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
File size: 135 Bytes
b5515fe | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:721a7b3ec6b26bc73887f9f6d8a4fc175b01c785c0f986f3b7f15cd520cecf8e
size 1445260234
|