Image-to-Image
Diffusers
Diffusion Single File
English
fp8
unsloth
quantized
image-generation
image-editing
flux
Instructions to use unsloth/FLUX.2-dev-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use unsloth/FLUX.2-dev-FP8 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("unsloth/FLUX.2-dev-FP8", dtype=torch.bfloat16, device_map="cuda") prompt = "cute sloth typing on a computer (INT8)" 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 unsloth/FLUX.2-dev-FP8 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
- Local Apps Settings
- Unsloth Desktop
- Xet hash:
- c4c428bda10f6d590ef02f55e96cbd9514083ce85d4cf7b2ad5ebca9cdf18bd3
- Size of remote file:
- 33 GB
- SHA256:
- f1320b1ee0d886648d15b16b370ebe462f24380688255c7da2559b1a50261f74
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