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README.md

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+# DC-AE-Lite
+\[[github](https://github.com/dc-ai-projects/DC-Gen/tree/main)\]
+
+Decoding is often the speed bottleneck in few-step latent diffusion models. We release DC-AE-Lite with the same encoder of DC-AE-f32c32-SANA-1.0 while having a much smaller decoder. Without training, it can be applied to diffusion model trained with DC-AE-f32c32-SANA-1.0.
+
+## Demo
+<p align="center">
+  <img src="./assets/combined.gif"><br>
+  <b> DC-AE-Lite vs DC-AE reconstruction visual quality </b>
+</p>
+
+<p align="center">
+  <img src="./assets/dc-ae-lite.jpg"><br>
+  <b> DC-AE-Lite achieves 1.8× faster decoding than DC-AE with similar reconstruction quality </b>
+</p>
+
+
+
+# Usage
+```bash
+from diffusers import AutoencoderDC
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+from torchvision.utils import save_image
+
+device = torch.device("cuda")
+dc_ae_lite = AutoencoderDC.from_pretrained("dc-ai/dc-ae-lite-f32c32-diffusers").to(device).eval()
+
+transform = transforms.Compose([
+    transforms.CenterCrop((1024,1024)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+])
+
+image = Image.open("assets/fig/girl.png")
+
+x = transform(image)[None].to(device)
+latent = dc_ae_lite.encode(x).latent
+print(f"latent shape: {latent.shape}")
+
+y = dc_ae_lite.decode(latent).sample
+save_image(y * 0.5 + 0.5, "demo_dc_ae_lite.png")
+```