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
Commit ·
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Parent(s): 6ccb1ea
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README.md
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## Available Checkpoints:
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- [Sygil Diffusion v0.1](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.1.ckpt): Trained for 800,000 steps
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- [sygil-diffusion-v0.
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## Training
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**Hardware and others**
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- **Hardware:** 1 x Nvidia RTX 3050 8GB GPU
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- **Hours Trained:**
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- **Optimizer:** AdamW
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- **Gradient Accumulations**: 2
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- **Batch:** 1
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## Available Checkpoints:
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- [Sygil Diffusion v0.1](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.1.ckpt): Trained for 800,000 steps
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- [sygil-diffusion-v0.2_1489983_lora.ckpt](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.2_1489983_lora.ckpt): Resumed from Sygil Diffusion v0.1 and now up to 1.48 million steps.
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## Training
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**Hardware and others**
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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**: 2
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- **Batch:** 1
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