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
| { | |
| "_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 | |
| } | |