DrUM (Draw Your Mind)

This repository hosts the official implementation of:

Hyungjin Kim, Seokho Ahn, and Young-Duk Seo, Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models, [iccv] [supp] [arXiv]

News

• [2025.11.04]: ICCV paper and supplementary materials released
• [2025.08.05]: Pre-trained weights available on 🤗 HuggingFace
• [2025.08.05]: Repository created

Introduction

DrUM enables personalized text-to-image (T2I) generation by integrating reference prompts into T2I diffusion models. It works with foundation T2I models such as Stable Diffusion v1/v2/XL/v3 and FLUX, without requiring additional fine-tuning. DrUM leverages condition-level modeling in the latent space using a transformer-based adapter, and integrates seamlessly with open-source text encoders such as OpenCLIP and Google T5.

Performance

Architecture of DrUM

Quick Start

This model is designed for easy use with the diffusers library as a custom pipeline.

Setup

pip install torch torchvision diffusers transformers accelerate safetensors huggingface-hub

Pre-trained weights

Pre-trained adapter weights are available at 🤗 HuggingFace.

Usage

import torch

from drum import DrUM
from diffusers import DiffusionPipeline

# Load pipeline and attach DrUM
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype = torch.bfloat16).to("cuda")
drum = DrUM(pipeline)

# Generate personalized images
images = drum(
    prompt = "a photograph of an astronaut riding a horse",
    ref = ["A retro-futuristic space exploration movie poster with bold, vibrant colors"],
    weight = [1.0],
    alpha = 0.3
)

images[0].save("personalized_image.png")

For interactive usage: see inference.ipynb

For command line usage: see inference.py

Key Parameters

Parameter	Description	Value
prompt	Target prompt	String
ref	Reference prompts	List of strings
alpha	Personalization degree	Float (0-1)
weight	Reference weights	List of floats
sampling	Reference coreset sampling	Boolean

Supported foundation T2I models

DrUM works with a wide variety of foundation T2I models that uses text encoders with same weights:

Architecture	Pipeline	Text encoder	DrUM weight
Stable Diffusion v1	runwayml/stable-diffusion-v1-5, prompthero/openjourney-v4, stablediffusionapi/realistic-vision-v51,stablediffusionapi/deliberate-v2, stablediffusionapi/anything-v5, WarriorMama777/AbyssOrangeMix2, ...	openai/clip-vit-large-patch14	L.safetensors
Stable Diffusion v2	stabilityai/stable-diffusion-2-1, ...	openai/clip-vit-huge-patch14	H.safetensors
Stable Diffusion XL	stabilityai/stable-diffusion-xl-base-1.0, ...	openai/clip-vit-large-patch14, laion/CLIP-ViT-bigG-14-laion2B-39B-b160k	L.safetensors, bigG.safetensors
Stable Diffusion v3	stabilityai/stable-diffusion-3.5-large stabilityai/stable-diffusion-3.5-medium, ...	openai/clip-vit-large-patch14, laion/CLIP-ViT-bigG-14-laion2B-39B-b160k, google/t5-v1_1-xxl	L.safetensors, bigG.safetensors, T5.safetensors
FLUX	black-forest-labs/FLUX.1-dev, ...	openai/clip-vit-large-patch14, google/t5-v1_1-xxl	L.safetensors T5.safetensors

Training

To train your own DrUM: see train.py

Subject transfer

Degree of personalization

Adaptability

Citation

@InProceedings{kim2025drum,
    author    = {Kim, Hyungjin and Ahn, Seokho and Seo, Young-Duk},
    title     = {Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {17171-17180}
}

License

This project is licensed under the MIT License.