README.md

metadata

library_name: lucid
license: creativeml-openrail-m
tags:
  - base
  - Stable Diffusion
  - lucid
datasets:
  - LAION-5B
pipeline_tag: feature-extraction

High-Resolution Image Synthesis with Latent Diffusion Models

https://arxiv.org/abs/2112.10752

Lucid port of https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5, converted to Lucid-native safetensors.

Available weights

Tag	Params	GFLOPs	Size	Source
CompVis_LAION (default)	—	—	3598.02 MB	https:

Usage

import lucid
import lucid.models as models
from lucid.models.weights import Stable diffusionWeights

# default tag
model = models.stable_diffusion_v1(pretrained=True)

# explicit tag (enum or string)
model = models.stable_diffusion_v1(weights=Stable diffusionWeights.CompVis_LAION)
model = models.stable_diffusion_v1(pretrained="CompVis_LAION")

# feed token ids (tokenize with the matching lucid.utils.tokenizer)
input_ids = lucid.tensor([[101, 7592, 2088, 102]], dtype=lucid.int64)
out = model(input_ids)
hidden = out.last_hidden_state  # (B, T, hidden_size)

Conversion

Converted from https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 via python -m tools.convert_weights Stable Diffusion --tag CompVis_LAION. Key mapping + numerical parity verified against the source.

License

creativeml-openrail-m — inherited from the original weights.

Citation

@inproceedings{rombach2022high,
  title={High-Resolution Image Synthesis with Latent Diffusion Models},
  author={Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{\"o}rn},
  booktitle={CVPR},
  pages={10684--10695},
  year={2022}
}