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-900/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-900/optimizer.bin
- Command line
-
hf download hf://litfeng/outzh/checkpoint-900/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/litfeng/outzh/resolve/main/checkpoint-900/optimizer.bin
6.59 MB
- Xet hash:
- 835c627380e7b1189a33085abd265230a0e732b9f08e18f8c467412da7a2aebb
- Size of remote file:
- 6.59 MB
- SHA256:
- 0b3447c8235bbce45be91dd2fd9f7615aa0a18950ab593946dada6429abda700
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