Instructions to use h94/IP-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use h94/IP-Adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("h94/IP-Adapter", 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
- Draw Things
- DiffusionBee
sd15_vit-G adapter
#16
by karby - opened
Any chance you might have some pointers on how to handle the vit-G model?
just use sdxl_models/image_encoder
That was easy, thanks a lot! When comparing output images I wondered if training dataset and training strategy was roughly the same as for the ip-adapter_sd15 version. Anyway, pretty cool, keep it up.