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@ -19,7 +19,8 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
## 🔥 Latest News!!
* Mar 03, 2025: 👋 Wan2.1GP v1.3: Support for Image to Video with multiples images for different images / prompts combinations (requires *--multiple-images* switch), and added command line *--preload x* to preload in VRAM x MB of the main diffusion model if you find there is too much unused VRAM and you want to (slightly) accelerate the generation process
* Mar 03, 2025: 👋 Wan2.1GP v1.3: Support for Image to Video with multiples images for different images / prompts combinations (requires *--multiple-images* switch), and added command line *--preload x* to preload in VRAM x MB of the main diffusion model if you find there is too much unused VRAM and you want to (slightly) accelerate the generation process.
If you upgrade you will need to do a 'pip install -r requirements.txt' again.
* Mar 03, 2025: 👋 Wan2.1GP v1.2: Implemented tiling on VAE encoding and decoding. No more VRAM peaks at the beginning and at the end
* Mar 03, 2025: 👋 Wan2.1GP v1.1: added Tea Cache support for faster generations: optimization of kijai's implementation (https://github.com/kijai/ComfyUI-WanVideoWrapper/) of teacache (https://github.com/ali-vilab/TeaCache)
* Mar 02, 2025: 👋 Wan2.1GP by DeepBeepMeep v1 brings:
@ -73,10 +74,10 @@ conda activate wan2gp
pip install torch==2.6.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu124
# 2. Install pip dependencies
python -m pip install -r requirements.txt
pip install -r requirements.txt
# 3.1 optional Sage attention support (30% faster, easy to install on Linux but much harder on Windows)
python -m pip install sageattention==1.0.6
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
@ -84,7 +85,7 @@ cd SageAttention
pip install -e .
# 3.2 optional Flash attention support (easy to install on Linux but much harder on Windows)
python -m pip install flash-attn==2.7.2.post1
pip install flash-attn==2.7.2.post1
```
@ -168,7 +169,7 @@ You will find prebuilt Loras on https://civitai.com/ or you will be able to buil
--compile : turn on pytorch compilation\
--attention mode: force attention mode among, sdpa, flash, sage, sage2\
--profile no : default (4) : no of profile between 1 and 5\
--preload no : number in Megabytes to preload partially the diffusion model in VRAM , may offer speed gains especially on
--preload no : number in Megabytes to preload partially the diffusion model in VRAM , may offer slight speed gains especially on older hardware
### Profiles (for power users only)
You can choose between 5 profiles, but two are really relevant here :