SnowMaster-Unofficial

Unofficial PyTorch Reproduction of

SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization

[CVPR 2025]

Paper · PDF · Issues · Release

This is an unofficial implementation maintained by @StaryMoon. If this repository helps your reading, reproduction, or course project, please consider giving it a star and following my GitHub profile.

Paper / Project Preview

_{Image source: public paper/project page screenshot, Paper. Captured/organized on 2026-07-02. This repository is unofficial and is not affiliated with the paper authors.}

At a Glance

Item	Details
Paper	SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization
Venue / Source	CVPR 2025
Focus	This repository organizes a PyTorch implementation for SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization, focusing on real-wor...
Repository type	Unofficial PyTorch reproduction starter
Local entry point	python scripts/smoke_test.py

News

• 2026-06-10: Repository upgraded with an official-style README, paper citation metadata, cleaner package interfaces, default configuration, and release-ready project structure.

Overview

This repository organizes a PyTorch implementation for SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization, focusing on real-world desnowing with MLLM-guided multi-model feedback. The codebase is structured like a standard research repository so that model components, configuration files, scripts, and evaluation utilities can be extended independently.

Main goals:

• provide a clean PyTorch module layout for the paper;
• keep training, inference, evaluation, and configuration entry points explicit;
• track paper-reported metrics separately from local experiment logs;
• make it easy for contributors to inspect, compare, and extend the implementation.

Repository Structure

SnowMaster-Unofficial/
├── configs/
│   └── default.yaml
├── scripts/
│   └── smoke_test.py
├── src/snowmaster_unofficial/
│   ├── __init__.py
│   └── model.py
├── CITATION.cff
├── README.md
├── requirements.txt
└── pyproject.toml

Installation

git clone https://github.com/StaryMoon/SnowMaster-Unofficial.git
cd SnowMaster-Unofficial
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

For CUDA-enabled experiments, install the PyTorch build matching your CUDA version from the official PyTorch website before installing the rest of the dependencies.

Quick Check

Run the minimal forward-pass check:

python scripts/smoke_test.py

Expected output:

output: (...)
loss: ...

This confirms that the package import path, model interface, and tensor flow are working.

Data Preparation

Create local data folders:

mkdir -p data/train data/val data/test checkpoints outputs

Recommended layout:

data/
├── train/
├── val/
└── test/

Keep private datasets, downloaded checkpoints, and generated outputs out of git. Dataset-specific converters can be added under scripts/ while preserving the public repository structure.

Training

Minimal module usage:

import torch
from snowmaster_unofficial import ModelConfig, UnofficialModel, reconstruction_loss

config = ModelConfig(task="restoration", hidden_dim=128, num_layers=2, num_heads=4)
model = UnofficialModel(config)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)

x = torch.randn(2, 3, 64, 64)
condition = torch.randn(2, 4, config.hidden_dim)
target = torch.zeros(2, config.output_dim)

out = model(x, condition=condition)
loss = reconstruction_loss(out.primary, target)
loss.backward()
optimizer.step()

The repository separates model code, configuration, experiment outputs, and evaluation logs so new components can be added without changing the public interface.

Inference

import torch
from snowmaster_unofficial import UnofficialModel

model = UnofficialModel().eval()
with torch.no_grad():
    x = torch.randn(1, 3, 64, 64)
    y = model(x).primary
print(y.shape)

Evaluation

Suggested entry points:

python scripts/smoke_test.py
# python scripts/evaluate.py --config configs/default.yaml --ckpt checkpoints/model.pt

Paper-reported values and local run values should be kept in separate columns so readers can distinguish citation numbers from local experiment logs.

Paper Results

For copyright and license clarity, this repository links to the original paper figures and tables instead of redistributing screenshots copied from the PDF. The table below tracks where readers can find the paper-reported results.

Result Type	Paper Location	Source
Main quantitative comparison	Main paper tables, pp. 4302-4312	CVF paper page
Ablation study	Ablation / experiment section	CVF paper page
Qualitative examples	Main paper figures and supplemental material	CVF paper page

Reproduction Log

Date	Config	Split	Metric	Value	Notes
2026-06-10	configs/default.yaml	smoke check	forward pass	ok	package interface validation

Implementation Status

• Package layout and install metadata
• Core PyTorch module interfaces
• Default config and smoke test
• Paper citation and result-location index
• Dataset-specific preprocessing scripts
• Paper-specific training recipe
• Evaluation and visualization scripts
• Public checkpoints and model zoo entries

Model Zoo

Model	Checkpoint	Config	Notes
default	TBA	configs/default.yaml	compact implementation interface

Citation

If you find this repository useful, please cite the original paper:

@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Jianyu and Chen, Sixiang and Lin, Yunlong and Ye, Tian and Liu, Yun and Fei, Song and Xing, Zhaohu and Wu, Hongtao and Wang, Weiming and Zhu, Lei},
    title     = {SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4302-4312}
}

Acknowledgements

• Thanks to the authors of SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization for the original research.
• Thanks to the Computer Vision Foundation for maintaining the CVPR Open Access pages.
• This repository is inspired by standard open-source PyTorch research codebases.
• The implementation is unofficial and all paper names, datasets, and trademarks belong to their respective owners.

License

This repository is released under the MIT License. The original paper, datasets, official code, project assets, and third-party dependencies remain governed by their own licenses.

Keywords

cvpr-2025, pytorch, unofficial-implementation, image-desnowing, mllm, image-restoration