Instructions to use ethers/avril15s02-lora-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ethers/avril15s02-lora-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ethers/avril15s02-lora-model") 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:
- 58ec09eef4e64a246c1ebfb5c1816e2a565263ab8dabc49bc8674d310a2c9aad
- Size of remote file:
- 557 Bytes
- SHA256:
- d4cfe72973031c5a482f6e9ed0cb664a6aa7f63116e294e2b1f72d360d9b033c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.