custom-diffusion
Research repository for multi-concept customization in text-to-image synthesis using diffusion models.
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Decision brief
Custom-Diffusion is a research-driven repository focusing on enhancing text-to-image generation tasks through multi-concept customization capabilities in diffusion models and fine-tuning techniques.
Good fit when
- Use Custom-Diffusion when your project requires incorporating multiple custom concepts into text-to-image synthesis, given its emphasis on handling multi-concept scenarios.
- Consider this tool if you are working with limited labeled data for customization purposes due to its support for few-shot learning capabilities.
Avoid when
- Avoid using Custom-Diffusion for immediate production deployments or simple image generation tasks as it is a research repository without extensive documentation meant for broader usability.
- Do not opt for Custom-Diffusion if your project prioritizes speed over customization quality, given its focus on high-quality outputs through complex model fine-tuning processes.
- Requirements:
- Min 8 GB RAM
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (91d since push)
- As of today
- Provenance
- Not a fork · Organization account
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- Security (OSV)
- No lockfile
- As of 1mo
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Install
pip install custom-diffusionHow it fits your stack(1)
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Evidence and technical details
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Overview
A Python-based project based on PyTorch for enhancing text-to-image generation with customization capabilities in computer vision tasks through diffusion models and fine-tuning techniques.
Capability facts
- Languages
- python
Source: github.language · Aug 24, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 24, 2026)
pip install clip-retrieval tqdmSource link
Tags
README
Getting Started
git clone https://github.com/adobe-research/custom-diffusion.git
cd custom-diffusion
git clone https://github.com/CompVis/stable-diffusion.git
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip install clip-retrieval tqdm
Our code was developed on the following commit #21f890f9da3cfbeaba8e2ac3c425ee9e998d5229 of stable-diffusion.
Download the stable-diffusion model checkpoint
wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
For more details, please refer here.
Dataset: we release some of the datasets used in paper here. Images taken from UnSplash are under UnSplash LICENSE.
Models: all our models can be downloaded from here.
install requirements
pip install accelerate>=0.24.1 pip install modelcards pip install transformers>=4.31.0 pip install deepspeed pip install diffusers==0.21.4 accelerate config export MODEL_NAME="CompVis/stable-diffusion-v1-4"
**Single-Concept fine-tuning**
For agents
This page has a .md twin and JSON over the API.