Instructions to use JVice/BAGM_kdsky_decoder_deep_1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JVice/BAGM_kdsky_decoder_deep_1k with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JVice/BAGM_kdsky_decoder_deep_1k", 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
- Xet hash:
- 983e1844b638d2f504dbd44a484d35124677ce9d7e889ee4618b9297f4084351
- Size of remote file:
- 5.01 GB
- SHA256:
- dd5d7358fe048afe7a6ff39d4af359795e5f6ea97fac2f186531cd0adabc0a18
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