Instructions to use FartingBackwards/wan2.2-fun-vace-14B-int8-convrot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use FartingBackwards/wan2.2-fun-vace-14B-int8-convrot with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Wan2.2 Fun-VACE A14B INT8 ConvRot
Converted from original BF16 weights (alibaba-pai/Wan2.2-VACE-Fun-A14B, via Comfy-Org repack) with silveroxides/convert_to_quant:
ctq -i <source>.safetensors -o <output>.safetensors \
--int8 --scaling_mode row --simple \
--convrot --convrot-group-size 256 \
--wan --exclude-layers "vace_blocks\.\d+\.(before|after)_proj" \
--comfy_quant --save-quant-metadata
VACE proj layers kept unquantized
VACE architecture adds 8 context blocks (vace_blocks.0–.7) that inject into the main transformer via before_proj (block 0 only) and after_proj (all 8 blocks). These are zero-initialized and only lightly adapter-tuned on a frozen base, so their weight magnitudes are much smaller than a normally-trained layer. Row-wise INT8 quantization at the same settings as the rest of the model risks burying that signal in quantization noise, silencing VACE conditioning while the base model still generates normally. These 9 tensors are excluded and kept at source precision; the rest of vace_blocks (attention/FFN/norms) is quantized the same as the base model.
License
Apache 2.0, inherited from the base weights (Alibaba PAI).
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Model tree for FartingBackwards/wan2.2-fun-vace-14B-int8-convrot
Base model
Wan-AI/Wan2.2-T2V-A14B