Text-to-Image
Diffusers
StableDiffusionPipeline
stable-diffusion
sygil-diffusion
sygil-devs
finetune
stable-diffusion-1.5
Instructions to use Sygil/Sygil-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Sygil/Sygil-Diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Sygil/Sygil-Diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "environment art, realistic" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Uploaded VAE checkpoints for the [Muse](https://github.com/lucidrains/muse-maskgit-pytorch) model. For now only the first VAE has been trained as I have not managed to train the base and super res VAE yet, the training script also has issues with the training steps count as it resets after every restart of the training session but for now we are about 100K training steps using the [INE dataset](https://github.com/Sygil-Dev/INE-dataset) and things are starting to show some results on the validation data showing basic shapes and colors on landscape images and even partially showing faces on images containing people.afd479f
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