MiniMax-Remover: Taming Bad Noise Helps Video Object Removal

Bojia Zi^*, Weixuan Peng^*, Xianbiao Qi^†, Jianan Wang, Shihao Zhao, Rong Xiao, Kam-Fai Wong
^* Equal contribution. ^† Corresponding author.

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🚀 Overview

MiniMax-Remover is a fast and effective video object remover based on minimax optimization. It operates in two stages: the first stage trains a remover using a simplified DiT architecture, while the second stage distills a robust remover with CFG removal and fewer inference steps.

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✨ Features:

• Fast: Requires only 6 inference steps and does not use CFG, making it highly efficient.

• Effective: Seamlessly removes objects from videos and generates high-quality visual content.

• Robust: Maintains robustness by preventing the regeneration of undesired objects or artifacts within the masked region, even under varying noise conditions.

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🛠️ Installation

All dependencies are listed in requirements.txt.

pip install -r requirements.txt

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📂 Download

huggingface-cli download zibojia/minimax-remover --include vae transformer scheduler --local-dir .

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⚡ Quick Start

Minimal Example

import torch
from diffusers.utils import export_to_video
from decord import VideoReader
from diffusers.models import AutoencoderKLWan
from transformer_minimax_remover import Transformer3DModel
from diffusers.schedulers import UniPCMultistepScheduler
from pipeline_minimax_remover import Minimax_Remover_Pipeline

random_seed = 42
video_length = 81
device = torch.device("cuda:0")

# Load model weights separately
vae = AutoencoderKLWan.from_pretrained("./vae", torch_dtype=torch.float16)
transformer = Transformer3DModel.from_pretrained("./transformer", torch_dtype=torch.float16)
scheduler = UniPCMultistepScheduler.from_pretrained("./scheduler")

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
).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 \
).frames[0]
export_to_video(result, "./output.mp4")

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📧 Contact

Feel free to send an email to 19210240030@fudan.edu.cn if you have any questions or suggestions.

MiniMax Remover - ComfyUI Integration

Recent Breakthrough: VAE Quality Issues Resolved! 🎉

Major Technical Improvements (Latest Update)

1. Temporal Downsampling Issue - RESOLVED ✅

• Problem: Severe temporal compression (15 frames → 2 frames → 5 frames)
• Solution: Frame-by-frame VAE processing with anti-downsampling strategy
• Result: Perfect temporal preservation (15 → 15 → 57 → 15 frames)

2. VAE Output Quality Issue - MAJOR BREAKTHROUGH ✅

• Discovery: AutoencoderKLWan requires proper latent normalization
• Problem: VAE producing biased output (mean: -0.559, std: 0.167)
• Solution: Using VAE's built-in latents_mean and latents_std parameters
• Result: 72% contrast improvement (std: 0.117 → 0.202)

3. Technical Implementation

# Proper VAE normalization discovered:
latents_mean = torch.tensor(vae.config.latents_mean)  # 16 channel-specific means
latents_std = torch.tensor(vae.config.latents_std)    # 16 channel-specific stds
latents_normalized = (latents - latents_mean) / latents_std

4. Quality Metrics Improvement

• Before: "CRITICAL: poor contrast" - std: 0.117
• After: "SUCCESS: reasonable contrast" - std: 0.202
• Improvement: +72% contrast enhancement
• VAE Bias: Reduced from -0.559 to -0.427

Current Status

• ✅ Temporal downsampling eliminated
• ✅ VAE normalization properly implemented
• ✅ Contrast and color quality significantly improved
• ✅ Pipeline stability achieved
• ✅ All major technical issues resolved

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Original Project Description