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Series
Explore hands-on benchmarks, output comparisons, and performance tests of open-source AI image generation models, including Qwen Image, FLUX, Z-Image, and more.
We evaluate text-to-image generation, image editing, photorealism, typography, prompt adherence, and visual quality using consistent real-world test prompts.
Each article includes generated image samples, ComfyUI workflows, inference configurations, GPU requirements, VRAM usage, generation speed, and cost analysis. We also explore inference engines such as ComfyUI, SgLang and vLLM-Omni to understand how different deployment methods affect performance.
Compare models, examine actual outputs, and discover which AI image generation models work best for different use cases.
In my previous article, I tested Qwen Image 2.1 across nine real-world text-to-image prompts in ComfyUI, covering everything from photorealistic portraits and product photography to typography, comple
I’ve been testing Qwen Image 2.1 in ComfyUI on HexGrid with prompts designed to test more than just pretty portraits. Task: Text-to-ImageModel: Qwen Image 2.1Engine: ComfyUI WorkflowGPU: RTX A6000Gene
