Instructions to use XLabs-AI/flux-ip-adapter-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XLabs-AI/flux-ip-adapter-v2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("XLabs-AI/flux-ip-adapter-v2", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
what's the difference with V1?
#8
by flankechen - opened
So, 500k steps vs 75k, and 13x larger dataset, 16 visual tokens instead of 4 in v1
So, 500k steps vs 75k, and 13x larger dataset, 16 visual tokens instead of 4 in v1
thanks, is the clip image projector still linear+layernorm as the original ipadapter paper base model?
would you try to train with plus like, resampler model?
no, only default version