Hey, this may have been discussed before or diffusers might not be the correct place to discuss. (let me know) I am coming from the point of view of purely being a consumer of models and noticing the large amount of potential copies and re-downloads.

🚀 Concept

Many Hugging Face model variants—whether fine‑tunes, adapters, or language‑specific forks—share large portions of their weights with a common base checkpoint. Similarly, tasks like diffusion often reuse the same T5 text encoder across multiple pipelines. Yet today, every variant or pipeline bundles the full encoder + decoder (or diffusion U‑Net), leading to redundant uploads and downloads.

A lightweight “package manager” layer would let you:

• Declare one or more bases (e.g.
  • Core transformer: t5-base@v1.2.0
  • Diffusion U‑Net: stable-diffusion-unet@v2.1.0)
• Upload only the components you’ve changed (e.g. a new decoder head or adapter weights)
• Download smartly, pulling shared pieces from bases you already have in cache

🎯 Why It Matters

• Bandwidth & Storage: CI pipelines, Docker builds, and on‑prem deployments fetch only the modified components—be they large (text encoder) or small (classification head)—instead of full checkpoints every time.
• Cache Harmony: Local caches (~/.cache/huggingface) track components by content hash, preventing duplicate copies of popular encoders or U‑Nets.
• Provenance & Security: Variants reference signed base packages; you audit and trust the core model separately from custom deltas.
• Reproducibility: A lockfile (e.g. hf-lock.json) pins exact versions of all referenced packages and deltas, similar to package-lock.json or Pipfile.lock.

🤝 Analogy to npm / pip

• npm: projects list dependencies in package.json and fetch only missing modules.
• pip: requirements.txt or Pipfile pins exact versions; wheels avoid rebuilding the same code.
• Our ask: let model repos declare dependencies on other model “packages,” so from_pretrained("my-diffusion-pipeline") behaves like npm install—reusing existing parts and pulling only what’s new.