---
title: ID-V2V
emoji: 🪞
colorFrom: pink
colorTo: purple
sdk: gradio
sdk_version: 6.22.0
app_file: app.py
python_version: "3.12"
startup_duration_timeout: 1h
pinned: false
license: apache-2.0
short_description: Identity-preserving video restyle & relight
models:
  - Eyeline-Labs/ID-V2V
  - Wan-AI/Wan2.1-T2V-14B
  - Wan-AI/Wan2.1-I2V-14B-480P
  - facebook/sam3
tags:
  - video-to-video
  - video-editing
  - identity-preservation
  - wan
  - vace
---

# ID-V2V

Demo of [**Eyeline-Labs/ID-V2V**](https://huggingface.co/Eyeline-Labs/ID-V2V) — restyle or
relight a video while keeping the people in it recognisable.

Inputs: a **source video** (supplies the motion), a **stylized first frame** (supplies the
target look and identity), and a **prompt**.

## Pipeline

Ported from the authors' reference implementation
([github.com/Eyeline-Labs/ID-V2V](https://github.com/Eyeline-Labs/ID-V2V)):

1. **SAM3** promptable concept segmentation (`"person"`) tracks the subjects across the clip;
   masks are cleaned with the repo's *Secret Panda* routine (hole-fill → close → bridge →
   hole-fill), per object and then on the union.
2. The masked pixels are composited over **gray 127** — this *foreground-on-gray* video is
   the single **VACE** control stream ID-V2V was trained on.
3. **Wan2.1 I2V-14B DiT + VACE ControlNet**, both taken from the finetuned `idv2v.pth`, run
   with the stylized frame as the I2V anchor *and* as the **SVI anti-drift reference pad**
   (`ref_pad_num = -1`), `cfg_scale = 5`, `vace_scale = 1`.

The authors' `diffsynth` fork (their custom multi-control VACE + SVI `WanVideoPipeline`) is
vendored under `diffsynth/`.

## Deviations from the reference, and why

| Reference | Here | Reason |
|---|---|---|
| 1280×720 | 832×480 (the repo's own CLI default) | 720p is ~2.5× the tokens; it does not fit one ZeroGPU slot |
| 81 frames/clip, multi-clip chaining for long videos | 17/33/49 frames, single clip | each extra clip is a full extra denoise pass |
| Consecutive source frames | selectable source-frame stride (default 2, output fps divided to match) | a 33-frame clip then still covers the whole action |
| SAM3 at source resolution, then crop/resize | crop/resize to 832×480, then SAM3 | same masks, much faster segmentation |
| Secret Panda morphology via `scipy.ndimage` | same operations via OpenCV (`border_value=0`) | identical results, ~10× faster |
| bf16 weights with CPU offload across 8×A100 | bf16 weights fully resident on one 96 GB ZeroGPU slot | no offload on ZeroGPU |

`idv2v.pth` ships as **fp32** (≈78 GB); it is memory-mapped, cast to bf16 (≈39 GB), and the
fp32 blob is deleted before ZeroGPU packs the weights.

## Credits

- Model & code: Eyeline Labs — [paper](https://huggingface.co/papers/2607.22830)
- Base model: [Wan-AI/Wan2.1-I2V-14B-720P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) + VACE
- Segmentation: [facebook/sam3](https://huggingface.co/facebook/sam3)
- Example clips are the authors' own `test_samples/`.