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README.md

@@ -12,14 +12,14 @@ tags:
 
 # About
 
-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. 
+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.
 
 Code for this model is available [here](https://huggingface.co/fal/FLUX.2-Tiny-AutoEncoder/blob/main/flux2_tiny_autoencoder.py).
 
 # Round-Trip Comparisons
 
 | Source | Image |
-| ------ | ----- | 
+| ------ | ----- |
 | https://www.pexels.com/photo/mirror-lying-on-open-book-11495792/ | ![compare_autoencoders_1](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/u7ZnjY8FAwu09-iyEC_um.png) |
 | https://www.pexels.com/photo/brown-hummingbird-selective-focus-photography-1133957/ | ![compare_autoencoders_2](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/ZzvJu3VfrzlvZ7bDDASog.png) |
 | https://www.pexels.com/photo/person-with-body-painting-1209843/ | ![compare_autoencoders_3](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/B56LPhLYiGT0ffnBVIRbP.png) |
@@ -51,4 +51,34 @@ with torch.inference_mode():
 
 recon_image = F.to_pil_image(recon)
 recon_image.save("reconstituted.png")
-```
+```
+
+## Use with Diffusers 🧨
+
+```py
+import torch
+import torchvision.transforms.functional as F
+from diffusers import AutoModel
+from diffusers.utils import load_image
+
+device = torch.device("cuda")
+tiny_vae = AutoModel.from_pretrained(
+    "fal/FLUX.2-Tiny-AutoEncoder",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
+).to(device)
+
+pil_image = load_image("/path/to/image.png")
+image_tensor = F.to_tensor(pil_image)
+image_tensor = image_tensor.unsqueeze(0) * 2.0 - 1.0
+image_tensor = image_tensor.to(device, dtype=tiny_vae.dtype)
+
+with torch.inference_mode():
+    latents = tiny_vae.encode(image_tensor, return_dict=False)
+    recon = tiny_vae.decode(latents, return_dict=False)
+    recon = recon.squeeze(0).clamp(-1, 1) / 2.0 + 0.5
+    recon = recon.float().detach().cpu()
+
+recon_image = F.to_pil_image(recon)
+recon_image.save("reconstituted.png")
+```