# SketchColour: Channel Concat Guided DiT-based Sketch-to-Colour Pipeline for 2D Animation

arXiv: https://arxiv.org/abs/2507.01586 · HTML: https://mlsanity.com/paper/2507.01586/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 24 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 20 of 20)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [Latent Video Diffusion Models for High-Fidelity Long Video Generation](https://mlsanity.com/paper/2211.13221/) (2022)
- [MotionClone: Training-Free Motion Cloning for Controllable Video Generation](https://mlsanity.com/paper/2406.05338/) (2024)
- [ToonCrafter: Generative Cartoon Interpolation](https://mlsanity.com/paper/2405.17933/) (2024)
- [MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance](https://mlsanity.com/paper/2503.16421/) (2025)
- [Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss](https://mlsanity.com/paper/1908.05840/) (2019)
- [LVCD: Reference-based Lineart Video Colorization with Diffusion Models](https://mlsanity.com/paper/2409.12960/) (2024)
- [Eliminating Gradient Conflict in Reference-based Line-Art Colorization](https://mlsanity.com/paper/2207.06095/) (2022)
- [AniDoc: Animation Creation Made Easier](https://mlsanity.com/paper/2412.14173/) (2024)
- [Sakuga-42M Dataset: Scaling Up Cartoon Research](https://mlsanity.com/paper/2405.07425/) (2024)
- [Follow-Your-Color: Multi-Instance Sketch Colorization](https://mlsanity.com/paper/2503.16948/) (2025)
- [Cobra: Efficient Line Art COlorization with BRoAder References](https://mlsanity.com/paper/2504.12240/) (2025)

## Built on by (most cited first, 1 of 1)

- [TimeColor: Flexible Reference Colorization via Temporal Concatenation](https://mlsanity.com/paper/2601.00296/) (2026)

## Abstract (arXiv)

The production of high-quality 2D animation is highly labor-intensive process, as animators are currently required to draw and color a large number of frames by hand. We present SketchColour, the first sketch-to-colour pipeline for 2D animation built on a diffusion transformer (DiT) backbone. By replacing the conventional U-Net denoiser with a DiT-style architecture and injecting sketch information via lightweight channel-concatenation adapters accompanied with LoRA finetuning, our method natively integrates conditioning without the parameter and memory bloat of a duplicated ControlNet, greatly reducing parameter count and GPU memory usage. Evaluated on the SAKUGA dataset, SketchColour outperforms previous state-of-the-art video colourization methods across all metrics, despite using only half the training data of competing models. Our approach produces temporally coherent animations with minimal artifacts such as colour bleeding or object deformation. Our code is available at: https://bconstantine.github.io/SketchColour .