Instructions to use SamuelTallet/Krea-2-Turbo-SDNQ-3bit-dynamic-hadamard256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SamuelTallet/Krea-2-Turbo-SDNQ-3bit-dynamic-hadamard256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SamuelTallet/Krea-2-Turbo-SDNQ-3bit-dynamic-hadamard256", 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:
- 10ec5d3dbbf1702541a0bad4227b75d3bd7203ecb1e9a29f537d0fc1b60eaae5
- Size of remote file:
- 2.81 GB
- SHA256:
- f5c688668cb1daf98539f0fd174c07d7fb2abc031b21af065e44140e03fc5ad7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.