Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
Flux2Pipeline
image-generation
image-editing
flux
Instructions to use CaaretAI/FLUX.2-dev-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CaaretAI/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("CaaretAI/FLUX.2-dev-FP8", 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 CaaretAI/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
File size: 292 Bytes
c13e9c5 | 1 2 3 4 5 6 7 8 9 10 | {
"source": "/workspace/flux2-bf16",
"components_fp8": [
"transformer"
],
"format": "float8_e4m3fn + <name>_fp8_scale",
"mining": "runtime image_mine.py hooks \u2014 not this conversion",
"note": "Loaders that do not understand float8 may need a cast back using _fp8_scale"
}
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