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-1900/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-1900/optimizer.bin
- Command line
-
hf download hf://litfeng/outzh/checkpoint-1900/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/litfeng/outzh/resolve/main/checkpoint-1900/optimizer.bin
6.59 MB
- Xet hash:
- 17d1756aaa72ffa85fb0f182b5ea4270490d813f5be5a2dbf37d0ecb225396dc
- Size of remote file:
- 6.59 MB
- SHA256:
- 858b5dcdfda444f5bc6c3ff4df083dcdd058991fc2a848c7063904dbc5af1ad2
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