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CIFAR-10 EDM2 large runs A and B: best EMA + final full checkpoints

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  1. cifar_edm2/README.md +32 -0
  2. cifar_edm2/run_a_linear/args.json +133 -0
  3. cifar_edm2/run_a_linear/ema_step_0300000.pt +3 -0
  4. cifar_edm2/run_a_linear/latest_step_0402000.pt +3 -0
  5. cifar_edm2/run_a_linear/log_fid.jsonl +17 -0
  6. cifar_edm2/run_b_trunc_std1/args.json +143 -0
  7. cifar_edm2/run_b_trunc_std1/ema_step_0125000.pt +3 -0
  8. cifar_edm2/run_b_trunc_std1/latest_step_0402000.pt +3 -0
  9. cifar_edm2/run_b_trunc_std1/log_fid.jsonl +17 -0
cifar_edm2/README.md ADDED
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+ # EqFM on CIFAR-10, EDM2 U-Net, from scratch (DeltaAI, 4x GH200, global batch 512)
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+
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+ Code: github.com/justinlin26/EqFM, branch `mnist-smf-ablation` (`scripts/train_stage2_flowmap_mnist.py`,
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+ `eqfm/models/edm2_flowmap.py`); recipe notes in `docs/RUN_ME_edm2_v2.md`.
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+ Unconditional; the flow map is X_sigma(x) = x + sigma * v(x, sigma). Network: the flow-map paper's EDM2 CIFAR
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+ U-Net (Boffi et al., arXiv 2505.18825), 54.67M params. Shifted MeanFlow loss with a live self-teacher.
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+
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+ | Folder | Run | Recipe |
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+ |---|---|---|
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+ | `run_b_trunc_std1/` | `edm2_large_b512_trunc_std1_s0` | truncated clock a=0.9, noise std 1 (sigma_data 1); diag weight 768 (256 from step 211277); LR 4e-3, 8e-4 at 130k, 1e-4 at 250k |
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+ | `run_a_linear/` | `edm2_large_b512_s0` | linear clock, noise std 0.5 (sigma_data 0.5); diag weight 768 (3072 from step 60298); LR 4e-3, cosine to 1e-4 from 242k |
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+
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+ Files per run:
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+ - `ema_step_XXXXXXX.pt`: best EMA checkpoint (EMA weights + training args). Run B: 125k. Run A: 300k.
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+ - `latest_step_0402000.pt`: final full checkpoint (raw weights `model`, `ema`, AdamW `optimizer`, `step`) -- resumable.
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+ - `args.json`: trainer arguments at the last launch; `log_fid.jsonl`: in-run FID-2k (EMA and raw) every 25k steps.
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+
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+ FID (CIFAR-10 train statistics, clean-fid legacy_tensorflow, expuniform sampler, EMA):
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+
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+ | Checkpoint | Metric | 1 | 2 | 4 | 8 | 16 steps |
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+ |---|---|---|---|---|---|---|
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+ | Run A, 300k | FID-50k | 11.30 | 12.81 | 10.54 | 7.50 | 7.04 |
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+ | Run A, 402k | FID-50k | 14.24 | 15.77 | 13.27 | 8.41 | 7.10 |
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+ | Run B, 125k | FID-2k (in-run) | 20.8 | 19.2 | 21.2 | 20.3 | – |
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+ | Run B, 402k | FID-2k (in-run) | 25.0 | 20.7 | 22.1 | 20.9 | – |
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+
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+ (In run A, FID-2k read 13.1 +- 0.2 higher than FID-50k; run B's FID-50k is being measured.)
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+
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+ Evaluate: `python scripts/eval_fid_cifar.py --mode eqfm --ckpt <file> --nfe 1 2 4 8 16 --schedule expuniform
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+ --num-gen 50000 --batch-size 500 --seed 2024` (reads clock and noise std from the checkpoint).
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+ Resume: full checkpoint via `--resume-from latest_step_0402000.pt`; from an EMA snapshot via
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+ `--resume-from ema_step_XXXXXXX.pt --rewarmup 1000` (optimizer reset), into a new run directory.
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+ "data_root": "/work/nvme/biqs/mdrieux/cifar-hth/data",
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+ }
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