ +├── seedvr2_ema_3b_fp8/ +└── loras/ ``` -## 使用技巧 +具体目录名取决于下载的 checkpoint；模型选择器只会列出当前 ComfyUI 中存在 +的目录。 -- 从默认设置开始，根据硬件情况调整 -- 使用 TeaCache，阈值设为 0.1-0.2 可显著提速 -- 在显存有限时启用内存优化 -- 量化可减少内存使用但可能影响质量 -- 串联多个 LoRA 实现复杂风格组合 +## 开发 -## 故障排除 - -- **内存不足**：启用内存优化或使用量化 -- **生成速度慢**：启用 TeaCache 或减少推理步数 -- **找不到模型**：检查 `ComfyUI/models/lightx2v/` 中的模型路径 - -## 示例工作流 - -`examples/` 目录中提供了示例工作流 JSON 文件： - -- `wan_i2v.json` - 基础图像生成视频 -- `wan_i2v_with_distill_lora.json` - 带蒸馏 LoRA 的 I2V -- `wan_t2v_with_distill_lora.json` - 带蒸馏 LoRA 的 T2V - -## 贡献指南 - -我们欢迎社区贡献！在提交代码之前，请确保遵循以下步骤： - -### 安装开发依赖 - -```bash -pip install ruff pre-commit -``` - -### 代码质量检查 - -在提交代码之前，请运行以下命令： +安装 pre-commit 并运行 CI 使用的检查： ```bash +python -m pip install pre-commit +pre-commit install pre-commit run --all-files ``` -这将自动检查代码格式、语法错误和其他代码质量问题。 - -### 贡献流程 - -1. Fork 本仓库 -2. 创建功能分支 (`git checkout -b feature/amazing-feature`) -3. 提交更改 (`git commit -m 'Add some amazing feature'`) -4. 推送到分支 (`git push origin feature/amazing-feature`) -5. 创建 Pull Request +贡献流程和 workflow 验证要求见 [`CONTRIBUTING.md`](CONTRIBUTING.md)。 diff --git a/file_handlers.py b/file_handlers.py index 3e98d21..d003727 100644 --- a/file_handlers.py +++ b/file_handlers.py @@ -142,34 +142,6 @@ class AudioFileHandler(FileHandler): return waveform - def _save_with_wave(self, path: str, waveform: np.ndarray, sample_rate: int): - """Save audio using wave module.""" - import wave - - with wave.open(path, "wb") as wav_file: - wav_file.setnchannels(1 if waveform.ndim == 1 else waveform.shape[-1]) - wav_file.setsampwidth(2) # 16-bit - wav_file.setframerate(sample_rate) - - if waveform.dtype != np.int16: - waveform = (waveform * 32767).astype(np.int16) - - wav_file.writeframes(waveform.tobytes()) - - def _load_with_wave(self, path: str) -> Tuple[np.ndarray, int]: - """Load audio using wave module.""" - import wave - - with wave.open(path, "rb") as wav_file: - sample_rate = wav_file.getframerate() - frames = wav_file.readframes(wav_file.getnframes()) - waveform = np.frombuffer(frames, dtype=np.int16) - - if wav_file.getnchannels() > 1: - waveform = waveform.reshape(-1, wav_file.getnchannels()) - - return waveform, sample_rate - class ImageFileHandler(FileHandler): """Handler for image files.""" diff --git a/pyproject.toml b/pyproject.toml index 69a9c6c..617fc2d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,6 +2,8 @@ name = "lightx2vwrapper" description = "ComfyUI-Lightx2vWrapper is an inference wrapper for Lightx2v designed for use with ComfyUI." version = "1.1.0" +readme = "README.md" +requires-python = ">=3.10" license = { file = "LICENSE" } [project.urls] @@ -19,10 +21,8 @@ line-length = 150 indent-width = 4 [tool.ruff.lint] -extend-select = ["I", "F401"] -ignore = ["F"] +extend-select = ["I"] [tool.ruff.lint.per-file-ignores] "**/__init__.py" = ["F401"] "**/lightx2v_kernel/*" = ["F401"] -"**/{cookbook,docs}/*" = ["E402", "F401", "F811", "F841"]