chore: clean up project metadata and docs
This commit is contained in:
gaclove
@@ -6,6 +6,7 @@ on:
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- "v*.*.*"
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permissions:
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contents: read
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issues: write
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|
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jobs:
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@@ -0,0 +1,27 @@
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name: Quality checks
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on:
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push:
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pull_request:
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permissions:
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contents: read
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jobs:
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pre-commit:
|
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name: Ruff and repository checks
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||||
runs-on: ubuntu-latest
|
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steps:
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- name: Check out code
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Install pre-commit
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run: python -m pip install pre-commit
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- name: Run checks
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run: pre-commit run --all-files
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@@ -0,0 +1,32 @@
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# Contributing
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|
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## Development setup
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The wrapper follows the Python version supported by the bundled LightX2V
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submodule (Python 3.10 or newer).
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```bash
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git clone --recursive https://github.com/ModelTC/ComfyUI-Lightx2vWrapper.git
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cd ComfyUI-Lightx2vWrapper
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python -m pip install -r lightx2v/requirements.txt
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python -m pip install -r requirements.txt pre-commit
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||||
pre-commit install
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```
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||||
|
||||
## 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.
|
||||
@@ -1,202 +1,97 @@
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[](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://github.com/comfyanonymous/ComfyUI) [](https://huggingface.co/lightx2v) [](https://deepwiki.com/GacLove/ComfyUI-Lightx2vWrapper)
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||||
[](LICENSE)
|
||||
[](https://www.python.org/downloads/)
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[](https://github.com/comfyanonymous/ComfyUI)
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[](https://huggingface.co/lightx2v)
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# ComfyUI-Lightx2vWrapper
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[中文版](./README_CN.md) | English
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ComfyUI custom nodes for [LightX2V](https://github.com/ModelTC/LightX2V). The
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wrapper exposes LightX2V's modular configuration and runner APIs, together
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with ComfyUI-friendly file inputs and output previews.
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A ComfyUI custom node wrapper for LightX2V, enabling modular video generation with advanced optimization features.
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[中文文档](README_CN.md)
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## Features
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## Supported workflows
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- **Modular Configuration System**: Separate nodes for each aspect of video generation
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- **Text-to-Video (T2V) and Image-to-Video (I2V)**: Support for both generation modes
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- **Advanced Optimizations**:
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- TeaCache acceleration (up to 3x speedup)
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- Quantization support (int8, fp8)
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- Memory optimization with CPU offloading
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- Lightweight VAE options
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- **LoRA Support**: Chain multiple LoRA models for customization
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- **Multiple Model Support**: wan2.1, hunyuan architectures
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The repository includes runnable workflow examples under
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[`example_workflows/`](example_workflows/):
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- [Wan 2.2](example_workflows/lighx2v_wan2.2.json) text/image-to-video
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- [SekoTalk](example_workflows/lightx2v_seko_talk.json) and [static
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SekoTalk](example_workflows/lightx2v_seko_talk_static.json)
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- [Multi-person SekoTalk](example_workflows/lightx2v_seko_talk_multi_talk.json)
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- [SeedVR2](example_workflows/lightx2v_seedvr.json) video restoration
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- [SwiftVR image](example_workflows/lightx2v_swiftvr_image.json) and [SwiftVR
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video](example_workflows/lightx2v_swiftvr_video.json) restoration
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The examples are versioned with the wrapper and are the best reference for
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current node names and connections.
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## Installation
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1. Clone this repository with submodules into your ComfyUI's `custom_nodes` directory:
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Clone the repository with its LightX2V submodule into ComfyUI's
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`custom_nodes` directory:
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```bash
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cd ComfyUI/custom_nodes
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git clone --recursive https://github.com/gaclove/ComfyUI-Lightx2vWrapper.git
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git clone --recursive https://github.com/ModelTC/ComfyUI-Lightx2vWrapper.git
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cd ComfyUI-Lightx2vWrapper
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python -m pip install -r lightx2v/requirements.txt
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python -m pip install -r requirements.txt
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```
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If you already cloned without submodules, initialize them:
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For an existing checkout, update the submodule with:
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```bash
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cd ComfyUI-Lightx2vWrapper
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git submodule update --init --recursive
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```
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2. Install dependencies:
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Download the model files from [LightX2V on Hugging Face](https://huggingface.co/lightx2v)
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and place them under `ComfyUI/models/lightx2v/`.
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```bash
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cd ComfyUI-Lightx2vWrapper
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# Install lightx2v submodule dependencies
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pip install -r lightx2v/requirements.txt
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# Install ComfyUI wrapper dependencies
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pip install -r requirements.txt
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```
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## Node groups
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3. Download models and place them in `ComfyUI/models/lightx2v/` directory
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- **Configuration:** `LightX2VInferenceConfig`, `LightX2VTeaCache`,
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`LightX2VQuantization`, and `LightX2VMemoryOptimization`.
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- **Composition:** `LightX2VConfigCombinerV2` and `LightX2VConfigCombinerV3`.
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- **Inference:** `LightX2VModularInferenceV2`.
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- **Talk inputs:** single-object, file-list, JSON, and multi-object talk
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combiners.
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- **Video inputs and restoration:** `LightX2VInputVideoPath`, SeedVR2 and
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SwiftVR loaders/samplers, plus `LightX2VOutputVideoPreview`.
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- **LoRA:** `LightX2VLoRALoader`.
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## Node Overview
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The inference node currently accepts the model classes exposed by
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`model_utils.support_model_cls_list`: Wan 2.1/2.2 variants, SekoTalk,
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Qwen Image, and SeedVR2. The retired `*_distill` values remain selectable as
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compatibility aliases for saved ComfyUI workflows and are normalized before
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LightX2V's runner is built.
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|
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### Configuration Nodes
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## Model files
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#### 1. LightX2V Inference Config
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Place each model in its own directory under `ComfyUI/models/lightx2v/`. LoRA
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files can be placed in either `ComfyUI/models/lightx2v/loras/` or
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`ComfyUI/models/loras/`.
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Basic inference configuration for video generation.
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- **Inputs**: model, task_type, inference_steps, seed, cfg_scale, width, height, video_length, fps
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- **Output**: Base configuration object
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#### 2. LightX2V TeaCache
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Feature caching acceleration configuration.
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- **Inputs**: enable, threshold (0.0-1.0), use_ret_steps
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- **Output**: TeaCache configuration
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- **Note**: Lower threshold = more speedup (0.1 ~2x, 0.2 ~3x)
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#### 3. LightX2V Quantization
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Model quantization settings for memory efficiency.
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- **Inputs**: dit_precision, t5_precision, clip_precision, backend, sensitive_layers_precision
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- **Output**: Quantization configuration
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- **Backends**: Auto-detected (vllm, sgl, q8f)
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#### 4. LightX2V Memory Optimization
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Memory management strategies.
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- **Inputs**: optimization_level, attention_type, enable_rotary_chunking, cpu_offload, unload_after_generate
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- **Output**: Memory optimization configuration
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#### 5. LightX2V Lightweight VAE
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VAE optimization options.
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|
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- **Inputs**: use_tiny_vae, use_tiling_vae
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- **Output**: VAE configuration
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|
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#### 6. LightX2V LoRA Loader
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|
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Load and chain LoRA models.
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|
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- **Inputs**: lora_name, strength (0.0-2.0), lora_chain (optional)
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- **Output**: LoRA chain configuration
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|
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### Combination Node
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#### 7. LightX2V Config Combiner
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Combines all configuration modules into a single configuration.
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- **Inputs**: All configuration types (optional)
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- **Output**: Combined configuration object
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|
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### Inference Node
|
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#### 8. LightX2V Modular Inference
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Main inference node for video generation.
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|
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- **Inputs**: combined_config, prompt, negative_prompt, image (optional), audio (optional)
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- **Outputs**: Generated video frames
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|
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## Usage Examples
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### Basic T2V Workflow
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1. Create LightX2V Inference Config (task_type: "t2v")
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2. Use LightX2V Config Combiner
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3. Connect to LightX2V Modular Inference with text prompt
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4. Save video output
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### I2V with Optimizations
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1. Load input image
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2. Create LightX2V Inference Config (task_type: "i2v")
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3. Add LightX2V TeaCache (threshold: 0.26)
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4. Add LightX2V Memory Optimization
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5. Combine configs with LightX2V Config Combiner
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6. Run LightX2V Modular Inference
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|
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### With LoRA
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|
||||
1. Create base configuration
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2. Load LoRA with LightX2V LoRA Loader
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3. Chain multiple LoRAs if needed
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4. Combine all configs
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5. Run inference
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|
||||
## Model Directory Structure
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||||
|
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Download models from: <https://huggingface.co/lightx2v>
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|
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Models should be placed in:
|
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|
||||
```txt
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```text
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ComfyUI/models/lightx2v/
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├── Wan2.1-I2V-14B-720P-xxx/ # Main model checkpoints
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├── Wan2.1-I2V-14B-480P-xxx/ # Main model checkpoints
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├── loras/ # LoRA models
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├── Wan2.2-I2V-A14B-distill-fp8/
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├── seedvr2_ema_3b_fp8/
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└── loras/
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```
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|
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## Tips
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The exact directory names depend on the downloaded checkpoint. The model
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picker only lists directories that exist on the local ComfyUI installation.
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|
||||
- Start with default settings and adjust based on your hardware
|
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- Use TeaCache with threshold 0.1-0.2 for significant speedup
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||||
- Enable memory optimization if running on limited VRAM
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||||
- 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.
|
||||
|
||||
+55
-168
@@ -1,202 +1,89 @@
|
||||
[](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://github.com/comfyanonymous/ComfyUI) [](https://huggingface.co/lightx2v) [](https://deepwiki.com/GacLove/ComfyUI-Lightx2vWrapper)
|
||||
[](LICENSE)
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://github.com/comfyanonymous/ComfyUI)
|
||||
[](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：<https://huggingface.co/lightx2v>
|
||||
|
||||
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)。
|
||||
|
||||
@@ -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."""
|
||||
|
||||
+3
-3
@@ -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"]
|
||||
|
||||
Reference in New Issue
Block a user