CamiloTDH/Sub-Memory-Efficient-Merging-FluxKreaSchnell

This is a minimal prototype (1 star, 0 forks, no velocity) demonstrating a specific model merging approach for Flux.1 variants. The contribution is narrow: blending two existing proprietary models from Black Forest Labs using standard merging techniques integrated with the Diffusers library. The code is not production-hardened, has no community adoption, and the technique itself (model averaging/merging) is well-established in the literature. The project depends entirely on Flux.1 models maintained by Black Forest Labs—a commercial entity—making it vulnerable to upstream changes. Diffusers itself already supports multiple image generation models natively, and the platform (Hugging Face) could trivially absorb optimized merging strategies as built-in utilities. Incumbents like Stability AI, Replicate, and commercial fine-tuning services already offer memory-efficient model variants. The 236-day age with zero activity and zero community signals indicates abandoned or one-off experimentation. Displacement risk is immediate: (1) Black Forest Labs could release official merged model checkpoints; (2) Hugging Face could add native model merging utilities to Diffusers; (3) the specific efficiency gains are likely marginal compared to using schnell or Krea-dev individually. This is a tutorial-grade optimization without defensible IP, replicable ecosystem effects, or switching costs.