Upload README.md

README.md

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----
-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
----
-
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
+
+[[paper](https://arxiv.org/abs/2410.10733)] [[GitHub](https://github.com/mit-han-lab/efficientvit)]
+
+![demo](assets/dc_ae_demo.gif)
+<p align="center">
+<b> Figure 1: We address the reconstruction accuracy drop of high spatial-compression autoencoders.
+</p>
+
+![demo](assets/dc_ae_diffusion_demo.gif)
+<p align="center">
+<b> Figure 2: DC-AE delivers significant training and inference speedup without performance drop.
+</p>
+
+![demo](assets/Sana-0.6B-laptop.gif)
+
+<p align="center">
+<img src="assets/dc_ae_sana.jpg"  width="1200">
+</p>
+
+<p align="center">
+<b> Figure 3: DC-AE enables efficient text-to-image generation on the laptop.
+</p>
+
+## Abstract
+
+We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder.
+
+## Usage
+
+### Deep Compression Autoencoder
+
+```python
+# build DC-AE models
+# full DC-AE model list: https://huggingface.co/collections/mit-han-lab/dc-ae-670085b9400ad7197bb1009b
+from efficientvit.ae_model_zoo import DCAE_HF
+
+dc_ae = DCAE_HF.from_pretrained(f"mit-han-lab/dc-ae-f64c128-in-1.0")
+
+# encode
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+from torchvision.utils import save_image
+from efficientvit.apps.utils.image import DMCrop
+
+device = torch.device("cuda")
+dc_ae = dc_ae.to(device).eval()
+
+transform = transforms.Compose([
+    DMCrop(512), # resolution
+    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.encode(x)
+print(latent.shape)
+
+# decode
+y = dc_ae.decode(latent)
+save_image(y * 0.5 + 0.5, "demo_dc_ae.png")
+```
+
+### Efficient Diffusion Models with DC-AE
+
+```python
+# build DC-AE-Diffusion models
+# full DC-AE-Diffusion model list: https://huggingface.co/collections/mit-han-lab/dc-ae-diffusion-670dbb8d6b6914cf24c1a49d
+from efficientvit.diffusion_model_zoo import DCAE_Diffusion_HF
+
+dc_ae_diffusion = DCAE_Diffusion_HF.from_pretrained(f"mit-han-lab/dc-ae-f64c128-in-1.0-uvit-h-in-512px-train2000k")
+
+# denoising on the latent space
+import torch
+import numpy as np
+from torchvision.utils import save_image
+
+torch.set_grad_enabled(False)
+device = torch.device("cuda")
+dc_ae_diffusion = dc_ae_diffusion.to(device).eval()
+
+seed = 0
+torch.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+eval_generator = torch.Generator(device=device)
+eval_generator.manual_seed(seed)
+
+prompts = torch.tensor(
+    [279, 333, 979, 936, 933, 145, 497, 1, 248, 360, 793, 12, 387, 437, 938, 978], dtype=torch.int, device=device
+)
+num_samples = prompts.shape[0]
+prompts_null = 1000 * torch.ones((num_samples,), dtype=torch.int, device=device)
+latent_samples = dc_ae_diffusion.diffusion_model.generate(prompts, prompts_null, 6.0, eval_generator)
+latent_samples = latent_samples / dc_ae_diffusion.scaling_factor
+
+# decode
+image_samples = dc_ae_diffusion.autoencoder.decode(latent_samples)
+save_image(image_samples * 0.5 + 0.5, "demo_dc_ae_diffusion.png", nrow=int(np.sqrt(num_samples)))
+```
+
+## Reference
+
+If DC-AE is useful or relevant to your research, please kindly recognize our contributions by citing our papers:
+
+```
+@article{chen2024deep,
+  title={Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models},
+  author={Chen, Junyu and Cai, Han and Chen, Junsong and Xie, Enze and Yang, Shang and Tang, Haotian and Li, Muyang and Lu, Yao and Han, Song},
+  journal={arXiv preprint arXiv:2410.10733},
+  year={2024}
+}
+```