The quality of image generation models trained with diffusion or flow matching is largely fixed by the training budget: at inference, the only available lever is the number of sampling steps, and additional steps improve quality only up to a point. We present a training recipe, Scout-Refine-Correct (SRC), that instead gives a flow-matching generator a genuine inference-time refinement capability. At each sampling step the model takes a scout rollout -- K steps ahead along its own trajectory -- extrapolates a full-sample estimate, and feeds that estimate back to itself as a self-conditioning signal before taking the real step. This can be repeated for R refinement rounds. Two correction losses, applied during training at states the model's own rollouts actually reach rather than only on the ground-truth path, teach the model to use this signal to reach new samples instead of collapsing to a suboptimal one. On ImageNet-256, SRC improves LightningDiT-B from FID14.5 to FID7.26 at NFE550, matching a privileged best-of-N baseline that uses an oracle FID verifier to choose among samples. SRC uses no external verifier, no reward signal, and, after ablation, almost no extra parameters over the base architecture. Combining SRC with best-of-N improves the result further, to FID3.81 at NFE~4400, showing that the two axes are complementary. We ablate the main design choices -- the architectural pathway for the scout signal, the scout horizon, each correction loss, and the training schedule to demonstrate key working components of training.
Figures & tables
Figure 2: In-place refinement of a basic self-conditioning model (no scout, no correction loss): FID vs. refinement rounds R . Quality saturates at ≈ 13.3 almost immediately – extra refinement compute buys nothing without the correction losses of Sec. 3.2 .
symbol
meaning
value
ρ
self-cond fraction of batch
0.5
δ
scout step size (fixed)
0.02
Kmin..Kmax
scout-count range
1..3
N
re-noise draws per rollout point
2
λsc,λm
loss weights
1,1
Table 1: Training recipe hyperparameters.
Figure 3: FID vs. NFE on ImageNet-256: sampling-step scaling alone (base model, flat), vanilla self-conditioning R -scaling (plateaus at ≈ 13.3), and SRC’s single-sample K×R frontier (global best FID 7.26 at NFE 550).
NFE
SRC ( K=2,R=1 )+Bo N
baseline Bo N
200
7.89
8.25
400
6.53
6.96
800
5.45 ( N=4 )
5.97 ( N=8 )
1600
4.12
-
Table 2: Same-budget comparison: SRC anchored at K=2,R=1 versus the baseline best-of- N oracle
N
SRC ( K=4,R=2 )+best-of- N
NFE
1
7.26
550
2
5.99
1100
4
5.02
2200
8
3.81 (new global best)
4400
Table 3: Scaling SRC+best-of- N past where the baseline best-of- N oracle was ever measured.
Figure 4: SRC K/R refinement compared with scaling the number of inference steps and with scaling the classifier-free guidance weight.
Figure 5: (a) FID vs. inference-time K at R=1 . (b) FID at four decisive operating cells. main-corr is the single most critical term; scout-corr is also clearly load-bearing, while K -conditioning doesn’t introduce significant gains.
R=2
R=3
with self-conditioned FM
0.013
0.003
without self-conditioned FM
0.164
0.183
Table 4: Mean LPIPS between refinement round R and round R+1 , averaged over images and sampling steps. The two rows are the self-conditioning baseline that keeps the term and the later model that drops it.
Figure 6: Pixel differences for SRC refinement and for classifier-free guidance scaling, on the same examples.
Kmax
best FID
2
7.21
3
7.18
4
7.49
6
8.30
8
9.99
Table 5: Training-time scout horizon ( Kmax ). Kmax=3 is the best of the tested values.
(a,b,c) steps
FID @ best KR
(120k, 0, 90k) – no stage b
7.85
(120k, 90k, 0) – no stage c
8.88
(120k, 60k, 30k)
7.18
(120k, 30k, 60k)
7.53
(90k, 90k, 30k)
7.34
Table 6: Training-stage budget. The (120k,60k,30k) split is the best of the five schedules tested.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: The architecture used from the self-conditioning baseline through SRC: s enters only as concatenated tokens from a bias-free embedder, and one AdaLN slot is driven by time and class. K is not a network input.
Figure 8: Matched-seed comparison of SRC refinement and vanilla self-conditioning refinement. Left: SRC at NFE {50,100,200,550} . Right: vanilla self-conditioning at the same NFEs (reference, R=1 , R=3 , R=10 ). The right-hand family is the baseline that plateaus in Fig. 2 .
Flow-based generative modeling learns probability transports from simple noise to data for fast sampling, density estimation, and controllable generation.