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

• [2023.06.15] All models ranging from 100M to 3B parameters are supported by vLLM !
• [2023.06.11] 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

Method	params	training	tokens	FID (256x256)	weight
LlamaGen-B	111M	DDP	16x16	5.46	c2i_B_256.pt
LlamaGen-B	111M	DDP	24x24	6.09	c2i_B_384.pt
LlamaGen-L	343M	DDP	16x16	3.80	c2i_L_256.pt
LlamaGen-L	343M	DDP	24x24	3.07	c2i_L_384.pt
LlamaGen-XL	775M	DDP	24x24	2.62	c2i_X_384L.pt
LlamaGen-XXL	1.4B	FSDP	24x24	2.34	c2i_XXL_384.pt
LlamaGen-3B	3.1B	FSDP	24x24	2.18	c2i_3B_384.pt

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 (released before July 1st)

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

Method	params	tokens	data	weight
LlamaGen-XL	775M	24x24	LAION COCO (50M)	t2i_XL_stage1_256.pt
LlamaGen-XL	775M	32x32	internal data (10M)	t2i_XL_stage2_512.pt

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}
}