Both replace the character in a video and both ship open weights. The difference is what each one needs before it can start, and how long it runs. Wan Animate takes a character image plus a preprocessing pass for pose, face, mask and background. Viggle-Animate takes the clip and one of its own frames, repainted — then 3 forward passes instead of 40.
Four sigma boundaries mean three passes between them, for the whole 124-frame clip in one window. Wan runs a 20-step sampler, and because its window is 77 frames a 124-frame clip becomes two chained segments — 40 passes in total. The clip labels below count sampler steps per segment (4 against 20); the table counts total passes (3 against 40). Both describe the same two runs. Viggle-Animate is also the bigger model at 33.1B against 17.3B, so it is not winning by being lighter, and 26s is what one B200 does — your own hardware will differ.
Edit one frame of your own clip in any image editor, and that frame carries the character through the shot. No pose skeleton to extract, no mask to clean up, no face tracker to fight. What you painted is what propagates.
Character replacement usually breaks exactly when things move quickly. In side-by-side renders Wan2.2-Animate-14B smears around rapidly moving heads and limbs, while Viggle-Animate keeps detail and matches stance width, arm extension and the timing of extreme poses — with no pose input at all.
Because nothing extracts a pose, the same two-input interface handles animals, stylized and illustrated characters, and non-humanoid objects — the airliner is the hardest published case. A pipeline built around a pose estimator can only go as far as the estimator understands the subject.
Choose Wan Animate if…
Choose Viggle-Animate if…
6.1× per render and 10.3× counting sampling alone, measured on the same machine and the same B200 GPU with the same source videos at 124 frames, 24 fps and 480×832: 26s against 160s, and 13.6s against 140s of sampling. The 160s figure does not include Wan’s preprocessing pass, so the practical gap is wider. On pass counts: Viggle-Animate runs 3 passes over the whole 124-frame clip in one window, while Wan runs a 20-step sampler over a 77-frame window, so 124 frames becomes two chained segments and 40 passes in total — which is why comparison clips labelled “4 steps” and “20 steps” describe the same runs as the 3-against-40 row. Viggle-Animate is the larger model of the two — 33.1B against 17.3B — so the speed comes from running 3 forward passes instead of 40, not from being a smaller network.
Not for the hosted version — it runs in the browser and you only upload a character image. For local use the weights are on Hugging Face and already run in WanGP and community ComfyUI nodes. Bear in mind the 26s number is a B200 benchmark; on a consumer card it will take longer, and at 33.1B parameters it needs more VRAM than Wan’s 17.3B.
No. Once the repainted frame is prepared, video inference runs with zero auxiliary models — no pose estimator, no segmenter, no face tracker, no text encoder. Wan Animate runs a preprocessing pass for pose, face, mask and background before sampling starts.
It is one frame taken from your own driving video with the character edited in an image editor — the published method uses gpt-image. It is a step Wan does not have, and it is the one place Wan is simpler: Wan takes a plain character image instead. The trade is that the repainted frame removes the pose, mask and background preprocessing entirely, and it is a single image edit rather than a per-clip pipeline.
Viggle-Animate, and that is where the published comparisons separate most. Wan2.2-Animate-14B tends to lose detail and smear around rapidly moving heads and limbs, while Viggle-Animate keeps character detail sharper and follows the source pose more closely — stance width, arm extension, limb displacement, and the timing of extreme poses — despite receiving no pose skeleton.
Both publish open weights on Hugging Face, and both run in WanGP and ComfyUI. The difference in access is the hosted side: Viggle runs a first-party hosted version you can use without any local setup, while Wan Animate is only available hosted through third parties.
When you are running locally and 17.3B fits your VRAM where 33.1B does not; when you need pose, mask and background as separate inputs you can inspect and override; or when you already have a Wan-based ComfyUI pipeline and the switching cost outweighs the render time you would save.
The weights are free to download and run yourself. The hosted version is free to try, and heavier use moves onto a paid plan — see the pricing page for the current tiers.
Try it on your own clip and compare the result yourself.
One clip, one character image, no pose rig and no prompt. Try it in the browser, or download the weights and run it yourself.