T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs

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This repository contains the official implementation of T2T-VICL, a collaborative framework for studying cross-task visual in-context learning (VICL). In standard VICL, the demonstration pair and the query usually belong to the same visual task. T2T-VICL studies a harder setting: the visual prompt comes from Task A, while the query image requires Task B.

The core idea is to translate mismatched visual demonstrations into implicit textual guidance. A large teacher VLM first describes the relationship between two low-level vision tasks without explicitly naming the tasks. A smaller Qwen-VL student is then fine-tuned to generate such content-dependent prompts from three images: Task A input, Task A output, and Task B input. The generated prompt guides a frozen image-editing VLM, and multiple candidates are evaluated with PSNR, SSIM, and VIEScore.

Method Overview

T2T-VICL has three main stages:

1. Implicit task relationship generation

   • A teacher VLM, Qwen2.5-VL-32B-Instruct in the paper, receives two paired examples: (Task A input, Task A output) and (Task B input, Task B output).
   • It produces a structured implicit description covering image content, visual changes, and task differences.
   • Descriptions are filtered for diversity with sentence-embedding based deduplication.

2. VLM-to-sVLM knowledge transfer

   • The teacher descriptions are converted into Qwen-VL conversation format.
   • Qwen3-VL-4B-Instruct is fine-tuned as a lightweight prompt generator.
   • At inference time, the student only sees (Task A input, Task A output, Task B input) and predicts the implicit prompt.

3. Cross-task inference and score-based selection

   • The generated implicit prompt is passed to an image-editing VLM together with the three input images.
   • Candidate outputs are evaluated using PSNR, SSIM, and VIEScore.
   • For stochastic APIs such as Gemini and Seedream, the paper samples multiple candidates and selects the highest-PSNR output before reporting SSIM and VIEScore.

Vision Tasks

The benchmark covers 12 low-level vision tasks:

Category	Tasks
Restoration	deblurring, dehazing, demoireing, denoising, deraining
Removal	reflection removal, shadow removal
Generation / Enhancement	colorization, harmonization, inpainting, light enhancement, style transfer

The released training set contains implicit descriptions for 26 cross-task pairs, with 2,000 samples per pair in the current local dataset. Evaluation files contain 100 samples per pair for the cross-task benchmark, plus same-task files for ablation experiments.

Structure

Task-Transfer/
|-- data.py                     # Generate teacher implicit descriptions
|-- data_process.py             # Convert descriptions to Qwen-VL SFT format
|-- eval.py                     # Main Gemini-based cross-task evaluation pipeline
|-- eval_*.py                   # Evaluation backends and ablations
|-- eval.sh                     # Example evaluation commands
|-- data/
|   |-- tasks/                  # Paired task data: task/input and task/output
|   |-- dataset/                # Train/eval JSON files
|   `-- output/                 # Raw output saving location
|-- Qwen3-VL/                   # Qwen3-VL
|-- VIEScore/                   # VIEScore

Installation

The project was developed with Python, PyTorch, Transformers, Diffusers, Qwen-VL utilities, VIEScore, and optional API clients for Gemini and Seedream. The included requirements.txt is an environment snapshot from the authors' machine, and you may use it as a reference for reproducing.

conda create -n vicl python=3.10
conda activate vicl

# Install PyTorch according to your CUDA driver first
# Example:
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118  # CUDA 11.8
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124  # CUDA 12.4
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126  # CUDA 12.6

# Find a Flash Attention 2 version matching your CUDA/PyTorch version:
# https://github.com/Dao-AILab/flash-attention/releases
# Example:
pip install flash-attn==2.7.4.post1 --no-build-isolation
pip install -U flash-attn --no-build-isolation

# Install project dependencies
# pip install transformers accelerate peft diffusers qwen-vl-utils scikit-image pillow tqdm google-genai fal-client requests
pip install -r requirements.txt

For Qwen3-VL fine-tuning, also install the packages required by Qwen3-VL/qwen-vl-finetune/README.md, including DeepSpeed and FlashAttention if your hardware supports them.

Note: For baseline models' evaluation process (including FireRed, Flux 2, OmniGen 2, Qwen-Image), the required packages might slightly differ. Check their official installation guidelines if you have any questions.

Data Preparation

Expected task data layout:

data/tasks/
|-- deblurring/
|   |-- input/
|   `-- output/
|-- dehazing/
|   |-- input/
|   `-- output/
`-- ...

Training and evaluation metadata are stored under data/dataset/:

data/dataset/
|-- train_dataset.json          # Teacher-generated implicit descriptions
|-- converted_dataset.json      # Qwen-VL SFT format
|-- eval_dataset.json           # Cross-task evaluation pairs
|-- eval_dataset_1.json         # Single-task evaluation data
`-- eval_dataset_2.json         # Same-task ablation pairs

To generate teacher descriptions for a new ordered task pair:

python data.py deblurring dehazing

The data.py loads Qwen2.5-VL-32B-Instruct by default and writes to data/dataset/train_dataset.json. Adjust model paths, sample counts, and dataset paths in the script before running large-scale generation.

To regenerate the Qwen-VL SFT file from train_dataset.json:

python data_process.py

Note: To download original data (tasks and dataset), you need to contact authors to get the Google Drive folder shared link, or sign User Agreement to gain access to Hugging Face dataset.

Fine-Tuning

The student prompt generator is trained with the converted Qwen-VL conversation data. First, register the dataset in:

Qwen3-VL/qwen-vl-finetune/qwenvl/data/__init__.py

Make sure annotation_path points to data/dataset/converted_dataset.json and data_path points to data/tasks/. Then run the Qwen3-VL SFT script:

cd Qwen3-VL/qwen-vl-finetune
bash finetune.sh  # Or you can choose any shell script file from Qwen3-VL/qwen-vl-finetune/scripts

Note: The default training script uses the dataset name VICL, which is already registered in qwenvl/data/__init__.py. Update the hard-coded absolute paths in that file before launching training.

The evaluation scripts expect the fine-tuned checkpoint at:

Qwen3-VL/qwen-vl-finetune/output/checkpoint-4875

If you save the checkpoint elsewhere, update CHECKPOINT_PATH in the corresponding eval*.py script.

Evaluation

Main experiments on Gemini:

• Run cross-task evaluation with the learned Qwen prompt.

  python eval.py --use_qwen_for_prompt

• Run the fixed-prompt baseline.

  python eval.py --fixed_prompt "This is a visual in-context learning task. The first two images are an input and output of Task A. The third image is the input for Task B. The goal is to perform Task B on the third image and generate output image, learning from Task A."

Additional models:

# Closed-source
python eval_seedream.py --use_qwen_for_prompt  # --fixed_prompt "xxx"

# Open-source
python eval_qwen.py --use_qwen_for_prompt      # --fixed_prompt "xxx"
python eval_flux.py --use_qwen_for_prompt      # --fixed_prompt "xxx"
python eval_omnigen.py --use_qwen_for_prompt   # --fixed_prompt "xxx"
python eval_firered.py --use_qwen_for_prompt   # --fixed_prompt "xxx"

Same-task ablations:

python eval_1.py
python eval_2.py --use_qwen_for_prompt

All commands above can be found in eval.sh.

Outputs are written under data/output/. Each task-pair directory contains generated images, evaluation_log.jsonl, and averaged evaluation_results.json.

API Keys and Paths

Some scripts contain placeholder keys or machine-specific paths from the original experiments. Before running them, update:

• GEMINI_API_KEY and BASE_URL in eval.py, eval_1.py, eval_2.py, eval_lumina.py, and related Gemini scripts.
• FAL_KEY in eval_seedream.py for Seedream evaluation.
• viescore_path in evaluation scripts if your checkout path differs from the authors' machine.
• annotation_path and data_path for the VICL entry in Qwen3-VL/qwen-vl-finetune/qwenvl/data/__init__.py.
• CHECKPOINT_PATH if the Qwen prompt generator checkpoint is saved elsewhere.

Our Results

The qualitative examples below show how Gemini adapts across diverse cross-task pairs under the implicit prompt.

The quantitive results on Gemini 2.5 Flash and Seedream 4.0 are also shown below. Each "Ours" column uses the Qwen-enhanced implicit prompt, compared with the fixed-prompt baseline.

Citation

@article{xia2025t2tvicl,
  title={T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs},
  author={Xia, Shao-Jun and Zhang, Huixin and Tu, Zhengzhong},
  journal={arXiv preprint arXiv:2511.16107},
  year={2025}
}

Acknowledgements

This repository builds on Qwen-VL, VIEScore, and several recent image-editing VLMs. Please also follow the licenses and usage terms of the underlying datasets, pretrained models, and API providers.