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import argparse
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
import hashlib
import json
from pathlib import Path
import sys
import time
import torch
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
sys.path.insert(0, str(ROOT / "scripts"))
from anima_style_probe.factor_interventions import apply_intervention # noqa: E402
from anima_style_probe.feature_parts import completed_part_records, write_feature_part # noqa: E402
from anima_style_probe.image_preprocess import PREPROCESS_VERSION, image_tensor # noqa: E402
from anima_style_probe.style_dataset import ( # noqa: E402
assigned_shards,
iter_prefetched_batches,
iter_style_samples,
)
from extract_anima_feature_parts import encode_independent_stills # noqa: E402
from extract_anima_introspective import configure_comfy # noqa: E402
def read_jsonl(path: Path) -> list[dict]:
with path.open(encoding="utf-8") as handle:
return [json.loads(line) for line in handle if line.strip()]
def flush_part(
output_dir: Path,
worker_index: int,
part_index: int,
features: list[torch.Tensor],
records: list[dict],
*,
blocks: list[int],
sigma: float,
noise_seed: int,
manifest_sha256: str,
selection: str,
) -> None:
write_feature_part(
output_dir,
"anima",
worker_index,
part_index,
{"features": torch.cat(features).to(torch.bfloat16)},
{
"kind": f"factor_intervention_anima_{selection}",
"blocks": blocks,
"sigma": sigma,
"noise_seed": noise_seed,
"preprocess_version": PREPROCESS_VERSION,
"transform_resolution": 768,
"manifest_sha256": manifest_sha256,
"records": records,
},
)
def main() -> int:
parser = argparse.ArgumentParser(description="Extract transformed-Anima pilot features.")
parser.add_argument("--comfy-root", type=Path, required=True)
parser.add_argument("--config", type=Path, required=True)
parser.add_argument("--dataset-root", type=Path, required=True)
parser.add_argument("--manifest", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--worker-index", type=int, required=True)
parser.add_argument("--num-workers", type=int, default=4)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--source-batch-size", type=int, default=8)
parser.add_argument("--decode-workers", type=int, default=8)
parser.add_argument("--part-size", type=int, default=1024)
parser.add_argument("--image-size", type=int, default=768)
parser.add_argument("--blocks", type=int, nargs="+", default=[8, 18, 26])
parser.add_argument("--sigma", type=float, default=0.1)
parser.add_argument("--noise-seed", type=int, default=20260715)
parser.add_argument("--selection", choices=("pilot", "train", "all"), default="pilot")
args = parser.parse_args()
if min(args.batch_size, args.source_batch_size, args.decode_workers, args.part_size) < 1:
raise ValueError("batch and worker sizes must be positive")
if args.image_size != 768:
raise ValueError("the Anima pilot is locked to 768x768")
config = json.loads(args.config.read_text(encoding="utf-8"))
manifest_sha256 = hashlib.sha256(args.manifest.read_bytes()).hexdigest()
all_rows = read_jsonl(args.manifest)
if args.selection == "pilot":
rows = [row for row in all_rows if row.get("anima_pilot")]
elif args.selection == "train":
rows = [row for row in all_rows if row.get("split") == "train"]
else:
rows = all_rows
worker_shards = {
path.name
for path in assigned_shards(args.dataset_root, args.worker_index, args.num_workers)
}
rows = [row for row in rows if row["shard"] in worker_shards]
by_source: dict[str, list[dict]] = defaultdict(list)
for row in rows:
by_source[str(row["source_record_id"])].append(row)
completed, part_index = completed_part_records(
args.output_dir, "anima", args.worker_index
)
for path in args.output_dir.glob(f"anima-w{args.worker_index}-p*.json"):
metadata = json.loads(path.read_text(encoding="utf-8"))
if metadata.get("manifest_sha256") != manifest_sha256:
raise RuntimeError(f"manifest mismatch with completed part: {path}")
pending_by_source = {
source_id: [row for row in variants if row["record_id"] not in completed]
for source_id, variants in by_source.items()
}
pending_by_source = {key: value for key, value in pending_by_source.items() if value}
nodes, model_management = configure_comfy(args.comfy_root)
if not torch.cuda.is_available():
raise RuntimeError("CUDA is required")
run_started = time.perf_counter()
torch.cuda.reset_peak_memory_stats()
vae = nodes.VAELoader().load_vae(config["comfy"]["vae_name"])[0]
clip = nodes.CLIPLoader().load_clip(
config["comfy"]["clip_name"],
config["comfy"].get("clip_type", "stable_diffusion"),
"default",
)[0]
empty_conditioning = nodes.CLIPTextEncode().encode(clip, "")[0]
if len(empty_conditioning) != 1:
raise RuntimeError("expected one empty-prompt conditioning item")
raw_cross, raw_metadata = empty_conditioning[0]
raw_ids = raw_metadata["t5xxl_ids"].flatten().detach().cpu()
raw_weights = raw_metadata["t5xxl_weights"].flatten().detach().cpu()
raw_cross = raw_cross.detach().cpu()
del clip
model_management.unload_all_models()
model_management.soft_empty_cache()
model = nodes.UNETLoader().load_unet(config["comfy"]["unet_name"], "default")[0]
model_management.load_models_gpu([model])
device = model.load_device
dtype = model.model.get_dtype_inference()
diffusion_model = model.model.diffusion_model
block_indices = [block - 1 for block in args.blocks]
if min(block_indices) < 0 or max(block_indices) >= len(diffusion_model.blocks):
raise ValueError(f"invalid blocks for {len(diffusion_model.blocks)}-block Anima model")
with torch.inference_mode():
context = diffusion_model.preprocess_text_embeds(
raw_cross.to(device=device, dtype=dtype),
raw_ids.unsqueeze(0).to(device=device),
t5xxl_weights=raw_weights.unsqueeze(0).unsqueeze(-1).to(
device=device, dtype=dtype
),
)
captured: dict[int, list[torch.Tensor]] = {}
def capture(block: int):
def hook(_module, _inputs, output):
spatial_dims = tuple(range(1, output.ndim - 1))
value = output.detach().float()
mean = value.mean(dim=spatial_dims)
log_std = value.var(dim=spatial_dims, unbiased=False).clamp_min(1e-8).sqrt().log()
captured.setdefault(block, []).append(torch.cat((mean, log_std), dim=-1).cpu())
return hook
handles = [
diffusion_model.blocks[index].register_forward_hook(capture(block))
for block, index in zip(args.blocks, block_indices, strict=True)
]
decode_pool = ThreadPoolExecutor(
max_workers=args.decode_workers, thread_name_prefix="anima-intervention"
)
extraction_started = time.perf_counter()
processed = 0
feature_parts: list[torch.Tensor] = []
part_records: list[dict] = []
feature_buffer: list[tuple[torch.Tensor, dict]] = []
def decode_source(sample):
decoded = []
for spec in pending_by_source[str(sample.metadata["record_id"])]:
full, _face = apply_intervention(
sample.full_bytes,
sample.face_bytes,
sample.metadata,
spec,
size=args.image_size,
)
decoded.append(
(
image_tensor(full).permute(1, 2, 0),
{**spec, "source_shard": sample.shard},
)
)
return decoded
def process_batch(batch: list[tuple[torch.Tensor, dict]]) -> None:
nonlocal processed, part_index, feature_parts, part_records
pixels, records = map(list, zip(*batch, strict=True))
with torch.inference_mode():
latents = encode_independent_stills(vae, pixels, model_management)
clean = model.model.process_latent_in(latents).to(device=device, dtype=dtype)
if clean.shape[0] != len(batch):
raise RuntimeError(f"latent batch has {clean.shape[0]} rows for {len(batch)} records")
generator = torch.Generator(device="cpu").manual_seed(args.noise_seed)
noise = torch.randn(clean[:1].shape, generator=generator, dtype=torch.float32).to(
device=device, dtype=dtype
).expand_as(clean)
sigma = torch.full((clean.shape[0],), args.sigma, device=device, dtype=torch.float32)
noised = args.sigma * noise + (1.0 - args.sigma) * clean
captured.clear()
model.model.apply_model(
noised,
sigma,
c_crossattn=context.expand(clean.shape[0], *context.shape[1:]),
)
if set(captured) != set(args.blocks):
raise RuntimeError(f"captured blocks {sorted(captured)}, expected {args.blocks}")
features = torch.stack(
[torch.cat(captured[block], dim=0) for block in args.blocks], dim=1
)
if features.shape != (len(batch), len(args.blocks), 4096):
raise RuntimeError(f"unexpected Anima feature shape: {tuple(features.shape)}")
feature_parts.append(features)
part_records.extend(records)
processed += len(batch)
if len(part_records) >= args.part_size:
flush_part(
args.output_dir,
args.worker_index,
part_index,
feature_parts,
part_records,
blocks=args.blocks,
sigma=args.sigma,
noise_seed=args.noise_seed,
manifest_sha256=manifest_sha256,
selection=args.selection,
)
part_index += 1
feature_parts, part_records = [], []
print(
json.dumps(
{
"worker": args.worker_index,
"new_records": processed,
"expected": len(rows),
"already_complete": len(completed),
"elapsed_seconds": round(time.perf_counter() - extraction_started, 1),
}
),
flush=True,
)
try:
samples = iter_style_samples(
args.dataset_root,
worker_index=args.worker_index,
num_workers=args.num_workers,
include_record_ids=set(pending_by_source),
)
for source_batch in iter_prefetched_batches(samples, args.source_batch_size):
for group in decode_pool.map(decode_source, source_batch):
feature_buffer.extend(group)
while len(feature_buffer) >= args.batch_size:
process_batch(feature_buffer[: args.batch_size])
del feature_buffer[: args.batch_size]
if feature_buffer:
process_batch(feature_buffer)
finally:
decode_pool.shutdown()
for handle in handles:
handle.remove()
if part_records:
flush_part(
args.output_dir,
args.worker_index,
part_index,
feature_parts,
part_records,
blocks=args.blocks,
sigma=args.sigma,
noise_seed=args.noise_seed,
manifest_sha256=manifest_sha256,
selection=args.selection,
)
if processed + len(completed) != len(rows):
raise RuntimeError(f"worker coverage mismatch: {processed} + {len(completed)} != {len(rows)}")
summary = {
"status": "complete",
"worker_index": args.worker_index,
"manifest_sha256": manifest_sha256,
"records": len(rows),
"already_complete": len(completed),
"new_records": processed,
"setup_seconds": extraction_started - run_started,
"elapsed_seconds": time.perf_counter() - extraction_started,
"total_seconds": time.perf_counter() - run_started,
"peak_vram_bytes": torch.cuda.max_memory_allocated(),
"blocks": args.blocks,
"sigma": args.sigma,
"noise_seed": args.noise_seed,
"selection": args.selection,
}
path = args.output_dir / f"anima-worker-{args.worker_index}.json"
path.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
print(json.dumps(summary, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
|