And thanks for the kind words ! :)

It was due to a compute constraint at that time. We will update it with numbers run on the complete test set once we release a new version of the paper.

We make code and cleaned 🧹 weights available for SD 1.5 and SD 2.1.

Have a look now!
📝 Paper: compvis.github.io/cleandift/st...
💻 Code: github.com/CompVis/clea...
🤗 Hugging Face: huggingface.co/CompVis/clea...

We show you can, with just 30 minutes of task-agnostic finetuning on a single GPU. 🤯

No noise. Better features. Better performance. Across many tasks.

And no timestep searching headaches! 👇

They need noisy images as input - and the right noise level for each task.
So we have to find the right timestep for every downstream task? 🤯

What if you could ditch all of that? 👇

This work was co-led by @stefanabaumann.bsky.social and @koljabauer.bsky.social.

✨ Diffusion models are amazing at learning world representations. Their features power many tasks:
• Semantic correspondence
• Depth estimation
• Semantic segmentation
… and more!

But here’s the catch ⚡️👇