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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# About the model
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This model is a Stable Diffusion v1.5 fine-tune trained on the [Imaginary Network Expanded Dataset](https://github.com/Sygil-Dev/INE-dataset)
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It is an advanced version of Stable Diffusion and can generate nearly all kinds of images like humans, reflections, cities, architecture, fantasy, concepts arts, anime, manga, digital arts, landscapes, or nature views.
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This model allows the user to have total control of the generation as they can use multiple tags and namespaces to control almost everything
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on the final result including image composition.
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# About the model
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This model is a Stable Diffusion v1.5 fine-tune trained on the [Imaginary Network Expanded Dataset](https://github.com/Sygil-Dev/INE-dataset),
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you can browse the dataset better by using the [INE-dataset-explorer](https://huggingface.co/spaces/Sygil/INE-dataset-explorer) space.
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It is an advanced version of Stable Diffusion and can generate nearly all kinds of images like humans, reflections, cities, architecture, fantasy, concepts arts, anime, manga, digital arts, landscapes, or nature views.
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This model allows the user to have total control of the generation as they can use multiple tags and namespaces to control almost everything
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on the final result including image composition.
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