Autoregressive Model Beats Diffusion: 🦙 Llama for Scalable Image Generation
This repo contains pre-trained model weights and training/sampling PyTorch(torch>=2.1.0) codes used in
Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, Zehuan Yuan
HKU, ByteDance
You can find more visualizations on 
🔥 Update
- [2024.06.28] Image tokenizers and AR models for text-conditional image generation are released ! Try it !
- [2024.06.15] All models ranging from 100M to 3B parameters are supported by vLLM !
- [2024.06.11] Image tokenizers and AR models for class-conditional image generation are released !
- [2024.06.11] Code and Demo are released !
🌿 Introduction
We introduce LlamaGen, a new family of image generation models that apply original next-token prediction paradigm of large language models to visual generation domain. It is an affirmative answer to whether vanilla autoregressive models, e.g., Llama, without inductive biases on visual signals can achieve state-of-the-art image generation performance if scaling properly. We reexamine design spaces of image tokenizers, scalability properties of image generation models, and their training data quality.
In this repo, we release:
- Two image tokenizers of downsample ratio 16 and 8.
- Seven class-conditional generation models ranging from 100M to 3B parameters.
- Two text-conditional generation models of 700M parameters.
- Online demos in
for running pre-trained models.
- Supported vLLM serving framework to enable 300% - 400% speedup.
🦄 Class-conditional image generation on ImageNet
VQ-VAE models
| Method |
params |
tokens |
rFID (256x256) |
weight |
| vq_ds16_c2i |
72M |
16x16 |
2.19 |
vq_ds16_c2i.pt |
| vq_ds16_c2i |
72M |
24x24 |
0.94 |
above |
| vq_ds16_c2i |
72M |
32x32 |
0.70 |
above |
| vq_ds8_c2i |
70M |
32x32 |
0.59 |
vq_ds8_c2i.pt |
AR models
Demo
Please download models, put them in the folder ./pretrained_models, and run
python3 autoregressive/sample/sample_c2i.py --vq-ckpt ./pretrained_models/vq_ds16_c2i.pt --gpt-ckpt ./pretrained_models/c2i_L_384.pt --gpt-model GPT-L --image-size 384
# or
python3 autoregressive/sample/sample_c2i.py --vq-ckpt ./pretrained_models/vq_ds16_c2i.pt --gpt-ckpt ./pretrained_models/c2i_XXL_384.pt --gpt-model GPT-XXL --from-fsdp --image-size 384
The generated images will be saved to sample_c2i.png.
Gradio Demo 
You can use our online gradio demo or run gradio locally:
python app.py
🚀 Text-conditional image generation
VQ-VAE models
| Method |
params |
tokens |
data |
weight |
| vq_ds16_t2i |
72M |
16x16 |
LAION COCO (50M) + internal data (10M) |
vq_ds16_t2i.pt |
AR models
Demo
Before running demo, please refer to language readme to install the required packages and language models.
Please download models, put them in the folder ./pretrained_models, and run
python3 autoregressive/sample/sample_t2i.py --vq-ckpt ./pretrained_models/vq_ds16_t2i.pt --gpt-ckpt ./pretrained_models/t2i_XL_stage1_256.pt --gpt-model GPT-XL --image-size 256
# or
python3 autoregressive/sample/sample_t2i.py --vq-ckpt ./pretrained_models/vq_ds16_t2i.pt --gpt-ckpt ./pretrained_models/t2i_XL_stage2_512.pt --gpt-model GPT-XL --image-size 512
The generated images will be saved to sample_t2i.png.
Local Gradio Demo
⚡ Serving
We use serving framework vLLM to enable higher throughput. Please refer to serving readme to install the required packages.
python3 autoregressive/serve/sample_c2i.py --vq-ckpt ./pretrained_models/vq_ds16_c2i.pt --gpt-ckpt ./pretrained_models/c2i_XXL_384.pt --gpt-model GPT-XXL --from-fsdp --image-size 384
The generated images will be saved to sample_c2i_vllm.png.
Getting Started
See Getting Started for installation, training and evaluation.
License
The majority of this project is licensed under MIT License. Portions of the project are available under separate license of referred projects, detailed in corresponding files.
BibTeX
@article{sun2024autoregressive,
title={Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation},
author={Sun, Peize and Jiang, Yi and Chen, Shoufa and Zhang, Shilong and Peng, Bingyue and Luo, Ping and Yuan, Zehuan},
journal={arXiv preprint arXiv:2406.06525},
year={2024}
}
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