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

*Structured English summary of a Chinese forum report comparing mainstream text-to-image models.*

Background

Text-to-image generation has advanced from GAN-based exploration to the diffusion era (OpenAI's DALL·E, 2021), then community explosion via Stable Diffusion's open-source release (2022). By 2024–2026, image generation matured from a novelty into a content-production tool producing high-resolution photorealistic imagery. A clear competitive landscape emerged: open models emphasize flexibility, controllability, and local deployment; closed models lead in stability and commercial readiness.

Open-Source Models

• FLUX (Black Forest Labs, Aug 2024): Created by original Stable Diffusion core members. Three variants: [dev] (non-commercial license), [schnell] (Apache 2.0), [pro] (closed API). At 12B parameters it was the largest open T2I model; Elo scores beat Midjourney v6, DALL·E 3, and SD3. Noted for photorealistic portraits and multi-reference-image support (up to 10 reference images for identity/style consistency). Rich ecosystem via ComfyUI and LoRA fine-tuning.
• Stable Diffusion 3.5 (Stability AI): Latest flagship of the most influential open series. Improved text understanding, image quality, and in-image text rendering. SD 3.5 Medium (2B params) achieves near-large-model performance. Deepest community ecosystem (LoRA, ControlNet, Civitai). Community License free for individuals and companies under $1M annual revenue.
• AuraFlow (Fal.ai, Aug 2024): Latent-diffusion open model positioned against SD3; claimed to beat Midjourney v6 and DALL·E 3 HD on some metrics at launch.
• Ideogram 2.0: Specialized in text rendering; 0.97 English OCR accuracy, accurate multilingual (incl. CJK) typography. Apache 2.0; quantized nf4 version runs in 6–8GB VRAM.
• Wan 2.2 (Alibaba): Open image/video generation family (T2V-A14B, TI2V-5B) with MoE architecture (8B total / ~1.3B active); 720P 24fps video on consumer GPUs. Apache 2.0; 14B variant needs 40GB+ VRAM. Strong on Chinese-style content and long-instruction "think-then-edit" chains.
• Qwen-Image 2.0 (Alibaba, Feb 2026): 7B unified generation/editing model, native 2K output. Best-in-open multilingual (esp. Chinese) text rendering; multi-reference consistency; Lightning distillation cuts 40 steps to 4 (~10x speedup). Apache 2.0.
• Z-Image Turbo (Alibaba, 2026): 6B speed-optimized model using Decoupled-DMD distillation; 8-step inference gives ~1s per image on H100, 5–10s on 16GB consumer GPUs. Suited for real-time and bulk e-commerce generation. Apache 2.0.
• ERNIE-Image (Baidu, open-sourced Apr 2026): 8B single-stream DiT, dubbed an "open-source pocket rocket." SuperCLUE Chinese T2I score 76.37 (China #1, global #4 after Nano Banana 2/Pro and GPT-Image-1.5); LongText-Bench 0.9733 (global #1 among open models). Strong complex-instruction following, dense text rendering, structured images, multi-language CJK typography, and comic/anime consistency. Runs on 24GB consumer GPUs; Apache 2.0 with ComfyUI and Unsloth LoRA support; 50+ platforms integrated.
• Others: GLM-Image (Zhipu AI, AR + diffusion decoder, strong on dense Chinese text and knowledge-rich posters/infographics); Boogu-Image series (unified generation/editing).

Closed-Source Models

• DALL·E 3 (OpenAI): Integrated into ChatGPT; GPT-4 expands prompts automatically. Strong prompt understanding and in-image text; style considered conservative vs Midjourney. Available via ChatGPT Plus / Microsoft Image Creator.
• Midjourney v6: Art-first photorealism; leading anatomy, skin, and hand detail; slower generation; Discord/web only, subscription-based.
• Adobe Firefly: Embedded in Photoshop/Creative Cloud; vector generation, commercially-safe training data with built-in copyright filtering; favors practical, brand-compliant output.
• Nano Banana Pro (Google, 2025): SuperCLUE global #1 (83.73); top-tier realism and multilingual text rendering; API-only.
• GPT-Image 2.0 (OpenAI, Apr 2026, codename "Spud"): Abandons diffusion; autoregressive multimodal generation inside GPT-4o with image tokens, eliminating prompt-translation semantic loss. "Thinking mode" 8-step workflow (task decomposition, web retrieval, self-review) enables multi-panel comics with consistent characters; >99% character accuracy in text rendering; native 2K output; tops Image Arena at 1512 Elo (+242 over Nano Banana Pro).
• Imagen 4 (Google, GA Aug 2025): First-class text rendering and spatial/context understanding; Google Workspace integration; 2K output; slower generation.

Open vs. Closed: Comparison Dimensions

• Control & customization: Open models allow LoRA fine-tuning, ControlNet, custom datasets; closed models restrict users to prompt engineering.
• Data sovereignty: Open models support private on-premise deployment; closed models send data to vendor clouds.
• Deployment cost: Open models need significant hardware (FLUX.2 dev ~32GB VRAM at FP8; A100 80GB/H100 typical; SD 3.5 Large runs on consumer cards). Closed models are pay-per-image APIs—low upfront cost, potentially high at scale.
• Quality & consistency: Closed models generally lead in complex-prompt adherence, text rendering, and batch consistency; open models can match them after community fine-tuning.
• Licensing & risk: Some open licenses restrict commercial use (SD Community License >$1M revenue); community fine-tunes carry copyright risk. Closed vendors absorb copyright risk for a fee.

Guidance: choose open models for maximum controllability, privacy, and internal customization; choose closed models for fast, stable, commercial delivery (e-commerce imagery, daily content, design drafts).

Evaluation Dimensions & Benchmarks

• Image quality/realism: SuperCLUE ranks image quality; Nano Banana 2 scores highest (89.00).
• Prompt adherence: SuperCLUE's text-image consistency dimension; complex layout and multi-object accuracy.
• Text rendering: LongText-Bench and OCR-accuracy metrics (ERNIE-Image 0.9733, Ideogram 0.97, GPT-Image 2.0 >99% character accuracy).
• Leaderboards: Image Arena / LMArena Elo rankings (GPT-Image 2.0 at 1512; Wan models the only open entries in LMArena image-editing top seven).

Conclusion

The 2024–2026 period shows open-source models (FLUX, Qwen-Image, ERNIE-Image, Z-Image Turbo) narrowing—and in niches like Chinese text rendering and local deployment—even exceeding closed-model capabilities, while closed models (GPT-Image 2.0, Nano Banana Pro, Imagen 4) retain the lead in overall quality ceiling, consistency, and turnkey commercial delivery. The trend points toward autoregressive unified multimodal architectures and aggressive distillation for real-time generation.