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custom-diffusion

adobe-research/custom-diffusion

Custom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion

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GraphCanon updated Sep 30, 2026 · GitHub synced Sep 20, 2026

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2.0k stars139 forksLast push May 24, 2026 Python Other

Decision brief

Custom Diffusion is a Python-based tool for fine-tuning text-to-image diffusion models for multi-concept customization, leveraging PyTorch and Stable Diffusion.

Good fit when

When you need to fine-tune text-to-image models for multiple concepts simultaneously, as Custom Diffusion supports multi-concept customization.
If you are working with datasets that require few-shot learning capabilities, as Custom Diffusion is designed to handle such scenarios effectively.

Avoid when

Avoid using Custom Diffusion if your project requires a single-concept fine-tuning approach, as it is optimized for multi-concept customization.
Do not use this tool if you are looking for a solution that does not rely on PyTorch and Stable Diffusion, as Custom Diffusion is built on these frameworks.

Requirements:: Min 8 GB RAM; Ensure you have a compatible environment setup with PyTorch and Stable Diffusion.; Installation requires specific versions of packages like accelerate, transformers, deepspeed, and diffusers.

Observed Sep 30, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report

Maintenance: Slowing (117d since push); As of Sep 19, 2026
Provenance: Not a fork · Organization account; As of Sep 19, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install custom-diffusion

PyPI

How it fits your stack(1)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

stable-diffusion

Custom Diffusion from Adobe focuses on multi-concept customization in text-to-image synthesis also using diffusion models, sharing a similar goal but possibly different execution pathway.

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A repository for customizing text-to-image diffusion models for multi-concept fine-tuning, based on PyTorch and Stable Diffusion.

Capability facts

Languages: python
Source: github.language · Sep 19, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 19, 2026)

pip install clip-retrieval tqdm

Source link

README

Getting Started Our code was developed on the following commit of stable diffusion. Download the stable diffusion model checkpoint For more details, please refer here. Dataset: we release some of the datasets used in paper here. Images taken from UnSplash are under UnSplash LICEN...

For agents

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