feat: Add Compatibility for Windows

[C0nsumption]

Windows?

feat: Add Compatibility for Windows

Description

This pull request introduces several changes to ensure compatibility with Windows and the most recent versions of various modules. The following modifications have been made:

1. Module Upgrades:

   • Upgraded most modules to their latest versions, including torch, diffusers, etc.

2. Downgrade NumPy:

   • Downgraded NumPy to a version below 2.0.0 to avoid compatibility issues with modules compiled using NumPy 1.x.

     pip install "numpy<2.0.0"

3. Conditional Import and Usage of deepspeed:

   • Added a try-except block to conditionally import deepspeed and add its related arguments only if deepspeed is available.
   • This avoids potential ImportError and makes the script more robust.

4. Enhance Image-Saving Logic:

   • Introduced the get_next_index function to handle non-integer filenames during the image-saving process.
   • This change ensures that the script can handle filenames that do not conform to an integer pattern without breaking.

Installation Instructions

1. Clone the Repository:

   git clone https://github.com/tencent/HunyuanDiT
   cd HunyuanDiT

2. Install Dependencies:

   Using a Virtual Environment

   python -m venv venv
   venv\Scripts\activate
   pip install "huggingface_hub[cli]"
   mkdir ckpts
   huggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./ckpts
   
   python.exe -m pip install --upgrade pip
   
   pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121   
   pip install loguru
   pip install diffusers
   pip install transformers
   pip install timm
   pip install einops
   pip install peft
   pip install sentencepiece
   pip install protobuf
   pip install "numpy<2.0.0"    

   Note: These versions are not the ones used in the requirements.txt, but they allow the use of CUDA 12.1 and the newest versions of diffusers and pytorch. (Same as ComfyUI I believe)

   Using Conda for Closer Compatibility with Original Repository

   conda create -n HunyuanDit python=3.9 -c conda-forge -y
   conda activate HunyuanDit
   
   # Install CUDA 11.7 from conda-forge
   conda install cudatoolkit=11.7 -c conda-forge
   
   set CUDA_HOME=%CONDA_PREFIX%
   set PATH=%CUDA_HOME%\bin;%PATH%
   
   python -m pip install --upgrade pip
   pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
   pip install loguru==0.7.2
   pip install diffusers==0.21.2
   pip install timm==0.9.5
   pip install einops==0.7.0
   pip install transformers==4.39.1
   pip install peft==0.10.0
   pip install "numpy<2.0"
   pip install sentencepiece==0.1.99
   pip install setuptools==65.5.1
   pip install protobuf==3.19.0
   pip install wheel
   pip install packaging

3. Run Inference:

   python sample_t2i.py --prompt "a woman" --no-enhance

WHY?

• Standard install on main repo leads to:

Error Encountered

• NumPy Compatibility:

  A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.0 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some modules may need to rebuild instead e.g. with 'pybind11>=2.12'.

• Missing Modules:
  • flash_attn: No module named 'flash_attn'
  • deepspeed: No module named 'deepspeed'

Solution

1. Downgrade NumPy:

   pip install "numpy<2.0.0"

2. Changes in hydit/modules/models.py:

   use_flash_attn = args.infer_mode == 'fa' or getattr(args, 'use_flash_attn', False)

   • Explanation: This function call attempts to access args.use_flash_attn. If the attribute does not exist, it returns False instead of raising an AttributeError. This maintains the intended behavior without causing an error.

3. Changes in hydit/config.py:

   try:
       import deepspeed
   
       # Add DeepSpeed-specific arguments
       parser = deepspeed.add_config_arguments(parser)
       parser.add_argument('--local_rank', type=int, default=None,
                           help='local rank passed from distributed launcher.')
       parser.add_argument('--deepspeed-optimizer', action='store_true',
                           help='Switching to the optimizers in DeepSpeed')
       parser.add_argument('--remote-device', type=str, default='none', choices=['none', 'cpu', 'nvme'],
                           help='Remote device for ZeRO-3 initialized parameters.')
       parser.add_argument('--zero-stage', type=int, default=1)
       parser.add_argument("--async-ema", action="store_true", help="Whether to use multi stream to excut EMA.")
   except ImportError:
       print("DeepSpeed not available. Skipping related arguments...")

   • Explanation: Added a try-except block to conditionally import deepspeed and add its related arguments only if deepspeed is available. This ensures that deepspeed related arguments are only included when deepspeed is successfully imported, avoiding potential ImportError and making the script more robust.

4. Changes in sample_t2i.py:

   def get_next_index(save_dir):
       all_files = list(save_dir.glob('*.png'))
       indices = []
       for f in all_files:
           try:
               indices.append(int(f.stem))
           except ValueError:
               logger.warning(f"Skipping file with non-integer name: {f}")
       return max(indices, default=-1) + 1
   
   # Find the first available index
   start = get_next_index(save_dir)

   • Explanation: Introduced the get_next_index function to handle non-integer filenames gracefully during the image-saving process. This ensures that the script can handle filenames that do not conform to an integer pattern without breaking.

Testing

• Only tested inference with:

  python sample_t2i.py --prompt "a picture" --no-enhance

Known Bugs

• deepspeed and flash_attn are currently not supported and training is not supported.

[jroubi]

Which python version did you used. In the conda environment it is explicitly said 3.9

But did you use the last version of python for the virtual env ?

[C0nsumption]

Which python version did you used. In the conda environment it is explicitly said 3.9

But did you use the last version of python for the virtual env ?

3.10.9

My bad for the delay bud, no github notification. Think it only comes up when quoted. But honestly if you are just doing inference my assumption is you should be fine even with 3.11 and maybe 3.12. Cause all the errors that pop up without these changes are all training related. It has nothing to do with actual inference.