# Public names follow the paper; code/utils/config.py accepts both these and legacy keys.
# Numerical settings and resource identities are unchanged. See ../../docs/paper-to-code.md.


# Initialization and checkpoint compatibility. Only the generator is optimized.
initial_generator_checkpoint: /path/to/video-generation-assets/checkpoints/ode_init.pt
initial_generator_checkpoint_sha256: b5396b8076ab3b920c9e4f4a2b52daa2c98c9983fb5e067ae5160fdf778dce21
resume_checkpoint: null

# Distributed execution: wrap units and precision; score-model settings below are legacy/inactive.
generator_fsdp_wrap_strategy: transformer
real_score_fsdp_wrap_strategy: size
fake_score_fsdp_wrap_strategy: size
text_encoder_fsdp_wrap_strategy: size
uses_score_models: false
uses_real_score: false
uses_fake_score: false

# Autoregressive rollout: four candidate denoising times, with stochastic training exit steps.
causal: true
denoising_step_list:
- 1000
- 750
- 500
- 250
warp_denoising_step: true
timestep_shift: 5.0
guidance_scale: 3.0
mixed_precision: true

# Training seed 0 requests a randomly chosen seed; use an explicit positive value for repeatable input draws.
seed: 0

# Paper Sec. 3.1: match generated videos in frozen representation spaces, not score-model outputs.
training_objective: distribution_matching
training_loop: distribution_matching
feature_space: frozen_video_encoders
mmd_estimator: hybrid_nystrom_monte_carlo
objective_weight: 1.0

# Each encoder loss M uses M / (stop_gradient(M) + epsilon); encoder weights follow this scaling.
normalize_encoder_losses: true
encoder_loss_normalization_epsilon: 0.0001

# Reference features are read from disk. "Teacher" in legacy keys means reference data, not a live network.
reference_feature_source: matched_encoder_features
reference_feature_manifest: /path/to/video-generation-assets/teacher_banks/triple_video_new8682_824/manifest.json
reference_feature_manifest_sha256: 1001c568c0dd2c53c17b7c7b147d01968b304a52f8b8ebe849246e1c6e34f36d
reference_feature_content_hash: 0e2431c6443f94a32418fddd93328d60c33bd78e2cc2c0751f9bce3960f36912

# Reference video/prompt correspondence and immutable asset identities.
reference_video_manifest: /path/to/video-generation-assets/experiments/triple_video_new8682_824/source/new8682_824_matched.jsonl
matched_manifest_sha256: fa8e8e24011e6d292920f63d55b7d4de24fd07ebf46e175a064deee30d2728e1
reference_video_count: 8682
matched_source_csv_sha256: d109dd7998a48916158dcc1a1d372c4e3a136f455257d4d272cbcb887a3a5b14
matched_sample_identity_sha256: c342bee98db8c4096726f06c9e840681516c27c8b39e54d4bbbf210abcc79637
matched_sampling_algorithm: sha256_seed_step_position_mod_n_v1
matched_sampling_with_replacement: true

# Paper Sec. 3.2: combine a persistent Nyström summary with the current reference minibatch.
triple_token_sampling: uniform_fixed
triple_nystrom_attraction_ema_enabled: false

# Nyström:Monte Carlo = 2:1, corresponding to paper mixture alpha = 1/3.
nystrom_reference_weight: 2.0
triple_nystrom_current_teacher_attraction_enabled: true
monte_carlo_reference_weight: 1.0
triple_nystrom_repulsion_fifo_size: 0

# The legacy triple_nystrom_* reference fields belong to V-JEPA 2; other encoders have separate packs.
triple_nystrom_reference_path: /path/to/video-generation-assets/experiments/triple_video_new8682_824/references/vjepa2_l15_last64_k1024_m1024_matched5k.pt
triple_nystrom_reference_sha256: 22a23b9bdd8283cf0f5cb41f1a3edfe2aa5badaad4e2bb033bc4a886d5c59b9a
triple_nystrom_landmarks: 1024
triple_nystrom_kernel_chunk_size: 32
triple_nystrom_seed: 0

# Paper Eq. (3): encoder weights after normalization; divide by the explicit divisor, not their sum.
dino_loss_weight: 10.0
vjepa_loss_weight: 1.0
videomae_loss_weight: 1.0
encoder_loss_divisor: 3.0

# DINOv3: 20 selected frames -> four groups of five -> 4 x 256 spatial tokens.
dinov3_repo_path: /path/to/video-generation-assets/models/dinov3-src
dinov3_weight_path: /path/to/video-generation-assets/models/dinov3_vitl16_lvd1689m.pth
dinov3_source_commit: 6876159a11b4df116f30f667f8c9888617df0751
dinov3_checkpoint_sha256: b361ce4cccaae083fccaa7a732bd9abb143b5a3736175152f28f331b600f8eec
dinov3_dense_geometry: temporal_mean5_full256
dinov3_dense_source_num_frames: 81
dinov3_dense_frame_indices:
- 0
- 4
- 8
- 13
- 17
- 21
- 25
- 29
- 34
- 38
- 42
- 46
- 51
- 55
- 59
- 63
- 67
- 72
- 76
- 80
dinov3_dense_input_size: 256
dinov3_dense_patch_size: 16
dinov3_dense_spatial_axis_indices:
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
dinov3_dense_temporal_groups: 4
dinov3_dense_temporal_pool_size: 5
dinov3_dense_forward_chunk_frames: 4
dinov3_dense_gradient_checkpointing: false
dinov3_nystrom_reference_path: /path/to/video-generation-assets/experiments/triple_video_new8682_824/references/dinov3_vitl16_f20mean5_k1024_m256_matched5k.pt
dinov3_nystrom_reference_sha256: e6e3fe8d2aca39bf68e5a1a2c384ae40cc1033345d88739fd9e97e4f880f5a3c
dinov3_nystrom_landmarks: 256
dinov3_nystrom_kernel_chunk_size: 32
dinov3_nystrom_seed: 0

# V-JEPA 2: last 64 of 81 frames, block index 15 (the 16th block), 1024 selected tokens.
vjepa2_model_id: /path/to/video-generation-assets/models/vjepa2-vitl-fpc64-256
vjepa2_reference_model_id: vjepa2-vitl-fpc64-256
vjepa2_model_sha256: 25466aef85727d16546c6cf8c99f12fcfad9cbca8225d45f23685e2e025b786b
vjepa2_dense_layers:
- 15
vjepa2_dense_layer: 15
vjepa2_dense_num_frames: 64
vjepa2_dense_source_num_frames: 81
vjepa2_dense_frame_sampling: last_contiguous
vjepa2_student_source_num_frames: 81
vjepa2_student_frame_sampling: last_contiguous
vjepa2_dense_num_tokens: 1024
vjepa2_dense_resize_short_side: 292
vjepa2_dense_crop_size: 256
vjepa2_dense_gradient_checkpointing: true

# VideoMAEv2: skip frame 0, split the remaining 80 frames into five 16-frame segments.
videomae_v2_model_id: /path/to/video-generation-assets/models/VideoMAEv2-Large
videomae_v2_model_revision: 9981a9c8f77118c421e5228e1b219468a4b0238d
videomae_v2_download_manifest_content_hash: 5e362cfe6fdb1bec9e3e195e5ef43cdfebbdfaec2625b1f60550efd05ead0d7d
videomae_v2_local_files_only: true
videomae_v2_dense_layers:
- 15
videomae_v2_dense_layer: 15
videomae_v2_encoder_count: 5
videomae_v2_skip_leading_frames: 1
videomae_v2_segment_num_frames: 16
videomae_v2_source_num_frames: 81

# 256 tokens per segment, hence 1280 tokens for the complete video.
videomae_v2_dense_num_tokens: 256
videomae_v2_dense_resize_short_side: 224
videomae_v2_dense_crop_size: 224
videomae_v2_dense_gradient_checkpointing: true
videomae_v2_nystrom_reference_path: /path/to/video-generation-assets/experiments/triple_video_new8682_824/references/videomae_v2_l15_fixed5x16_k256_m256_matched5k.pt
videomae_v2_nystrom_reference_sha256: 526cad0c388c560c486cb460b3e7df6a4b91826825e3e2b0af10f6ffe8f56a4a
videomae_v2_nystrom_landmarks: 256
videomae_v2_nystrom_kernel_chunk_size: 32
videomae_v2_nystrom_seed: 0
videomae_v2_nystrom_attraction_weight: 2.0
videomae_v2_nystrom_current_teacher_attraction_weight: 1.0

# Paper Sec. 3.3: separate distribution batch B, differentiation batch b, and reference minibatch B_ref.
per_device_microbatch_size: 1
distribution_samples_per_rank: 64

# Replay the configured per-rank suffix; this release does not draw the paper pseudocode's uniform subset.
differentiation_samples_per_rank: 32
fresh_video_partial_gradient: true
differentiate_full_distribution_loss: true

# Apply B/b once on replay gradients, separately from FSDP averaging compensation.
differentiation_gradient_scale: 2.0
distribution_batch_size: 256
reference_minibatch_size: 128

# Recorded estimator geometry: [B, b, B_ref]. Released self-kernel mean includes diagonal pairs and divides by B^2.
partial_mmd_geometry:
- 256
- 128
- 128
partial_mmd_normalization_identity: full_student_population_denominators_v1
partial_mmd_kss_denominator: 65536
partial_mmd_fixed_attraction_denominator: 256
partial_mmd_live_cross_denominator: 32768
partial_mmd_teacher_self_denominator: 16384
partial_mmd_gradient_compensation: 2.0
fresh_video_replay_atol: 1.0e-05
fresh_video_replay_rtol: 0.0001

# Parameter EMA is disabled; inference uses the trained generator weights directly.
ema_weight: 0.0
sharding_strategy: full

# Generator optimizer. Learning-rate schedule parameters below remain unchanged.
generator_learning_rate: 2.5e-06
beta1: 0.0
beta2: 0.999
weight_decay: 0.0
max_grad_norm_generator: 10.0
negative_prompt: ''
dfake_gen_update_ratio: 1

# Latent tensor [batch, time, channels, height, width]; 21 latent frames decode to 81 RGB frames at 480 x 832.
latent_video_shape:
- 1
- 21
- 16
- 60
- 104
denoising_loss_type: flow

# Activation recomputation saves GPU memory; it is unrelated to saving model checkpoints.
gradient_checkpointing: true
num_frame_per_block: 3
load_raw_video: false
model_kwargs:
  model_name: Wan2.1-T2V-1.3B
  timestep_shift: 5.0

# Checkpoint/logging policy. Full states include optimizer and per-rank RNG state.
log_iters: 5
save_full_state: true
save_full_state_final_only: false
checkpoint_keep_last: 0
checkpoint_model_min_free_gib: 12
checkpoint_full_min_free_gib: 32
allow_resume_world_size_change: false

# Optional in-training evaluation is disabled; use scripts/infer.sh for generation.
eval_interval: 0
eval_seed: 12345
eval_num_prompts: 0
eval_prompt_path: prompts/test_8.txt
num_workers: 0
stdout_log_iters: 1

# Optimizer updates, not epochs or diffusion timesteps. Do not shorten the configured cosine horizon.
training_updates: 150
num_train_timestep: 1000

# Clean cached context; blocks share the sampled training exit step. Frozen encoders still pass input gradients.
context_noise: 0
independent_first_frame: false
same_step_across_blocks: true
last_step_only: false
i2v: false
num_training_frames: 21
gc_interval: 25

# Opaque feature/data identities are retained so existing reference packs and checkpoints still match.
feature_protocol_identity: triple_dinov3_f20mean5_k1024_vjepa2_l15_last64_k1024_videomae_v2_l15_5x16_k256_new8682_824_v1
matched_video_root: /path/to/new8682_824
newwan_dataset_alias: new8682_824

# 1.3B: cosine decay over updates 0..125, then the floor through update 150.
generator_lr_schedule: cosine_window
generator_lr_cosine_start_step: 0
generator_lr_cosine_end_step: 125
generator_lr_min: 5.0e-07