File size: 2,603 Bytes
f065e53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | # SpatialDiffuseSlot — Semanticist DiT-L/ViT (tok_L init) + OUR 85 multi-res
# spatial tokens + OUR spatial-align attention mask. (2026-07-15 설계 확정본)
# Phase 1 (this config): DiT trunk FROZEN (freeze_dit) — encoder/pool/cond-embedder
# adapt first with LONG warmup. Phase 2: resume with freeze_dit=false + low blr.
trainer:
target: semanticist.engine.diffusion_trainer.DiffusionTrainer
params:
num_epoch: 400
valid_size: 64
blr: 2.5e-5 # Semanticist recipe (batch 256 -> lr 2.5e-5)
cosine_lr: True
warmup_epochs: 100 # 25% warmup (Semanticist recipe, 우리 기존 2%의 교훈)
batch_size: 32 # per-gpu; 4 GPU -> eff 128 (large-batch regime 회피)
num_workers: 8
pin_memory: True
grad_accum_steps: 2 # eff 256 == Semanticist
precision: 'bf16'
max_grad_norm: 3.0
save_every: 10000
sample_every: 5000
fid_every: 50000
result_folder: "./output/tokenizer/models_l_spatial"
log_dir: "./output/tokenizer/models_l_spatial/logs"
cfg: 3.0
compile: False
enable_ema: True
model:
target: semanticist.stage1.spatial_diffuse_slot.SpatialDiffuseSlot
params:
encoder: 'vit_base_patch16'
enc_img_size: 256
enc_causal: True
num_slots: 256 # KEEP 256: tok_L encoder loads 100% (slots unused)
slot_dim: 16
norm_slots: True
enable_nest: False
enable_nest_after: -1
dit_model: 'DiT-L-2'
vae: 'xwen99/mar-vae-kl16'
num_sampling_steps: '250'
use_repa: True
repa_encoder_depth: 8
repa_loss_weight: 1.0
eval_fid: True
fid_stats: 'fid_stats/adm_in256_stats.npz'
# --- SpatialDiffuseSlot 전용 ---
level_sizes: [8, 4, 2, 1] # 85 multi-res cond tokens
pool_depth: 2 # 우리 L_repa attn-pool depth
freeze_dit: True # Phase-1: pretrained DiT trunk frozen
init_from: /NHNHOME/WORKSPACE/0226010398_A/sr_diffusion/clevr_sudoku/new_eval_spatial_reasoning0430/eval_assets/semanticist_pretrained/semanticist_tok_L.pkl
ckpt_path: null # trainer가 무조건 참조 (resume용; init은 init_from)
dataset:
target: semanticist.utils.datasets.ImageNet
params:
root: ./dataset/imagenet/
split: train
aug: 'randcrop'
img_size: 256
test_dataset:
target: semanticist.utils.datasets.ImageNet
params:
root: ./dataset/imagenet/
split: val
aug: 'centercrop'
img_size: 256
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