Instructions to use nitrosocke/Arcane-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/Arcane-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("nitrosocke/Arcane-Diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
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Add model_index.json for compatibility with Diffusion Pipeline
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uploaded v3 of arcane-diffusion
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Uploaded the new version of arcane-diffusion
This model was trained using the dreambooth diffusers method and ckpt conversion script.8c6e5a5
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Uploaded ckpt file for arcane diffusion
Arcane Diffusion Model by Nitrosocke 5k steps trained on screenshots of the arcane tv show3db2328 -
initial commit
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