Add training and inference source code

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# Public names follow the paper; code/utils/config.py accepts both these and legacy keys.
# Three-encoder recipe used by the human and VLM evaluations. See ../docs/evaluated-settings.md.

# Step 80 uses the first 80 updates of the original 200-step cosine schedule.

# Initialization and checkpoint compatibility. Only the generator is optimized.
initial_generator_checkpoint: /path/to/video-generation-assets/checkpoints/causal-ode-init.safetensors
initial_generator_checkpoint_sha256: 01a21da77adf0e1e803d74ae73ac9c2bc397b1a05a74cbf89709a653282f7ab6
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_bank/manifest.json
reference_feature_manifest_sha256: c755ccc39ddddca7c9551ce4657c3e95f202453a0943711f0f9cd90b47d106e4
reference_feature_content_hash: 6b8f249733c39d865a8a9d4b1963779d5578c6a6d68aa5d96002a4b706c766e9

# Reference video/prompt correspondence and immutable asset identities.
reference_video_manifest: /path/to/video-generation-assets/source/matched.jsonl
matched_manifest_sha256: a047d08b22757f979f40787af816fa76bf31c68742242250ffbb6177cb815a02
reference_video_count: 8000
matched_source_csv_sha256: 5dd5e93f25d7fec7adfee10cfdf4ba85851f40624c3274cceef068ec1f0c36e9
matched_sample_identity_sha256: 43c752141b38dadb8791ca3e16507b2bc2c0deae3345c5ca609e03ae82a3a021
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/references/vjepa2.pt
triple_nystrom_reference_sha256: caa965b372e8dd75ba3757adf9caefa7c9b0bbc758d39c9d566a0056b2c19e20
triple_nystrom_landmarks: 1024
triple_nystrom_kernel_chunk_size: 32
triple_nystrom_seed: 0

# All three frozen encoders contribute; normalize each branch, then combine with weights 10:1:1.
active_feature_encoders:
- dinov3
- vjepa2
- videomae_v2
vjepa_loss_weight: 1.0
videomae_loss_weight: 1.0
encoder_loss_divisor: 3.0

# DINOv3: 20 sampled frames, five-frame temporal means, 1024 patch tokens.
dino_loss_weight: 10.0
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/references/dinov3.pt
dinov3_nystrom_reference_sha256: bc488a15ea499cc55eaaf201401eecfe37f9a92ccbe43d6e8d3e0fc37415f011
dinov3_nystrom_landmarks: 256
dinov3_nystrom_kernel_chunk_size: 32
dinov3_nystrom_seed: 0
dinov3_feature_metadata_preserve_runtime_paths: true

# 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_share_encoder_weights: true
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/references/videomae_v2.pt
videomae_v2_nystrom_reference_sha256: 17284c8eba334b091ab9668621ed9acccb045911e2acac51fd2cf3e4f4d56a36
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: 2
fresh_video_logical_micro_batch_size: 16
fresh_video_physical_replay_batch_size: 1
distribution_samples_per_rank: 32

# Replay the configured per-rank suffix; this release does not draw the paper pseudocode's uniform subset.
differentiation_samples_per_rank: 16
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
expected_world_size: 8
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
fsdp_backward_prefetch: post
optimizer_foreach: false
optimizer_fused: false
optimizer_state_cpu_offload_during_backward: true

# Generator optimizer. Learning-rate schedule parameters below remain unchanged.
generator_learning_rate: 2.5e-06

# 14B: retain the original 200-update cosine curve while stopping at update 80.
lr_schedule: cosine
lr_final: 5.0e-07
lr_schedule_total_steps: 200
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
generator_replay_activation_cpu_offload: true
generator_replay_activation_cpu_offload_block_stride: 0
generator_replay_frozen_encoder_cpu_offload: true
num_frame_per_block: 3
load_raw_video: false
model_kwargs:
  model_name: Wan2.1-T2V-14B
  timestep_shift: 5.0

# Checkpoint/logging policy. Full states include optimizer and per-rank RNG state.
log_iters: 10
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: 80
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: distribution_matching_14b_three_encoder_step80
matched_video_root: /path/to/video-generation-assets/videos
dataset_identity:
  name: wan21_14b_reference_8000
  sample_count: 8000
  pixel_geometry:
  - 81
  - 480
  - 832
  latent_geometry:
  - 21
  - 60
  - 104
  caption_type: source_wan_dataset_prompt
  source_manifest_sha256: a047d08b22757f979f40787af816fa76bf31c68742242250ffbb6177cb815a02
  teacher_bank_content_hash: 6b8f249733c39d865a8a9d4b1963779d5578c6a6d68aa5d96002a4b706c766e9
  reference_manifest_sha256: bc6041e517b6aa37d149c33cc9e5da69693e9efca515387e6bdb16a015307c4d
  checkpoint_policy: every_10_keep_all
fresh_video_progress_logs: true

	# Public names follow the paper; code/utils/config.py accepts both these and legacy keys.
	# Three-encoder recipe used by the human and VLM evaluations. See ../docs/evaluated-settings.md.

	# Step 80 uses the first 80 updates of the original 200-step cosine schedule.

	# Initialization and checkpoint compatibility. Only the generator is optimized.
	initial_generator_checkpoint: /path/to/video-generation-assets/checkpoints/causal-ode-init.safetensors
	initial_generator_checkpoint_sha256: 01a21da77adf0e1e803d74ae73ac9c2bc397b1a05a74cbf89709a653282f7ab6
	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_bank/manifest.json
	reference_feature_manifest_sha256: c755ccc39ddddca7c9551ce4657c3e95f202453a0943711f0f9cd90b47d106e4
	reference_feature_content_hash: 6b8f249733c39d865a8a9d4b1963779d5578c6a6d68aa5d96002a4b706c766e9

	# Reference video/prompt correspondence and immutable asset identities.
	reference_video_manifest: /path/to/video-generation-assets/source/matched.jsonl
	matched_manifest_sha256: a047d08b22757f979f40787af816fa76bf31c68742242250ffbb6177cb815a02
	reference_video_count: 8000
	matched_source_csv_sha256: 5dd5e93f25d7fec7adfee10cfdf4ba85851f40624c3274cceef068ec1f0c36e9
	matched_sample_identity_sha256: 43c752141b38dadb8791ca3e16507b2bc2c0deae3345c5ca609e03ae82a3a021
	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/references/vjepa2.pt
	triple_nystrom_reference_sha256: caa965b372e8dd75ba3757adf9caefa7c9b0bbc758d39c9d566a0056b2c19e20
	triple_nystrom_landmarks: 1024
	triple_nystrom_kernel_chunk_size: 32
	triple_nystrom_seed: 0

	# All three frozen encoders contribute; normalize each branch, then combine with weights 10:1:1.
	active_feature_encoders:
	- dinov3
	- vjepa2
	- videomae_v2
	vjepa_loss_weight: 1.0
	videomae_loss_weight: 1.0
	encoder_loss_divisor: 3.0

	# DINOv3: 20 sampled frames, five-frame temporal means, 1024 patch tokens.
	dino_loss_weight: 10.0
	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/references/dinov3.pt
	dinov3_nystrom_reference_sha256: bc488a15ea499cc55eaaf201401eecfe37f9a92ccbe43d6e8d3e0fc37415f011
	dinov3_nystrom_landmarks: 256
	dinov3_nystrom_kernel_chunk_size: 32
	dinov3_nystrom_seed: 0
	dinov3_feature_metadata_preserve_runtime_paths: true

	# 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_share_encoder_weights: true
	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/references/videomae_v2.pt
	videomae_v2_nystrom_reference_sha256: 17284c8eba334b091ab9668621ed9acccb045911e2acac51fd2cf3e4f4d56a36
	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: 2
	fresh_video_logical_micro_batch_size: 16
	fresh_video_physical_replay_batch_size: 1
	distribution_samples_per_rank: 32

	# Replay the configured per-rank suffix; this release does not draw the paper pseudocode's uniform subset.
	differentiation_samples_per_rank: 16
	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
	expected_world_size: 8
	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
	fsdp_backward_prefetch: post
	optimizer_foreach: false
	optimizer_fused: false
	optimizer_state_cpu_offload_during_backward: true

	# Generator optimizer. Learning-rate schedule parameters below remain unchanged.
	generator_learning_rate: 2.5e-06

	# 14B: retain the original 200-update cosine curve while stopping at update 80.
	lr_schedule: cosine
	lr_final: 5.0e-07
	lr_schedule_total_steps: 200
	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
	generator_replay_activation_cpu_offload: true
	generator_replay_activation_cpu_offload_block_stride: 0
	generator_replay_frozen_encoder_cpu_offload: true
	num_frame_per_block: 3
	load_raw_video: false
	model_kwargs:
	model_name: Wan2.1-T2V-14B
	timestep_shift: 5.0

	# Checkpoint/logging policy. Full states include optimizer and per-rank RNG state.
	log_iters: 10
	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: 80
	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: distribution_matching_14b_three_encoder_step80
	matched_video_root: /path/to/video-generation-assets/videos
	dataset_identity:
	name: wan21_14b_reference_8000
	sample_count: 8000
	pixel_geometry:
	- 81
	- 480
	- 832
	latent_geometry:
	- 21
	- 60
	- 104
	caption_type: source_wan_dataset_prompt
	source_manifest_sha256: a047d08b22757f979f40787af816fa76bf31c68742242250ffbb6177cb815a02
	teacher_bank_content_hash: 6b8f249733c39d865a8a9d4b1963779d5578c6a6d68aa5d96002a4b706c766e9
	reference_manifest_sha256: bc6041e517b6aa37d149c33cc9e5da69693e9efca515387e6bdb16a015307c4d
	checkpoint_policy: every_10_keep_all
	fresh_video_progress_logs: true