2024-2026 Text-to-Image Model Survey and Comparison Report

Forum topic · ✨步子哥 · 2026-08-02

Summary

This comprehensive report surveys and compares mainstream text-to-image (T2I) AI models from 2024 to 2026, covering both open-source and closed-source systems. Open-source models reviewed include Black Forest Labs' FLUX series (12B parameters, with dev/schnell/pro variants), Stability AI's Stable Diffusion 3.5, Fal.ai's AuraFlow, Ideogram 2.0 (renowned for text rendering, OCR accuracy 0.97), and Alibaba's Wan 2.2 video generation series, Qwen-Image 2.0 (7B, native 2K, multilingual text rendering), Z-Image Turbo (6B, 8-step sub-second generation), and Baidu's ERNIE-Image (8B DiT, ranked first in China on SuperCLUE with 76.37). Closed-source models include OpenAI's DALL·E 3 and GPT-Image 2.0 (Image Arena leader at 1512, >99% text accuracy), Midjourney v6, Adobe Firefly, Google's Nano Banana Pro (SuperCLUE top score 83.73) and Imagen 4. The report compares open vs. closed models across controllability, data privacy, deployment cost, generation quality, licensing, and business risk, and outlines evaluation dimensions such as image quality, prompt adherence, text rendering, and consistency, helping readers choose models suited to their use cases.

Key points

This report surveys the leading text-to-image (T2I) models of 2024–2026, covering architecture, performance, licensing, and ecosystems.

Background

T2I has evolved from GANs to diffusion models (DALL·E 2021, Stable Diffusion 2022) into a mature content-production technology by 2024–2026.
The competitive landscape: open-source models emphasize flexibility, controllability, and local deployment; closed-source models lead in stability and commercial readiness.

Notable open-source models

FLUX (Black Forest Labs): 12B-parameter suite ([dev] non-commercial, [schnell] Apache 2.0, [pro] API). Outperformed Midjourney v6, DALL·E 3, and SD3 on Elo benchmarks at release; supports up to 10 reference images for identity/style consistency.
Stable Diffusion 3.5 (Stability AI): Maturest community ecosystem (LoRA, ControlNet, Civitai); improved text rendering; Community License free for individuals and companies under $1M revenue.
AuraFlow (Fal.ai): Open-source challenger claiming to beat Midjourney v6 and DALL·E 3 HD on several metrics at launch.
Ideogram 2.0: Specialized in text rendering (English OCR accuracy 0.97), multilingual typography; Apache 2.0; nf4 quantized version runs on 6–8GB VRAM.
Wan 2.2 (Alibaba): Open video generation (T2V-A14B, TI2V-5B); 720P/24fps on consumer GPUs; MoE architecture; Apache 2.0 but 14B variant needs 40GB+ VRAM.
Qwen-Image 2.0 (Alibaba): 7B unified generation/editing model, native 2K output, near-commercial multilingual (Chinese/English) text rendering; Lightning distillation cuts 40 steps to 4 (~10x speedup); Apache 2.0.
Z-Image Turbo (Alibaba): 6B fast-generation model using Decoupled-DMD distillation (8 steps, ~1s on H100, 5–10s on 16GB consumer GPUs); Apache 2.0.
ERNIE-Image (Baidu): 8B single-stream DiT, open-sourced April 2026; SuperCLUE Chinese T2I score 76.37 (1st in China, 4th globally); LongText-Bench 0.9733 (1st among open models); runs on 24GB GPUs; Apache 2.0 with ComfyUI and Unsloth LoRA support.
Others: GLM-Image (Zhipu AI, AR+diffusion hybrid, strong in dense Chinese text), Boogu-Image series.

Notable closed-source models

DALL·E 3 (OpenAI): Integrated in ChatGPT with GPT-4 prompt expansion; strong text embedding and prompt understanding; conservative artistic style.
Midjourney v6: Best-in-class photorealism and artistic style (anatomy, skin, hands); slower generation; subscription/Discord only.
Adobe Firefly: Deep Creative Cloud integration, vector graphics generation, copyright-safe commercial training data.
Nano Banana Pro (Google): SuperCLUE global #1 at 83.73; exceptional realism and multilingual text rendering.
GPT-Image 2.0 (OpenAI, codename "Spud"): Autoregressive multimodal architecture replacing diffusion; "thinking mode" 8-step workflow (task decomposition, retrieval, self-check); >99% character accuracy; 2K output; Image Arena top score 1512 (+242 over Nano Banana Pro).
Imagen 4 (Google): First-class text rendering, strong spatial/contextual understanding, 2K output, Google Workspace integration; slower generation.

Open vs. closed comparison

Control/customization: Open models allow LoRA fine-tuning, ControlNet, dataset customization; closed models only prompt engineering.
Data sovereignty: Open models support private on-premise deployment; closed models send data to cloud.
Cost: Open models require significant hardware (e.g., FLUX.2 dev FP8 ~32GB VRAM; production on A100/H100); closed APIs are pay-per-image with no hardware overhead.
Quality/stability: Closed models generally lead in complex prompt adherence, text rendering, and batch consistency; open models can match them after fine-tuning.
Licensing/risk: Some open licenses restrict commercial use (SD Community License >$1M revenue); closed providers assume copyright risk for a fee.

Evaluation dimensions

Benchmarks such as SuperCLUE assess image quality (Nano Banana 2: 89.00), prompt adherence/text-image consistency, text rendering, and multi-object consistency.

Conclusion

Open-source models suit scenarios demanding maximum controllability and data privacy (enterprise customization, research); closed models fit commercial workflows requiring fast, stable, high-quality output (e-commerce imagery, content publishing, design drafts). Choice depends on resources, licensing needs, and business requirements.