calcuis/humo-gguf

---

license: apache-2.0 base_model:

• bytedance-research/HuMo

pipeline_tag: image-to-video tags:

• gguf-node

widget:

• text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset tracking camera parameters: negative_prompt: blurry ugly bad output: url: workflow-demo1.webp

• text: >-

a conversation between two little monster kids parameters: negative_prompt: blurry ugly bad output: url: workflow-demo2.webp

• text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset tracking camera parameters: negative_prompt: blurry ugly bad output: url: workflow-demo3.webp

---

humo-gguf

• drag humo to > ./ComfyUI/models/diffusionmodels
• drag cow-umt5xxl [3.67GB] to > ./ComfyUI/models/textencoders
• drag pig [254MB] to > ./ComfyUI/models/vae

!screenshot

s2v workflow

• drag humo to > ./ComfyUI/models/diffusionmodels
• anyone below, drag it to > ./ComfyUI/models/textencoders

- option 1: just cow-umt5xxl [3.67GB] - option 2: just umt5xxl [3.47GB] (rebuild tokenizer process) - option 3: both cat-umt5xxl [3.66GB] and tokenizer [4.55MB]

• drag whisper3 [3.23GB] to > ./ComfyUI/models/audioencoders
• drag pig [254MB] to > ./ComfyUI/models/vae

!screenshot

note: output seems different from wan; don't expect too much; get the lite lora for 4/8-step operation here, the lora works for 17b only; but 1.7b itself is fast enough

!screenshot

reference

• gguf-node (pypi|repo|pack)