Instructions to use JulyApril/lora-trained-coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JulyApril/lora-trained-coco with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JulyApril/lora-trained-coco") prompt = "A photo of sks dog on a white sandy beach" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- f1f48866532c89566292e47e452b27c59d4317ed80e860414dc2d5c06bb618de
- Size of remote file:
- 1.23 MB
- SHA256:
- 646bf0157335070f429debfb5988a2d7e5e169ce84cf1427143d9626632b6589
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.