Wan-Animate-2_GGUFs

About

What Wan-Animate-2_GGUFs is

Wan-Animate-2 14B — GGUF "</video GGUF quantizations of Wan-AI/Wan2.2-Animate-2-14B, converted for ComfyUI with ComfyUI-GGUF. Wan-Animate-2 is a character animation model that takes a reference image and a driving video and transfers the performance directly — no intermediate skeleton or motion extractor in between. This repo exists so it runs on 8-12 GB cards instead of requiring the full 30 GB bf16. All original licensing terms and usage restrictions carry over from the base model. ⚠️ These require a custom loader node Load these with Rebels W3A8 Loader, not the stock GGUF loader. Wan-Animate-2 doesn't announce itself through tensor names. ComfyUI selects it from a config blob in the safetensors metadata that sets modeltype: "animate2" — and GGUF has no equivalent field, so any GGUF of this model loads as a plain Wan 2.1 I2V. The failure is quiet, which is what makes it worth spelling out: the model loads, sampling runs, a video comes out with your reference character in it — and the driving video is completely ignored. Random motion, no performance transfer. Nothing errors. The loader node supplies that value explicitly: 1. Install ComfyUI-GGUF and Rebels W3A8 Loader 2. Use GGUF Unet Loader + modeltype (Rebels) in place of the stock Unet Loader (GGUF) 3. Set modeltype to animate2 4. Everything else in the standard Wan-Animate-2 workflow stays as it is Confirm it took — the console prints on load: model class=WANAnimate2 is the proof. Anything else and you're in the silent-failure case above. Getting good motion transfer The reference image must roughly match the driving video's opening pose. This is the single biggest quality factor and it isn't a quantization thing — it applies to the full bf16 model too. A front-facing portrait against a side-on driving video loses context and the model falls back to invented motion. Frame the reference the way the performance starts. Files Converted from the official bf16 weights. Every file was verified after quantization to confirm the precision-sensitive layers were preserved. Download a single file rather than the whole repo: Required companion files The GGUF replaces only the diffusion model. Everything else comes fro

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How video models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys wan-animate-2-ggufs for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (wan-animate-2-ggufs below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"wan-animate-2-ggufs","prompt":"a drone shot over a forest"}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

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