Multi-Concept Customization of Text-to-Image Diffusion

1 CMU    2Tsinghua University    3Adobe Research

CVPR 2023

Code Paper Project Gallery Slides Data

Custom Diffusion

While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for example, their family, pets, or items). Can we teach a model to quickly acquire a new concept, given a few examples? Furthermore, can we compose multiple new concepts together?

We propose Custom Diffusion, an efficient method for augmenting existing text-to-image models. We find that only optimizing a few parameters in the text-to-image conditioning mechanism is sufficiently powerful to represent new concepts while enabling fast tuning. Additionally, we can jointly train for multiple concepts or combine multiple fine-tuned models into one via closed-form constrained optimization. Our fine-tuned model generates variations of multiple new concepts in novel unseen settings.

Our method is fast (~6 minutes on 2 A100 GPUs) and has low storage requirements (75MB) for each additional concept model apart from the pretrained model. This can be further compressed to 5 - 15 MB by only saving a low-rank approximation of the weight updates.

CustomConcept101 dataset

We also introduced a new dataset of 101 concepts for evaluating model customization methods along with text prompts for single-concept and multi-concept compositions. For more details and results please refer to the dataset webpage and code.


Given a set of target images, our method first retrieves (generates) regularization images with similar captions as target images. The final training dataset is union of target and regularization images. During fine-tuning we update the key and value projection matrices of the cross-attention blocks in the diffusion model with the standard diffusion training loss. All our experiments are based on Stable Diffusion.

Single-Concept Results

We show results of our fine-tuning method on various category of new/personalized concept including scene, style, pet, personal toy, and objects. For more generations and comparison with concurrent methods please refer to our Gallery page.

Target Images

Moongate in snowy ice

Moongate at a beach with a view of seashore

Target Images (Credit: Mia Tang)

Painting of dog in the style of V* art

Plant painting in the style of V* art

Target Images

V* tortoise plushy sitting at the beach with a view of sea

V* tortoise plushy wearing sunglasses

Target Images (Credit: Aaron Hertzmann)

Painting of dog in the style of V* art

Plant painting in the style of V* art

Target Images

V* dog wearing sunglasses

A sleeping V* dog

Target Images

V* cat in times square

Painting of V* cat at a beach by artist claude monet

Target Images

V* table and an orange sofa

V* table with a vase of rose flowers on it

Target Images

V* chair near a pool

A watercolor painting of V* chair in a forest

Target Images

V* barn in fall season with leaves all around

Painting of V* barn in the style of van gogh

Target Images

A vase filled with V* flower on a table

V* flower with violet color petals

Target Images

V* teddybear in grand canyon

V* teddybear swimming in pool

Target Images

V* wooden pot with mountains and sunset in background

Rose flowers in V* wooden pot on a table

Multi-Concept Results

In multi-concept fine-tuning we show composition of scene or object with a pet, and composition of two objects. For more generations and comparison with concurrent methods please refer to our Gallery page.

Target Images

V2* chair with the V1* cat sitting on it near a beach

Watercolor painting of V1* cat sitting on V2* chair

Target Images

V2* dog wearing sunglasses in front of moongate

A digital illustration of the V2* dog in front of moongate

Target Images

The V1* cat is sitting inside a V2* wooden pot and looking up

The V1* cat sculpture in the style of a V2* wooden pot

Target Images

Photo of a V1* table and the V2* chair

Watercolor painting of a V1* table and a V2* chair

Target Images

V2* flower in the V1* wooden pot on a table

V2* flower engraving on the V1* wooden pot

Sample Qualitative Comparison with Concurrent Works

Below image shows qualitative comparison of our method with DreamBooth and Textual Inversion on single-concept fine-tuning. DreamBooth fine-tunes all the parameters in the diffusion model, keeping the text transformer frozen, and uses generated images as the regularization dataset. Textual Inversion only optimizes a new word embedding token for each concept. Please see our Gallery page for more sample generations on the complete evaluation set of text-prompts.

Sample generations on multi-concept by our (joint) training method, ours optimization based method, and DreamBooth. For more samples on the complete evaluation set of text-prompts, please see our Gallery page.

Model Compression

We can further reduce the storage requirement for each fine-tuned model by saving the low-rank approximation of the difference between the pretrained model and fine-tuned model updated weights.

Sample generations with different level of compression. The storage requirements of models from left to right are 75MB, 15MB, 5MB, 1MB, 0.1MB, and 0.08MB (to save the optimized V*). Even with 5x compression with top 60% singular values, the performance remains similar.


Our method has still various limitations. Difficult compositions, e.g., a pet dog and a pet cat, remain challenging. In many case, the pre-trained model also faces a similar difficulty, and we believe that our model inherits these limitations. Additionally, composing increasing three or more concepts together is also challenging.

First column shows the sample target images used for fine-tuning the model with our joint training method. Second column shows the failed compositional generation by our method. Third column shows generations from the pretrained model with similar text prompt as input.


  author = {Kumari, Nupur and Zhang, Bingliang and Zhang, Richard and Shechtman, Eli and Zhu, Jun-Yan},
  title = {Multi-Concept Customization of Text-to-Image Diffusion},
  booktitle = {CVPR},
  year = {2023},

Related and Concurrent Works


We are grateful to Nick Kolkin, David Bau, Sheng-Yu Wang, Gaurav Parmar, John Nack, and Sylvain Paris for their helpful comments and discussion, and to Allie Chang, Chen Wu, Sumith Kulal, Minguk Kang, Yotam Nitzan, and Taesung Park for proofreading the draft. We also thank Mia Tang and Aaron Hertzmann for sharing their artwork. Some of the datasets are downloaded from Unsplash. This work was partly done by Nupur Kumari during the Adobe internship. The work is partly supported by Adobe Inc. The website template is taken from DreamFusion project page.