| --- |
| title: Video Watermark Remover |
| emoji: 🦎 |
| colorFrom: green |
| colorTo: blue |
| sdk: gradio |
| sdk_version: "4.44.0" |
| python_version: "3.10" |
| app_file: app.py |
| pinned: false |
| license: apache-2.0 |
| --- |
| |
| # Video Watermark Remover |
|
|
| Self-hosted Hugging Face Space for removing static, opaque watermarks from video footage. |
|
|
| ## Modes |
|
|
| | Mode | Model | Speed | Best for | |
| |------|-------|-------|----------| |
| | **Fast** | LaMa (per-frame) | Seconds | Sky, water, foliage — low-frequency backgrounds | |
| | **Quality** | Wan2.1-VACE-14B + lightx2v 4-step distill, FP8 on H200 | ~2-3 min | Structured or textured backgrounds | |
|
|
| ## How it works |
|
|
| 1. Upload a video clip — up to 60 s; the first 15 s is processed at 1080p |
| 2. Paint over the watermark on the first frame using the brush, or: |
| - **Snap to Rectangle** — scribble roughly, click to convert to a clean filled rectangle |
| - **Clear Mask** — start the drawing over |
| 3. Choose **Fast** or **Quality** mode |
| 4. Hit **Remove Watermark** — output is composited back at the source resolution |
|
|
| ### Crop-inpaint-composite |
|
|
| The pipeline never runs the model on the full 1920×1080 frame: |
| - The mask determines a tight crop, expanded to a model-friendly resolution with surrounding context |
| - Only that crop (~7× fewer pixels) is processed |
| - The result is feather-blended back into the original frame |
| - All other pixels are **byte-identical** to the source |
|
|
| V-Log / HDR colour metadata is preserved via FFmpeg flag passthrough (10-bit H.265 output for HDR sources). |
|
|
| ## Platform |
|
|
| - ZeroGPU (Nvidia H200 MIG slice) |
| - PyTorch + diffusers ≥0.34 |
| - ffmpeg via `packages.txt` |
|
|
| ## Upstream protection |
|
|
| All model files come from a private mirror at [JackIsNotInTheBox/Video_Watermark_Remover_Checkpoints](https://huggingface.co/JackIsNotInTheBox/Video_Watermark_Remover_Checkpoints): |
|
|
| - LaMa: `lama/big-lama.pt` — `LAMA_MODEL_URL` defaults to the mirror, prefetched into `torch.hub` cache before `simple_lama_inpainting` can reach for its hardcoded GitHub release URL |
| - VACE-14B: `vace-14b/` — full diffusers package, loaded with `local_files_only=True` so any cache miss errors loudly instead of silently fetching from upstream HF Hub |
| - Distill LoRA: `loras/wan2.1_t2v_14b_lora_rank64_lightx2v_4step.safetensors` — same `local_files_only=True` enforcement |
|
|
| The Space stays functional even if upstream Wan-AI / lightx2v / GitHub release sources are deleted: at runtime nothing reaches for them. |
|
|
| On the first deploy, ~75 GB of VACE weights are downloaded from the mirror to the persistent cache in a background thread. Fast mode works immediately; Quality mode blocks until prewarm finishes (the UI shows a progress message during the wait). |
|
|
| ## License |
|
|
| - Pipeline code: Apache 2.0 |
| - LaMa: Apache 2.0 |
| - Wan2.1-VACE-14B: Apache 2.0 ([Wan-AI repo](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-diffusers)) |
| - lightx2v 4-step distill LoRA: Apache 2.0 |
|
|
