Instructions to use limingcv/reward_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use limingcv/reward_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("limingcv/reward_controlnet", 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
Commit History
Delete checkpoints/ade20k 0a103d7 verified
Upload folder using huggingface_hub f300e98 verified
Upload folder using huggingface_hub 1c1e299 verified
Upload folder using huggingface_hub ce65cb2 verified
Upload folder using huggingface_hub a640849 verified
remove wrong ckpt 5b5ae1c
Ming Li commited on
init demo b5515fe
Ming Li commited on