[Submitted on 15 Oct 2026]
Title:FlashControl: One-Step Controllable Generator via Distillation-Friendly Single-Stream Teachers
View a PDF of the paper titled FlashControl: One-Step Controllable Generator via Distillation-Friendly Single-Stream Teachers, by Ngan Nguyen and Dung Nguyen and Quan Dao and Dimitris N. Metaxas and Minh Hoai and Cuong Pham and Anh Tran
View PDF HTML (experimental)
Abstract:Diffusion models have become a leading approach in visual generative modeling, particularly effective in text-to-image and controllable generation. Their main drawback is slow inference as it requires many sampling steps. While recent distillation methods enable one-step text-to-image generation, extending these techniques to controllable generation is still challenging. Existing controllable models, such as ControlNet, rely on dual-branch design that make distillation difficult. In this work, we adopt a simpler strategy. Instead of adding extra conditional branches, we finetune a selected subset of layers in the original text-to-image diffusion model to directly enable controllable generation. This results in DeltaControl, a single-flow, multi-step diffusion model capable of supporting spatial-condition generation while matching the performance of dual-branch approaches. Building on this teacher model, we further apply step-distillation to obtain FlashControl, a one-step model capable of spatial controllable generation with a single forward pass.
Comments: Accepted at The 40th Annual Conference on Neural Information Processing Systems (NeurIPS 2026)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.22537 [cs.CV]
(or arXiv:2610.22537v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.22537
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ngan Nguyen [view email] [v1] Thu, 15 Oct 2026 18:32:24 UTC (14,591 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled FlashControl: One-Step Controllable Generator via Distillation-Friendly Single-Stream Teachers, by Ngan Nguyen and Dung Nguyen and Quan Dao and Dimitris N. Metaxas and Minh Hoai and Cuong Pham and Anh Tran
View PDF
HTML (experimental)
TeX Source
view license
Additional Features
Audio Summary
Current browse context:
cs.CV
new | recent | 2026-10
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)