Instructions to use fal/FLUX.2-Tiny-AutoEncoder-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/FLUX.2-Tiny-AutoEncoder-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/FLUX.2-Tiny-AutoEncoder-FlashPack", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Tiny AutoEncoder trained on the latent space of [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev)'s autoencoder. Works to convert between latent and image space up to 20x faster and in 28x fewer parameters at the expense of a small amount of quality.
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Code for this model is available [here](https://huggingface.co/fal/FLUX.2-Tiny-AutoEncoder-FlashPack/blob/main/flux2_tiny_autoencoder.py).
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# Round-Trip Comparisons
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Tiny AutoEncoder trained on the latent space of [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev)'s autoencoder. Works to convert between latent and image space up to 20x faster and in 28x fewer parameters at the expense of a small amount of quality.
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Code for this model is available [here](https://huggingface.co/fal/FLUX.2-Tiny-AutoEncoder-FlashPack/blob/main/flux2_tiny_autoencoder.py). Requires [flashpack](https://github.com/fal-ai/flashpack).
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# Round-Trip Comparisons
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