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Running on Zero
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f7dc16a | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import argparse
import os
from .config import resolve_args
def parse_args():
p = argparse.ArgumentParser(
description="Multics hierarchical multi-player inference"
)
p.add_argument(
"--dataset_path",
type=str,
default=None,
help="Dataset root (CSV paths resolved relative to this).",
)
p.add_argument(
"--metadata_file_name",
type=str,
default=None,
help="CSV path or name under dataset_path.",
)
p.add_argument(
"--ckpt_path",
type=str,
default=None,
help="DiT state_dict (.ckpt from Lightning on_save_checkpoint).",
)
p.add_argument(
"--wan_model_root",
type=str,
default=None,
help="Directory containing Wan2.1-T2V-1.3B and Wan2.2-TI2V-5B.",
)
p.add_argument(
"--target_rgb_frames",
type=int,
default=81,
help="RGB frame count. Must be 4n+1 and match training. Default 81.",
)
p.add_argument(
"--blank_prompt_emb_path",
type=str,
default=None,
help="Unused at inference. Prompts are encoded from the CSV. Training reads asset/blank_string_prompt_emb.pth when text is dropped or missing.",
)
p.add_argument("--vae_latent_channels", type=int, default=48)
p.add_argument("--num_players", type=int, default=2)
p.add_argument(
"--use_simplex_rope",
action="store_true",
default=True,
help="Use simplex 4D RoPE instead of learned player embeddings.",
)
p.add_argument("--simplex_pool_size", type=int, default=4)
p.add_argument("--simplex_gamma", type=float, default=1.0)
p.add_argument(
"--agent_vertex_ids",
type=int,
nargs="+",
default=None,
help="Simplex vertex id for each player. Defaults to 0 .. num_players-1.",
)
p.add_argument("--output_dir", type=str, default="outputs")
p.add_argument("--dataloader_num_workers", type=int, default=0)
p.add_argument("--cfg_scale", type=float, default=5.0)
p.add_argument("--num_inference_steps", type=int, default=50)
p.add_argument("--seed", type=int, default=0)
p.add_argument(
"--training_stage",
type=str,
default="stage2",
choices=["stage2"],
help=argparse.SUPPRESS,
)
p.add_argument(
"--attention_mode", type=str, default="hier_hub", choices=["hier_hub"]
)
p.add_argument("--local_attn_size", type=int, default=24)
p.add_argument("--num_frame_per_block", type=int, default=7)
p.add_argument("--num_local_hub_tokens", type=int, default=5)
p.add_argument("--num_global_hub_tokens", type=int, default=6)
p.add_argument(
"--hier_no_local_hub",
action="store_true",
help="Ablation: remove local hubs and connect every player directly to global hub.",
)
p.add_argument(
"--hier_local_hub_mode",
type=str,
default="shared",
choices=["shared", "per_group"],
help="hier_hub local seed parameterization: shared=[K_L,D], per_group=[N,K_L,D].",
)
p.add_argument(
"--hier_direct_schedule",
type=str,
default="none",
choices=["none", "uniform", "explicit"],
)
p.add_argument("--hier_direct_frac", type=float, default=0.0)
p.add_argument("--hier_direct_layers", type=str, default=None)
p.add_argument(
"--hier_infer_mode",
type=str,
default="cached",
choices=["full_sequence", "cached"],
help="hier_hub inference mode: cached uses the KV cache; full_sequence is a slower reference path without it.",
)
p.add_argument("--group_path_column", type=str, default="group_path")
p.add_argument(
"--hier_group_format",
type=str,
default="labels",
choices=["labels", "membership"],
help="hier_hub grouping tensor format. labels=partition (default); membership=cover.",
)
p.add_argument(
"--hier_group_mode",
type=str,
default="csv",
choices=["csv", "random_static", "random_block"],
)
p.add_argument("--hier_group_seed", type=int, default=0)
p.add_argument("--hier_random_preserve_sizes", type=int, default=1, choices=[0, 1])
p.add_argument(
"--group_label_player_ids",
type=str,
default=None,
help="1-based player ids used to select columns from group labels, e.g. 1,2,3,4.",
)
p.add_argument(
"--use_camera_pose",
action="store_true",
help="Add relative camera conditioning to self-attention Q and K. The CSV must contain camera_pose_path* or camera12_path*.",
)
p.add_argument(
"--camera_dim", type=int, default=12, help="Camera-pose embedding size."
)
p.add_argument(
"--camera_dropout_p",
type=float,
default=0.1,
help="Camera-adapter dropout used in training. Disabled when the model is evaluated.",
)
p.add_argument("--attn_impl", type=str, default="sdpa", choices=["sdpa", "flex"])
p.add_argument(
"--loss_type",
type=str,
default="diffusion_forcing",
choices=["diffusion_forcing"],
help="Training objective. Fixed to diffusion forcing.",
)
p.add_argument(
"--causal_num_frames",
type=int,
default=257,
help="RGB frame count for diffusion_forcing / causal_inference only.",
)
p.add_argument(
"--max_batches",
type=int,
default=None,
help="If set, only run this many batches.",
)
p.add_argument(
"--precomputed_consecutive_rows",
type=int,
default=1,
help="For precomputed N-player data, concatenate this many consecutive CSV rows along time before trimming to target_rgb_frames. Default 1 preserves existing behavior.",
)
p.add_argument(
"--parity_atol",
type=float,
default=0.5,
help="Relative threshold for the parity check, max|delta| / block mean. Default 0.5.",
)
p.add_argument(
"--no_save_grid",
action="store_true",
help="Do not write grid MP4. N-player precomputed inference writes 2xP grid (gen / GT).",
)
p.add_argument(
"--no_save_separate_players",
action="store_true",
help="Do not write per-player generated MP4s.",
)
p.add_argument("--grid_fps", type=int, default=20, help="FPS for written MP4s.")
args = resolve_args(p, "inference")
if args.hier_group_format == "membership":
infer_kernel = os.environ.get("WAN_HIER_INFER_KERNEL", "sdpa").strip().lower()
if (
infer_kernel in ("segmented", "tiled128")
or args.attn_impl == "hier_tiled_ste"
):
raise ValueError(
f"hier_group_format=membership is not supported with WAN_HIER_INFER_KERNEL={infer_kernel} or attn_impl={args.attn_impl}; use WAN_HIER_INFER_KERNEL=sdpa or packed_varlen with attn_impl=flex"
)
return args
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