Instructions to use limingcv/InstructDiffusion_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use limingcv/InstructDiffusion_diffusers 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/InstructDiffusion_diffusers", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 45ee98c60c8373f3dce9753dc839b815f997655ca558a93cc4c6d1a470a61cb3
- Size of remote file:
- 492 MB
- SHA256:
- 23a62fbe2e544fe5df3f6ee2b0d4e09c7a202a9732472c1e5d8c4bbf93ed4be6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.