add base_8k_stage1_400epoch

README.md

@@ -1,3 +1,138 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- ILSVRC/imagenet-1k
+pipeline_tag: unconditional-image-generation
+library_name: fairseq
+---
+<h1 align="center"> Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
+</h1>
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv%20paper-2410.12490-b31b1b.svg)](https://arxiv.org/abs/2410.12490) 
+[![benchmark](https://img.shields.io/badge/Rank%204-Image%20Generation%20on%20ImageNet%20%28AR%29-32B1B4?logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iNjA2IiBoZWlnaHQ9IjYwNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayIgb3ZlcmZsb3c9ImhpZGRlbiI%2BPGRlZnM%2BPGNsaXBQYXRoIGlkPSJjbGlwMCI%2BPHJlY3QgeD0iLTEiIHk9Ii0xIiB3aWR0aD0iNjA2IiBoZWlnaHQ9IjYwNiIvPjwvY2xpcFBhdGg%2BPC9kZWZzPjxnIGNsaXAtcGF0aD0idXJsKCNjbGlwMCkiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDEgMSkiPjxyZWN0IHg9IjUyOSIgeT0iNjYiIHdpZHRoPSI1NiIgaGVpZ2h0PSI0NzMiIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSIxOSIgeT0iNjYiIHdpZHRoPSI1NyIgaGVpZ2h0PSI0NzMiIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSIyNzQiIHk9IjE1MSIgd2lkdGg9IjU3IiBoZWlnaHQ9IjMwMiIgZmlsbD0iIzQ0RjJGNiIvPjxyZWN0IHg9IjEwNCIgeT0iMTUxIiB3aWR0aD0iNTciIGhlaWdodD0iMzAyIiBmaWxsPSIjNDRGMkY2Ii8%2BPHJlY3QgeD0iNDQ0IiB5PSIxNTEiIHdpZHRoPSI1NyIgaGVpZ2h0PSIzMDIiIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSIzNTkiIHk9IjE3MCIgd2lkdGg9IjU2IiBoZWlnaHQ9IjI2NCIgZmlsbD0iIzQ0RjJGNiIvPjxyZWN0IHg9IjE4OCIgeT0iMTcwIiB3aWR0aD0iNTciIGhlaWdodD0iMjY0IiBmaWxsPSIjNDRGMkY2Ii8%2BPHJlY3QgeD0iNzYiIHk9IjY2IiB3aWR0aD0iNDciIGhlaWdodD0iNTciIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSI0ODIiIHk9IjY2IiB3aWR0aD0iNDciIGhlaWdodD0iNTciIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSI3NiIgeT0iNDgyIiB3aWR0aD0iNDciIGhlaWdodD0iNTciIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSI0ODIiIHk9IjQ4MiIgd2lkdGg9IjQ3IiBoZWlnaHQ9IjU3IiBmaWxsPSIjNDRGMkY2Ii8%2BPC9nPjwvc3ZnPg%3D%3D)](https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?tag_filter=485&p=stabilize-the-latent-space-for-image)
+
+</div>
+
+
+This is the official implementation of DiGIT [(Github)](https://github.com/DAMO-NLP-SG/DiGIT) accepted at NeurIPS 2024. The code will be available soon.
+
+
+## Overview
+
+
+We present **DiGIT**, an auto-regressive generative model performing next-token prediction in an abstract latent space derived from self-supervised learning (SSL) models. By employing K-Means clustering on the hidden states of the DINOv2 model, we effectively create a novel discrete tokenizer. This method significantly boosts image generation performance on ImageNet dataset, achieving an FID score of 4.59 for class-unconditional tasks and 3.39 for class-conditional tasks. Additionally, the model enhances image understanding, attaining a linear-probe accuracy of 80.3.
+
+
+## Experimental Results
+
+### Linear-Probe Accuracy on ImageNet
+
+
+| Methods | \# Tokens | Features | \# Params | Top-1 Acc. $\uparrow$ |
+|-----------------------------------|-------------|----------|------------|-----------------------|
+| iGPT-L | 32 $\times$ 32 | 1536 | 1362M | 60.3 |
+| iGPT-XL | 64 $\times$ 64 | 3072 | 6801M | 68.7 |
+| VIM+VQGAN | 32 $\times$ 32 | 1024 | 650M | 61.8 |
+| VIM+dVAE | 32 $\times$ 32 | 1024 | 650M | 63.8 |
+| VIM+ViT-VQGAN | 32 $\times$ 32 | 1024 | 650M | 65.1 |
+| VIM+ViT-VQGAN | 32 $\times$ 32 | 2048 | 1697M | 73.2 |
+| AIM | 16 $\times$ 16 | 1536 | 0.6B | 70.5 |
+| **DiGIT (Ours)** | 16 $\times$ 16 | 1024 | 219M | 71.7 |
+| **DiGIT (Ours)** | 16 $\times$ 16 | 1536 | 732M | **80.3** |
+
+### Class-Unconditional Image Generation on ImageNet (Resolution: 256 $\times$ 256)
+
+| Type | Methods | \# Param | \# Epoch | FID $\downarrow$ | IS $\uparrow$ |
+|-------|-------------------------------------|----------|----------|------------------|----------------|
+| GAN | BigGAN | 70M | - | 38.6 | 24.70 |
+| Diff. | LDM | 395M | - | 39.1 | 22.83 |
+| Diff. | ADM | 554M | - | 26.2 | 39.70 |
+| MIM | MAGE | 200M | 1600 | 11.1 | 81.17 |
+| MIM | MAGE | 463M | 1600 | 9.10 | 105.1 |
+| MIM | MaskGIT | 227M | 300 | 20.7 | 42.08 |
+| MIM | **DiGIT (+MaskGIT)** | 219M | 200 | **9.04** | **75.04** |
+| AR | VQGAN | 214M | 200 | 24.38 | 30.93 |
+| AR | **DiGIT (+VQGAN)** | 219M | 400 | **9.13** | **73.85** |
+| AR | **DiGIT (+VQGAN)** | 732M | 200 | **4.59** | **141.29** |
+
+### Class-Conditional Image Generation on ImageNet (Resolution: 256 $\times$ 256)
+
+
+
+| Type | Methods | \# Param | \# Epoch | FID $\downarrow$ | IS $\uparrow$ |
+|-------|----------------------|----------|----------|------------------|----------------|
+| GAN | BigGAN | 160M | - | 6.95 | 198.2 |
+| Diff. | ADM | 554M | - | 10.94 | 101.0 |
+| Diff. | LDM-4 | 400M | - | 10.56 | 103.5 |
+| Diff. | DiT-XL/2 | 675M | - | 9.62 | 121.50 |
+| Diff. | L-DiT-7B | 7B | - | 6.09 | 153.32 |
+| MIM | CQR-Trans | 371M | 300 | 5.45 | 172.6 |
+| MIM+AR | VAR | 310M | 200 | 4.64 | - |
+| MIM+AR | VAR | 310M | 200 | 3.60* | 257.5* |
+| MIM+AR | VAR | 600M | 250 | 2.95* | 306.1* |
+| MIM | MAGVIT-v2 | 307M | 1080 | 3.65 | 200.5 |
+| AR | VQVAE-2 | 13.5B | - | 31.11 | 45 |
+| AR | RQ-Trans | 480M | - | 15.72 | 86.8 |
+| AR | RQ-Trans | 3.8B | - | 7.55 | 134.0 |
+| AR | ViTVQGAN | 650M | 360 | 11.20 | 97.2 |
+| AR | ViTVQGAN | 1.7B | 360 | 5.3 | 149.9 |
+| MIM | MaskGIT | 227M | 300 | 6.18 | 182.1 |
+| MIM | **DiGIT (+MaskGIT)** | 219M | 200 | **4.62** | **146.19** |
+| AR | VQGAN | 227M | 300 | 18.65 | 80.4 |
+| AR | **DiGIT (+VQGAN)** | 219M | 200 | **4.79** | **142.87** |
+| AR | **DiGIT (+VQGAN)** | 732M | 200 | **3.39** | **205.96** |
+
+*: VAR is trained with classifier-free guidance while all the other models are not.
+
+
+## Checkpoints
+The K-Means npy file and model checkpoints can be downloaded from:
+
+| Model | Link |
+|:----------:|:-----:|
+| HF weights🤗 | [Huggingface](https://huggingface.co/DAMO-NLP-SG/DiGIT) |
+| Google Drive | [Google Drive](https://drive.google.com/drive/folders/1QWc51HhnZ2G4xI7TkKRanaqXuo8WxUSI?usp=share_link) |
+
+For the base model we use [DINOv2-base](https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_reg4_pretrain.pth) and [DINOv2-large](https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_reg4_pretrain.pth) for large size model. The VQGAN we use is the same as [MAGE](https://drive.google.com/file/d/13S_unB87n6KKuuMdyMnyExW0G1kplTbP/view?usp=sharing).
+
+```
+DiGIT
+└── data/
+├── ILSVRC2012
+├── dinov2_base_short_224_l3
+├── km_8k.npy
+├── dinov2_large_short_224_l3
+├── km_16k.npy
+└── outputs/
+├── base_8k_stage1
+├── ...
+└── models/
+├── vqgan_jax_strongaug.ckpt
+├── dinov2_vitb14_reg4_pretrain.pth
+├── dinov2_vitl14_reg4_pretrain.pth
+```
+
+The training and inference code can be found at our github repo https://github.com/DAMO-NLP-SG/DiGIT
+
+
+## Citation
+
+If you find our project useful, hope you can star our repo and cite our work as follows.
+
+```bibtex
+
+
+@misc
+
+{zhu2024stabilizelatentspaceimage,
+title={Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective},
+author={Yongxin Zhu and Bocheng Li and Hang Zhang and Xin Li and Linli Xu and Lidong Bing},
+year={2024},
+eprint={2410.12490},
+archivePrefix={arXiv},
+primaryClass={cs.CV},
+url={https://arxiv.org/abs/2410.12490},
+}
+```