Rate-Adaptive One-Step Diffusion Compression for AIGC Images

Nitiz Khanal

cs.CVSep 25, 2026

Rate-Adaptive One-Step Diffusion Compression for AIGC Images

Authors: Nitiz Khanal

Organizations: Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Nepal

Abstract

We describe our entry to the LoViF 2026 AIGC Image Compression Challenge, a benchmark for ultra-low-bitrate coding of AI-generated images under a strict global rate budget of 0.025 bits per pixel (BPP). Generated imagery poses a distinct challenge for compression: it frequently contains rendered typography, synthetic edges, repeated motifs, UI-like layout, and stylized micro-texture that conventional distortion-oriented codecs erase at this rate, while unconstrained generative decoders can restore plausible-looking detail that no longer matches the source geometry or symbols. We treat this as a rate-perception allocation problem. Our system fine-tunes four rate-specialized checkpoints of the AEIC one-step diffusion codec, generates per-image candidates from all four, including one latent refined through encoder-side test-time optimization (TTO) with periodic entropy-conditioning refresh, entropy-codes every candidate with practical rANS coding, and selects exactly one bitstream per image with an exact multiple-choice knapsack solved over true coded file sizes. A fixed, zero-additional-bit residual restoration network is applied at decode time. Every submitted bitstream is independently decodable by the shipped decoder, which uses no source image or external side information. We report the full pipeline, an ablation history spanning 77 logged experiments, and a set of negative results, including why PSNR could not be pushed to parity with rate-distortion-oriented competitors under this architecture, useful to future participants. This is a challenge report: our entry scored 31.527739 (PSNR 27.02 dB, MS-SSIM 0.9176, LPIPS 0.0778, DISTS 0.0390 at 0.02495 BPP), the second-best DISTS on the leaderboard, ranking 5th on the final test-phase leaderboard announced August 4, 2026.

Figures & tables

Panel of Figure 1 — Figure 1: Final compression pipeline. Dashed flow denotes encoder-only access to the source for candidate evaluation and rate allocation; the submitted decoder receives only the selected bitstream and reconstructs without any source access.

Checkpoint	Init	Steps	Corpus	Target rate
aigc8_5000	ft8 (zero-shot, $\lambda_{8}$ )	5k	train-800 $8\times$ + initial synth.	0.020 BPP
r2_18000	aigc4_5000 ( $\lambda_{4}$ )	16k+	train-800 $8\times$ + synth.	0.037 BPP
r3l4_8000	r2_18000	8k	train-800 $8\times$ + SD3.5/SDXL synth.	0.037 BPP
r3l8_8000	aigc8_5000	8k	train-800 $8\times$ + SD3.5 synth.	0.026 BPP

Table 1: Training configuration of the four shipped checkpoints. All use AEIC’s codec loss

\lambda_{R}R+\lambda_{2}\|x{-}\hat{x}\|_{2}^{2}+\lambda_{D}\mathcal{L}_{\mathrm{DISTS}}+\lambda_{C}\mathcal{L}_{\mathrm{CLIP}}+\lambda_{A}\mathcal{L}_{\mathrm{adv}}+\lambda_{L}\mathcal{L}_{\mathrm{LPIPS}}

. Exact weights

(\lambda_{R},\lambda_{2},\lambda_{D},\lambda_{C},\lambda_{A},\lambda_{L})

are

(8,2,1,.1,.1,1)

for aigc8_5000;

(4,2,1.5,.1,.1,2)

for r2_18000 and r3l4_8000; and

(8,2,1.5,.1,.1,2)

for r3l8_8000. These values are retained in the four released training configuration files. Target rate is the checkpoint’s measured natural operating region, not an imposed per-image constraint.

Checkpoint	BPP	PSNR	MS-SSIM	LPIPS	DISTS	$\Delta$ PSNR	$\Delta$ MS-SSIM	$\Delta$ LPIPS	$\Delta$ DISTS	selected
	Raw checkpoint					Restorer change				Final
aigc8_5000	.019739	25.6427	.891992	.112686	.063923	+.1848	+.000272	-.001876	-.001634	20
r2_18000	.036242	27.0023	.921605	.082632	.049385	+.2166	+.000084	-.001581	-.001376	12
r3l4_8000	.036478	27.0471	.922338	.081227	.048786	+.2114	+.000063	-.001420	-.001200	40
r3l8_8000	.025387	26.3583	.907997	.091023	.052620	+.1948	+.000164	-.001377	-.001219	28

Table 2: Per-checkpoint full-test results before global selection, retained from the actual coded candidates. These are descriptive measurements of frozen, already-generated challenge candidates and were not used to update checkpoint weights. Restorer columns are restored-minus-raw metric changes (negative is better for LPIPS/DISTS); BPP is unchanged. “Final selected” counts the checkpoint-ID bytes in the 100 submitted streams, including streams whose latent was refined by TTO.

Checkpoint	Nominal rate	Score	BPP
ft8 (zero-shot)	low	106.19	0.017
ft4 (zero-shot)	mid	109.21	0.0239
ft2 (zero-shot)	high	111.28	0.031
Early zero-shot mix (mean-BPP solver)	–	109.55	0.0250

Table 3: Zero-shot AEIC-ME rate ladder on the challenge validation set, before any AIGC fine-tuning, TTO, or restoration (

S

scale, i.e. leaderboard scale

+80

; see Sec. 4.3 ). A single checkpoint at the legal rate already trails a per-image mix of unrelated rate points, motivating the checkpoint-bank design used throughout the rest of the system. Under the same early (mean-BPP-constrained, later superseded) solver, adding three fine-tuned checkpoints to this pool raised the mix from 109.55 to 109.89 (+0.34); this same-methodology signal fine-tuning was worth adding, not comparable to Tab. 6 ’s later rows.

Quantity	Final test entry
Official score ( $B{=}S{-}80$ )	31.527739
PSNR	27.022356 dB
MS-SSIM	0.917594
LPIPS	0.077794
DISTS	0.038970
Average BPP	0.024946

Table 4: Official organizer-recalculated final-test metrics for the submitted entry.

Rank	Team	Score	BPP	PSNR	MS-SSIM	LPIPS	DISTS
1	Ashes	34.004381	0.025000	29.820286	0.930888	0.076335	0.051785
2	rrrrty	33.557593	0.024987	28.926965	0.934522	0.071049	0.046815
3	NeFIC++	33.271115	0.024153	28.601395	0.932167	0.071724	0.044575
4	Freedom	32.849392	0.025000	28.779010	0.933339	0.077461	0.054115
5	ZeroR	31.527739	0.024946	27.022356	0.917594	0.077794	0.038970
6	prophet1	30.349026	0.024962	26.303701	0.908165	0.087057	0.038851

Table 5: Official final test-phase leaderboard, organizer-recalculated and announced August 4, 2026, at full precision. Our DISTS (0.038970) is second-lowest overall (only prophet1’s 0.038851 is lower) and lowest among every entry ranked above us; our LPIPS is not a leadership position. PSNR trails the top four by 1.6–2.8 dB ( Sec. 5 ).

Panel of Figure 2 — Figure 2: Subjective stage comparison on hand-selected test image 000009; bottom row enlarges the same poster region. The fine-tuned decode is 0.01774 BPP; TTO produces the selected 0.01885-BPP stream; restoration adds no bits, and the last column is the submitted reconstruction. The comparison isolates visible stage effects, not aggregate performance; see Sec. 4.6 for full-set evidence.

Candidate pool (cumulative)	$S$	BPP	Status
Early 11-way mixed pool (v3)	109.94	0.0250	confirmed
+ expanded fine-tuning, 300-step TTO (v17)	111.53	0.0250	confirmed
+ refresh, MILP pool (v20)	111.66	0.0250	confirmed
+ 500-step TTO (v21)	111.83	0.0250	dev-set est.
Matched pool without TTO	111.18	0.0250	dev-set ablation
Matched pool with TTO	111.51	0.0250	dev-set ablation

Table 6: Legal-rate (

\leq 0.025

BPP) results. Progression and matched-ablation rows use validation; the last row is the official test set and is not directly comparable across datasets. In particular, v20 was not evaluated on test, so v20

\to

v21 transfer cannot be inferred from the official test score. The matched TTO comparison uses the same four checkpoints, restorer, and MILP allocation, differing only in the availability of TTO candidates.

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