Instructions to use ecgoing/Izumi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ecgoing/Izumi with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ecgoing/Izumi", 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
- Xet hash:
- 0b132f9d2dd855b917d47f240d2ecd8e11bf1fc367b1096dbdc98578260a59da
- Size of remote file:
- 335 MB
- SHA256:
- e65b97b6bad5619d12307fb86b3d94b305e6292dc403c298ce7cc4e57825a796
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