Descriptions:
Fahd Mirza installs and tests Baidu’s ERNIE Image Turbo locally, an open-weights text-to-image model built on a single-stream diffusion transformer architecture that generates images in just eight inference steps. The model is served using SGLang on an Nvidia RTX A6000 with 48GB VRAM, consuming approximately 30GB during generation, and accessed through a custom Gradio interface.
Mirza runs a structured sequence of prompts covering different capability dimensions: an architectural scene (an ancient Beijing hutong at golden hour), a studio portrait with specific object placement, a multi-subject composition requiring four specific cat breeds in correct positional order with a GoPro camera, and a vintage travel poster for Sofia, Bulgaria testing text rendering. Results are evaluated across composition quality, instruction following accuracy, cultural authenticity, and known weak spots. The model performs strongly on complex scene construction and multi-subject placement, handles common text strings like ‘Visit Sofia’ correctly, but struggles with less common words (‘Balkans’ rendered as ‘Barkins’) and shows the typical diffusion model weakness on human hands and fine finger detail.
The video includes a component-level architecture breakdown explaining the roles of the positional encoding weights, PE tokenizer, denoising scheduler, text encoder, core diffusion transformer, and VAE — making it useful both as a practical setup guide and as an introduction to how modern single-stream diffusion transformers differ from earlier multi-stage architectures. The distilled model is compared implicitly against FLUX through the qualitative assessment rather than a formal side-by-side test.
📺 Source: Fahd Mirza · Published April 14, 2026
🏷️ Format: Benchmark Test
