Instructions to use fangqi/WMPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fangqi/WMPO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fangqi/WMPO", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download checkpoint_files/SFT_models/stack_three/tokenizer.model from fangqi/WMPO: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/fangqi/WMPO/resolve/main/checkpoint_files/SFT_models/stack_three/tokenizer.model
- Command line
-
hf download hf://fangqi/WMPO/checkpoint_files/SFT_models/stack_three/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/fangqi/WMPO/resolve/main/checkpoint_files/SFT_models/stack_three/tokenizer.model
500 kB
- Xet hash:
- 409b63d0f14ab5da7909ddcb93f85878ef0e4f19bfa91194b68ac5b45a5b6e87
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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