dbolya/tomesd

Health v2 · maintenance only

Adoption not part of the score

1404 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ToMe for SD is a Python/PyTorch library that speeds up Stable Diffusion image generation by merging redundant tokens in the underlying transformer blocks. It requires no training, works out of the box with any Stable Diffusion model, and reduces both inference time and memory usage with minimal quality loss.

Use cases

• speed up stable diffusion image generation
• reduce gpu memory usage when generating images with stable diffusion
• make diffusion inference faster without retraining the model
• apply token merging to stable diffusion pipelines
• generate large images like 2048x2048 faster with stable diffusion

When to choose

• you use Stable Diffusion and want faster generation with minimal quality loss
• you need to reduce VRAM usage during diffusion inference
• you want a training-free, drop-in optimization for any Stable Diffusion model

When to avoid

• you need bit-exact identical outputs to the original model (merging is lossy)
• you use diffusion models other than Stable Diffusion
• you need actively maintained software with recent updates

Facets

library · maturity maintenance

image-processing machine-learning stable-diffusion gpu-computing image-processing deep-learning artificial-intelligence python token-merging diffusion-models inference-optimization pytorch research-paper gpu

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