huggingface/diffusers

[Proposals Welcome] Fal Flashpack integration for faster model loading

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#12564 opened on Oct 31, 2025

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 (4 comments) (3 reactions) (0 assignees)Python (22,190 stars) (4,562 forks)batch import
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Description

Hey! 👋

We've had a request to explore integrating Fal's Flashpack for faster DiT and Text Encoder loading (https://github.com/huggingface/diffusers/issues/12550). Before we jump into implementation, we wanted to open this up to the community to gather ideas and hear from anyone who's experimented with this.

We'd love your input on:

  1. Performance: Has anyone tried it? What kind of speedups did you see? Are there any performance trade-offs?
  2. Integration Design: How would you approach it if you were to integrating this into Diffusers? Describe your design at a high level - how would we support this in our existing framework and what would the API look like?

We're looking for proposals and ideas rather than PRs at this stage. We're genuinely interested in hearing different approaches and perspectives from the community on this.

Feel free to share your thoughts!

Contributor guide

Tech stack
pythonpytorch
Domain
machine learningperformance
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Familiarity with Diffusers libraryUnderstanding of Fal FlashpackBasic knowledge of PyTorch model loading
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
35
Research direction
Explore the existing discussion in issue #12550 for initial context. Investigate Flashpack's API and evaluate integration points in the diffusers loading pipeline (e.g., how models are loaded via from pretrained). Propose a high level design that maintains backward compatibility and enhances performance.