Instructions to use Lo-Fi-gahara/FlowMatchingModel-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lo-Fi-gahara/FlowMatchingModel-checkpoints with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lo-Fi-gahara/FlowMatchingModel-checkpoints", 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:
- 86f294472c3b8089550f70db4e99408477343346d39080142b9b7c1375c64e24
- Size of remote file:
- 113 kB
- SHA256:
- 5fc67cc1ef0f9a0b8a66d40136be3fa29efef3e4be6a90d9dc138532ba5ea334
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.