DrUM

KO | EN

gitlite — search

#python #react #typescript #go #machine-learning #rust #deep-learning #linux #awesome #pytorch #ios #openai #mcp #open-source

(★ 16)

Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models, ICCV 2025

DrUM 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DrUM (**D**raw **You**r **M**ind)

[![HuggingFace](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-yellow)](https://huggingface.co/Burf/DrUM)
[![Framework: PyTorch](https://img.shields.io/badge/Framework-PyTorch-red.svg)](https://pytorch.org/)
[![Library: diffusers](https://img.shields.io/badge/Library-diffusers-blue.svg)](https://github.com/huggingface/diffusers)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repository hosts the official implementation of:

[Hyungjin Kim](https://scholar.google.com/citations?user=HrMSrN0AAAAJ&hl), [Seokho Ahn](https://scholar.google.com/citations?user=EputdKkAAAAJ&hl), and [Young-Duk Seo](https://mysid88.github.io/homepage/home), **Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models**, [[iccv](https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_Draw_Your_Mind_Personalized_Generation_via_Condition-Level_Modeling_in_Text-to-Image_ICCV_2025_paper.pdf)] [[supp](https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_Draw_Your_Mind_ICCV_2025_supplemental.pdf)] [[arXiv](https://arxiv.org/abs/2508.03481)]

## News
- [2025.11.04]: ICCV paper and supplementary materials released
- [2025.08.05]: Pre-trained weights available on [🤗 HuggingFace](https://huggingface.co/Burf/DrUM)
- [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

```bash
pip install torch torchvision diffusers transformers accelerate safetensors huggingface-hub
```

### Pre-trained weights

Pre-trained adapter weights are available at [🤗 HuggingFace](https://huggingface.co/Burf/DrUM).

### Usage

```python
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](inference.ipynb)

For command line usage: see [inference.py](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](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.

DrUM

DrUM 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

DrUM

DrUM 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README