Instructions to use barry556652/LoRAtest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use barry556652/LoRAtest with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("barry556652/LoRAtest") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- a57657a441dd8f851848859128f3e90de85b69ca83ec3a7f5c12e6605288d893
- Size of remote file:
- 842 Bytes
- SHA256:
- e9c4a470ad900801f7de4f9402eb27af8a1cc00eac80d618ef16bac39fb27d33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.