# Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements

arXiv: https://arxiv.org/abs/2609.05234 · HTML: https://mlsanity.com/paper/2609.05234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 40 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Builds on (most cited first, 30 of 33)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment](https://mlsanity.com/paper/2310.11513/) (2023)
- [Universal Guidance for Diffusion Models](https://mlsanity.com/paper/2302.07121/) (2023)
- [FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model](https://mlsanity.com/paper/2303.09833/) (2023)
- [AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea](https://mlsanity.com/paper/2411.15738/) (2024)
- [Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models](https://mlsanity.com/paper/2311.12092/) (2023)
- [Stable Flow: Vital Layers for Training-Free Image Editing](https://mlsanity.com/paper/2411.14430/) (2024)
- [DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation](https://mlsanity.com/paper/2402.11929/) (2024)
- [Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions](https://mlsanity.com/paper/2403.17064/) (2024)
- [DreamOmni: Unified Image Generation and Editing](https://mlsanity.com/paper/2412.17098/) (2024)
- [Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis](https://mlsanity.com/paper/2412.02168/) (2024)
- [SliderSpace: Decomposing the Visual Capabilities of Diffusion Models](https://mlsanity.com/paper/2502.01639/) (2025)
- [LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs](https://mlsanity.com/paper/2507.16193/) (2025)
- [SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control](https://mlsanity.com/paper/2511.09715/) (2025)
- [Decoupling Training-Free Guided Diffusion by ADMM](https://mlsanity.com/paper/2411.12773/) (2024)
- [Composition and Alignment of Diffusion Models using Constrained Learning](https://mlsanity.com/paper/2508.19104/) (2025)
- [Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation](https://mlsanity.com/paper/2412.19853/) (2024)
- [PairEdit: Learning Semantic Variations for Exemplar-based Image Editing](https://mlsanity.com/paper/2506.07992/) (2025)
- [Addressing Text Embedding Leakage in Diffusion-based Image Editing](https://mlsanity.com/paper/2412.04715/) (2024)
- [UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying](https://mlsanity.com/paper/2508.03142/) (2025)
- [I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models](https://mlsanity.com/paper/2512.04660/) (2025)
- [AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models](https://mlsanity.com/paper/2508.02151/) (2025)
- [FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video](https://mlsanity.com/paper/2511.00103/) (2025)
- [LumiSculpt: Enabling Consistent Portrait Lighting in Video Generation](https://mlsanity.com/paper/2410.22979/) (2024)
- [Can Diffusion Models Disentangle? A Theoretical Perspective](https://mlsanity.com/paper/2504.00220/) (2025)
- [All-in-One Slider for Attribute Manipulation in Diffusion Models](https://mlsanity.com/paper/2508.19195/) (2025)
- [Improving Controllable Generation: Faster Training and Better Performance via x0-Supervision](https://mlsanity.com/paper/2604.05761/) (2026)

## Abstract (arXiv)

Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffusion sliders derive their axes from text or learned representations, leaving their scales disconnected from observable image properties. Consequently, we cannot tell in advance which attributes are learnable, compare control strengths directly, or anticipate interference when multiple controls are combined. We propose Measured Sliders, a framework that defines continuous controls through closed-form differentiable image measurements. A common measurement space unifies the pipeline. Before training, an observability test identifies usable supervision. During training, a measurement-guided objective learns target movement while suppressing non-target changes. After training, decoded calibration expresses controls in comparable units of realized image change. Multiple LoRA branches are stored in one checkpoint and composed without training on joint activations. Across SDXL and FLUX.1-dev, the resulting controls are ordered, selective, and composable. On 553 prompts, lighting direction reaches rho = 0.995 and 98.9% monotone sweeps. A five-attribute checkpoint achieves average selectivity 2.59, compared with 1.50 for the strongest baseline, and preserves every requested direction in 96.7% of pair and 86.1% of triple compositions. The observability test also separates every subsequently successful measurement from the failed candidate. Overall, image-space measurement provides a common basis for learning, diagnosing, calibrating, and composing continuous generative controls.