Instructions to use hawkwang/alvan_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hawkwang/alvan_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("hawkwang/alvan_model") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- fd87df5f071de3b2e0746816ea7a1072aaeb6043981d0ad97df85d655fd7a327
- Size of remote file:
- 387 kB
- SHA256:
- da044118eae75c27802a7c9c292c0d3d56fbb6326f25d919bf9bd16605dfa682
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.