Advances, Model Types, and Open Challenges of AI‑Generated Content (AIGC) with XiaoBu’s Image Generation Progress

AIGC (Artificial Intelligence Generated Content) is a new form of content creation that leverages AI techniques, offering higher efficiency than PGC and more stable quality than UGC while being cost‑effective and highly scalable.

The main evaluation dimensions for AIGC are Performance (e.g., FID, Perplexity, FOV), Diversity (distribution variance), and Novelty (maximizing distance from training data), with the latter still lacking robust quantitative metrics.

Various model families are used in AIGC:

GFlowNet – originally for molecular generation, optimizes the distribution of high‑reward samples via reinforcement learning.

Diffusion models – transform structured data into noise and reverse the process; typical algorithms include DDPM, NCSN, and SDE.

Generative Adversarial Networks (GAN) – consist of a generator and a discriminator; popular variants include WGAN, BigGAN, StyleGAN series, etc.

Variational AutoEncoders (VAE) – latent‑variable models used for fast inference in pipelines like Stable Diffusion.

Energy‑based models – e.g., Boltzmann machines and RBMs, applied in multimodal pre‑training.

Autoregressive models – such as PixelRNN, Gated PixelCNN, GPT‑3/ChatGPT; predict the next token based on previous ones.

Normalizing flow models – e.g., GLOW and Residual Flow, provide exact likelihood estimation and efficient reversible generation.

XiaoBu’s recent AIGC image‑generation work builds on Stable Diffusion with extensive fine‑tuning techniques (DreamBooth, Textual Inversion, ControlNet) and employs RLHF for prompt rewriting to improve aesthetic quality and CLIPScore while preserving user intent.

Technical innovations include the CETNets architecture (convolutional blocks added to VIP models), a three‑stage cross‑modal training pipeline (single‑modal pre‑training, modality alignment, end‑to‑end task training), and a customized AIGC drawing architecture that integrates these components.

Open issues highlighted are the limited self‑assessment results of GPT‑4, critiques from Yann LeCun about the narrow focus of current models, third‑party evaluations showing gaps in logical reasoning, and broader challenges such as the lack of inductive‑deductive integration, missing representations for many intelligences, and the need to combine language models with mathematical‑logic AI.

The article concludes with a summary of these shortcomings and a call for future research to bridge the gaps between data models, theoretical models, and real‑world intelligent behavior.