  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. The outcome of this
exploration consists of: (1) An image tokenizer with downsample ratio of 16,
reconstruction quality of 0.94 rFID and codebook usage of 97% on ImageNet
benchmark. (2) A series of class-conditional image generation models ranging
from 111M to 3.1B parameters, achieving 2.18 FID on ImageNet 256x256
benchmarks, outperforming the popular diffusion models such as LDM, DiT. (3) A
text-conditional image generation model with 775M parameters, from two-stage
training on LAION-COCO and high aesthetics quality images, demonstrating
competitive performance of visual quality and text alignment. (4) We verify the
effectiveness of LLM serving frameworks in optimizing the inference speed of
image generation models and achieve 326% - 414% speedup. We release all models
and codes to facilitate open-source community of visual generation and
multimodal foundation models.
