Piece it Together: Part-Based Concepting with IP-Priors

Elad Richardson, Kfir Goldberg, Yuval Alaluf, Daniel Cohen-Or
Tel Aviv University, Bria AI

Advanced generative models excel at synthesizing images but often rely on text-based conditioning. Visual designers, however, often work beyond language, directly drawing inspiration from existing visual elements. In many cases, these elements represent only fragments of a potential concept-such as an uniquely structured wing, or a specific hairstyle-serving as inspiration for the artist to explore how they can come together creatively into a coherent whole. Recognizing this need, we introduce a generative framework that seamlessly integrates a partial set of user-provided visual components into a coherent composition while simultaneously sampling the missing parts needed to generate a plausible and complete concept. Our approach builds on a strong and underexplored representation space, extracted from IP-Adapter+, on which we train IP-Prior, a lightweight flow-matching model that synthesizes coherent compositions based on domain-specific priors, enabling diverse and context-aware generations. Additionally, we present a LoRA-based fine-tuning strategy that significantly improves prompt adherence in IP-Adapter+ for a given task, addressing its common trade-off between reconstruction quality and prompt adherence.

Using a dedicated prior for the target domain, our method, Piece it Together (PiT), effectively completes missing information by seamlessly integrating given elements into a coherent composition while adding the necessary missing pieces needed for the complete concept to reside in the prior domain.

Description 📜

Official implementation of the paper "Piece it Together: Part-Based Concepting with IP-Priors"

Table of contents

• Piece it Together: Part-Based Concepting with IP-Priors
  • Description 📜
  • Table of contents
  • Getting started with PiT 🚀
    • Setup your environment
  • Inference with PiT
  • Training PiT
  • Inference with IP-LoRA
  • Training IP-LoRA
    • Preparing your data
    • Running the training script
  • Exploring the IP+ space
    • Finding new directions
    • Editing images with found directions
  • Acknowledgments
  • Citation

Getting started with PiT 🚀

Setup your environment

1. Clone the repo:

git clone https://github.com/eladrich/PiT
cd PiT

2. Install uv:

Instructions taken from here.

For linux systems this should be:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

3. Install the dependencies:

uv sync

4. Activate your .venv and set the Python env:

source .venv/bin/activate
export PYTHONPATH=${PYTHONPATH}:${PWD}

Inference with PiT

Domain	Examples	Link
Characters		Here
Products		Here
Toys		Here

Training PiT

Data Generation

PiT assumes that the data is structured so that the the target images and part images are in the same directory with the naming convention being image_name.jpg for hte base image and image_name_i.jpg for the parts.

To use a generated data see the sample scripts

python -m scripts.generate_characters

python -m scripts.generate_products

Training

For training see the training/coach.py file and the example below

bash python -m scripts.train --config_path=configs/train/train_characters.yaml

PiT Inference

For inference see scripts.infer.py with the corresponding configs under configs/infer

python -m scripts.infer  --config_path=configs/infer/infer_characters.yaml

Inference with IP-LoRA

1. Download the IP checkpoint and the LoRAs

ip_lora_inference/download_ip_adapter.sh
ip_lora_inference/download_loras.sh

2. Run inference with your preferred model

example for running the styled-generation LoRA

python ip_lora_inference/inference_ip_lora.py --lora_type "character_sheet" --lora_path "weights/character_sheet/pytorch_lora_weights.safetensors" --prompt "a character sheet displaying a creature, from several angles with 1 large front view in the middle, clean white background. In the background we can see half-completed, partially colored, sketches of different parts of the object" --output_dir "ip_lora_inference/character_sheet/" --ref_images_paths "assets/character_sheet_default_ref.jpg"
--ip_adapter_path "weights/ip_adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin"

Training IP-LoRA

Preparing your data

The expected data format for the training script is as follows:

--base_dir/
----targets/
------img1.jpg
------img1.txt
------img2.jpg
------img2.txt
------img3.jpg
------img3.txt
.
.
.
----refs/
------img1_ref.jpg
------img2_ref.jpg
------img3_ref.jpg
.
.
.

Where imgX.jpg is the target image for the input reference image imgX_ref.jpg with the prompt imgX.txt

Running the training script

For training a character-sheet styled generation LoRA, run the following command:

 python ./ip_lora_train/train_ip_lora.py \
 --rank 64 \
 --resolution 1024 \
 --validation_epochs 1 \
 --num_train_epochs 100 \
 --checkpointing_steps 50 \
 --train_batch_size 2 \
 --learning_rate 1e-4 \
 --dataloader_num_workers 1 \
 --gradient_accumulation_steps 8 \
 --dataset_base_dir <base_dir> \
 --prompt_mode character_sheet \
 --output_dir ./output/train_ip_lora/character_sheet
 

and for the text adherence LoRA, run the following command:

 python ./ip_lora_train/train_ip_lora.py \
 --rank 64 \
 --resolution 1024 \
 --validation_epochs 1 \
 --num_train_epochs 100 \
 --checkpointing_steps 50 \
 --train_batch_size 2 \
 --learning_rate 1e-4 \
 --dataloader_num_workers 1 \
 --gradient_accumulation_steps 8 \
 --dataset_base_dir <base_dir> \
 --prompt_mode creature_in_scene \
 --output_dir ./output/train_ip_lora/creature_in_scene

Exploring the IP+ space

Start by downloading the needed IP+ checkpoint and the directions presented in the paper:

ip_plus_space_exploration/download_directions.sh
ip_plus_space_exploration/download_ip_adapter.sh

Finding new directions

To find a direction in the IP+ space from "class1" (e.g. "scrawny") to "class2" (e.g. "muscular"):

1. Create class1_dir and class2_dir containing images of the source and target classes respectively

2. Run the find_direction script:

python ip_plus_space_exploration/find_direction.py --class1_dir <path_to_source_class> --class2_dir <path_to_target_class> --output_dir ./ip_directions --ip_model_type "plus"

Editing images with found directions

Use the direction found in the previous stage, or one downloaded from HuggingFace in the previous stage.

python ip_plus_space_exploration/edit_by_direction.py --ip_model_type "plus" --image_path <source_image> --direction_path <path_to_chosen_direction> --direction_type "ip" --output_dir "./edit_by_direction/"

Acknowledgments

Code is based on

• https://github.com/pOpsPaper/pOps
• https://github.com/cloneofsimo/minRF by the great @cloneofsimo

Citation

If you use this code for your research, please cite the following paper:

@misc{richardson2025piece,
      title={Piece it Together: Part-Based Concepting with IP-Priors}, 
      author={Richardson, Elad and Goldberg, Kfir and Alaluf, Yuval and Cohen-Or, Daniel},
      year={2025},
      eprint={2503.10365},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.10365}, 
}