With all the new stuff coming out I've been seeing a lot of posts and error threads being opened for various issues with cuda/pytorch/sage attantion/triton/flash attention. I was tired of digging links up so I initially made this as a cheat sheet for myself but expanded it with hopes that this will help some of you get your venvs and systems running smoothly.

Jump To:

• 1. Check Installed Python Versions
• 2. Set Default Python Version by Changing PATH
• 3. Check the Currently Active CUDA Version
• 4. Download and Install the Correct CUDA Toolkit
• 5. Change System CUDA Version in PATH
• 6. Check All Your Versions Easy
• 7. Install to a VENV
• 8. Install PyTorch
• 9. Install Triton
• 10. Install SageAttention
• 11. Install FlashAttention
• 12. Installing A Fresh Venv
• Notes

1. Check Installed Python Versions

To list all installed versions of Python on your system, open cmd and run:

py -0p 

2. Set Default System Python Version by Changing PATH

You can have multiple versions installed on your system. The version of Python that runs when you type python is determined by the order of Python directories in your PATH variable. The first python.exe found is used as the default.

Steps:

1. Open the Start menu, search for Environment Variables, and select Edit system environment variables.
2. In the System Properties window, click Environment Variables.
3. Under System variables (or User variables), find and select the Path variable, then click Edit.
4. Move the entry for your desired Python version (for example, C:\Users\<yourname>\AppData\Local\Programs\Python\Python310\ and its Scripts subfolder) to the top of the list, above any other Python versions.
5. Click OK to save and close all dialogs.

6. Restart your command prompt and run:

   python --version

It should now display your chosen Python version.

3. Check the Currently Active CUDA Version

To see which CUDA version is currently active, run:

nvcc --version 

4. Download and Install the Correct CUDA Toolkit

Download and install from the official NVIDIA CUDA Toolkit page:
https://developer.nvidia.com/cuda-toolkit-archive

Install the version that you need. Multiple version can be installed.

5. Change System CUDA Version in PATH

1. Search for env in the Windows search bar.
2. Open Edit the system environment variables.
3. In the System Properties window, click Environment Variables.
4. Under System Variables, locate CUDA_PATH.

5. If it doesn't point to your intended CUDA version, change it. Example value:

   C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4

6. Install to a VENV

From this point to install any of these to a virtual environment you first need to activate it. For system you just skip this part and run as is.

Open a command prompt in your venv/python folder (folder name might be different) and run:

Scripts\activate 

You will now see (venv) in your cmd. You can now just run the pip commands as normal.

7. Check All Your Installed Dependencies

Make a versioncheck.py file. Edit it with any text/code editor and paste the code below. Open a CMD to the root folder and run with: python versioncheck.py

This will print the versions for torch, CUDA, torchvision, torchaudio, CUDA, Triton, SageAttention, FlashAttention. To use this in a VENV activate the venv first then run the script.

import torch import torchvision import torchaudio print("torch version:", torch.__version__) print("cuda version (torch):", torch.version.cuda) print("torchvision version:", torchvision.__version__) print("torchaudio version:", torchaudio.__version__) print("cuda available:", torch.cuda.is_available()) try: import flash_attn print("flash-attention version:", flash_attn.__version__) except ImportError: print("flash-attention is not installed or cannot be imported") try: import triton print("triton version:", triton.__version__) except ImportError: print("triton is not installed or cannot be imported") try: import sageattention print("sageattention version:", sageattention.__version__) except ImportError: print("sageattention is not installed or cannot be imported") except AttributeError: print("sageattention is installed but has no __version__ attribute") 

This will print the versions for torch, CUDA, torchvision, torchaudio, CUDA, Triton, SageAttention, FlashAttention.

torch version: 2.6.0+cu126 cuda version (torch): 12.6 torchvision version: 0.21.0+cu126 torchaudio version: 2.6.0+cu126 cuda available: True flash-attention version: 2.7.4 triton version: 3.2.0 sageattention is installed but has no version attribute 

8. Install PyTorch

Use the official install selector to get the correct command for your system:
Install PyTorch

9. Install Triton

To install Triton for Windows, run:

pip install triton-windows 

For a specific version:

pip install triton-windows==3.2.0.post18 

Triton Windows releases and info:

• Triton Windows Releases
• Main Triton Readme
• Special Notes for ComfyUI
• Test Triton

10. Install Sage Attention

Get the correct prebuilt Sage Attention wheel for your system here:

• woct0rdho SageAttention Releases
• sdbds/SageAttention-for-windows Releases

pip install sageattention "path to downloaded wheel"

Example :

pip install sageattention "D:\sageattention-2.1.1+cu124torch2.5.1-cp310-cp310-win_amd64.whl"

sageattention-2.1.1+cu124torch2.5.1-cp310-cp310-win_amd64.whl This translates to being compatible with Cuda 12.4 | Py Torch 2.5.1 | Python 3.10.

11. Install Flash Attention

Get the correct prebuilt Flash Attention wheel compatible with your python version here:

• FlashAttention Releases

pip install "path to downloaded wheel"

12. Installing A Fresh Venv

You can install a new python venv in your root folder by using the following command. You can change C:\path\to\python310 to match your required version of python.

"C:\path\to\python310\python.exe" -m venv venv 

To activate and start installing dependencies

your_env_name\Scripts\activate 

Most projects will come with a requirements.txt to install this to your venv

pip install -r requirements.txt 

Notes

• Make sure all versions (Python, CUDA, PyTorch, Triton, SageAttention) are compatible this is the primary reason for most issues.
• Each implementation will have its own requirements which is why we use a standalone environment.
• Restart your command prompt after making changes to environment variables or PATH.
• If I've missed anything please leave a comment and I will add it to the post.

https://github.com/MNeMoNiCuZ/FramePack-Batch

FramePack Batch Processor

FramePack Batch Processor is a command-line tool that processes a folder of images and transforms them into animated videos using the FramePack I2V model. This tool enables you to batch process multiple images without needing to use the Gradio web interface, and it also allows you to extract and use the prompt used in your original image, if it's saved in the EXIF metadata (like A1111 or other tools does).

Original Repository

https://github.com/lllyasviel/FramePack

Features

• Process multiple images in a single command
• Generate smooth animations from static images
• Customize video length, quality, and other parameters
• Extract prompts from image metadata (optional)
• Works in both high and low VRAM environments
• Skip files that already have generated videos
• Final videos will be copied to the input folder, matching the same name as the input image

Requirements

• Python 3.10
• PyTorch with CUDA support
• Hugging Face Transformers
• Diffusers
• VRAM: 6GB minimum (works better with 12GB+)

Installation

1. Clone or download the original repository
2. Clone or download the scripts and files from this repository into the same directory
3. Run venv_create.bat to set up your environment:
   • Choose your Python version when prompted
   • Accept the default virtual environment name (venv) or choose your own
   • Allow pip upgrade when prompted
   • Allow installation of dependencies from requirements.txt
4. Install the new requirements by running pip install -r requirements-batch.txt in your virtual environment

The script will create:

• A virtual environment
• venv_activate.bat for activating the environment
• venv_update.bat for updating pip

Usage

• Place your images in the input folder
• Activate the virtual environment:venv_activate.bat
• Run the script with desired parameters:

​

python batch.py [optional input arguments] 

1. Generated videos will be saved in both the outputs folder and alongside the original images

Command Line Options (Input Arguments)

--input_dir PATH Directory containing input images (default: ./input) --output_dir PATH Directory to save output videos (default: ./outputs) --prompt TEXT Prompt to guide the generation (default: "") --seed NUMBER Random seed, -1 for random (default: -1) --use_teacache Use TeaCache - faster but may affect hand quality (default: True) --video_length FLOAT Total video length in seconds, range 1-120 (default: 1.0) --steps NUMBER Number of sampling steps, range 1-100 (default: 5) --distilled_cfg FLOAT Distilled CFG scale, range 1.0-32.0 (default: 10.0) --gpu_memory FLOAT GPU memory preservation in GB, range 6-128 (default: 6.0) --use_image_prompt Use prompt from image metadata if available (default: True) --overwrite Overwrite existing output videos (default: False) 

Examples

Basic Usage

Process all images in the input folder with default settings:

python batch.py 

Customizing Output

Generate longer videos with more sampling steps:

python batch.py --video_length 10 --steps 25 

Using a Custom Prompt

Apply the same prompt to all images:

python batch.py --prompt "A character doing some simple body movements" 

Using Image Metadata Prompts

Extract and use prompts embedded in image metadata:

python batch.py --use_image_prompt 

Overwriting Existing Videos

By default, the processor skips images that already have corresponding videos. To regenerate them:

python batch.py --overwrite 

Processing a Custom Folder

Process images from a different folder:

python batch.py --input_dir "my_images" --output_dir "my_videos" 

Memory Optimization

The script automatically detects your available VRAM and adjusts its operation mode:

• High VRAM Mode (>60GB): All models are kept in GPU memory for faster processing
• Low VRAM Mode (<60GB): Models are loaded/unloaded as needed to conserve memory

You can adjust the amount of preserved memory with the --gpu_memory option if you encounter out-of-memory errors.

Tips

• For best results, use square or portrait images with clear subjects
• Increase steps for higher quality animations (but slower processing)
• Use --video_length to control the duration of the generated videos
• If experiencing hand/finger issues, try disabling TeaCache with --use_teacache false
• The first image takes longer to process as models are being loaded
• Use the default skip behavior to efficiently process new images in a folder

HF demo: https://huggingface.co/spaces/InstantX/InstantCharacter

I know for Flux there's FluxGym, which makes it pretty straightforward to train LoRAs specifically for Flux models.

Is there an equivalent tool or workflow for training LoRAs that are compatible with HiDream AI? Any pointers or resources would be super appreciated. Thanks in advance!