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Add isolated Minecraft and RE10K baseline evaluation suite
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# WorldMem diffusion training config
# Action + PRoPE conditioning, no text conditioning, CFG on actions only
trainer: diffusion
causal: true
mixed_precision: true
seed: 0
# Data
# CSV columns: video_path, action_path (WorldMem format)
data_path: data/train_wm_gamecraft/v2/worldmem_train_v2_shuffle.csv
num_frames: 60 # latent frame count; dataset loads (21-1)*4+1=81 video frames
load_raw_video: true
sample_strategy: random # overfit: always start from SKIP_FRAMES; use "random" for normal training
# CFG: probability of dropping action conditioning (zeros actions + identity viewmats)
proportion_empty_prompts: 0.1
# Model
# 81 video frames @ 640x352 -> 21 latent frames @ 80x44 (WAN 4x temporal, 8x spatial)
image_or_video_shape:
- 1 # batch_size placeholder (overridden at runtime)
- 60 # latent frames
- 16 # latent channels
- 44 # latent height (352/8)
- 80 # latent width (640/8)
model_kwargs:
timestep_shift: 5.0
local_attn_size: -1
sink_size: 0
use_action_encoder: true
action_dim: 25
latent_channels: 16
# Training hyperparams
num_train_timestep: 1000
timestep_shift: 5.0
guidance_scale: 1.0
teacher_forcing: true
noise_augmentation_max_timestep: 0
generator_ckpt: exps/dense_short/checkpoint_model_022000/model.pt
lr: 2.0e-5
beta1: 0.9
beta2: 0.999
weight_decay: 0.01
batch_size: 1
gradient_checkpointing: true
num_frame_per_block: 4
sharding_strategy: hybrid_full
# Logging / saving
no_save: false
no_visualize: true
log_iters: 500
# ema_weight: 0.999
# ema_start_step: 500
gc_interval: 100
wandb_host: "https://api.wandb.ai"
wandb_key:
wandb_entity: ""
wandb_project: ""
negative_prompt: ""
# FSDP wrap strategy
generator_fsdp_wrap_strategy: size
text_encoder_fsdp_wrap_strategy: size