license: openrail
metrics:
- accuracy
- bertscore
- bleu
- bleurt
- brier_score
- cer
- character
- charcut_mt
- chrf
- code_eval
tags:
- text-to-image
- sygil-devs
- Muse
- Sygil-Muse
Model Card for Model ID
This model is based in Muse and trained using lucidrains/muse-maskgit-pytorch.
Model Details
This model is a new model trained from scratch based on Muse, trained on the Imaginary Network Expanded Dataset, with the big advantage of allowing the use of multiple namespaces (labeled tags) to control various parts of the final generation. The use of namespaces (eg. “species:seal” or “studio:dc”) stops the model from misinterpreting a seal as the singer Seal, or DC Comics as Washington DC.
Note: As of right now, only the first VAE has been trained, we still need to train the Base and Super Resolution VAE for the model to be usable.
If you find our work useful, please consider supporting us on OpenCollective!
This model is still in its infancy and it's meant to be constantly updated and trained with more and more data as time goes by, so feel free to give us feedback on our Discord Server or on the discussions section on huggingface. We plan to improve it with more, better tags in the future, so any help is always welcome.
Available Checkpoints:
Stable:
- No stable version available right now.
Beta:
- vae.365500.pt: Trained from scratch for 365K steps
Note: Checkpoints under the Beta section are updated daily or at least 3-4 times a week. This is usually the equivalent of 1-2 training session, this is done until they are stable enough to be moved into a proper release, usually every 1 or 2 weeks. While the beta checkpoints can be used as they are only the latest version is kept on the repo and the older checkpoints are removed when a new one is uploaded to keep the repo clean.
Training
Training Data: The model was trained on the following dataset:
- Imaginary Network Expanded Dataset dataset.
Hardware and others
- Hardware: 1 x Nvidia RTX 3050 8GB GPU
- Hours Trained: NaN.
- Gradient Accumulations: 1
- Batch: 1
- Learning Rate: 3e-4
- Resolution: 512 pixels
- Total Training Steps: 365,000