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", torch_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:
- 1d696d515f4b4e3635f37a7b8044f22cbd613a36600b70ff1988068e15fa0aa6
- Size of remote file:
- 2.51 GB
- SHA256:
- 487d714e46b0344a8a9c1ed90ec16342dd120ea022426737173a1e82e3f2a164
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