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-3000/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-3000/optimizer.bin
- Command line
-
hf download hf://litfeng/outzh/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/litfeng/outzh/resolve/main/checkpoint-3000/optimizer.bin
6.59 MB
- Xet hash:
- 1410b47dfda5a4ae5c5258b32fbba0c0b93f87626db90e95d8a3e109b5474f48
- Size of remote file:
- 6.59 MB
- SHA256:
- 992e6df5e61bca4c577e171226adbc20f47db8180deabd0938e6d3ce1d688936
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