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README.md

@@ -1,113 +1,9 @@
-# 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}
-}
-```
+---
+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]