gguf quantized version of wan 1.3b models

• run it straight with gguf-connector
• opt a gguf file in the current directory to interact with by:

ggc w2

GGUF file(s) available. Select which one to use:

1. wan2.1-t2v-1.3b-q4_0.gguf
2. wan2.1-t2v-1.3b-q8_0.gguf
3. wan2.1-vace-1.3b-q4_0.gguf
4. wan2.1-vace-1.3b-q8_0.gguf

Enter your choice (1 to 4): _

run it with gguf-node via comfyui

• drag wan to > ./ComfyUI/models/diffusion_models
• drag umt5 to > ./ComfyUI/models/text_encoders
• drag pig to > ./ComfyUI/models/vae

review

• wan architecture; should work on both comfyui-gguf and gguf nodes
• full set gguf works right away (model + encoder + vae); gguf node is recommended for full gguf
• vace model is recommended, since it doesn't need vision clip to work (for i2v and v2v) and run faster than fun model a lot, according to the initial test results
• upgrade your node for umt5 gguf encoder support
• note: for umt5 gguf, you might encounter oom after rebuilding your tokenizer in the first prompt (once built the tokenizer alive during the session; before you kill it); don't panic, prompt again it should work

reference

• base model from wan-ai
• comfyui from comfyanonymous
• comfyui-gguf city96 (special thanks; fully compatible)
• pig architecture from connector
• gguf-node (pypi|repo|pack)
• gguf-connector (pypi)