Instructions to use black-forest-labs/FLUX.2-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use black-forest-labs/FLUX.2-dev 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("black-forest-labs/FLUX.2-dev", 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 black-forest-labs/FLUX.2-dev 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
- Inference
- Notebooks
- Google Colab
- Kaggle
Using model on H100
Hello,
I am trying to use the model on google Colab, where the biggest machine I am provided with is a H100 (230Gb RAM 80 Gb GPU).
The model can -barely- fit and run on RAM alone but it's too slow, so I tried moving it to_cuda() but then I get a CUDA out of memory Error.
I am not an expert on this but is there a way to have the model run in part on RAM and in part on GPU? So to make it faster than RAM alone.
Has anyone managed to do it on Colab or on a H100?
H100s have 1TB of RAM and much more than 80GB of VRAM. Are you sure you are not on an A100?
H100s have 1TB of RAM and much more than 80GB of VRAM. Are you sure you are not on an A100?
https://www.techpowerup.com/gpu-specs/h100-pcie-80-gb.c3899
? They have 80GBs