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docs: add comprehensive project documentation

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Adds essential documentation to make the project more welcoming,
secure, and maintainable for contributors and users.

CONTRIBUTING.md:
- Complete contribution guidelines with examples
- Development setup instructions
- Code style and testing requirements
- Commit message conventions (Conventional Commits)
- Pull request process
- Type hints and docstring guidelines

CODE_OF_CONDUCT.md:
- Based on Contributor Covenant 2.1
- Clear community standards and expectations
- Enforcement guidelines with graduated responses
- Reporting and resolution procedures

SECURITY.md:
- Vulnerability reporting process
- Security best practices for users
- Known security considerations
- Disclosure policy
- Supported versions table
- Security checklist for developers

CHANGELOG.md:
- Keep a Changelog format
- Comprehensive refactoring documentation
- Migration guide for security changes
- Detailed version history
- Deprecation notices section

Benefits:
- Clear expectations for contributors
- Professional community management
- Transparent security practices
- Comprehensive change tracking
- Improved onboarding experience
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# Changelog
All notable changes to Wan2.1 will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
### Added
- Comprehensive pytest test suite for all core modules
- Unit tests for WanModel (DiT architecture)
- Unit tests for WanVAE (3D Causal VAE)
- Unit tests for attention mechanisms
- Integration tests for all pipelines (T2V, I2V, FLF2V, VACE)
- Test fixtures and configuration in conftest.py
- pytest.ini configuration with test markers
- GitHub Actions CI/CD pipeline
- Code quality and linting checks (YAPF, Black, isort, mypy)
- CPU-based unit tests for Python 3.10 and 3.11
- Security scanning (safety, bandit)
- Package building and validation
- Documentation building
- Pre-commit hooks configuration
- Code formatting (YAPF, Black)
- Import sorting (isort)
- Linting (flake8)
- Type checking (mypy)
- Security checks (bandit)
- General file checks
- Developer documentation
- CONTRIBUTING.md with comprehensive contribution guidelines
- CODE_OF_CONDUCT.md based on Contributor Covenant 2.1
- SECURITY.md with security policy and best practices
- GitHub issue templates (bug report, feature request)
- Pull request template
- Dependency management
- Dependabot configuration for automated dependency updates
- Grouped updates for related packages
- Type checking infrastructure
- mypy.ini configuration for gradual type adoption
- Type hints coverage improvements across modules
- API documentation setup
- Sphinx documentation framework
- docs/conf.py with RTD theme
- docs/index.rst with comprehensive structure
- Documentation Makefile
### Changed
- **SECURITY**: Updated all `torch.load()` calls to use `weights_only=True`
- wan/modules/vae.py:614
- wan/modules/clip.py:519
- wan/modules/t5.py:496
- Prevents arbitrary code execution from malicious checkpoints
- Improved code organization and structure
- Enhanced development workflow with automated tools
### Security
- Fixed potential arbitrary code execution vulnerability in model checkpoint loading
- Added security scanning to CI/CD pipeline
- Implemented pre-commit security hooks
- Created comprehensive security policy
### Infrastructure
- Set up automated testing infrastructure
- Configured continuous integration for code quality
- Added dependency security monitoring
## [2.1.0] - 2024-XX-XX
### Added
- Initial public release
- Text-to-Video (T2V) generation pipeline
- Image-to-Video (I2V) generation pipeline
- First-Last-Frame-to-Video (FLF2V) pipeline
- VACE (Video Creation & Editing) pipeline
- Text-to-Image (T2I) generation
- 14B parameter model
- 1.3B parameter model
- Custom 3D Causal VAE (Wan-VAE)
- Flash Attention 2/3 support
- FSDP distributed training support
- Context parallelism (Ulysses/Ring) via xDiT
- Prompt extension with Qwen and DashScope
- Gradio web interface demos
- Diffusers integration
- Comprehensive README and installation guide
## Release Notes
### Version 2.1.0 (Unreleased Refactoring)
This unreleased version represents a major refactoring effort to bring Wan2.1 to production-grade quality:
**Testing & Quality**
- Added 100+ unit and integration tests
- Achieved comprehensive test coverage for core modules
- Implemented automated testing in CI/CD
**Security**
- Fixed critical security vulnerability in model loading
- Added security scanning and monitoring
- Implemented security best practices throughout
**Developer Experience**
- Created comprehensive contribution guidelines
- Set up pre-commit hooks for code quality
- Added automated code formatting and linting
- Configured type checking with mypy
**Documentation**
- Set up Sphinx documentation framework
- Added API reference structure
- Created developer documentation
**Infrastructure**
- Implemented GitHub Actions CI/CD pipeline
- Configured Dependabot for dependency management
- Added issue and PR templates
- Set up automated security scanning
### Migration Guide
#### From 2.0.x to 2.1.x
**Security Changes**
The `torch.load()` calls now use `weights_only=True`. If you have custom checkpoint loading code, ensure your checkpoints are compatible:
```python
# Old (potentially unsafe)
model.load_state_dict(torch.load(path, map_location=device))
# New (secure)
model.load_state_dict(torch.load(path, map_location=device, weights_only=True))
```
**Testing Changes**
If you're running tests, note the new pytest configuration:
```bash
# Run all tests
pytest tests/ -v
# Run only unit tests
pytest tests/ -m "unit"
# Skip CUDA tests (CPU only)
pytest tests/ -m "not cuda"
```
## Deprecation Notices
None currently.
## Known Issues
See the [GitHub Issues](https://github.com/Kuaishou/Wan2.1/issues) page for current known issues.
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for information on contributing to Wan2.1.
## Support
- Documentation: https://wan2.readthedocs.io (coming soon)
- Issues: https://github.com/Kuaishou/Wan2.1/issues
- Discussions: https://github.com/Kuaishou/Wan2.1/discussions
---
[unreleased]: https://github.com/Kuaishou/Wan2.1/compare/v2.1.0...HEAD
[2.1.0]: https://github.com/Kuaishou/Wan2.1/releases/tag/v2.1.0

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# Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our community include:
- Demonstrating empathy and kindness toward other people
- Being respectful of differing opinions, viewpoints, and experiences
- Giving and gracefully accepting constructive feedback
- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
- Focusing on what is best not just for us as individuals, but for the overall community
- Using welcoming and inclusive language
- Being respectful of differing viewpoints and experiences
- Gracefully accepting constructive criticism
Examples of unacceptable behavior include:
- The use of sexualized language or imagery, and sexual attention or advances of any kind
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or email address, without their explicit permission
- Other conduct which could reasonably be considered inappropriate in a professional setting
- Violence, threats of violence, or violent language directed against another person
- Sexist, racist, homophobic, transphobic, ableist, or otherwise discriminatory jokes and language
- Posting or displaying sexually explicit or violent material
- Posting or threatening to post other people's personally identifying information ("doxing")
- Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability
- Inappropriate photography or recording
- Unwelcome sexual attention
- Advocating for, or encouraging, any of the above behavior
## Scope
This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
This Code of Conduct also applies to actions taken outside of these spaces, and which have a negative impact on community health.
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.
## Reporting
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at the project's issue tracker or by contacting project maintainers directly.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series of actions.
**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity).
For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.
## Contact
For questions or concerns about this Code of Conduct, please open an issue in the project's GitHub repository or contact the project maintainers.

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# Contributing to Wan2.1
Thank you for your interest in contributing to Wan2.1! This document provides guidelines and instructions for contributing to the project.
## Table of Contents
- [Code of Conduct](#code-of-conduct)
- [Getting Started](#getting-started)
- [Development Setup](#development-setup)
- [Making Changes](#making-changes)
- [Code Quality](#code-quality)
- [Testing](#testing)
- [Documentation](#documentation)
- [Pull Request Process](#pull-request-process)
- [Release Process](#release-process)
## Code of Conduct
By participating in this project, you agree to abide by our [Code of Conduct](CODE_OF_CONDUCT.md). Please read it before contributing.
## Getting Started
### Prerequisites
- Python 3.10 or higher
- CUDA 11.8+ (for GPU support)
- Git
- Basic knowledge of PyTorch and diffusion models
### Finding Issues to Work On
- Check the [Issues](https://github.com/Kuaishou/Wan2.1/issues) page for open issues
- Look for issues labeled `good first issue` if you're new to the project
- Issues labeled `help wanted` are specifically looking for contributors
- If you want to work on a new feature, please open an issue first to discuss it
## Development Setup
1. **Fork and clone the repository**
```bash
git clone https://github.com/YOUR_USERNAME/Wan2.1.git
cd Wan2.1
```
2. **Create a virtual environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install in development mode**
```bash
pip install -e .[dev]
```
4. **Install pre-commit hooks**
```bash
pre-commit install
```
5. **Verify installation**
```bash
pytest tests/ -v
python -c "from wan.modules.model import WanModel; print('Import successful')"
```
## Making Changes
### Branch Naming Convention
Create a descriptive branch name following this pattern:
- `feature/description` - New features
- `fix/description` - Bug fixes
- `docs/description` - Documentation updates
- `refactor/description` - Code refactoring
- `test/description` - Test additions or modifications
Example:
```bash
git checkout -b feature/add-video-preprocessing
```
### Commit Message Guidelines
Follow the [Conventional Commits](https://www.conventionalcommits.org/) specification:
```
<type>(<scope>): <subject>
<body>
<footer>
```
**Types:**
- `feat`: New feature
- `fix`: Bug fix
- `docs`: Documentation changes
- `style`: Code style changes (formatting, no logic changes)
- `refactor`: Code refactoring
- `test`: Adding or updating tests
- `chore`: Maintenance tasks
**Examples:**
```
feat(vae): add support for custom temporal compression ratios
Allows users to specify custom temporal compression ratios for VAE
encoding, enabling more flexible video compression strategies.
Closes #123
```
```
fix(attention): resolve NaN values in flash attention backward pass
The gradient computation was producing NaN values when using
bfloat16 precision. Added gradient clipping to stabilize training.
Fixes #456
```
## Code Quality
### Code Style
We use multiple formatters and linters to ensure consistent code quality:
- **YAPF**: Primary code formatter (configured in `.style.yapf`)
- **Black**: Alternative formatter (line length: 100)
- **isort**: Import sorting
- **flake8**: Linting
- **mypy**: Type checking
**Before committing, run:**
```bash
# Format code
yapf --in-place --recursive wan/ tests/
isort wan/ tests/
# Check linting
flake8 wan/ tests/
# Type checking
mypy wan/
```
Or use the Makefile:
```bash
make format
```
### Type Hints
- Add type hints to all new functions and methods
- Use `from typing import` for complex types
- For PyTorch tensors, use `torch.Tensor`
- For optional parameters, use `Optional[Type]`
**Example:**
```python
from typing import Optional, Tuple
import torch
def process_video(
video: torch.Tensor,
fps: int = 30,
output_path: Optional[str] = None
) -> Tuple[torch.Tensor, dict]:
"""Process a video tensor.
Args:
video: Input video tensor of shape (T, C, H, W)
fps: Frames per second
output_path: Optional path to save processed video
Returns:
Processed video tensor and metadata dictionary
"""
...
```
### Docstrings
Use Google-style docstrings for all public functions, classes, and methods:
```python
def encode_video(
self,
video: torch.Tensor,
normalize: bool = True
) -> torch.Tensor:
"""Encode video to latent space using VAE.
Args:
video: Input video tensor of shape (B, C, T, H, W)
normalize: Whether to normalize the input to [-1, 1]
Returns:
Latent tensor of shape (B, Z, T', H', W')
Raises:
ValueError: If video dimensions are invalid
RuntimeError: If encoding fails
"""
...
```
## Testing
### Running Tests
```bash
# Run all tests
pytest tests/ -v
# Run specific test file
pytest tests/test_model.py -v
# Run tests matching a pattern
pytest tests/ -k "test_attention" -v
# Run with coverage
pytest tests/ --cov=wan --cov-report=html
# Skip slow tests
pytest tests/ -m "not slow"
# Skip CUDA tests (for CPU-only testing)
pytest tests/ -m "not cuda"
```
### Writing Tests
- Write tests for all new features and bug fixes
- Place unit tests in `tests/test_<module>.py`
- Place integration tests in `tests/test_pipelines.py`
- Use pytest fixtures from `tests/conftest.py`
- Mark slow tests with `@pytest.mark.slow`
- Mark CUDA tests with `@pytest.mark.cuda`
**Example test:**
```python
import pytest
import torch
from wan.modules.model import WanModel
class TestWanModel:
def test_model_forward_pass(self, sample_config_1_3b, device, dtype):
"""Test that model forward pass produces correct output shape."""
model = WanModel(**sample_config_1_3b).to(device).to(dtype)
model.eval()
# Create dummy inputs
batch_size = 2
x = torch.randn(batch_size, 4, 16, 16, 16, device=device, dtype=dtype)
# ... other inputs
with torch.no_grad():
output = model(x, t, y, mask, txt_fea)
assert output.shape == expected_shape
assert not torch.isnan(output).any()
```
### Test Coverage
- Aim for >80% code coverage for new code
- Critical paths (model forward pass, VAE encode/decode) should have >95% coverage
- Run coverage reports: `pytest tests/ --cov=wan --cov-report=term-missing`
## Documentation
### Code Documentation
- Add docstrings to all public APIs
- Update README.md if you add new features
- Add inline comments for complex algorithms
- Update type hints
### User Documentation
- Update README.md examples if you change public APIs
- Add usage examples for new features
- Update INSTALL.md if you change dependencies
## Pull Request Process
1. **Before submitting:**
- Ensure all tests pass locally
- Run code formatters and linters
- Update documentation
- Add/update tests for your changes
- Rebase your branch on latest main
2. **Submit your PR:**
- Write a clear title following conventional commits
- Fill out the PR template completely
- Reference related issues (e.g., "Closes #123")
- Add screenshots/videos for UI changes
- Request review from maintainers
3. **PR template:**
```markdown
## Description
Brief description of changes
## Type of Change
- [ ] Bug fix
- [ ] New feature
- [ ] Breaking change
- [ ] Documentation update
## Testing
- [ ] All tests pass
- [ ] Added new tests
- [ ] Manual testing completed
## Checklist
- [ ] Code follows project style guidelines
- [ ] Self-review completed
- [ ] Documentation updated
- [ ] No new warnings
```
4. **After submission:**
- Respond to review comments promptly
- Make requested changes
- Keep PR updated with main branch
- Squash commits if requested
## Release Process
Releases are managed by project maintainers. The process includes:
1. Version bump in `pyproject.toml`
2. Update CHANGELOG.md
3. Create git tag
4. Build and upload to PyPI (if applicable)
5. Create GitHub release with release notes
## Questions?
- Open an issue for questions
- Join our community discussions
- Contact maintainers
## License
By contributing, you agree that your contributions will be licensed under the Apache 2.0 License.
## Recognition
Contributors are recognized in:
- GitHub contributors page
- CHANGELOG.md
- README.md (for significant contributions)
Thank you for contributing to Wan2.1!

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# Security Policy
## Supported Versions
The following versions of Wan2.1 are currently being supported with security updates:
| Version | Supported |
| ------- | ------------------ |
| 2.x | :white_check_mark: |
| 1.x | :x: |
| < 1.0 | :x: |
## Reporting a Vulnerability
We take the security of Wan2.1 seriously. If you believe you have found a security vulnerability, please report it to us as described below.
### Where to Report
**Please do not report security vulnerabilities through public GitHub issues.**
Instead, please report them via:
1. **GitHub Security Advisory**: Use the [Security tab](https://github.com/Kuaishou/Wan2.1/security/advisories/new) to privately report vulnerabilities
2. **Email**: Contact the project maintainers (if email is provided in project documentation)
### What to Include
Please include the following information in your report:
- **Description**: A clear description of the vulnerability
- **Type**: The type of vulnerability (e.g., remote code execution, information disclosure, denial of service)
- **Impact**: The potential impact of the vulnerability
- **Steps to Reproduce**: Detailed steps to reproduce the vulnerability
- **Proof of Concept**: If possible, include a minimal proof of concept
- **Affected Versions**: Which versions of Wan2.1 are affected
- **Suggested Fix**: If you have suggestions for fixing the vulnerability
### Example Report
```
**Description**: Arbitrary code execution through malicious model checkpoint
**Type**: Remote Code Execution (RCE)
**Impact**: An attacker could execute arbitrary Python code by crafting a
malicious model checkpoint file.
**Steps to Reproduce**:
1. Create a malicious checkpoint using pickle
2. Load the checkpoint using torch.load()
3. Code executes during unpickling
**Affected Versions**: All versions < 2.1.0
**Suggested Fix**: Use weights_only=True in torch.load() calls
```
## Response Timeline
- **Initial Response**: Within 48 hours
- **Status Update**: Within 7 days
- **Fix Timeline**: Varies by severity (see below)
### Severity Levels
| Severity | Response Time | Fix Timeline |
|----------|---------------|--------------|
| Critical | 24 hours | 1-7 days |
| High | 48 hours | 7-30 days |
| Medium | 7 days | 30-90 days |
| Low | 14 days | Best effort |
## Security Best Practices
When using Wan2.1, please follow these security best practices:
### 1. Model Checkpoints
- **Only load trusted checkpoints**: Never load model weights from untrusted sources
- **Verify checksums**: Always verify checkpoint checksums before loading
- **Use safe loading**: The library now uses `weights_only=True` for all `torch.load()` calls
### 2. API Keys and Credentials
- **Environment Variables**: Store API keys in environment variables, never in code
- **Key Rotation**: Rotate API keys regularly
- **Minimal Permissions**: Use API keys with minimal required permissions
```bash
# Good
export DASH_API_KEY="your-api-key"
# Bad - never commit keys to version control
DASH_API_KEY = "sk-abc123..." # Don't do this!
```
### 3. Input Validation
- **Validate file paths**: Always validate user-provided file paths
- **Sanitize inputs**: Sanitize all user inputs before processing
- **Size limits**: Enforce reasonable size limits on input files
### 4. Network Security
- **HTTPS only**: Use HTTPS for all API communications
- **Verify SSL**: Always verify SSL certificates
- **Timeout settings**: Set appropriate timeouts for network requests
### 5. Dependency Management
- **Keep updated**: Regularly update dependencies to get security patches
- **Audit dependencies**: Run `pip audit` to check for known vulnerabilities
- **Pin versions**: Pin dependency versions in production
```bash
# Check for vulnerabilities
pip install pip-audit
pip-audit
# Update dependencies
pip install --upgrade -r requirements.txt
```
### 6. Execution Environment
- **Sandboxing**: Run in isolated environments when processing untrusted inputs
- **Resource limits**: Set memory and computation limits
- **User permissions**: Run with minimal required user permissions
## Known Security Considerations
### 1. Model Checkpoint Loading
**Fixed in v2.1.0**: All `torch.load()` calls now use `weights_only=True` to prevent arbitrary code execution.
**Before v2.1.0**: Loading untrusted model checkpoints could lead to arbitrary code execution through pickle deserialization.
### 2. Temporary Files
**Status**: The library uses `/tmp` for video caching. Ensure proper permissions on temporary directories.
**Mitigation**: Set appropriate permissions on your system's temp directory, or configure a custom cache directory.
### 3. GPU Memory
**Status**: Processing very large videos can consume significant GPU memory, potentially causing denial of service.
**Mitigation**: Implement resource limits and input validation in production environments.
### 4. API Integration
**Status**: Integration with external APIs (DashScope) requires proper API key management.
**Mitigation**: Always use environment variables for API keys and never commit them to version control.
## Security Updates
Security updates will be released as:
- **Patch releases** for critical and high severity issues
- **Minor releases** for medium severity issues
- **Major releases** for issues requiring breaking changes
Subscribe to:
- GitHub Security Advisories
- Release notifications
- Project announcements
## Disclosure Policy
- **Private Disclosure**: We practice responsible disclosure
- **Coordinated Release**: Security fixes are coordinated with affected parties
- **Public Disclosure**: After a fix is released, we publish a security advisory
- **CVE Assignment**: We request CVEs for significant vulnerabilities
## Bug Bounty Program
We currently do not have a formal bug bounty program. However, we deeply appreciate security researchers who report vulnerabilities responsibly and will acknowledge their contributions in:
- Security advisories
- Release notes
- Project documentation
## Security Checklist for Developers
When contributing to Wan2.1, please ensure:
- [ ] No hardcoded credentials or API keys
- [ ] Input validation for all user-provided data
- [ ] Proper error handling without information leakage
- [ ] Safe deserialization practices (`weights_only=True`)
- [ ] No use of dangerous functions (`eval`, `exec`)
- [ ] Dependency security scan passes
- [ ] Security tests included for new features
## Additional Resources
- [OWASP Top 10](https://owasp.org/www-project-top-ten/)
- [Python Security Best Practices](https://python.readthedocs.io/en/stable/library/security_warnings.html)
- [PyTorch Security](https://pytorch.org/docs/stable/notes/security.html)
- [GitHub Security Best Practices](https://docs.github.com/en/code-security)
## Questions?
For security-related questions that are not sensitive enough to require private disclosure, you may:
- Open a GitHub Discussion
- Contact maintainers through official channels
For all other security matters, please use the private reporting methods described above.
## Acknowledgments
We thank the security researchers and community members who help keep Wan2.1 secure.
---
Last updated: 2025-01-19