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A scale-and-add method enables a 5B model to unify multimodal video generation and editing five times faster.

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T0 review · grok-4.3

2026-06-28 02:13 UTC pith:NVFBI2PQ

load-bearing objection LoomVideo's Scale-and-Add latent trick is the real novelty for cutting editing compute, but the claim that it handles complex non-rigid edits without extra tokens rests on thin evidence so far. the 2 major comments →

arxiv 2606.06042 v2 pith:NVFBI2PQ submitted 2026-06-04 cs.CV

LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing

Jianzong Wu , Hao Lian , Jiongfan Yang , Dachao Hao , Ye Tian , Yunhai Tong , Jingyuan Zhu , Biaolong Chen
show 7 more authors
Qiaosong Qi Aixi Zhang Wanggui He Mushui Liu Jinlong Liu Pipei Huang Hao Jiang
This is my paper
classification cs.CV
keywords video generationvideo editingmultimodal inputsdiffusion transformerMLLMlatent conditioninginference speed
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper aims to show that video generation and editing can be unified in a single efficient model that accepts interleaved multimodal inputs. It replaces the costly token concatenation used in prior work with a simple scaling and addition of the source video latent to the target latent in the diffusion process. This change keeps sequence length the same and allows the 5B model to deliver competitive or better results on benchmarks while running at least 5.41 times faster. The result would matter if it makes advanced video tools usable without massive compute resources.

Core claim

LoomVideo is a 5B-parameter unified model for video generation and editing that employs an MLLM for input encoding and Deepstack injection to align features with the DiT. Its central innovation is the zero-overhead Scale-and-Add conditioning, where the clean source video latent is scaled and added directly to the noised target latent to support editing without increasing sequence length or attention cost. Negative Temporal RoPE handles multiple reference images. The model achieves state-of-the-art or highly competitive performance on comprehensive benchmarks, with exceptional results in e-commerce and fashion, and at least 5.41x faster inference.

What carries the argument

The zero-overhead Scale-and-Add conditioning approach that scales and adds the clean source video latent to the noised target latent.

Load-bearing premise

The assumption that scaling and directly adding the clean source video latent to the noised target latent will preserve robust editing performance for complex non-rigid edits without requiring token concatenation.

What would settle it

Running the model on a test set of videos requiring complex non-rigid edits and observing whether edit quality drops significantly compared to concatenation-based models.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The computational complexity of self-attention stays constant instead of quadrupling when conditioning on source video.
  • A model of only 5B parameters can match or exceed the performance of models with 13B or more parameters.
  • Inference speed improves by a factor of at least 5.41 compared to models of similar capabilities.
  • Complex non-rigid video edits remain feasible under the new conditioning scheme.

Where Pith is reading between the lines

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

  • The Scale-and-Add technique might apply to image generation and editing tasks to achieve similar efficiency gains.
  • Smaller unified models could open video foundation models to more users and applications.
  • The method's strength in fashion scenarios may stem from better preservation of visual consistency across frames.
  • Further tests could check if the approach scales to longer videos without additional modifications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces LoomVideo, a compact 5B-parameter unified architecture for video generation and editing that replaces the text encoder with an MLLM, uses Deepstack injection to align multi-layer MLLM features with the DiT, and proposes a zero-overhead Scale-and-Add conditioning method (scaling and directly adding the clean source video latent to the noised target latent) to avoid token concatenation and its quadratic self-attention cost. It further integrates Negative Temporal RoPE for multiple reference images and claims SOTA or highly competitive performance across benchmarks (with particular strength in e-commerce/fashion) plus at least 5.41x inference speedup relative to comparable models.

Significance. If the empirical claims hold, the work would be significant for practical video foundation models: the zero-overhead conditioning directly addresses the sequence-length bottleneck that has limited unified editing models, while the 5B scale and domain-specific strengths could enable broader deployment. The approach is conceptually elegant and, if robust, offers a clear efficiency gain without sacrificing multimodal unification.

major comments (2)
  1. [Scale-and-Add conditioning description] Scale-and-Add conditioning (abstract and method description): The central claim that scaling and directly adding the clean source video latent to the noised target latent 'maintains robust capabilities for complex, non-rigid edits' is load-bearing for both the efficiency and performance assertions, yet rests on an untested latent-space mixing assumption. No derivation, information-theoretic argument, or ablation is supplied showing why the additive mixture supplies sufficient structural/temporal cues without destructive interference or signal loss when noise dominates (e.g., large deformations, viewpoint changes, or object interactions). This directly underpins the 5.41x speedup and 'maintains robust capabilities' statements.
  2. [Experimental results and claims] Experimental validation (abstract): The assertions of 'state-of-the-art or highly competitive performance across comprehensive benchmarks' and 'at least a 5.41x acceleration' are presented without reference to specific benchmark definitions, baseline models, ablation results isolating the Scale-and-Add contribution, or measurement protocol for the speedup (e.g., hardware, sequence lengths, batching). These details are required to substantiate the central empirical claims.
minor comments (2)
  1. The abstract would benefit from naming the exact benchmarks and baselines used to support the SOTA/competitive claims.
  2. Clarify whether 'Deepstack injection' and 'Negative Temporal RoPE' are novel contributions or adaptations of prior techniques, with appropriate citations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point-by-point below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Scale-and-Add conditioning description] Scale-and-Add conditioning (abstract and method description): The central claim that scaling and directly adding the clean source video latent to the noised target latent 'maintains robust capabilities for complex, non-rigid edits' is load-bearing for both the efficiency and performance assertions, yet rests on an untested latent-space mixing assumption. No derivation, information-theoretic argument, or ablation is supplied showing why the additive mixture supplies sufficient structural/temporal cues without destructive interference or signal loss when noise dominates (e.g., large deformations, viewpoint changes, or object interactions). This directly underpins the 5.41x speedup and 'maintains robust capabilities' statements.

    Authors: We agree that the current manuscript would be strengthened by explicit justification for the Scale-and-Add mechanism. While the empirical results in the full paper show competitive performance on complex edits, no dedicated ablation isolating the additive mixing or information-theoretic argument was provided. In the revised manuscript we will add (i) a short paragraph motivating why scaled addition preserves structural and temporal cues and (ii) an ablation study comparing Scale-and-Add against token-concatenation baselines on deformation-heavy and viewpoint-change cases. revision: yes

  2. Referee: [Experimental results and claims] Experimental validation (abstract): The assertions of 'state-of-the-art or highly competitive performance across comprehensive benchmarks' and 'at least a 5.41x acceleration' are presented without reference to specific benchmark definitions, baseline models, ablation results isolating the Scale-and-Add contribution, or measurement protocol for the speedup (e.g., hardware, sequence lengths, batching). These details are required to substantiate the central empirical claims.

    Authors: The Experiments section of the full manuscript reports the benchmarks, baselines, and speedup measurements, but these specifics are not referenced in the abstract or method overview. We will revise the abstract and the opening of the method section to explicitly name the benchmarks, list the baseline models, reference the Scale-and-Add ablation, and state the exact inference protocol (hardware, sequence lengths, batch size) used to obtain the 5.41x figure. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents an architectural design choice (Scale-and-Add latent conditioning) and reports empirical benchmark results for a 5B model. No derivation chain reduces any claimed prediction or first-principles result to fitted parameters or self-citations by construction. Performance and speedup claims are stated as outcomes of experiments, not tautological outputs of the method's own equations. The conditioning approach is introduced as an ansatz for efficiency, not derived from prior equations in the paper. No self-citation load-bearing steps or uniqueness theorems appear. This is the normal case of a self-contained empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim depends on the empirical effectiveness of newly introduced architectural components whose performance is asserted via benchmarks but not derived from first principles or external independent evidence.

pith-pipeline@v0.9.1-grok · 5853 in / 1287 out tokens · 38752 ms · 2026-06-28T02:13:35.335012+00:00 · methodology

0 comments
read the original abstract

Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier field. Existing unified frameworks predominantly rely on massive models (typically 13B parameters or more) and incorporate source video conditions for editing by concatenating sequence tokens. This concatenation inevitably doubles the sequence length, quadrupling the computational complexity of the self-attention mechanism and introducing prohibitive overhead. To address these bottlenecks, we present LoomVideo, a highly efficient 5B-parameter unified architecture for both video generation and editing. LoomVideo replaces the standard text encoder with a Multimodal Large Language Model (MLLM) and employs Deepstack injection mechanism to align multi-layer MLLM features with the Diffusion Transformer (DiT). Crucially, we introduce a zero-overhead Scale-and-Add conditioning approach for video editing. By scaling and directly adding the clean source video latent to the noised target latent, this elegant design eliminates the need for token concatenation, drastically reducing computational cost while maintaining robust capabilities for complex, non-rigid edits. Furthermore, a Negative Temporal RoPE strategy is seamlessly integrated to handle multiple reference images. Extensive experiments demonstrate that our compact 5B model achieves state-of-the-art or highly competitive performance across comprehensive benchmarks, exhibiting exceptional superiority in e-commerce and fashion generation scenarios. Benefiting from the zero-overhead conditioning mechanism, LoomVideo achieves at least a 5.41x acceleration in inference speed compared to models of similar capabilities, paving the way for highly practical and efficient video foundation models.

Figures

Figures reproduced from arXiv: 2606.06042 by Aixi Zhang, Biaolong Chen, Dachao Hao, Hao Jiang, Hao Lian, Jianzong Wu, Jingyuan Zhu, Jinlong Liu, Jiongfan Yang, Mushui Liu, Pipei Huang, Qiaosong Qi, Wanggui He, Ye Tian, Yunhai Tong.

Figure 1
Figure 1. Figure 1: Showcase of LoomVideo across diverse video generation and editing scenarios, including foundational video generation & editing, reference-image-guided video generation and editing. 2 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overall Architecture of LoomVideo. LoomVideo seamlessly processes interleaved multimodal inputs using an MLLM. It employs two key designs: (1) A Deepstack injection mechanism, which extracts feature embeddings from every layer of the MLLM and injects them into the corresponding layers of the DiT via cross￾attention. (2) A zero-overhead Scale-and-Add conditioning approach for video editing, which scales the… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison of LoomVideo and other baselines. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Low-quality generation cases of LoomVideo. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results for LoomVideo on Text-to-Video and Instuction Editing tasks. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results for LoomVideo on Instuction Editing task. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results for LoomVideo on Instuction-Image Editing task. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative results for LoomVideo on our benchmark FashionVideoBench. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Evaluation prompt template for video product editing. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Evaluation prompt template for video model replacement editing. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Evaluation prompt template for freeform video editing. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Evaluation prompt template for video item replacement with reference image. [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Evaluation prompt template for motion-transfer video generation. [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Evaluation prompt template for multi-reference-image-to-video generation. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships

    cs.CV 2026-07 conditional novelty 6.0

    Writing editing instructions that explicitly bind each attribute to a reference image via `<Image_N>` tokens substantially improves multi-reference video editing, and a specialized MLLM trained with GRPO generates the...

Reference graph

Works this paper leans on

17 extracted references · cited by 1 Pith paper

  1. [1]

    - 5:Perfect Preservation

    The first score: Subject Consistency Objective:Evaluate how well the edited product maintains its identity and appearance throughout the video. - 5:Perfect Preservation. Product identity is flawlessly maintained with all attributes correctly edited.- 4:High Preservation. Product is highly consistent with only minor detail loss.- 3:Moderate Preservation. P...

  2. [3]

    - 5:Excellent

    The third score: Visual Quality Objective:Evaluate the general aesthetic quality, temporal consistency, and motion smoothness. - 5:Excellent. Exceptional visual aesthetics and perfect temporal consistency.- 4:Good. High overall quality with only minor artifacts.- 3:Fair. Acceptable but noticeably flawed.- 2:Poor. Significantly degraded quality.- 1:Unaccep...

  3. [4]

    - 5:Perfect Consistency

    The first score: Subject Consistency Objective:Evaluate how well the new model maintains consistency throughout the video. - 5:Perfect Consistency. Model identity is flawlessly maintained across all frames.- 4:High Consistency. Model is highly consistent with only minor flickering.- 3:Moderate Consistency. Model is recognizable but shows some instability....

  4. [5]

    - 5:Perfect Alignment

    The second score: Prompt Following Objective:Assess how accurately the video executes the model replacement instruction. - 5:Perfect Alignment. The model is perfectly replaced as specified.- 4:Good Alignment. The model is correctly replaced with minor deviations.- 3:Partial Alignment. The model is partially replaced but with noticeable errors.- 2:Weak Ali...

  5. [6]

    - 5:Excellent

    The third score: Visual Quality Objective:Evaluate the general aesthetic quality, temporal consistency, and motion smoothness. - 5:Excellent. Exceptional visual aesthetics and perfect temporal consistency.- 4:Good. High overall quality with only minor artifacts.- 3:Fair. Acceptable but noticeably flawed.- 2:Poor. Significantly degraded quality.- 1:Unaccep...

  6. [7]

    - 5:Perfect Consistency

    The first score: Subject Consistency Objective:Evaluate how well the edited content maintains consistency throughout the video. - 5:Perfect Consistency. Edited content is flawlessly maintained across all frames.- 4:High Consistency. Content is highly consistent with only minor flickering.- 3:Moderate Consistency. Content is recognizable but shows some ins...

  7. [8]

    - 5:Perfect Alignment

    The second score: Prompt Following Objective:Assess how accurately the video executes the editing instruction. - 5:Perfect Alignment. Every detail of the instruction is perfectly rendered.- 4:Good Alignment. The core semantics of the prompt are successfully captured.- 3:Partial Alignment. The model captures the main idea but ignores specific nuances.- 2:W...

  8. [9]

    - 5:Excellent

    The third score: Visual Quality Objective:Evaluate the general aesthetic quality, temporal consistency, and motion smoothness. - 5:Excellent. Exceptional visual aesthetics and perfect temporal consistency.- 4:Good. High overall quality with only minor artifacts.- 3:Fair. Acceptable but noticeably flawed.- 2:Poor. Significantly degraded quality.- 1:Unaccep...

  9. [10]

    - 5:Perfect Match

    The first score: Subject Consistency Objective:Evaluate how well the replaced item matches the reference image and maintains consistency throughout the video. - 5:Perfect Match. The replaced item is identical to the reference image and consistent across all frames.- 4: High Match. The replaced item closely matches the reference with minor differences.- 3:...

  10. [11]

    - 5:Perfect Alignment

    The second score: Prompt Following Objective:Assess how accurately the video executes the item replacement instruction. - 5:Perfect Alignment. The item is perfectly replaced as specified.- 4:Good Alignment. The item is correctly replaced with minor deviations.- 3:Partial Alignment. The item is partially replaced but with noticeable errors. - 2:Weak Alignm...

  11. [12]

    - 5:Excellent

    The third score: Visual Quality Objective:Evaluate the general aesthetic quality, temporal consistency, and motion smoothness. - 5:Excellent. Exceptional visual aesthetics and perfect temporal consistency.- 4:Good. High overall quality with only minor artifacts.- 3:Fair. Acceptable but noticeably flawed.- 2:Poor. Significantly degraded quality.- 1:Unaccep...

  12. [13]

    - 5:Perfect

    The first score: Subject Consistency Objective:Evaluate both (a) how faithfully the generated video reproduces the person’s appearance (face, body, clothing) and background from the reference image, and (b) how consistently these elements are maintained across all frames of the generated video. - 5:Perfect. Reference appearance is faithfully reproduced an...

  13. [14]

    - 5:Perfect Alignment

    The second score: Prompt Following Objective:Assess how faithfully the generated video reproduces the movements, poses, and motion trajectory from the source video as specified by the instruction. - 5:Perfect Alignment. All movements, poses, and temporal dynamics from the source are flawlessly reproduced. - 4:Good Alignment. Core motion is accurately capt...

  14. [15]

    - 5:Excellent

    The third score: Visual Quality Objective:Evaluate the general aesthetic quality, temporal consistency, and motion smoothness. - 5:Excellent. Exceptional visual aesthetics and perfect temporal consistency.- 4:Good. High overall quality with only minor artifacts.- 3:Fair. Acceptable but noticeably flawed.- 2:Poor. Significantly degraded quality.- 1:Unaccep...

  15. [16]

    - 5:Perfect

    The first score: Subject Consistency Objective:Evaluate both (a) how faithfully the generated video reproduces the visual elements from the reference images (person identity, clothing details, background, product appearance, etc.), and (b) how consistently these elements are maintained across all frames of the generated video. - 5:Perfect. All referenced ...

  16. [17]

    - 5:Perfect Alignment

    The second score: Prompt Following Objective:Assess how accurately the video follows the text instruction (scene composition, actions, camera movements, timing, etc.). - 5:Perfect Alignment. Every detail of the instruction is perfectly rendered.- 4:Good Alignment. The core semantics of the prompt are successfully captured.- 3:Partial Alignment. The model ...

  17. [18]

    - 5:Excellent

    The third score: Visual Quality Objective:Evaluate the general aesthetic quality, temporal consistency, and motion smoothness. - 5:Excellent. Exceptional visual aesthetics and perfect temporal consistency.- 4:Good. High overall quality with only minor artifacts.- 3:Fair. Acceptable but noticeably flawed.- 2:Poor. Significantly degraded quality.- 1:Unaccep...