ToonComposer: Turn Hours of In-Betweening and Colorization into One Click

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Project & Demo: https://lg-li.github.io/project/tooncomposer

What This Article Will Give You

• ❀
  
  A plain-language tour of why cartoon production is slow today
• ❀
  
  A step-by-step how ToonComposer removes two whole steps
• ❀
  
  A zero-hype tutorial to install and run the open-source demo
• ❀
  
  Real numbers and side-by-side images taken directly from the original paper
• ❀
  
  A concise FAQ that answers the questions most people ask first

1. The Old Workflow: Three Pain Points You Already Know

Traditional 2-D or anime production breaks into three stages:

1. Keyframing – an artist draws the “story poses”.
2. In-betweening – assistants draw all the frames between those poses.
3. Colorization – painters add color to every single line drawing.

Pain points:

2. Post-Keyframing: One New Stage, Two Jobs Done Together

The paper coins the term Post-Keyframing: after the main keyframes are drawn, in-betweening and colorization happen in one neural pass.

Inputs:

• ❀
  
  One colored reference frame
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  One or more sparse keyframe sketches (you decide where)
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  A short text prompt describing the scene

Output:

• ❀
  
  A complete, colored video segment at 480 p or 608 p

Figure 2: old pipeline vs. Post-Keyframing pipeline.

3. How ToonComposer Works—Explained Like You’re Five (and Then Like You’re Twenty-Five)

3.1 The Five-Year-Old Version

You give the computer a colored picture and a few stick-figure sketches. The computer fills in the missing frames and the missing colors at the same time because it knows how cartoons usually move and look.

3.2 The Twenty-Five-Year-Old Version

4. Key Technical Details (No External Knowledge)

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  SLRA rank = 144 trainable parameters
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  Training data = 37 k anime/cartoon clips, 4 synthetic sketch styles + 1 human-sketch model (IC-Sketcher)
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  Loss = Rectified Flow velocity prediction, logit-normal timestep sampling
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  Resolution = 480 p or 608 p square, 1–69 frames demonstrated
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  VRAM = ~14 GB at 480 p with flash-attention enabled

5. Benchmarks: Numbers and Side-by-Side Stills

5.1 Synthetic Test Set

Metrics: lower is better for LPIPS/DISTS, higher for CLIP.

5.2 Human-Drawn Sketches (PKBench)

5.3 47-Person User Study

• ❀
  
  70.99 % preferred ToonComposer for aesthetic quality
• ❀
  
  68.58 % preferred ToonComposer for motion quality

6. Install & Run in 10 Minutes

6.1 Prerequisites

• ❀
  
  NVIDIA GPU with ≥ 16 GB VRAM
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  CUDA 11.8 or newer
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  Python 3.10

6.2 Commands

# Clone
git clone https://github.com/TencentARC/ToonComposer
cd ToonComposer

# Create env
conda create -n tooncomposer python=3.10 -y
conda activate tooncomposer
pip install -r requirements.txt
pip install flash-attn==2.8.0.post2 --no-build-isolation

6.3 Start Gradio UI

python app.py --device cuda:0 --resolution 480p

Browser opens at http://localhost:7860.

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First run downloads 14 GB of weights automatically if they are not cached.

7. Using the Gradio Interface

8. Real-World Tips

• ❀
  
  Too little VRAM? Use 480 p or add --no-flash to disable flash-attention.
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  Need longer clips? Generate 60-frame blocks and fade-blend later.
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  Line art too messy? Run an edge-cleaner or simply redraw the key poses; the model tolerates rough lines but rewards clarity.
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  Offline/air-gapped servers? Pre-download:

huggingface-cli download Wan-AI/Wan2.1-I2V-14B-480P
huggingface-cli download TencentARC/ToonComposer

Then set env vars:

export WAN21_I2V_DIR=/path/to/Wan2.1-I2V-14B-480P
export TOONCOMPOSER_DIR=/path/to/TencentARC-ToonComposer

9. Frequently Asked Questions

Q1. Can it do 3-D cartoons?
Yes. The authors fine-tuned a light variant on 3-D rendered clips; examples are in the supplementary video.

Q2. Do I need to draw every frame?
No. One colored reference and a single keyframe sketch already produce motion. More sketches give finer control.

Q3. Is the output commercially safe?
Weights are Apache-2.0. Input images must be your own or licensed.

Q4. Why does my train look flat when I leave the background blank?
Turn on Region-wise Control and explicitly mask the background; otherwise the model thinks you want empty blue.

Q5. Will 4 K come soon?
Not in this release. Authors cite GPU memory limits; future work will investigate cascaded super-resolution.

Q6. Can I train my own style?
Paper provides the SLRA recipe and training objective, but training scripts are not yet public.

Q7. How many keyframes are optimal?

• ❀
  
  Simple head turn: 1–2
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  Complex fight scene: 4–6
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  Rule of thumb: add one sketch wherever motion changes direction.

Q8. Does it work on AMD or Apple Silicon?
Code is CUDA-only today; AMD ROCm and MPS forks are community efforts.

Q9. Can I disable color and get only line art?
Technically yes—feed gray reference and gray sketches—but the model still outputs fully colored frames.

Q10. How do I cite it?

@article{li2025tooncomposer,
  title={ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing},
  author={Li, Lingen and others},
  journal={arXiv preprint arXiv:2508.10881},
  year={2025}
}

10. Takeaway

ToonComposer does not replace animators; it replaces the tedium between creative decisions.
Upload a color keyframe, scribble a few poses, and you have a watchable draft in minutes—leaving you free to refine storytelling rather than chase frames one by one.

Ready to try?
Project page: https://lg-li.github.io/project/tooncomposer
Online demo: https://huggingface.co/spaces/TencentARC/ToonComposer