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2025-06-05 15:10:54 +08:00
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@@ -12,10 +12,11 @@
<p align="center">
<a href="https://huggingface.co/zibojia/MiniMaxRemover"><img alt="Huggingface Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Model-brightgreen"></a>
<a href="https://github.com/zibojia/MiniMax-Remover"><img alt="Github" src="https://img.shields.io/badge/MiniMaxRemover-github-grey"></a>
<a href="https://github.com/zibojia/MiniMax-Remover"><img alt="Github" src="https://img.shields.io/badge/MiniMaxRemover-github-black"></a>
<a href="https://huggingface.co/spaces/zibojia/MiniMaxRemover"><img alt="Huggingface Space" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Space-1e90ff"></a>
<a href="https://arxiv.org/abs/2505.24873"><img alt="arXiv" src="https://img.shields.io/badge/MiniMaxRemover-arXiv-b31b1b"></a>
<a href="https://www.youtube.com/watch?v=KaU5yNl6CTc"><img alt="YouTube" src="https://img.shields.io/badge/Youtube-video-ff0000"></a>
<a href="https://minimax-remover.github.io"><img alt="Demo Page" src="https://img.shields.io/badge/Website-Demo%20Page-yellow"></a>
</p>
---
@@ -70,40 +71,17 @@ vae = AutoencoderKLWan.from_pretrained("./vae", torch_dtype=torch.float16)
transformer = Transformer3DModel.from_pretrained("./transformer", torch_dtype=torch.float16)
scheduler = UniPCMultistepScheduler.from_pretrained("./scheduler")
# Load video and mask
def load_video(path):
vr = VideoReader(path)
imgs = vr.get_batch(list(range(video_length))).asnumpy()
return torch.from_numpy(imgs) / 127.5 - 1.0 # [-1, 1]
def load_mask(path):
vr = VideoReader(path)
masks = vr.get_batch(list(range(video_length))).asnumpy()
masks = torch.from_numpy(masks)[:, :, :, :1]
masks[masks > 20] = 255
masks[masks < 255] = 0
return masks / 255.0 # [0, 1]
images = load_video("./video.mp4") # images in range [-1, 1]
masks = load_mask("./mask.mp4") # masks in range [0, 1]
images = # images in range [-1, 1]
masks = # masks in range [0, 1]
# Initialize the pipeline (pass the loaded weights as objects)
pipe = Minimax_Remover_Pipeline(
vae=vae,
transformer=transformer,
scheduler=scheduler,
torch_dtype=torch.float16
pipe = Minimax_Remover_Pipeline(vae=vae, transformer=transformer, \
scheduler=scheduler, torch_dtype=torch.float16
).to(device)
result = pipe(
images=images,
masks=masks,
num_frames=video_length,
height=480,
width=832,
num_inference_steps=12,
generator=torch.Generator(device=device).manual_seed(random_seed),
iterations=6
result = pipe(images=images, masks=masks, \
num_frames=video_length, height=480, width=832, \
num_inference_steps=12, generator=torch.Generator(device=device).manual_seed(random_seed), iterations=6 \
).frames[0]
export_to_video(result, "./output.mp4")
```
@@ -141,8 +119,8 @@ These control output resolution and the speed/quality tradeoff. Changing them ma
|-----------------------|----------------------------------------------------------------|---------|
| `iterations` | Mask dilation hyperparameter (controls inpainting margin) | 6 |
| `num_frames` | Number of frames to process | 81 |
| `height`, `width` | Output video resolution | 480,832 |
| `num_inference_steps` | Diffusion steps per frame (higher = better quality, slower) | 12 |
| `height`, `width` | Output video resolution | 480x832 |
| `num_inference_steps` | Diffusion steps | 12 |
Other parameters can be adjusted in the pipeline call.
@@ -150,20 +128,9 @@ Other parameters can be adjusted in the pipeline call.
## 📂 Model Weights
Place model weights in the following directories:
- `./vae/`
- `./transformer/`
- `./scheduler/`
You should load each component separately (as shown above) and pass the loaded objects to the pipeline.
---
## 💡 Notes
- `transformer_minimax_remover` and `pipeline_minimax_remover` are included as project modules and do **not** require separate installation.
- This repository is intended for **academic research only**. **Commercial use is strictly prohibited.**
```shell
huggingface-cli download zibojia/minimax-remover --include vae transformer scheduler --local-dir .
```
---