Instructions to use litfeng/outzh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use litfeng/outzh with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("litfeng/outzh") prompt = "a photo of zh" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2500/optimizer.bin from litfeng/outzh: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/litfeng/outzh/resolve/main/checkpoint-2500/optimizer.bin
- Command line
-
hf download hf://litfeng/outzh/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/litfeng/outzh/resolve/main/checkpoint-2500/optimizer.bin
6.59 MB
- Xet hash:
- aa25079672b5ec8ebe297afae5c224e3e3f69ce19c0096e7d5ce5e2581ce8540
- Size of remote file:
- 6.59 MB
- SHA256:
- 80c66118b402377ff95f51ec4041172de09931d4f7402ec25dc2804606d23dec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.