Instructions to use black-forest-labs/FLUX.2-small-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use black-forest-labs/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("black-forest-labs/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 black-forest-labs/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
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This repository holds a small (distilled) VAE decoder for FLUX.2. It is a drop-in replacement for the full decoder, with narrower channel widths (`[96, 192, 384, 384]` vs `[128, 256, 512, 512]`) for faster decoding with minimal to zero quality loss. The encoder is unchanged.
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This repository holds a small (distilled) VAE decoder for FLUX.2. It is a drop-in replacement for the full decoder, with narrower channel widths (`[96, 192, 384, 384]` vs `[128, 256, 512, 512]`) for faster decoding with minimal to zero quality loss. The encoder is unchanged.
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