Fibo 2.0 vs MOSS Image: ComfyUI setup and tests

This ComfyUI tutorial compares Fibo 2.0 and MOSS Image for text-to-image generation and image editing, with Flux 3 Image Klein included in the early tests. The presenter repeats prompts with synchronized seeds and discusses differences in composition, text accuracy and texture.

Setup covers model loaders, text encoders and the Flux 2 VAE shared by all three models. MOSS Image Base is described as the choice for fine-tuning, while SFT is intended for everyday generation. The Fibo Dev workflow uses a Turbo LoRA at 12 steps; later tests remove it and use 30 steps. The MOSS editing section requires an updated ComfyUI installation, an edit LoRA and the appropriate native conditioning node.

The presenter prefers MOSS Image in the cafe selfie, Chinese street-sign and Mars greenhouse examples. Its JSON layout workflow uses bounding boxes for objects and text, with a graphical editor available through Nodes 2.0. Fibo also accepts JSON prompts in these experiments, though some labels differ from the requested text.

For the sweater color edit, the presenter finds that Fibo preserves more skin and facial detail, while MOSS produces a brighter navy blue and loses some detail. These examples help viewers choose a local AI image workflow, but the speaker's preferences and sample results do not establish a universal ranking.