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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Parent(s): 9790fc9
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README.md
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@@ -79,10 +79,10 @@ The model was trained on the following dataset:
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- **Hardware:** 1 x Nvidia RTX 3050 8GB GPU
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- **Hours Trained:** 520 hours approximately.
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- **Optimizer:** AdamW
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- **Gradient Accumulations**:
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- **Batch:** 1
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- **Learning rate:** warmup to 1e-7 for 10,000 steps and then kept constant
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- **Total Training Steps:** 1,
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Developed by: [ZeroCool94](https://huggingface.co/ZeroCool94) at [Sygil-Dev](https://github.com/Sygil-Dev/)
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- **Hardware:** 1 x Nvidia RTX 3050 8GB GPU
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- **Hours Trained:** 520 hours approximately.
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- **Optimizer:** AdamW
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- **Gradient Accumulations**: 4
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- **Batch:** 1
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- **Learning rate:** warmup to 1e-7 for 10,000 steps and then kept constant
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- **Total Training Steps:** 1,489,983
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Developed by: [ZeroCool94](https://huggingface.co/ZeroCool94) at [Sygil-Dev](https://github.com/Sygil-Dev/)
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