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arxiv: 2606.27192 · v1 · pith:76E3XFVKnew · submitted 2026-06-25 · 💻 cs.CV

LISA: Likelihood Score Alignment for Visual-condition Controllable Generation

Yanghao Wang , Hongxu Chen , Jiazhen Liu , Zhenqi He , Rui Liu , Zhen Wang , Long Chen This is my paper

Pith reviewed 2026-06-26 05:43 UTC · model grok-4.3

classification 💻 cs.CV
keywords likelihood score alignmentcontrollable generationdual-branch architecturediffusion modelsvisual conditioningregularizationscore-based modelingside network features
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The pith

Aligning side-network features to an approximated likelihood score accelerates convergence and improves conditional results in dual-branch diffusion models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reexamines the dual-branch setup for visual-condition controllable generation, in which a side network encodes conditions and supplies intermediate features to a frozen main network. It frames the side network's contribution as implicitly supplying a likelihood score under score-based modeling while the main network supplies the unconditional prior. LISA adds a regularization term that hooks a side-network layer, routes its features through a lightweight decoder into score space, and penalizes deviation from an approximated likelihood score target. Joint optimization with the usual diffusion loss produces faster convergence, stronger final outputs, and more disentangled side features at negligible extra training cost and zero inference cost.

Core claim

LISA is a regularization method that hooks intermediate features from a chosen layer of the side network, projects them into the score latent space with a lightweight decoder, constructs an approximated likelihood score target from the diffusion process, and minimizes the distance between the decoder output and this target as an added loss term while jointly training the side network and decoder alongside the standard diffusion objective.

What carries the argument

Likelihood Score Alignment (LISA) regularization, which projects side-network features via a decoder and aligns them to an approximated likelihood score target derived from the diffusion process.

If this is right

  • Training convergence accelerates consistently across image and video tasks, architectures, and diffusion or flow models.
  • Final synthetic image and video quality improves under visual conditioning.
  • Side-network features become more disentangled, supporting better conditional modeling.
  • The added cost remains negligible during training and zero at inference time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same alignment idea might extend to conditioning signals other than visual inputs if an appropriate score target can be approximated.
  • More disentangled features could allow designers to shrink the side network without losing control strength.
  • Varying which layer is hooked for the projection might yield further gains in efficiency or performance.

Load-bearing premise

The approximated likelihood score target constructed from the diffusion process is a faithful and stable training signal for the side-network features.

What would settle it

A controlled experiment in which the LISA regularization loss is added but training convergence does not accelerate, final results do not improve, or side features do not become more disentangled would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.27192 by Hongxu Chen, Jiazhen Liu, Long Chen, Rui Liu, Yanghao Wang, Zhenqi He, Zhen Wang.

Figure 1
Figure 1. Figure 1: Likelihood score alignment (LISA) can improve training convergence and synthetic quality. Our framework, LISA, explicitly decomposes roles within the dual-branch paradigm: the main network and side network are responsible for the unconditional and likelihood score, respec￾tively. By aligning a certain feature of the side network with an approximated likelihood score via a lightweight decoder, LISA can achi… view at source ↗
Figure 2
Figure 2. Figure 2: The framework of LISA. The first forward w/o condition injection provides the uncon￾ditional score sθ(xt, t). By minusing it with the known ∇xt log pt(xt|x0), we can construct an approximated likelihood score ˆℓt(xt, c). In the second forward w/ condition injection, we align the feature of the side network with the ˆℓt(xt, c) via a decoder as an extra regularization objective. Taking the gradient with resp… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative examples across four image-condition generation tasks. LISA shows better condition following performance (see highlighted parts in blue boxes). 4.2 ABLATION STUDY We ablated two main hyperparameters: the feature depth used for alignment and the weight λ (c.f., Eq. (16)). We used the pose-conditioned image generation task and ControlNet+LISA (with 18K training iterations) as the default setting.… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons on the pose-condition video generation. LISA shows better condition following performance in the latter frame (see the highlighted parts in the blue boxes). module does not increase the memory footprint. In terms of training time per iteration, LISA takes 2.3s compared with 2.1s for ControlNet, introducing only 0.2s additional latency. As a highlight, during the inference stage, LIS… view at source ↗
Figure 5
Figure 5. Figure 5: Quantitative (left) and qualitative (right) results of compositional-condition genera￾tion. Benefit from the explicit role decomposition, LISA shows better feature composition property. 4.4 BONUS: COMPOSITIONAL-CONDITION GENERATION Since the alignment between the feature and the likelihood score can encourage the side network to model the condition more independently, the features under such regularization… view at source ↗
read the original abstract

The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pretrained main network, has shown remarkable success in visual-condition controllable generation. Despite its widespread adoption, the role of the side branch and its training efficiency remain underexplored. In this paper, we first revisit this mainstream paradigm through the lens of score-based generative modeling: 1) The main network preserves visual perceptual quality by providing a prior unconditional score. 2) The side network steers conditional control by implicitly contributing a likelihood score. Guided by this perspective, we propose LIkelihood Score Alignment (LISA), an effective regularization method that explicitly aligns the intermediate feature of the side network with an approximated likelihood score. Specifically, we first hook features from a designated layer of the side network and project them into the score latent space by a lightweight decoder. Then, we construct an approximated likelihood score target and calculate the distance between the decoder's output and this target as an additional regularization loss. Finally, we jointly optimize the side network and decoder with both standard diffusion loss and our regularization loss. Experiments across various image/video tasks, architectures, and diffusion/flow models demonstrated that LISA can not only consistently accelerate the training convergence and improve final synthetic results, but also encourage the side network's features to be more disentangled for conditional modeling with negligible additional training cost and zero extra inference cost.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper revisits the dual-branch paradigm for visual-condition controllable generation in diffusion models, interpreting the frozen main network as supplying an unconditional score and the side network as implicitly contributing a likelihood score. It proposes LISA, which hooks features from a designated layer of the side network, projects them via a lightweight decoder into score space, constructs an approximated likelihood score target from the diffusion process and main-network predictions, and adds a regularization loss aligning the projected features to this target. The method is claimed to accelerate convergence, improve synthesis quality, and yield more disentangled side-network features across image/video tasks, architectures, and diffusion/flow models, at negligible training cost and zero inference overhead.

Significance. If the approximated target is shown to be a stable and faithful proxy for the conditional score contribution, LISA would supply a lightweight, theoretically motivated regularizer that improves training dynamics without architectural changes or inference cost. This could influence practice in controllable generation pipelines that rely on side networks for conditioning.

major comments (3)
  1. [Abstract / Method] Abstract and method description: the construction of the 'approximated likelihood score target' from the diffusion forward process, main-network predictions, and a designated layer is presented as a practical construction without a derivation establishing that it is a close proxy to the true conditional likelihood score contribution or that it remains stable across training steps and condition types; this is load-bearing because the regularization loss is justified only if the target supplies a useful rather than misaligned signal.
  2. [Experiments] Experiments section: the claims of accelerated convergence, improved final results, and more disentangled features rest on empirical outcomes, but no ablation isolates whether performance gains arise from the specific form of the likelihood-score target versus generic regularization or decoder capacity; without such controls the causal role of the proposed alignment remains unclear.
  3. [Method] Method: the paper states that the side network 'implicitly contributing a likelihood score' is an interpretive perspective, yet the regularization is derived from this view; if the perspective does not hold exactly, the added loss could amount to fitting an auxiliary quantity rather than enforcing the intended decomposition.
minor comments (2)
  1. [Method] Notation for the projected features and the target score should be introduced with explicit equations rather than descriptive prose to allow readers to verify the distance computation.
  2. [Method] The choice of 'designated layer' for feature hooking is not motivated; a brief justification or sensitivity analysis would clarify reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below with clarifications and indicate where revisions will be made to improve the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Method] Abstract and method description: the construction of the 'approximated likelihood score target' from the diffusion forward process, main-network predictions, and a designated layer is presented as a practical construction without a derivation establishing that it is a close proxy to the true conditional likelihood score contribution or that it remains stable across training steps and condition types; this is load-bearing because the regularization loss is justified only if the target supplies a useful rather than misaligned signal.

    Authors: The target is constructed from the explicit diffusion forward process and the unconditional score of the frozen main network, following the standard additive decomposition of conditional scores in score-based models. While no full proof of closeness to the true conditional contribution is provided, the construction is deterministic given the known noising schedule. We will revise the method section to include a detailed step-by-step derivation of the approximation and additional plots showing stability of the target across training steps and condition types. revision: partial

  2. Referee: [Experiments] Experiments section: the claims of accelerated convergence, improved final results, and more disentangled features rest on empirical outcomes, but no ablation isolates whether performance gains arise from the specific form of the likelihood-score target versus generic regularization or decoder capacity; without such controls the causal role of the proposed alignment remains unclear.

    Authors: We agree that isolating the specific contribution of the likelihood-score target is important. In the revision we will add ablations that replace the target with random vectors or a standard feature regression loss while keeping the decoder identical, as well as varying decoder capacity, to demonstrate that the particular form of the target drives the reported gains. revision: yes

  3. Referee: [Method] Method: the paper states that the side network 'implicitly contributing a likelihood score' is an interpretive perspective, yet the regularization is derived from this view; if the perspective does not hold exactly, the added loss could amount to fitting an auxiliary quantity rather than enforcing the intended decomposition.

    Authors: The manuscript already qualifies the view as interpretive. The loss is a practical regularizer motivated by that perspective rather than a claim of exact decomposition. The consistent improvements in convergence speed, sample quality, and feature disentanglement across multiple architectures and tasks provide empirical support for its utility. We will clarify the wording to emphasize the heuristic nature of the alignment. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation is interpretive and empirically validated.

full rationale

The paper offers an interpretive lens on dual-branch diffusion models (main network as unconditional score provider, side network as implicit likelihood contributor) and then introduces LISA as a regularization that aligns projected side-network features to a constructed approximation of the likelihood score. No equations, self-citations, or derivations are exhibited that reduce the target construction, the alignment loss, or the claimed improvements to the inputs by definition. The approximation is presented as a practical engineering choice whose utility is assessed through experiments on multiple tasks, architectures, and models rather than by algebraic identity or self-referential fitting. This is the normal case of an independent empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the 'approximated likelihood score' is mentioned but its construction is not detailed.

pith-pipeline@v0.9.1-grok · 5798 in / 1136 out tokens · 16792 ms · 2026-06-26T05:43:50.124712+00:00 · methodology

discussion (0)

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Forward citations

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