This is a GUI for tech wizard kohya-ss's musubi tuner's inference script. https://github.com/kohya-ss/musubi-tuner
It allows inference with these models:
Hunyuan-t2v
Hunyuan-i2v
Hunyuan-v2v
WanX-t2v
WanX-i2v
WanX-v2v
SkyReels-i2v
SkyReels-t2v
If you are running out of vram use block swapping and some form of attention besides sdpa or torch and use split attention. Sage attention is the fastest/lowest vram but difficult to install in windows. I would say the easiest to get to run is xformers attention, you can usually get it with "pip install xformers".
Best quality will be obtained with only enabling block swapping and using the fp16 model with sdpa attention. You can speed things up with cfg skip, fp8 scaled, slg skip is small speedup, sage attention is fastest but all speedups come with quality degradations. You can use the video to video like you would img2img in Automatic1111 to increase quality possibly.
If you are using a lora that you didn't train with musubi you need to drag it to the convert lora tab and convert it to the default format. It should spit it out into the /lora folder.
This about the speed I get generating a 960x544 97 frame 40 step video without fp8 using sage attention and skyreels on a 4090: 80%|████████ | 24/30 [11:01<02:45, 27.54s/it]
If you need additional installation instructions or information create an issue and I will try to help. Also there are alot of settings notes on the musubi github linked above.
To use the Skyreels models, first download Kijai's awesome models:
https://huggingface.co/Kijai/SkyReels-V1-Hunyuan_comfy/resolve/main/skyreels_hunyuan_i2v_bf16.safetensors?download=true
And
https://huggingface.co/Kijai/SkyReels-V1-Hunyuan_comfy/resolve/main/skyreels_hunyuan_t2v_bf16.safetensors?download=true
Place the models inside the hunyuan folder inside of H1111 and select them at the bottom of the page by clicking DIT model
Use the i2v model for image to video.
Use the t2v model for video to video and text to video.
Most of the lora's for hunyuan will work with skyreels also.
To use wanX download these and toss them in the wan subfolder:
Download the T5 models_t5_umt5-xxl-enc-bf16.pth, vae Wan2.1_VAE.pth and CLIP models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth from the following page: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P/tree/main
Download the DiT weights from the following page: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
ie : wan2.1_i2v_720p_14B_fp16.safetensors
git pull to update the installation pip install -r requirements.txt
I have tested the 14B i2v and t2v models so far to be working
4/3/2025
Add support for hunyuan i2v model. Download the clip vision from https://huggingface.co/maybleMyers/H1111_Hunyuan_i2v And download the official model from hunyuan's website and rename it to mp_rank_00_model_states_i2v.pt https://huggingface.co/tencent/HunyuanVideo-I2V/tree/main/hunyuan-video-i2v-720p/transformers add both to your hunyuan folder.
3/29/2025
Added support for fun models! download dit from https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control and specify correct task type and dit location. I renamed it from diffusion_pytorch_model to Wan2.1-Fun-14B-control. Works in the normal WanX-i2v tab when you select the control option at the bottom of the page.
3/23/2025
Added Wanx cfg skip functionality to skip cfg guidance during inference for faster generations but less following of the prompt
3/22/2025
Added WanX-i2v end frame functionality
3/20/2025
Added WanX-v2v functionality.
3/18/2025
Added Skip Layer Guidance for WanX-i2v.
3/13/2025
Added extend video functionality to WanX-i2v. It kind of works .
3/12/2025
Added ability to send the last frame of a video to the input in WanX-i2v. Also you can now use this to extend the video. You can do multiple batches at each step and pick the best extended video then generate an even longer one.
3/9/2025
Added batching ability for a folder full of images in WanX-i2v tab. Added flash attn for windows prebuilt wheel.
3/8/2025
Added support for wan lora's. Remember to convert them first in the convert lora tab.
3/5/2025
Added ability to batch a folder of images with skyreels i2v, so you can make a video with every image in a folder.
3/2/2025
Added initial support for wanX-2.1 Image to Video and Text to Video inference.
3/1/2025
Added support for Skyreels Video to Video and Text to Video.
2/23/2025
Added initial support for skyreels using musubi's skyreel implementation. (thanks sdbds && Kijai :D)
download models from https://huggingface.co/Kijai/SkyReels-V1-Hunyuan_comfy and add them to your hunyuan folder
skyreels_hunyuan_i2v_bf16.safetensors
skyreels_hunyuan_t2v_bf16.safetensors
- Python 3.10
- CUDA 12.4
Tested on ubuntu 24
to update navigate to H1111 and git pull
git clone https://github.com/maybleMyers/H1111
cd H1111
#to download models
wget https://huggingface.co/tencent/HunyuanVideo/resolve/main/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt -P hunyuan
wget https://huggingface.co/tencent/HunyuanVideo/resolve/main/hunyuan-video-t2v-720p/vae/pytorch_model.pt -P hunyuan
wget https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged/resolve/main/split_files/text_encoders/llava_llama3_fp16.safetensors -P hunyuan
wget https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged/resolve/main/split_files/text_encoders/clip_l.safetensors -P hunyuan
#fp8 model
wget https://huggingface.co/kohya-ss/HunyuanVideo-fp8_e4m3fn-unofficial/resolve/main/mp_rank_00_model_states_fp8.safetensors -P hunyuan
python -m venv env
#(if you have another version of python do python3.10 -m venv env after you install it with sudo apt install python3.10 python3.10-venv python3.10-distutils)
source env/bin/activate
pip install torch==2.5.1 torchvision --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
pip install sageattention==1.0.6
might need python3.10-dev as well for sage attention to work
#download models
https://huggingface.co/tencent/HunyuanVideo/resolve/main/hunyuan-video-t2v-720p/vae/pytorch_model.pt
#fp8 dit model
place models in H1111/hunyuan folder
First, open PowerShell and navigate to your desired installation directory. Then run these commands:
git clone https://github.com/maybleMyers/H1111
cd H1111
python -m venv env
./env/scripts/activate
pip install torch==2.5.1 torchvision --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
python h1111.py
open 127.0.0.1:7860 in a browser
pip install --no-deps xformers --index-url https://download.pytorch.org/whl/cu124Note: This can take 1-5 hour to install even on a good CPU, but provides faster generation.
I have uploaded a wheel for windows users to match cuda 12.4 and python 3.10.(thanks lldacing)
https://huggingface.co/maybleMyers/wan_files_for_h1111/resolve/main/flash_attn-2.7.4%2Bcu124torch2.5.1cxx11abiFALSE-cp310-cp310-win_amd64.whl?download=true
pip install flash-attn --no-build-isolation
If you have downloaded the wheel you can install it with:
pip install "flash_attn-2.7.4+cu124torch2.5.1cxx11abiFALSE-cp310-cp310-win_amd64.whl"