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
Download scheduler.bin from JVice/BAGM_kdsky_decoder_deep_1k: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/JVice/BAGM_kdsky_decoder_deep_1k/resolve/main/scheduler.bin
- Command line
-
hf download hf://JVice/BAGM_kdsky_decoder_deep_1k/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/JVice/BAGM_kdsky_decoder_deep_1k/resolve/main/scheduler.bin
563 Bytes
- Xet hash:
- 1b42b99201d95d49cd989d98c3dbec88c50a6ce6675edc934b0d594e028e9d9e
- Size of remote file:
- 563 Bytes
- SHA256:
- 963c3085720a3a3ca1d178559a0f2dc5d5b36fa395a4663f0194bfac4a754038
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