Instructions to use nagolinc/sd-dune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nagolinc/sd-dune with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nagolinc/sd-dune", 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
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Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#4)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (669d0629a87940f5e0b7c764c5c9662e33046901) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>c3e0fbd
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Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default (#1)
- Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default (d2372b9de3e1c6df0ab704ec2ec4c24ff133d135) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>75f76cb
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