{"id":"07d1aee1-a8d3-47c6-adaf-163effbddfec","arxiv_id":"2508.20379","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A training-free framework that maps audio embeddings into Stable Diffusion's text space and fuses multiple audio/text prompts via per-patch residual noise selection, outperforming text-only editors on new audio-visual editing benchmarks.","lead":"This paper introduces a training-free way to edit images and videos using audio prompts, alone or alongside text. It maps sound embeddings into the text space of Stable Diffusion and combines multiple prompt signals by picking the strongest noise residual in each patch.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Eq. (3)'s learned linear map M is unspecified, leaving the 'training-free' claim and audio-semantic transfer unverified; this linchpin must be disclosed and validated.","rationale":"The reader's weakest assumption correctly identifies that the learned linear map M and its inversion are the load-bearing component of the audio-integration claim. My stress-test confirms this and adds two technical details that deepen the concern: (1) the forward map in Eq. (3) includes L2 normalization, so the inverse problem is not well-posed, and the ridge pseudo-inverse could amplify directions outside the range of M; (2) the scaling of cA by ||cinv|| in Eq. (4) is unjustified by the forward model, introducing an arbitrary scaling that can shift the embedding magnitude. These issues are not resolved by the paper's qualitative results, which show only a few examples. The proposed control experiment directly tests whether M is causally responsible for the observed audio-alignment scores; if M is irrelevant, the central claim simplifies to a text-label-driven effect, and if M is essential, the missing training disclosure violates the training-free premise. This concern is exactly the reader's weakest assumption, so my recommendation is unchanged: the paper should be conditionally accepted pending disclosure and validation of M. Other potential concerns, such as the evaluation using audio category names for CLIP_Audio or the absence of comparison to [27], are secondary and do not alter the main verdict.","tokens_in":12969,"tokens_out":6715,"duration_ms":75028,"concrete_test":"Ask the authors to release the exact matrix M and the procedure that produced it. Then run the A2I-edit benchmark (PIEBench-multi) with two controls: (i) replace M with the identity matrix; (ii) replace M with a random orthogonal matrix. If CLIP_Audio does not drop substantially under either control, M is not the mechanism of audio alignment and the 'mapping' is irrelevant; if performance collapses, M is essential and its undisclosed learning source must be provided to verify the training-free claim and the semantic validity of the inversion.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central promise is zero-shot, training-free audio guidance. That promise rests on Eq. (3), which defines a learned linear map M (no bias) from Stable Diffusion's pooled text embedding to CoDi's audio-aligned CLIP space, and Eq. (4), which inverts M via ridge regression to project audio embeddings back into the diffusion prompt space. The paper never states how M was learned, on what paired data, or whether it comes from a pre-trained model. If M was fit for this paper, the framework is not training-free; if it is adopted from elsewhere, it must be identified. Moreover, Eq. (3) includes L2 normalization, making the forward mapping non-injective; Eq. (4) arbitrarily scales the audio embedding by ||cinv|| before applying the pseudo-inverse, with no justification from the forward model. Nothing in the paper shows that the ridge inverse preserves audio semantics (e.g., 'thunder' maps to a thunder-like text embedding) nor reports the conditioning/rank of M. The only acknowledgment is a vague limitation in Sec. 4.7. This is the linchpin of the method; if M is wrong or arbitrary, the audio signal does not enter the diffusion process and the entire reported advantage over text-only editing disappears. Reproducing the paper without M is impossible.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes an audio-guided visual editing framework that integrates CoDi audio embeddings into Stable Diffusion without additional training, using a learned linear mapping (Eq. 3) and its Tikhonov-regularized pseudo-inverse (Eq. 4). It also introduces separate noise branching with adaptive patch-wise selection (Eq. 6) to combine multiple text and audio prompts. The authors contribute two new benchmarks, PIEBench-multi and DAVIS-multi, and report quantitative and user-study results showing improvement over text-only and audio-captioning baselines across image and video editing tasks.","tokens_in":13342,"tokens_out":3196,"duration_ms":38840,"significance":"If the central mapping is sound, the paper addresses a realistic gap: audio carries information that text prompts often cannot convey, and doing so without fine-tuning a diffusion model is practically attractive. The separate noise branching idea is simple and plausibly general, and the authors provide new benchmarks that could support future research. The paper also includes an ablation study and a user study, which strengthens the empirical contribution. However, the load-bearing component—the learned linear map M that transfers audio semantics into Stable Diffusion's prompt space—is not described, implemented, or validated. This makes the core claim unverifiable and the 'training-free' assertion ambiguous. The significance of the paper depends critically on resolving this gap.","major_comments":[{"comment":"The paper states 'M : C_SD -> C_CLIP is a learned linear mapping with no bias' but never specifies how M was obtained: what data was used, what loss was optimized, or whether M is a pretrained component from another model. If M was fit for this paper, the framework is not training-free; if it is taken from an existing model, that model must be cited. Furthermore, Eq. (3) includes L2 normalization, making the forward map non-injective, and Eq. (4) arbitrarily scales c_A by ||c_inv|| before applying the pseudo-inverse. The manuscript provides no derivation for this scaling, no condition-number or rank information for M, and no evidence that the inverse maps audio embeddings to semantically corresponding text embeddings. This is the linchpin of the method: if M is arbitrary or poorly conditioned, the audio signal does not actually enter the diffusion process, and the reported benefits over","section":"Sec. 3.2, Eqs. (3) and (4)"},{"comment":"The description states that for audio editing prompts, CLIP_Audio is computed 'using category names for audio editing prompts.' This means the score is CLIP similarity between the edited image and a text label (e.g., 'thunder'), not between the output and the actual audio signal. CLIP_Audio therefore measures class-label alignment, not fidelity to the specific audio input. To support the claim that audio content is being transferred, the paper needs an audio-based metric (e.g., CLAP score between the original audio and an audio-like representation of the output) or at least a user study where participants actually listen to the audio. The current user study setup is not described in enough detail to determine whether audio was presented to the raters; please clarify the protocol.","section":"Sec. 4.4, CLIP_Audio metric"},{"comment":"The proposed adaptive patch selection uses the per-pixel maximum of residual noise magnitudes. The paper claims this 'preserves high-frequency detail and prevents destructive interference' (Sec. 3.3), but no theoretical justification is given, and the only ablation compares against simple averaging. Max-norm selection is a strong heuristic; it could favor one prompt's effect at each spatial location or amplify outlier noise. The authors should compare against alternative fusion strategies (e.g., attention-weighted averaging, channel-wise pooling, or learned weights) and provide an analysis of why per-pixel max is the right choice. This is not as load-bearing as the M issue, but it affects the validity of the multi-prompt claim.","section":"Sec. 3.3, Eq. (6)"}],"minor_comments":[{"comment":"The phrase 'training-free integration' is used despite M being a 'learned linear mapping.' Please clarify whether 'training-free' means 'no fine-tuning of the diffusion model' or 'no learning whatsoever.' If M comes from a pretrained model, say so explicitly.","section":"Sec. 3.2"},{"comment":"The implementation uses Stable Diffusion v1.5 and A100 GPUs with seed 1. Providing the exact code or a link to a public repository would aid reproducibility; at minimum, list the hyperparameters for the inversion and sampling steps.","section":"Sec. 4.1"},{"comment":"In Tables 1 and 2, the 'Ours' rows sometimes lack user-study columns (e.g., Table 1, A2I-edit row). This makes the comparison of user-study results incomplete. Please either report the numbers or clearly indicate why they are omitted.","section":"Sec. 4.5"},{"comment":"The benchmark construction selects '28 classes of audio suitable for editing by filtering out low-quality content from VGGSound.' The filtering criteria are not described. Please specify the filtering procedure to make the benchmark creation transparent and reproducible.","section":"Sec. 4.2"},{"comment":"The limitation statement acknowledges that the mapping assumes close alignment between audio and textual counterparts, but this is precisely the assumption that needs to be tested. The reference to [25] is appropriate; please elaborate on how the observed modality gap might affect the proposed method in practice.","section":"Sec. 4.7"},{"comment":"Some captions in Figures 2 and 3 contain text that is difficult to read (e.g., overlapping labels). Please enlarge or reposition labels for clarity.","section":"General"}],"recommendation":"major_revision","confidential_remarks":"The central issue is the unspecified linear mapping M. If the authors provide a concrete definition, training details, and validation, the paper could become a solid contribution. However, in its current form, the method is not reproducible and the claim of being 'training-free' is ambiguous. I recommend major revision with a strong request to address the mapping construction and validation. The benchmarks and noise-fusion idea are promising, but they stand on the audio-mapping pillar."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"First, the good news: the separate noise branching with per-patch argmax residual selection (Eq. 6) is a clean, testable idea, and the ablation against noise averaging shows it does real work in preserving high-frequency detail. That part stands on its own. The benchmark extensions (PIEBench-multi, DAVIS-multi) are also a useful resource, and the user study is more than most editing papers bother with.\n\nThe problem is the other half of the pipeline. The audio integration rests on a learned linear map M (Eq. 3) that the paper never defines: no training data, no loss, no source. If M was fit for this paper, the 'training-free' framing is wrong. If it comes from an existing model, the authors need to say which. The L2 normalization makes the forward map non-injective, and the ||cinv|| scaling in Eq. (4) is thrown in without derivation. There's no check that the ridge inverse preserves audio semantics. This is the linchpin of the entire audio contribution, and right now it's unverifiable.\n\nAlso, the paper cites the closest zero-shot audio-visual editing baseline [27] but never compares to it, and no error bars are reported. Those are fixable omissions.\n\nThe limitations section is honest about the inheritance from PnP/TokenFlow and the modality-gap assumption, which is good, but it doesn't acknowledge how central the M-specification problem is.\n\nIf the authors can disclose M or replace it with a non-learned alignment, the paper becomes a solid contribution. As is, the fusion method is worth taking seriously, but the audio mapping claim is not yet substantiated.\n\nI'd send it to peer review with a strong demand for the M details. If those come out, I'd be happy to cite it; until then, I'd hold off.","headline":"The patch-selection fusion is a real win, but the unstated learned mapping M makes the audio integration unverifiable.","tokens_in":13763,"tokens_out":3255,"would_cite":false,"duration_ms":34474,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Audio can be converted into a diffusion-model text-prompt vector with a single matrix inversion, enabling zero-shot image and video editing from sound, alone or combined with text.","keywords":["audio-guided editing","diffusion models","multi-modal prompts","training-free","CLIP space mapping","noise branching","adaptive patch selection","image and video editing"],"falsifier":"Run the audio-to-prompt mapping on a set of sounds, use the recovered pseudo-prompt alone to drive Stable Diffusion, and measure whether images generated from the mapped vector are more CLIP-similar to each sound's true text caption than to randomly matched captions; if the mapped embeddings are no closer to their true captions than to unrelated ones, the linear-invertibility premise fails and the audio channel is not actually carrying semantics.","tokens_in":12923,"feed_emoji":"🎧","tokens_out":7496,"duration_ms":75700,"temperature":0.7,"pith_summary":"This paper sets out to make audio a first-class editing prompt for diffusion models, alongside text, with no training. It claims that a pre-trained aligned multi-modal encoder's audio embedding can be moved into Stable Diffusion's prompt space by a learned linear map and a matrix inversion, so a sound such as splashing water or thunder behaves like an invisible caption. For tasks that require text and audio together, it replaces naive noise averaging with separate noise branching per prompt and per-patch selection of the strongest residual relative to the inversion prompt. On audio-augmented versions of two existing benchmarks, the method is reported to beat text-only and caption-based baselines on audio-alignment and integrated-prompt metrics. If true, this gives editors a zero-shot way to specify effects through sound that text alone cannot capture.","feed_headline":"One matrix inversion turns audio into an edit prompt","feed_subtitle":"Sound alone or with text steers diffusion-based editing, preserving high-frequency detail through patch-wise noise selection.","key_machinery":"The work's load-bearing objects are two training-free bridges. The first is the linear audio-to-text bridge: a learned bias-free map from Stable Diffusion's pooled text embedding to an aligned multi-modal encoder's shared space, inverted by a Tikhonov pseudo-inverse so a sound becomes a pseudo text prompt. The second is the fusion rule: separate noise branching computes the noise prediction for each prompt, and adaptive patch-wise selection adds to the inversion noise, at each spatial patch, the residual with the largest channel-wise L2 norm, preserving detail that averaging destroys.","core_discovery":"The central claim is that audio can be made a first-class prompt for Stable-Diffusion-based editing by algebra rather than by training. The authors find that a pretrained aligned multi-modal encoder's audio embedding can be transported into Stable Diffusion's CLIP text space with a no-bias linear map M and then recovered as a pseudo prompt by solving a Tikhonov-regularized least-squares problem (their Eq. 4). For multiple simultaneous prompts, they reject naive averaging of the denoiser's noise estimates, which collapses high-frequency detail; instead they branch the frozen denoiser once per prompt, subtract the inversion prompt's noise, and at each spatial patch keep the largest-magnitude r","pith_inferences":["If the linear-map assumption holds broadly, the learned matrix M is itself a compressed audio-text alignment; learning M deliberately on paired audio-text data would be a natural way to test how much semantic weight the pseudo-inverse carries, since the paper does not specify how M was obtained.","The per-patch maximum rule implicitly assumes different prompts dominate spatially disjoint patches; when two prompts target the same region, the stronger residual wins outright, so the fused result may inherit one prompt rather than blending both.","The approach is bounded by the audio encoder's pretraining: sounds that co-occur with text in the encoder's training data will map cleanly, while rare or novel sounds may degrade to noise regardless of the matrix inversion.","A direct extension would be to use the recovered pseudo text prompt for text-to-audio or audio-to-video generation tasks, isolating whether the mapped vector is semantically meaningful outside the editing setup."],"forward_implications":["Any Stable-Diffusion editor built on DDIM inversion with feature and self-attention injection can accept audio prompts by inserting this mapping, with no paired training data.","Sound properties beyond semantic labels, such as volume, texture, and dynamics, can steer edits; the paper shows louder audio produces stronger effects.","Text and audio prompts can be combined in a single sampling pass, so an editor can specify a subject in text and an ambient sound in audio without training a condition adapter.","The same patch-wise maximum-residual rule is a generic parameter-free way to fuse any set of conditioning signals beyond text and audio, such as depth or edge maps.","The new audio-augmented benchmarks give future audio-guided editing methods a common testbed with 300 image-audio pairs and 267 video-audio pairs."],"supporting_citations":[{"why":"Supplies the pretrained aligned multi-modal encoder that yields audio embeddings in the shared CLIP space; it is the source of the audio representation used throughout.","marker":"[46]"},{"why":"Stable Diffusion, the latent diffusion model whose fixed CLIP text-encoder space the audio embeddings are mapped into; it is the model being edited.","marker":"[41]"},{"why":"PnP-Diffusion, the image-editing backbone whose spatial-feature and self-attention injection is used for structure-preserving audio-guided edits.","marker":"[48]"},{"why":"TokenFlow, the video-editing backbone that propagates edits across frames, used for the audio-guided and text-audio video tasks.","marker":"[10]"},{"why":"DDIM inversion, the reversible sampling scheme that provides the inversion prompt noise and trajectory on which both editing and the residual noise estimates depend.","marker":"[44]"},{"why":"PIEBench, the text-guided image editing benchmark that is extended into PIEBench-multi with audio editing prompts.","marker":"[17]"},{"why":"DAVIS, the video benchmark that is extended into DAVIS-multi with audio editing prompts for video editing evaluation.","marker":"[37]"}],"fun_headline_variants":["Audio becomes an edit prompt with one matrix solve","No training: sound prompts image edits via algebra","Matrix inversion turns audio into an edit command","Sound to prompt: a linear map for image editing","Audio guides diffusion editing without any training"],"cache_read_input_tokens":2688,"weakest_assumption_plain":"The load-bearing premise is that a pretrained audio encoder's embeddings live in a space so tightly linearly related to the diffusion model's pooled text-embedding space that the pseudo-inverse of a learned no-bias map returns a semantically faithful text-prompt vector; if that linear relation is weak or the audio-text alignment is inaccurate, the audio channel fails no matter how the noise predictions are fused.","fun_headline_variants_meta":{"raw":{"variants":["Audio becomes an edit prompt with one matrix solve","No training: sound prompts image edits via algebra","Matrix inversion turns audio into an edit command","Sound to prompt: a linear map for image editing","Audio guides diffusion editing without any training"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000497,"raw_usage":{"total_tokens":2242,"prompt_tokens":684,"completion_tokens":1558,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":428,"completion_tokens_details":{"reasoning_tokens":1503}},"tokens_in":428,"tokens_out":1558,"duration_ms":12910,"temperature":1.0,"reasoning_tokens":1503,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T15:05:35.064544+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the audio-to-prompt mapping on a set of sounds, use the recovered pseudo-prompt alone to drive Stable Diffusion, and measure whether images generated from the mapped vector are more CLIP-similar to each sound's true text caption than to randomly matched captions; if the mapped embeddings are no closer to their true captions than to unrelated ones, the linear-invertibility premise fails and the audio channel is not actually carrying semantics.","supporting_citations":[{"cited_title":"High-resolution image synthesis with latent diffusion models","cited_arxiv_id":null,"evidence_quote":"Stable Diffusion, the latent diffusion model whose fixed CLIP text-encoder space the audio embeddings are mapped into; it is the model being edited."}],"review_version":1}