- 03/07/2025: Version 6.1: Upgraded HunyanVideo Image to Video with new model released today (that obviously replaces the model of yesterday)\
- 03/06/2025: Version 6.0: Support for HunyanVideo Image to Video with Fast generation, Low VRAM (up to 12s video) and Lora support\ You need to do a pip install -r requirements.txt if you had already installed the app
- 02/27/2025: Version 5.1: Added Loras Preset to easily store and share combinations of loras and their multipliers
- 02/25/2025: Version 5.0: Out Of this World Release by DeepBeepMeep that lands only in HunyuanVideo GP: VRAM laws have been broken as VRAM consumption has been divided by 3 and 20%-50% faster at no quality loss !
You can now generate videos that lasts up to 10s of 1280x720 and 16s of 848x480 with 24 GB of VRAM with Loras and no quantization !!!
Welcome to low VRAM GPUs owners as from now on you can generate multiseconds videos.
Many thanks to RIFLEx (https://github.com/thu-ml/RIFLEx) and their very good released timing, for their positional embeddign breakthrough that allows generating videos longer than up to 10s that doesn't look like still life.
Please note that although there will be still sufficient VRAM left, generating video longer than 10s with Hunyuan current models is useless as the videos starts to get redundant
If you have already installed HunyuanVideoGP, you will need to run pip install -r requirements.txt. Upgrading to python 2.6.0 and the corresponding attention libaries is a plus for performance.
- 03/10/2025: Version 4.1: Improved lora presets, they can now include prompts and comments to guide the user
- 02/11/2025: Version 4.0 Quality of life features: fast abort video generation, detect automatically attention modes not supported, you can now change video engine parameters without having to restart the app
- 02/11/2025: Version 3.5 optimized lora support (reduced VRAM requirements and faster). You can now generate 1280x720 97 frames with Loras in 3 minutes only in the fastest mode
- 02/10/2025: Version 3.4 New --fast and --fastest switches to automatically get the best performance
- 02/10/2025: Version 3.3 Prefill automatically optimal parameters for Fast Hunyuan
- 02/07/2025: Version 3.2 Added support for Xformers attention and reduce VRAM requirements for sdpa attention
- 01/21/2025: Version 3.1 Ability to define a Loras directory and turn on / off any Lora when running the application
- 01/11/2025: Version 3.0 Multiple prompts / multiple generations per prompt, new progression bar, support for pretrained Loras
- 01/06/2025: Version 2.1 Integrated Tea Cache (https://github.com/ali-vilab/TeaCache) for even faster generations
- 01/04/2025: Version 2.0 Full leverage of mmgp 3.0 (faster and even lower RAM requirements ! + support for compilation on Linux and WSL)
- 12/22/2024: Version 1.0 First release
GPU Poor version by DeepBeepMeep. This great video generator can now run smoothly on any GPU.
This version has the following improvements over the original Hunyuan Video model:
- Reduce greatly the RAM requirements and VRAM requirements
- Much faster thanks to compilation and fast loading / unloading
- 5 profiles in order to able to run the model at a decent speed on a low end consumer config (32 GB of RAM and 12 VRAM) and to run it at a very good speed on a high end consumer config (48 GB of RAM and 24 GB of VRAM)
- Autodownloading of the needed model files
- Improved gradio interface with progression bar and more options
- Multiples prompts / multiple generations per prompt
- Support multiple pretrained Loras with 32 GB of RAM or less
- Switch easily between Hunyuan and Fast Hunyuan models and quantized / non quantized models
- Much simpler installation
This fork by DeepBeepMeep is an integration of the mmpg module on the gradio_server.py.
It is an illustration on how one can set up on an existing model some fast and properly working CPU offloading with changing only a few lines of code in the core model.
For more information on how to use the mmpg module, please go to: https://github.com/deepbeepmeep/mmgp
You will find the original Hunyuan Video repository here: https://github.com/Tencent/HunyuanVideo
If you are looking for a one click installation, just go to the Pinokio App store : https://pinokio.computer/
We provide an environment.yml file for setting up a Conda environment.
Conda's installation instructions are available here.
This app has been tested on Python 3.10.9 / 2.6.0 / Cuda 12.4.
(Pytorch compilation will not properly work for long video on Pytorch 2.5.1)
# 1 - conda. Prepare and activate a conda environment
conda env create -f environment.yml
conda activate HunyuanVideo
# OR
# 1 - venv. Alternatively create a python 3.10 venv and then do the following
pip install torch==2.6.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu124 # if pytorch 2.6.0
# 2. Install pip dependencies
python -m pip install -r requirements.txt
# 3.1 optional Flash attention support (easy to install on Linux but much harder on Windows)
python -m pip install flash-attn==2.7.2.post1
# 3.2 optional Sage attention support (30% faster, easy to install on Linux but much harder on Windows)
python -m pip install sageattention==1.0.6
# or for Sage Attention 2 (40% faster, sorry, only manual compilation for the moment)
git clone https://github.com/thu-ml/SageAttention
cd SageAttention
pip install -e .
# 3.3 optional Xformers attention support (same speed as sdpa attention but lower VRAM requirements, easy to install on Linux but much harder on Windows)
python -m pip install xformers==0.0.29
Note that Flash attention and Sage attention are quite complex to install on Windows but offers a better memory management (and consequently longer videos) than the default sdpa attention. Likewise Pytorch Compilation will work on Windows only if you manage to install Triton. It is quite a complex process (see below for links).
I provide here links to simplify the installation for Windows users with Python 3.10 / Pytorch 2.51 / Cuda 12.4. As I am not hosting these files I won't be able to provide support neither guarantee they do what they should do.
- Triton attention (needed for pytorch compilation and Sage attention)
pip install https://github.com/woct0rdho/triton-windows/releases/download/v3.2.0-windows.post9/triton-3.2.0-cp310-cp310-win_amd64.whl # triton for pytorch 2.6.0
- Xformers attention
pip install https://download.pytorch.org/whl/cu124/xformers-0.0.29.post1-cp310-cp310-win_amd64.whl
- Sage attention
pip install https://github.com/deepbeepmeep/SageAttention/raw/refs/heads/main/releases/sageattention-2.1.0-cp310-cp310-win_amd64.whl # for pytorch 2.6.0 (experimental, if it works, otherwise you you will need to install and compile manually, see above)
To run the Text to Video application:
python gradio_server.pyTo run the Image to Video application:
python gradio_server.py --i2vEvery lora stored in the subfoler 'loras' for t2v and 'loras_i2v' will be automatically loaded. You will be then able to activate / desactive any of them when running the application by selecting them in the area below "Activated Loras" .
For each activated Lora, you may specify a multiplier that is one float number that corresponds to its weight (default is 1.0) .The multipliers for each Lora shoud be separated by a space character or a carriage return. For instance:
1.2 0.8 means that the first lora will have a 1.2 multiplier and the second one will have 0.8.
Alternatively for each Lora's multiplier you may specify a list of float numbers multipliers separated by a "," (no space) that gives the evolution of this Lora's multiplier over the steps. For instance let's assume there are 30 denoising steps and the multiplier is 0.9,0.8,0.7 then for the steps ranges 0-9, 10-19 and 20-29 the Lora multiplier will be respectively 0.9, 0.8 and 0.7.
If multiple Loras are defined, remember that each multiplier associated to different Loras should be separated by a space or a carriage return, so we can specify the evolution of multipliers for multiple Loras. For instance for two Loras (press Shift Return to force a carriage return):
0.9,0.8,0.7
1.2,1.1,1.0
You can edit, save or delete Loras presets (combinations of loras with their corresponding multipliers) directly from the gradio Web interface. These presets will save the comment part of the prompt that should contain some instructions how to use the corresponding the loras (for instance by specifying a trigger word or providing an example).A comment in the prompt is a line that starts that a #. It will be ignored by the video generator. For instance:
# use they keyword ohnvx to trigger the Lora*
A ohnvx is driving a car
Each preset, is a file with ".lset" extension stored in the loras directory and can be shared with other users
Last but not least you can pre activate Loras corresponding and prefill a prompt (comments only or full prompt) by specifying a preset when launching the gradio server:
python gradio_server.py --lora-preset mylorapreset.lset # where 'mylorapreset.lset' is a preset stored in the 'loras' folderYou will find prebuilt Loras on https://civitai.com/ or you will be able to build them with tools such as kohya or onetrainer.
If you are a speed addict and are ready to accept some tradeoff on the quality I have added two switches:
- Fast Hunyuan Video enabled by default + Sage Attention + Teacache (an advanced acceleration algorithm x2 the speed for a small quality cost)
python gradio_server.py --fast- Fast Hunyuan Video enabled by default + Sage Attention + Teacache (an advanced acceleration algorithm x2 the speed for a small quality cost) + Compilation
python gradio_server.py --fastestPlease note that the first sampling step of the first video generation will take two minutes to perform the compilation. Consecutive generations will be very fast unless you trigger a new compilation by changing the resolution, duration of the video or add / remove loras.
For these two switches to work you will need to install Triton and Sage attention.
As you can change the prompt without causing a recompilation, theses switches work quite well with th Multiple prompts and / or Multiple Generations options.
With the --fastest switch activated a 1280x720 97 frames video takes with a Lora takes less than 4 minutes to be generated !
If you are looking for a good tradeoff between speed and quality I suggest you use the official HunyuanVideo model with Sage attention and pytorch compilation. You may as well turn on Teacache which will degrade less the video quality given there are more processing steps.
python gradio_server.py --attention sage --compile--profile no : default (4) : no of profile between 1 and 5
--quantize-transformer bool: (default True) : enable / disable on the fly transformer quantization
--lora-dir path : Path of directory that contains Loras in diffusers / safetensor format
--lora-preset preset : name of preset gile (without the extension) to preload
--verbose level : default (1) : level of information between 0 and 2
--server-port portno : default (7860) : Gradio port no
--server-name name : default (0.0.0.0) : Gradio server name
--open-browser : open automatically Browser when launching Gradio Server
--fast : start the app by loading Fast Hunyuan Video generator (faster but lower quality) + sage attention + teacache x2
--lock-config : prevent modifying the video engine configuration from the interface
--multiple-images : allow the users to choose multiple images as different starting points for new videos\
--compile : turn on pytorch compilation
--fastest : shortcut for --fast + --compile
--attention mode: force attention mode among, sdpa, flash, sage and xformers
--vae-mode: 0-5, defalt(0) : VAE tiling to be used for latents decoding
--preload no : number in Megabytes to preload partially the diffusion model in VRAM , may offer slight speed gains especially on older hardware. Works only with profile 2 and 4.
You can choose between 5 profiles, these will try to leverage the most your hardware, but have little impact for HunyuanVideo GP:
- HighRAM_HighVRAM (1): the fastest well suited for a RTX 3090 / RTX 4090 but consumes much more VRAM, adapted for fast shorter video
- HighRAM_LowVRAM (2): a bit slower, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos
- LowRAM_HighVRAM (3): adapted for RTX 3090 / RTX 4090 with limited RAM but at the cost of VRAM (shorter videos)
- LowRAM_LowVRAM (4): if you have little VRAM or want to generate longer videos
- VerylowRAM_LowVRAM (5): at least 24 GB of RAM and 10 GB of VRAM : if you don't have much it won't be fast but maybe it will work
Profile 2 (High RAM) and 4 (Low RAM)are the most recommended profiles since they are versatile (support for long videos for a slight performance cost).
However, a safe approach is to start from profile 5 (default profile) and then go down progressively to profile 4 and then to profile 2 as long as the app remains responsive or doesn't trigger any out of memory error.
-
Wan2GP: https://github.com/deepbeepmeep/Wan2GP :
Another great 3D Image to Video and Text to Video generator. It can run on very low config as one its models is only 1.5 B parameters -
Hunyuan3D-2GP: https://github.com/deepbeepmeep/Hunyuan3D-2GP :
A great image to 3D and text to 3D tool by the Tencent team. Thanks to mmgp it can run with less than 6 GB of VRAM -
FluxFillGP: https://github.com/deepbeepmeep/FluxFillGP :
One of the best inpainting / outpainting tools based on Flux that can run with less than 12 GB of VRAM. -
Cosmos1GP: https://github.com/deepbeepmeep/Cosmos1GP :
This application include two models: a text to world generator and a image / video to world (probably the best open source image to video generator). -
OminiControlGP: https://github.com/deepbeepmeep/OminiControlGP :
A Flux derived application very powerful that can be used to transfer an object of your choice in a prompted scene. With mmgp you can run it with only 6 GB of VRAM. -
YuE GP: https://github.com/deepbeepmeep/YuEGP :
A great song generator (instruments + singer's voice) based on prompted Lyrics and a genre description. Thanks to mmgp you can run it with less than 10 GB of VRAM without waiting forever.