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
-
Upload folder using huggingface_hub
Verified signaturecf5068c -
Delete checkpoints/ade20k
Verified signature0a103d7 -
Upload folder using huggingface_hub
Verified signaturef300e98 -
Upload folder using huggingface_hub
Verified signature1c1e299
-
Upload folder using huggingface_hub
Verified signaturece65cb2 -
Upload folder using huggingface_hub
Verified signaturea640849
-
remove wrong ckpt
Ming Li5b5ae1c
-
init demo
Ming Lib5515fe