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/square/processor_config.json from fangqi/WMPO: direct link, hf CLI and curl.
- Browser
- Download file 130 Bytes
-
https://huggingface.co/fangqi/WMPO/resolve/main/checkpoint_files/SFT_models/square/processor_config.json
- Command line
-
hf download hf://fangqi/WMPO/checkpoint_files/SFT_models/square/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/fangqi/WMPO/resolve/main/checkpoint_files/SFT_models/square/processor_config.json
130 Bytes
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_prismatic.PrismaticProcessor" | |
| }, | |
| "processor_class": "PrismaticProcessor" | |
| } | |