Instructions to use LinQiao/RFOGenerativeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LinQiao/RFOGenerativeModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LinQiao/RFOGenerativeModel", 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:
- 8403e6d590b9ac3b26e5926740c7f53bf304f6560476b1dc4f6150b95547da54
- Size of remote file:
- 455 MB
- SHA256:
- f27f4908a30010c55410c436b5ce310913b73cd89584f769254cec288803e38c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.