pith:I4KKES37
SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation
SlimDiffSR distills a diffusion model into a single-step student network that runs 200 times faster and uses 20 times fewer parameters for remote sensing super-resolution.
arxiv:2605.02198 v2 · 2026-05-04 · cs.CV
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\pithnumber{I4KKES37KHTVIWXYFA2AAVOJQD}
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Record completeness
Claims
SlimDiffSR attains up to 200× inference acceleration and a 20× reduction in model parameters compared with multi-step diffusion models, while achieving competitive perceptual quality and clearly outperforming existing lightweight diffusion baselines in efficiency.
That the uncertainty-guided timestep assignment and structured pruning with frequency- and direction-separable convolutions plus MMD distillation will preserve generative quality on remote sensing data without significant degradation or domain-specific failure modes.
SlimDiffSR uses uncertainty-guided timestep assignment and remote-sensing-tailored pruning plus MMD distillation to create a diffusion SR model with 200x faster inference and 20x fewer parameters while keeping competitive quality.
Receipt and verification
| First computed | 2026-05-20T00:04:33.585516Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
4714a24b7f51e7545af828340055c980e5c0d89c1353898192a3e25ea087fc4c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/I4KKES37KHTVIWXYFA2AAVOJQD \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4714a24b7f51e7545af828340055c980e5c0d89c1353898192a3e25ea087fc4c
Canonical record JSON
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"primary_cat": "cs.CV",
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