Zhichao Liao*‡, Xiaole Xian*‡, Qingyu Li, Wenyu Qin, Meng Wang, Weicheng Xie ✉️, Siyang Song, Pingfa Feng, Long Zeng ✉️, Liang Pan
Tsinghua University · Shenzhen University
Kling Team, Kuaishou Technology · University of Exeter · S-lab, Nanyang Technological University
* Equal contribution, ✉️ Corresponding author
‡ Work conducted during an internship at Kling Team, Kuaishou Technology
-
2026/03/27: 🔥 We have released the training and inference code on GitHub. Feel free to try it out! -
2026/03/08: 🔥 We released the technical report on arXiv. -
2026/02/21: 🔥 PureCC was accepted by CVPR 2026.
Thank you all for your attention! We are actively cleaning our technical report, models, and codes, and we will open source them soon.
- Technical Paper on arXiv
- Training and Inference code on GitHub
PureCC enables high-fidelity personalized concept customization while better preserving the original model behavior and generation capability.
🔥🔥🔥 The goal of an I2I editing or inpainting task is to perform a one-time visual modification on a given image, with the focus on transforming that specific image into the desired result. In contrast, PureCC aims to teach the model a new concept. Moreover, compared with other concept customization methods, it not only emphasizes concept fidelity, but also highlights “pure learning” — learning only the target concept itself while minimizing disruption to the original model’s behavior, distribution, and capabilities.
This base model is from Stable Diffusion 3.5 Medium.
Note: The model is gated. You must accept the Stability AI Community License before downloading.
Download the model:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="stabilityai/stable-diffusion-3.5-medium",
local_dir="/path/to/SD3.5-medium",
)
Or via CLI:
huggingface-cli download stabilityai/stable-diffusion-3.5-medium \
--local-dir /path/to/SD3.5-medium
Our inference code is similar to that of regular LoRA SD inference.
import torch
from diffusers import DiffusionPipeline
from peft import PeftModel
base_model_id = "stabilityai/stable-diffusion-3.5-medium"
lora_model_id = "/path/to/stage2/checkpoint"
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.bfloat16)
pipe.transformer = PeftModel.from_pretrained(pipe.transformer, lora_model_id)
pipe = pipe.to("cuda")
prompt = "a photo of sks robot toy on a wooden table"
image = pipe(prompt=prompt, generator=torch.manual_seed(42)).images[0]
image.save("output.png")
In the first stage, we train a LoRA on the target concept to build a specific representation extractor. This follows the official diffusers DreamBooth LoRA for SD3 training recipe.
First, install the required dependencies:
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
pip install -r examples/dreambooth/requirements_sd3.txt
Then launch Stage 1 training:
export MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers"
export INSTANCE_DIR="/path/to/subject/images"
export OUTPUT_DIR="./output/base_ckpt/robot_toy"
accelerate launch examples/dreambooth/train_dreambooth_lora_sd3.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--mixed_precision="bf16" \
--instance_prompt="a photo of sks robot toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--learning_rate=4e-4 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=1000 \
--rank=4 \
--seed=0
In the second stage, we introduce a PureCC loss to prevent disruption to the original model’s behavior and capabilities.
accelerate launch train_stage2_v2_sd3.py \
--pretrained_model_name_or_path /path/to/SD3.5-medium \
--data_path /path/to/dataset \
--csv_name robot_toy.csv \
--output_dir ./output \
--max_train_steps 800 \
--learning_rate 1e-4 \
--rank 4 \
--eta 2.0 \
--mixed_precision bf16
If you find PureCC useful for your research, welcome to 🌟 this repo and cite our work using the following BibTeX:
@misc{liao2026purecc,
title={PureCC: Pure Learning for Text-to-Image Concept Customization},
author={Zhichao Liao and Xiaole Xian and Qingyu Li and Wenyu Qin and Meng Wang and Weicheng Xie and Siyang Song and Pingfa Feng and Long Zeng and Liang Pan},
year={2026},
eprint={2603.07561},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.07561},
}