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31c7d49 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | #!/usr/bin/env python3
"""Generate an official PyTorch reference for the browser-controlled flow loop."""
from __future__ import annotations
import argparse
import json
import os
import time
from pathlib import Path
from dit_common import (
CAMERA_SHAPE,
FEATURE1_SHAPE,
FEATURE2_SHAPE,
LATENT_SHAPE,
choose_torch_device,
load_official_flow_model,
resolved_file,
sha256_file,
source_revision,
synchronize_torch,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--triposplat-repo", type=Path, required=True)
parser.add_argument("--weights", type=Path, required=True)
parser.add_argument("--input-fixture-dir", type=Path, required=True)
parser.add_argument("--output-fixture-dir", type=Path, required=True)
parser.add_argument("--device", choices=("cpu", "mps", "cuda", "auto"), default="cpu")
parser.add_argument(
"--internal-precision",
choices=("fp16", "fp32"),
default="fp16",
help="Official flow-model precision used for the reference (default: %(default)s).",
)
parser.add_argument("--steps", type=int, default=4)
parser.add_argument("--guidance-scale", type=float, default=3.0)
parser.add_argument("--shift", type=float, default=3.0)
parser.add_argument(
"--record-trajectory",
action="store_true",
help="Record official per-invocation sample, timestep, and raw predictions.",
)
args = parser.parse_args()
if args.steps <= 0:
parser.error("--steps must be positive")
if args.guidance_scale <= 1:
parser.error("--guidance-scale must be greater than one to exercise CFG")
if args.shift <= 0:
parser.error("--shift must be positive")
return args
def hard_link(source: Path, destination: Path) -> None:
destination.parent.mkdir(parents=True, exist_ok=True)
if destination.exists():
if os.path.samefile(source, destination):
return
destination.unlink()
os.link(source, destination)
def main() -> None:
try:
import numpy as np
import torch
except ImportError as exc:
raise SystemExit(f"PyTorch and NumPy are required: {exc}") from exc
args = parse_args()
repository = args.triposplat_repo.expanduser().resolve()
weights = resolved_file(args.weights, "TripoSplat flow-model weights")
input_dir = args.input_fixture_dir.expanduser().resolve()
output_dir = args.output_fixture_dir.expanduser().resolve()
shapes = {
"latent": LATENT_SHAPE,
"camera": CAMERA_SHAPE,
"feature1": FEATURE1_SHAPE,
"feature2": FEATURE2_SHAPE,
}
arrays = {}
for name, shape in shapes.items():
path = resolved_file(input_dir / f"{name}.f32", f"fixture {name}")
array = np.fromfile(path, dtype="<f4")
expected = int(np.prod(shape))
if array.size != expected:
raise ValueError(f"{path} has {array.size} floats; expected {expected}")
arrays[name] = array.reshape(shape)
device = choose_torch_device(torch, args.device)
commit, dirty = source_revision(repository)
print(f"Loading untouched official flow model at {commit} on {device}")
model, source = load_official_flow_model(
torch=torch,
triposplat_repo=repository,
weights=weights,
device=device,
internal_precision=args.internal_precision,
low_memory_construction=True,
)
noise = {
"latent": torch.from_numpy(arrays["latent"]).to(device=device, dtype=torch.float32),
"camera": torch.from_numpy(arrays["camera"]).to(device=device, dtype=torch.float32),
}
condition = {
"feature1": torch.from_numpy(arrays["feature1"]).to(device=device, dtype=torch.float32),
"feature2": torch.from_numpy(arrays["feature2"]).to(device=device, dtype=torch.float32),
}
negative_condition = {name: torch.zeros_like(value) for name, value in condition.items()}
sampler = source.pipeline_module.FlowEulerCfgSampler()
trajectory = []
class RecordingModel:
def __call__(self, x_t, timestep, cond):
prediction = model(x_t, timestep, cond)
trajectory.append(
{
"sample": {name: value.detach().clone() for name, value in x_t.items()},
"timestep": timestep.detach().clone(),
"prediction": {
name: value.detach().clone() for name, value in prediction.items()
},
}
)
return prediction
sampling_model = RecordingModel() if args.record_trajectory else model
synchronize_torch(torch, device)
started = time.perf_counter()
with torch.inference_mode():
result = sampler.sample(
sampling_model,
{name: value.clone() for name, value in noise.items()},
cond=condition,
neg_cond=negative_condition,
steps=args.steps,
guidance_scale=args.guidance_scale,
shift=args.shift,
)
synchronize_torch(torch, device)
duration_ms = (time.perf_counter() - started) * 1000
output_dir.mkdir(parents=True, exist_ok=True)
for name in shapes:
hard_link(input_dir / f"{name}.f32", output_dir / f"{name}.f32")
output_files = {}
for name, tensor in result.items():
path = output_dir / f"flow{args.steps}_{name}.f32"
np.ascontiguousarray(tensor.detach().float().cpu().numpy(), dtype="<f4").tofile(path)
output_files[name] = {
"path": path.name,
"shape": list(tensor.shape),
"sha256": sha256_file(path),
}
trajectory_files = []
if args.record_trajectory:
expected_invocations = args.steps * 2
if len(trajectory) != expected_invocations:
raise RuntimeError(
f"Recorded {len(trajectory)} model calls; expected {expected_invocations}"
)
trajectory_dir = output_dir / "trajectory"
trajectory_dir.mkdir(parents=True, exist_ok=True)
for index, record in enumerate(trajectory):
invocation = index + 1
prefix = f"invocation_{invocation:02d}"
paths = {}
tensors = {
"sample_latent": record["sample"]["latent"],
"sample_camera": record["sample"]["camera"],
"t": record["timestep"],
"pred_latent": record["prediction"]["latent"],
"pred_camera": record["prediction"]["camera"],
}
for name, tensor in tensors.items():
path = trajectory_dir / f"{prefix}_{name}.f32"
np.ascontiguousarray(tensor.detach().float().cpu().numpy(), dtype="<f4").tofile(path)
paths[name] = {
"path": str(path.relative_to(output_dir)),
"shape": list(tensor.shape),
"sha256": sha256_file(path),
}
trajectory_files.append(
{
"invocation": invocation,
"step": index // 2 + 1,
"pass": "conditional" if index % 2 == 0 else "unconditional",
"timestep": float(record["timestep"].flatten()[0].detach().cpu()),
"tensors": paths,
}
)
manifest = {
"source": {
"repository": str(repository),
"commit": commit,
"tracked_source_dirty": dirty,
"loader": "official triposplat.load_flow_model",
"sampler": "official triposplat.FlowEulerCfgSampler",
},
"settings": {
"steps": args.steps,
"guidance_scale": args.guidance_scale,
"shift": args.shift,
"internal_precision": args.internal_precision,
"conditional_invocations": args.steps,
"unconditional_invocations": args.steps,
},
"pytorch": {"device": str(device), "duration_ms": duration_ms},
"outputs": output_files,
}
if trajectory_files:
manifest["trajectory"] = trajectory_files
manifest_path = output_dir / "flow.json"
manifest_path.write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8")
print(f"Wrote {manifest_path} ({duration_ms:.1f} ms official PyTorch sampling)")
if __name__ == "__main__":
main()
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