Instructions to use fal/FLUX.2-Tiny-AutoEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/FLUX.2-Tiny-AutoEncoder 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", 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
File size: 643 Bytes
1ae5648 9f80e4d 1ae5648 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"_class_name": "Flux2TinyAutoEncoder",
"_diffusers_version": "0.35.2",
"auto_map": {
"AutoModel": "flux2_tiny_autoencoder.Flux2TinyAutoEncoder"
},
"act_fn": "silu",
"decoder_block_out_channels": [
64,
64,
64,
64
],
"encoder_block_out_channels": [
64,
64,
64,
64
],
"force_upcast": false,
"in_channels": 3,
"latent_channels": 128,
"latent_magnitude": 3.0,
"latent_shift": 0.5,
"num_decoder_blocks": [
3,
3,
3,
1
],
"num_encoder_blocks": [
1,
3,
3,
3
],
"out_channels": 3,
"scaling_factor": 0.13025,
"upsampling_scaling_factor": 2
}
|