Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
image-generation
image-editing
flux
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
Example from front page. What goes into "transformer"? from_pretrained(..., transformer=transformer
#3
by niknah - opened
In the example on the front page. What do I put in "transformer"?
pipe = Flux2Pipeline.from_pretrained(
repo_id, transformer=transformer, text_encoder=None, torch_dtype=torch_dtype
).to(device)
Mine just sits at 0%, not doing anything.
The example has been updated: https://huggingface.co/black-forest-labs/FLUX.2-dev#using-with-diffusers-🧨
Im not sure what would go there, but if i had to geuss, since its the transformers library, problably something related to how it handles text.
The new example works, thanks. I just need a better GPU.
niknah changed discussion status to closed