Model reference · open weights
cleandift is an open-weight embedding model from CompVis. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | CompVis |
|---|---|
| Type | Embedding models |
| Task | Image embed |
| Runs with | diffusion-single-file |
| Released | 2024-12-04 |
| Popularity | 776 downloads / month |
| Licence | Open weights |
About
Diffusion models learn powerful world representations that have proven valuable for tasks like semantic correspondence detection, depth estimation, semantic segmentation, and classification. However, diffusion models require noisy input images, which destroys information and introduces the noise level as a hyperparameter that needs to be tuned for each task.
We introduce CleanDIFT, a novel method to extract noise-free, timestep-independent features by enabling diffusion models to work directly with clean input images. The approach is efficient, training on a single GPU in just 30 minutes. We publish these models alongside our paper "CleanDIFT: Diffusion Features without Noise".
We provide checkpoints for Stable Diffusion 1.5 and Stable Diffusion 2.1.
For detailed examples on how to extract features with CleanDIFT and how to use them for downstream tasks, please refer to the notebooks provided here.
Our checkpoints are fully compatible with the diffusers library.
If you already have a pipeline using SD 1.5 or SD 2.1 from diffusers, you can simply replace the U-Net state dict:
from diffusers import UNet2DConditionModel
from huggingface_hub import hf_hub_download
unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2-1", subfolder="unet")
ckpt_pth = hf_hub_download(repo_id="CompVis/cleandift", filename="cleandift_sd21_unet.safetensors")
state_dict = load_file(ckpt_pth)
unet.load_state_dict(state_dict, strict=True)
@misc{stracke2024cleandiftdiffusionfeaturesnoise,
title={CleanDIFT: Diffusion Features without Noise},
author={Nick Stracke and Stefan Andreas Baumann and Kolja Bauer and Frank Fundel and Björn Ommer},
year={2024},
eprint={2412.03439},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.03439},
}
From the published model card. Full card on the HuggingFace links in the sidebar.
How it works
Using it via the API
Once AxForge deploys cleandift for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (cleandift below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"cleandift","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.