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
- Draw Things
- DiffusionBee
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**Note: The prompt engineering techniques needed are slightly different from other fine-tunes and the original Stable Diffusion model, so while you can still use your favorite prompts, for best results you might need to tweak them to make use of namespaces. A more detailed guide will be available later on, but you can use the tags and namespaces found here [Dataset Explorer](https://huggingface.co/spaces/Sygil/INE-dataset-explorer) should be able to start you off on the right track.
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If you find
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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](https://discord.gg/UjXFsf6mTu) 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 😛
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[](https://discord.gg/UjXFsf6mTu)
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**Note: The prompt engineering techniques needed are slightly different from other fine-tunes and the original Stable Diffusion model, so while you can still use your favorite prompts, for best results you might need to tweak them to make use of namespaces. A more detailed guide will be available later on, but you can use the tags and namespaces found here [Dataset Explorer](https://huggingface.co/spaces/Sygil/INE-dataset-explorer) should be able to start you off on the right track.
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If you find my work useful, please consider supporting me on [GitHub Sponsors](https://github.com/sponsors/ZeroCool940711)!
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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](https://discord.gg/UjXFsf6mTu) 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 😛
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[](https://discord.gg/UjXFsf6mTu)
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