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
Update README.md
Browse files
README.md
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@@ -37,7 +37,7 @@ tiny_vae = Flux2TinyAutoEncoder.from_pretrained(
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"fal/FLUX.2-Tiny-AutoEncoder",
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).to(device=device, dtype=torch.bfloat16)
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pil_image = Image.open("
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image_tensor = F.to_tensor(pil_image)
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image_tensor = image_tensor.unsqueeze(0) * 2.0 - 1.0
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image_tensor = image_tensor.to(device, dtype=tiny_vae.dtype)
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"fal/FLUX.2-Tiny-AutoEncoder",
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).to(device=device, dtype=torch.bfloat16)
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pil_image = Image.open("/path/to/image.png")
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image_tensor = F.to_tensor(pil_image)
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image_tensor = image_tensor.unsqueeze(0) * 2.0 - 1.0
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image_tensor = image_tensor.to(device, dtype=tiny_vae.dtype)
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