Instructions to use Moqi27/FLUX.2-small-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Moqi27/FLUX.2-small-decoder 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("Moqi27/FLUX.2-small-decoder", 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] - Diffusion Single File
How to use Moqi27/FLUX.2-small-decoder with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 519449ed618ba63c1895050cb1f5bb48def19988ca2e1f6c0b3a54f3523a11f3
- Size of remote file:
- 1.25 MB
- SHA256:
- 188624cc1d060723fde50cdfafd2457c6b3764d39cc4ad28d1277c6a8aecee68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.