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updated readme

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  1. README.md +6 -5
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@@ -19,7 +19,8 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
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  ## 🔥 Latest News!!
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- * 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
 
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  * 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
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  * 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)
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  * Mar 02, 2025: 👋 Wan2.1GP by DeepBeepMeep v1 brings:
@@ -73,10 +74,10 @@ conda activate wan2gp
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  pip install torch==2.6.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu124
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  # 2. Install pip dependencies
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- python -m pip install -r requirements.txt
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  # 3.1 optional Sage attention support (30% faster, easy to install on Linux but much harder on Windows)
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- python -m pip install sageattention==1.0.6
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  # or for Sage Attention 2 (40% faster, sorry only manual compilation for the moment)
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  git clone https://github.com/thu-ml/SageAttention
@@ -84,7 +85,7 @@ cd SageAttention
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  pip install -e .
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  # 3.2 optional Flash attention support (easy to install on Linux but much harder on Windows)
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- python -m pip install flash-attn==2.7.2.post1
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  ```
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@@ -168,7 +169,7 @@ You will find prebuilt Loras on https://civitai.com/ or you will be able to buil
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  --compile : turn on pytorch compilation\
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  --attention mode: force attention mode among, sdpa, flash, sage, sage2\
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  --profile no : default (4) : no of profile between 1 and 5\
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- --preload no : number in Megabytes to preload partially the diffusion model in VRAM , may offer speed gains especially on
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  ### Profiles (for power users only)
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  You can choose between 5 profiles, but two are really relevant here :
 
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  ## 🔥 Latest News!!
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+ * 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.
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+ If you upgrade you will need to do a 'pip install -r requirements.txt' again.
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  * 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
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  * 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)
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  * Mar 02, 2025: 👋 Wan2.1GP by DeepBeepMeep v1 brings:
 
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  pip install torch==2.6.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu124
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  # 2. Install pip dependencies
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+ pip install -r requirements.txt
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  # 3.1 optional Sage attention support (30% faster, easy to install on Linux but much harder on Windows)
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+ pip install sageattention==1.0.6
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  # or for Sage Attention 2 (40% faster, sorry only manual compilation for the moment)
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  git clone https://github.com/thu-ml/SageAttention
 
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  pip install -e .
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  # 3.2 optional Flash attention support (easy to install on Linux but much harder on Windows)
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+ pip install flash-attn==2.7.2.post1
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  ```
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  --compile : turn on pytorch compilation\
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  --attention mode: force attention mode among, sdpa, flash, sage, sage2\
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  --profile no : default (4) : no of profile between 1 and 5\
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+ --preload no : number in Megabytes to preload partially the diffusion model in VRAM , may offer slight speed gains especially on older hardware
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  ### Profiles (for power users only)
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  You can choose between 5 profiles, but two are really relevant here :