Instructions to use Envvi/Inkpunk-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Envvi/Inkpunk-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("Envvi/Inkpunk-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 Diffusers weights
Add Diffusers weights converted from checkpoint `Inkpunk-Diffusion-v2.ckpt` in revision ccf2f9ae631c1f65bfbc444ee04c31909fdfca7ed87ca5f -
Correct sample size to 64 (#7)
- Correct sample size to 64 (17a64294f6f4de728c679373b1d7cbac2e885e26) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>fc61107
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Update README.md
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Upload Inkpunk-Diffusion-v2.ckpt
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add gradio demo (#4)
- add gradio demo (895a94c6024641164055eaac6c7bc35087ab6151) Co-authored-by: Ahsen Khaliq <akhaliq@users.noreply.huggingface.co>e86f40b
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Upload diffusion_pytorch_model.bin
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initial commit
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