| --- |
| title: Viggle-Animate |
| emoji: 🕺 |
| colorFrom: indigo |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 5.50.0 |
| python_version: "3.10" |
| app_file: app.py |
| hf_oauth: true |
| pinned: false |
| models: |
| - Viggle/Viggle-Animate |
| short_description: Put the person from a still into a driving video, in 4 steps |
| --- |
| |
| Upload a driving video and a character still. The clip's first frame is repainted with |
| gpt-image-2 so it shows your character in that exact pose and framing, and that repainted |
| frame is the only conditioning the model gets besides the clip itself — no pose estimation, |
| no segmentation, no masks. |
|
|
| Takes 4–10 s of driving video and renders it at 4 sampling steps; anything longer is cut to |
| 10.1 s. The model itself only renders frame counts of the form 17k+5 and never fewer than 124 |
| (5.17 s), so shorter clips are held out to 124 frames with a frozen last frame and the render |
| is trimmed back — what comes out is exactly as long as what went in. |
|
|
| Weights: [`Viggle/Viggle-Animate`](https://huggingface.co/Viggle/Viggle-Animate). |
|
|
| **No sign-in.** The GPU comes out of your own ZeroGPU quota, and the render asks for more of |
| it than a free account's day holds — in practice rendering needs PRO. The repaint calls an |
| image model this Space pays for, on the CPU, before any GPU is involved, and is limited to |
| five a day per visitor. The finished examples at the bottom of the page need neither, and |
| are there so the model can be judged for free. |
|
|
| **Hardware.** The transformer is 62 GiB in bf16; with the VAEs that is 73.5 GiB on the card. |
| ZeroGPU `xlarge` (96 GB) is required — `large` is 48 GB and cannot hold it. `xlarge` bills at |
| 2× the normal quota rate, so a free account's day does not go far here; PRO gets 40 min. |
|
|
| The weights stay on the CPU at startup. Pre-staging them through ZeroGPU's tensor pack was |
| tried and cannot fit: the pack plus the source weights need ~159 GB on disk at once, and the |
| container enforces a write quota that `df` does not show — the pack's `fallocate` dies with |
| ENOSPC while the boot log prints over a terabyte free. The cost is one host-to-device copy on |
| the first render a cold worker handles (measured 14 s, from warm page cache); queued renders |
| after it skip the copy. What did land instead: the decode step quantizes to uint8 on the card |
| rather than walking the clip to fp32 numpy on the CPU (bit-identical frames, several seconds |
| of GPU time saved), the frame count rides along from `prepare` instead of being re-counted in |
| the billed window, and trimming plus watermarking run in the parent process after the GPU |
| window closes. |
|
|
| When AOT-compiled copies of the transformer's two repeated block classes exist in the weights |
| repo (`<ClassName>/package.pt2`, built once on this hardware from an owner-only maintenance |
| button), they are downloaded at boot and swapped in after the weights reach the card; without |
| them the Space renders the same model eagerly. |
|
|
