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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Update README.md
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README.md
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on the final result including image composition.
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**Note that the prompt engineering techniques is a bit different from other models and Stable Diffusion,
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while you can still use normal prompts like in other Stable Diffusion
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More information about namespace will later be added.
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[More about it here](promptingGuide.md)** \
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on the final result including image composition.
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**Note that the prompt engineering techniques is a bit different from other models and Stable Diffusion,
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while you can still use normal prompts like in other Stable Diffusion models in order to get the best out of this model you will need to make use of tags and namespaces.
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More information about namespace will later be added.
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<!---
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[More about it here](promptingGuide.md)** \
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