E3Diff — SAR2Opt

Efficient End-to-End Diffusion Model for One-Step SAR-to-Optical Translation, Qin, Zou, Li and Zhang, IEEE Geoscience and Remote Sensing Letters, 2024 (doi:10.1109/LGRS.2024.3506566; the year is the one in the authors' own citation block — a table that labels this row with a later issue year is referring to the same paper). Upstream code: DeepSARRS/E3Diff at commit 38601093ab8f8e4b478144621f20890b100a3b74.

This is the authors' two-stage method, retrained by us end to end on SAR2Opt, and it is the checkpoint behind the E3Diff row of the ReFlowSET comparison table.

These are our weights, not the authors' released checkpoint. The authors' own weights exist for a different dataset and are not what this row measures.

What is in this folder

file	bytes	what it is
`gen.pth`	768,426,944	the `sr3` UNet after stage 2, at absolute iteration 310,000

Architecturally this is the same network as the DDPM (SR3-class) row in this repository — E3Diff's two stages share one UNet. What differs is the training:

stage 1 (250,000 updates) is the eps-prediction conditional DDPM. Its checkpoint is the ddpm/ folder in this repository.
stage 2 (60,000 further updates, absolute iteration 310,000) fine-tunes that same network into a one-step generator: the sampler is run with gradients from pure noise for ddim_steps = 1, and the loss is taken directly on pixels.

UNet: inner_channel 64, channel_multiplier [1, 2, 4, 8, 16], res_blocks 1, norm_groups 16, no attention, 3 in / 3 out, 3-channel condition.

The condition is the same three-channel [PPB, Canny, SAR] stack described on the DDPM card: FAST_PPB speckle filtering (Deledalle 2009, P = 3, W = 10, h = 0.5), cv2.Canny(ppb, 50, 150, L2gradient=True), and the raw SAR image. This row cannot be run from a SAR PNG alone.

Training budget we used


generator updates released	250,000 (stage 1, inherited) + 60,000 (stage 2) = 310,000 absolute
batch size / resolution	4 @ 512 px
optimizer	Adam, lr 5e-5
stage-2 sampler during training	DDIM, 1 step, run with gradients from pure noise
stage-2 losses	L1 on pixels, + LPIPS (weight 5), + focal-frequency (weight 10), + a vision-aided CLIP GAN (λ_gan 0.5)
stage-1 losses	plain eps-prediction MSE (all auxiliary weights zero)
sampler (test)	DDIM, 1 step
EMA	decay 0.9999 (stage 1)
augmentation	flips, 90° rotations, and a random brightness jitter on the SAR/PPB pair (upstream's `transform_augment`)

The stage-2 budget is 24 % of the stage-1 budget, which follows the authors' own ratio (they resume at 640,000 and run to 800,000). Note that n_iter in this code base is an absolute step count continuing stage 1, not a stage-2 budget — a config that reads 310,000 buys 60,000 new iterations.

Measured on the SAR2Opt test set (n = 627, 512 px)

FID↓	DISTS↓	LPIPS↓	SSIM↑	PSNR↑
104.7	0.232	0.529	0.249	16.09

Evaluated on the official split's 627 test tiles, centre-cropped to 512 px. The tiles are 600 px natively; this benchmark crops and never resizes, in every method's training and in the evaluation. No subsampling: every metric on this page is measured over all 627 pairs.

These are our own re-evaluation numbers, measured by us on the images this checkpoint produced. No number here is copied from any paper. PSNR and SSIM are per-image torchmetrics with data_range=1; LPIPS is LPIPS-VGG on inputs mapped to [-1, 1] (the normalize=False convention); DISTS is the standard implementation on the same pairs; FID is pytorch-fid against the size-matched ground-truth test set. The LPIPS convention matters: the alternative [0, 1] convention gives a systematically different number and the two must never be mixed, or compared against a paper that used the other one.

Load it and translate one SAR image

Same entry point and same mechanics as the DDPM row, with "stage": 2 and ddim_steps: 1.

# 1. Build the condition channels for each SAR image, once:
#      ppb   = FAST_PPB(sar, P=3, W=10, h=0.5)
#      canny = cv2.Canny(ppb.astype('uint8'), 50, 150, L2gradient=True)
#    written to <root>/val/SAR-PPB/<name> and <root>/val/SAR-canny/<name>,
#    alongside <root>/val/SAR/<name> and <root>/val/EO/<name>.

# 2. Run phase 'val' with a config whose path.resume_state is the checkpoint
#    PREFIX -- no '_gen.pth' suffix -- and place gen.pth as <prefix>_gen.pth:
#      "stage": 2, "ddim_steps": 1,
#      "model": {"beta_schedule": {"val": {"n_timestep": 1, "ddim": 1, ...}}},
#      "datasets": {"val": {"data_len": -1, "r_resolution": 512}}
#    python main.py -c <val.json> -p val -enable_wandb "" --seed 1

Sampling is genuinely one network evaluation per image, which is the point of the method.

As on the DDPM card, the RGB loader is the one thing you must sort out: upstream hard-codes a grayscale EO target and a two-channel condition, and with three channels its ddim_sample() slices the condition and concatenates mismatched shapes. We patched SAR2EODataset.__getitem__ in memory to return HR = EO[0:3], LR = SAR[0:3], SR = cat(PPB[0:1], Canny[0:1], SAR[0:1]) and then ran the repository's main.py verbatim.

Read before using this checkpoint

This is not an oracle. Worth stating, because a one-step generator invites the question: the sampler's condition_x is the [PPB, Canny, SAR] stack, and the ground truth never enters it. The cell also passes our leak audit on both datasets.
Stage 2 needs the real vision-aided CLIP discriminator. Stage 1 has lambda_gan = 0 and can import a stub; stage 2 cannot. If a constant-output stub shadows the real vision_aided_loss package, stage 2 trains against a constant GAN loss and silently is not the authors' method. Assert on vision_aided_loss.__file__ before starting a run.
SoftPool. The code imports the SoftPool CUDA extension unconditionally. We used a pure-PyTorch drop-in with identical maths (softpool(x) = avgpool(x·eˣ) / avgpool(eˣ)) and identical autograd, which needs no build step.
The checkpoint loads with strict=False. A mismatched configuration loads nothing and raises no error. Verify the Loading pretrained model for G [...] log line and look at the first output.
-p val writes next to the checkpoint and then renames the sample directory to <prefix>_S<ssim>_P<psnr>_l2<l2>_Lp<lpips>. That rename raises if the target already exists — after the whole inference has been paid for.
-enable_wandb "" is mandatory; the flag defaults to the truthy string 'false'.
One change we made to the repository: core/logger.py no longer unconditionally overwrites CUDA_VISIBLE_DEVICES from the config's gpu_ids, which on a shared machine remapped every job onto another user's device. It is a scheduling fix and does not affect the model.

Licence — stated factually ⚠ no upstream licence exists

The upstream code base publishes no licence. DeepSARRS/E3Diff has no LICENSE, LICENCE, COPYING or NOTICE file at the repository root or at any depth; 1 of its 58 tracked files is licence-shaped, and it is SoftPool/LICENSE.txt, the MIT licence of a vendored third-party dependency (Copyright (c) 2020 Alexandros Stergiou, reproduced here as licenses/LICENSE-SoftPool-MIT.txt) — not a grant covering E3Diff. Its README has no licence section, and the GitHub API reports no declared licence, with the /license endpoint returning 404. Checked 2026-08-28.

Under default copyright that means all rights are reserved by the authors and no express permission to redistribute derived work has been granted to us or to you. We publish this checkpoint anyway, so that the benchmark is reproducible end to end, and we state the position plainly rather than implying a permission that does not exist. If you intend to redistribute this checkpoint or build on it, assess that for yourself, and consider asking the authors directly.

Lineage. E3Diff's README credits SR3 (Janspiry/Image-Super-Resolution-via-Iterative-Refinement, Apache-2.0), GaParmar/img2img-turbo (MIT) and alexandrosstergiou/SoftPool (MIT). Those licences cover the borrowed parts only, not the authors' own two-stage contribution. We ship only the SoftPool text, because it is the only one of the three that is vendored in the tree we trained from.

Please cite: Qin, Zou, Li and Zhang, Efficient End-to-End Diffusion Model for One-Step SAR-to-Optical Translation, IEEE Geoscience and Remote Sensing Letters, doi:10.1109/LGRS.2024.3506566. The full record of what we checked, per method, is in licenses/NO-UPSTREAM-LICENSE.md at the root of this repository.

Part of the ReFlowSET release. This folder holds one comparison-method checkpoint that we retrained ourselves on SAR2Opt; it is not ReFlowSET itself. Every comparison method in this repository was retrained by us on the same splits at the same resolution and scored through one evaluation pipeline, so the rows are directly comparable to each other — and, for the same reason, not directly comparable with the numbers in the methods' own papers.