Exploring the Performance Frontier of Compact Unified Image Generation Models

• URL: http://arxiv.org/abs/2608.20334v2
• Date: Fri, 21 Aug 2026 15:55:13 GMT
• Title: Exploring the Performance Frontier of Compact Unified Image Generation Models
• Abstract summary: Swift-Image is a compact unified model for text-to-image generation, single-image editing, and multi-image editing.<n>Swift-Image achieves leading aggregate performance among evaluated open-source models with only 6B parameters and 243K GPU training hours.
• Score: 24.485980902368706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through systematic training engineering under a constrained computational budget. Swift-Image adopts an efficient 6B single-stream DiT and a progressive training pipeline that evolves from broad semantic coverage to higher resolution, stronger visual quality, and unified generation-editing supervision. For post-training, we employ parallel expert reinforcement learning followed by multi-teacher on-policy distillation to alleviate interference among heterogeneous objectives. We further decouple high-level reasoning from pixel-level rendering with a Prompt Enhancer that translates user requests into generator-aligned visual specifications. For efficient deployment, structural pruning and few-step distillation produce 3B and accelerated variants. Swift-Image achieves leading aggregate performance among evaluated open-source models with only 6B parameters and 243K GPU training hours; the compressed 3B model incurs nearly no loss, while few-step distillation further improves aggregate editing performance with substantially fewer sampling steps. Our study also summarizes practical lessons for architecture, data curriculum, post-training, prompt enhancement, and model compression.

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