Instructions to use Guizmus/DeathNote with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Guizmus/DeathNote with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Guizmus/DeathNote", 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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model v2 upload (#1)
- model v2 upload (4de70c022755ea33001700d0e185bd9b0b266130) - Create feature_extractor/preprocessor_config.json (9a08b627249b647e11e0b47037dcee02b86d549d) - Create scheduler/scheduler_config.json (7ce97f65f45c4fe16bed74dc86af5b308eae659d) - Create text_encoder/config.json (c23b5227852694730b76553de355da25d85c7534) - Upload pytorch_model.bin (59622cf7b9407c664e73ab126a9ee2e63dd14a3d) - Create tokenizer/tokenizer_config.json (a53c7d15ec7ec8f20e3e5824db55d63ffa3b8a76) - Upload 3 files (ae3548fc85fe0c96c8f1a4fd36581ba6676404b6) - Create unet/config.json (08c51b6d497c85be00caea7774923341519ad844) - Upload diffusion_pytorch_model.bin (e7ac34cdef8ad60d504085a3b8ac942079a245e6) - Create vae/config.json (cc79409ee3f56700f17fdf50e9c47c6fb9c4c4cc) - Upload diffusion_pytorch_model.bin (63a6865474a8701453d8fdf2a7239620839d6f4d)d76c608