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SmartPhotoCrafter

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SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing

arxiv

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SmartPhotoCrafter is an end-to-end framework for automatic photographic image editing that reformulates traditional editing as a closed-loop reasoning-to-generation process. Unlike prior methods that rely on explicit user instructions, our approach autonomously identifies aesthetic deficiencies, reasons about improvement strategies, and performs targeted edits without human prompts.

✨ Highlights

Fully Automatic Editing – No user instructions or parameters required; the model completes the closed loop of quality assessment → reasoning → editing autonomously.
Dual Capability – Supports both image restoration (denoising, deblurring, low-light enhancement) and image retouching (color, tone, contrast enhancement).
Aesthetic Reasoning – Explicitly generates image quality analysis and editing suggestions, improving interpretability.
High-Fidelity Generation – Preserves original content structure while delivering photo-realistic outputs with high tonal/color semantic sensitivity.
Reinforcement Learning Optimization – Jointly optimizes reasoning and generation modules, aligning editing trajectories with human aesthetic preferences.

🖼️ Demo

📣 News

2026/04/07: We open-source the inference scripts and pretrained weights.

✅ To-Do List for SmartPhotoCrafter Release

✅ Release the inference code of SmartPhotoCrafter
[ ] Release the SmartPhotoCrafter pretrained weights

Requirements and Installation

Prepare Environment

Create a conda environment & install requirements

shell

# python==3.10.0 cuda==12.4 torch==2.5
conda create -n smartphotocrafter python==3.10.0
conda activate smartphotocrafter
pip install -r requirements.txt

📦 Pretrained Model Weights

Models	Download	Features
SmartPhotoCrafter	🤗 Huggingface 🤖 ModelScope
Qwen-Image-Edit-2509	🤗 Huggingface 🤖 ModelScope	Base Model

😉 Demo Inference

We provide scripts for both automatic and manual editing. Automatic editing only requires inputting one image, while manual editing requires adding a prompt.

Automatically edit reasoning scripts

bash scripts/inference/automatic-edit.sh

Manual edit reasoning scripts

bash scripts/inference/manual-eidt.sh

Script example

PowerShell

CUDA_VISIBLE_DEVICES=0 python infer.py \
    --model_path "ckpt/Qwen-Image-Edit-2509" \
    --dit_path "ckpt/DiT.safetensors" \
    --vlm_path "ckpt/text_encoder" \
    --image_path "example/841012.png" \
    --output_folder "example/output/automatic" \
    --seed 42 \

🚀 Training

Coming Soon ...

⭐ Acknowledgement

Our code is modified based on Edit-R1. We adopt Qwen-Image-Edit-2509 as the base model.

📜 License

All the materials, including code, checkpoints, and demos, are made available under the Creative Commons BY-NC-SA 4.0 license. You are free to copy, redistribute, remix, transform, and build upon the project for non-commercial purposes, as long as you give appropriate credit and distribute your contributions under the same license.

young98/SmartPhotoCrafter