Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping

Ayush Raina; Or Patashnik; Rishubh Parihar; R. Venkatesh Babu

arxiv: 2606.23682 · v1 · pith:KQVCPXNBnew · submitted 2026-06-22 · 💻 cs.CV

Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping

Rishubh Parihar , Ayush Raina , R. Venkatesh Babu , Or Patashnik This is my paper

Pith reviewed 2026-06-26 08:58 UTC · model grok-4.3

classification 💻 cs.CV

keywords reference conditioned generationtoken droppingdiffusion modelsefficient inferenceimage synthesissparse representationssubject-driven generationimage editing

0 comments

The pith

Fine-tuning reference diffusion models on randomly dropped tokens lets them generate from sparse reference inputs while preserving quality.

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

The paper establishes that reference-conditioned diffusion models can operate on far fewer reference tokens than the dense grids they currently use. It shows that dropping most reference tokens at inference time already works reasonably well, and that fine-tuning with random dropping at varying ratios makes the model robust to any chosen subset. At inference, task-aware selection then keeps only the most useful tokens, cutting compute sharply. The result is a 4x speedup on multi-reference tasks and 2x on single-reference tasks across editing and subject-driven generation, with no reported drop in output quality.

Core claim

Sparse Context constructs sparse reference representations by keeping only a reduced subset of reference tokens. Fine-tuning the model with random token dropping at varying ratios during training makes it robust to partial reference inputs, decoupling it from any particular selection rule. At inference, task-aware strategies replace random dropping and adapt the token budget to the specific input and task, yielding large speed gains without loss of visual quality in spatially-aligned editing and subject-driven generation.

What carries the argument

Random token dropping during fine-tuning, which trains the model to generate from incomplete reference token sets and thereby supports flexible task-aware selection at inference.

If this is right

Multi-reference generation runs approximately four times faster at inference.
Single-reference generation runs approximately two times faster at inference.
The same trained model supports both spatially-aligned editing and subject-driven generation without retraining.
Token budget can be adjusted per input and task while keeping generation quality intact.
The method works on existing reference-conditioned diffusion architectures without architectural changes.

Where Pith is reading between the lines

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

The same fine-tuning pattern could be tested on reference-based video or 3D generation models.
Memory footprint during inference would likely drop in proportion to the token reduction.
If the selection rule can be made differentiable, end-to-end training of the selector together with the generator becomes possible.

Load-bearing premise

That training the model with random drops at varying ratios will make it work equally well with any later task-aware selection rule chosen at inference time.

What would settle it

Measure visual quality on a held-out task where the selection rule at inference differs markedly from any random-drop pattern seen in training; a clear drop in quality relative to the full-token baseline would falsify the robustness claim.

Figures

Figures reproduced from arXiv: 2606.23682 by Ayush Raina, Or Patashnik, Rishubh Parihar, R. Venkatesh Babu.

**Figure 1.** Figure 1: Sparse Context enhances the efficiency of reference-conditioned generation by replacing dense spatial grids with a smaller set of sampled tokens. Constructing sparse representations of reference images, it significantly reduces memory usage and inference time while preserving the high-quality performance of state-of-the-art generative models. Abstract Reference-based diffusion models enable highly controll… view at source ↗

**Figure 2.** Figure 2: Redundancy in reference tokens. We drop a large number of reference tokens for a pretrained reference-conditioned image generator. Even when dropping 80%, the output resembles the coarse layout of the input scene. This finding confirms that reference tokens have high redundancy, which can be removed for efficiency. We observe that this full-resolution representation of reference images is often unnecessa… view at source ↗

**Figure 3.** Figure 3: Sparse Context Overview. Training: During training, we randomly drop the tokens from reference images with a keep fraction of f ∈ (0.05, 0.25) to obtain their sparse token representation y f i for conditioning to denoise the image tokens zt. Inference: During inference, user can randomly drop the reference tokens based on their budget and condition the DiT on the sparse tokens for reference based generatio… view at source ↗

**Figure 4.** Figure 4: Token selection for image editing. During inference, we use Canny edge map to concentrate token selection on structural boundaries rather than random sampling. This edge-prioritized strategy more effectively preserves the underlying image structure and scene identity during the editing process. image and use it as a probability distribution to sample reference tokens. As shown in [PITH_FULL_IMAGE:figu… view at source ↗

**Figure 5.** Figure 5: Token Selection for Personalization. We utilize a saliency map to localize the dominant scene object, allowing the selection process to concentrate on informative regions. By prioritizing these salient tokens, our method more effectively extracts fine-grained object details, leading to significantly higher identity preservation critical for high-fidelity image personalization. To train our model with toke… view at source ↗

**Figure 6.** Figure 6: Image Editing Results. We perform instruction based image editing with diverse editing prompts. Sparse Context preserves the structure of the reference image during editing while accurately performing the described edit. Evaluation Metrics. We report LPIPS Zhang et al. [2018], CLIP-Image (CLIP-I) Radford et al. [2021], and DINO Oquab et al. [2023] similarity scores between the reference and generated image… view at source ↗

**Figure 7.** Figure 7: Image Personalization Results. Our method preserves the object identity well even with only keeping 10% of reference tokens (f = 0.1). Naïve token dropping fails in preserving fine-grained object details such as colors of the rope or carpet pattern. the generation prompts. Finally, we report relative inference speedups averaged over 100 runs on a single NVIDIA A100 GPU, compared to the base model without t… view at source ↗

**Figure 8.** Figure 8: Speedup for multi-reference personalization [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 9.** Figure 9: Our method is complementary to KV-caching [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗

**Figure 10.** Figure 10: Results for efficient instruction-based image editing. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗

**Figure 11.** Figure 11: Results for single-reference personalization with [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗

**Figure 12.** Figure 12: Results for multi-reference personalization with [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗

**Figure 13.** Figure 13: Clock time for inference on A100 GPU A.6 Memory overhead We benchmark the additional dynamic memory overhead during the inference process in [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗

**Figure 14.** Figure 14: Peak memory during inference with number of reference image and differen token [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗

**Figure 15.** Figure 15: Limitation. The choice of the inference time token selection heuristic can depend on the type of edit. In example A stylization edit Canny based sampling is effective but for example B, it will focus only the object region that needs to be replaced, resulting in inferior quality of the background region. replace the object, and using canny based sampling can hurt the preservation of background region as t… view at source ↗

read the original abstract

Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are computationally expensive in runtime, and their cost scales severely with the number of input references. While the efficiency of diffusion models has been extensively studied in the context of prompt-driven generation, it remains largely under-explored in the realm of reference-based models. This setting presents unique challenges not addressed by methods focusing solely on generation. In particular, the wasteful representation of references as dense token grids offers significant opportunities for improvement. In this work, we present Sparse Context, a method for constructing sparse reference representations by retaining only a reduced subset of reference tokens. We observe that even without modifying the model, dropping a significant portion of reference tokens at inference time largely preserves its generation capabilities. To fully realize this potential, we fine-tune the model with random token dropping at varying ratios, encouraging robustness to partial reference representations. Crucially, this training strategy decouples the model from any specific token selection rule, allowing flexible control at inference time. At inference time, instead of random dropping, we apply task-aware token selection strategies that prioritize the most informative regions of the reference images, adapting the token budget to the input and task requirements. Extensive experiments show our method achieves a 4x increase in inference speed for multi-reference generation and an 2x for single reference generation. Importantly, this efficiency is achieved without compromising visual quality across both spatially-aligned editing and subject-driven generation.

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.

Desk Editor's Note private letter to a colleague

Random-drop fine-tuning lets reference diffusion models drop tokens at inference for 2-4x speedups, but the claim that this works for any task-aware selection rests on unshown transfer evidence.

read the letter

The core thing here is a training change: fine-tune the reference-conditioned diffusion model by randomly dropping varying fractions of reference tokens so it stays usable when you drop tokens later. At inference they switch to task-aware selection instead of random, and they report 4x speedup on multi-reference and 2x on single-reference cases with no visible quality drop in editing and subject-driven tasks.

What is actually new is the decoupling step. Earlier efficiency work mostly targets prompt-only models or fixed compression. Here the random-drop fine-tuning is meant to remove dependence on any one dropping rule, so you can plug in saliency or attention-based selection without retraining. That flexibility is the practical angle, and the paper shows the basic observation that even an unmodified model tolerates quite a bit of dropping.

The soft spot is exactly the transfer the stress-test flags. Random masks during training produce one distribution of kept tokens; task-aware methods keep different ones. The abstract asserts the model becomes insensitive to the choice, but without direct side-by-side metrics (same budget, random vs task-aware, same model) it is not clear the distributions are close enough for quality to stay flat. If the experiments only show overall results rather than that controlled comparison, the central robustness claim is weaker than stated.

The work is aimed at people who already run reference-based diffusion for editing or personalization and need to handle more references or faster iteration. A reader who cares about measured speed-quality trade-offs in that setting would find the method worth testing.

Send it to review. The idea is simple, the efficiency target is real, and the experiments can be checked directly once the full numbers and ablations are in front of referees.

Referee Report

2 major / 1 minor

Summary. The paper proposes Sparse Context, a method to accelerate reference-conditioned diffusion models by constructing sparse reference representations through token dropping. The approach fine-tunes the model using random token dropping at varying ratios to promote robustness to partial inputs, then applies task-aware selection strategies at inference time. The central claims are a 4x inference speedup for multi-reference generation and 2x for single-reference generation, achieved without compromising visual quality in spatially-aligned editing and subject-driven generation tasks.

Significance. If the empirical claims hold, the work addresses an under-explored efficiency gap in reference-based diffusion models, where cost scales with the number of references. The training strategy of random dropping to enable flexible inference-time selection could be broadly useful if the decoupling effect is confirmed, potentially improving practicality of controllable generation without requiring architecture changes.

major comments (2)

[Abstract] Abstract: The central claim that random-ratio token dropping during fine-tuning 'decouples the model from any specific token selection rule' is asserted without a direct empirical test. No section or result is described that compares generation quality (metrics or human preference) under random masks versus the task-aware strategies (e.g., saliency- or attention-based) at identical token budgets; this comparison is load-bearing for the robustness and flexibility assertions.
[Abstract] Abstract: The abstract states that 'extensive experiments show' 4x and 2x speedups 'without compromising visual quality,' yet supplies no quantitative metrics, baselines, ablation tables, or error analysis. This absence prevents evaluation of whether the quality-preservation claim holds at the reported speedups.

minor comments (1)

The abstract refers to 'spatially-aligned editing and subject-driven generation' without naming the specific datasets, tasks, or evaluation protocols used to support the quality claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and outline revisions to strengthen the presentation of our claims.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that random-ratio token dropping during fine-tuning 'decouples the model from any specific token selection rule' is asserted without a direct empirical test. No section or result is described that compares generation quality (metrics or human preference) under random masks versus the task-aware strategies (e.g., saliency- or attention-based) at identical token budgets; this comparison is load-bearing for the robustness and flexibility assertions.

Authors: We agree that the abstract does not explicitly present a direct side-by-side comparison of generation quality under random versus task-aware token selection at matched budgets. The manuscript demonstrates the benefit of random-ratio training by showing that task-aware selection at inference preserves quality after such training, but a dedicated ablation isolating the selection rule itself would more directly support the decoupling claim. We will add this comparison (including quantitative metrics and human preference results) to the experiments section and revise the abstract to reference it. revision: yes
Referee: [Abstract] Abstract: The abstract states that 'extensive experiments show' 4x and 2x speedups 'without compromising visual quality,' yet supplies no quantitative metrics, baselines, ablation tables, or error analysis. This absence prevents evaluation of whether the quality-preservation claim holds at the reported speedups.

Authors: The referee correctly notes that the abstract summarizes the speedup and quality claims without including supporting numbers. The body of the manuscript reports these details via FID, CLIP similarity, and user-study results across multiple tables and figures. To address the concern, we will revise the abstract to incorporate key quantitative metrics (e.g., specific FID values and speedup ratios with quality preservation) while remaining within length constraints. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical efficiency method

full rationale

The paper presents an empirical method: observe that random token dropping at inference preserves quality, then fine-tune with random-ratio dropping to encourage robustness, followed by task-aware selection at inference. No equations, derivations, or self-citations are shown that reduce the claimed 4x/2x speedups or quality preservation to fitted parameters, self-definitions, or prior author results by construction. The decoupling assertion is an empirical claim tied to the training procedure and validated experimentally, not a circular reduction. The work is self-contained against external benchmarks with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the domain assumption that diffusion models remain effective with sparse reference tokens and on the paper-specific training choice of random dropping; no free parameters or new entities are introduced in the abstract.

axioms (2)

domain assumption Reference images can be tokenized and partially dropped while preserving generation quality in diffusion models
Invoked when stating that dropping tokens largely preserves capabilities even without model changes.
ad hoc to paper Random token dropping during fine-tuning produces robustness to arbitrary task-aware selection at inference
This is the key training strategy that decouples the model from specific selection rules.

pith-pipeline@v0.9.1-grok · 5810 in / 1406 out tokens · 28835 ms · 2026-06-26T08:58:47.551123+00:00 · methodology

discussion (0)

Reference graph

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For the base model, our method achieves more than 2× speedup when 5% of the tokens are preserved

Our fine-tuning strategy significantly outperforms the Naïve token dropping strategy in terms of image fidelity and text alignment while being equally fast. For the base model, our method achieves more than 2× speedup when 5% of the tokens are preserved. This showcases the generalization of our approach in accelerating different model architectures, inclu...

arXiv