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Identity Preservation metrics:
- CLIP consistency: frame-to-frame and vs first-frame cosine similarity of image embeddings.
Uses simple resize-flatten-normalize embedding when CLIP is not available; optional CLIP when available.
- Face Embedding / Character ID retention: placeholder (optional insightface/torchreid).
"""
from __future__ import annotations
import argparse
import json
import os
from typing import Any
import numpy as np
from .common import discover_evals_videos, load_video_frames, load_video_frames_pil
try:
import cv2
HAS_CV2 = True
except ImportError:
HAS_CV2 = False
def _simple_embedding(frames: np.ndarray, size: tuple[int, int] = (64, 64)) -> np.ndarray:
"""Per-frame embedding: resize, flatten, normalize. Shape (N, D)."""
if not HAS_CV2 or frames.size == 0:
return np.zeros((0, 0))
h, w = size
out = []
for i in range(frames.shape[0]):
f = cv2.resize(frames[i], (w, h), interpolation=cv2.INTER_LINEAR)
v = f.astype(np.float32).flatten()
n = np.linalg.norm(v)
out.append(v / n if n > 0 else v)
return np.stack(out, axis=0)
def _cosine_sim(a: np.ndarray, b: np.ndarray) -> float:
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8))
def clip_consistency_simple(frames: np.ndarray) -> dict[str, float]:
"""
Consistency without CLIP: use simple embedding (resize+flatten+normalize), then
- mean consecutive cosine similarity
- min consecutive cosine similarity
- mean similarity to first frame
- min similarity to first frame
"""
emb = _simple_embedding(frames)
if emb.shape[0] < 2:
return {"mean_consecutive_sim": 1.0, "min_consecutive_sim": 1.0, "mean_to_first_sim": 1.0, "min_to_first_sim": 1.0}
first = emb[0]
consec_sims = [_cosine_sim(emb[i], emb[i + 1]) for i in range(emb.shape[0] - 1)]
to_first_sims = [_cosine_sim(emb[i], first) for i in range(1, emb.shape[0])]
return {
"mean_consecutive_sim": float(np.mean(consec_sims)),
"min_consecutive_sim": float(np.min(consec_sims)),
"mean_to_first_sim": float(np.mean(to_first_sims)),
"min_to_first_sim": float(np.min(to_first_sims)),
"embedding": "simple",
}
def _try_clip_embeddings(pil_list, device="cuda"):
"""Optional: load CLIP and return (N, D) normalized image features. Returns None if unavailable."""
try:
import sys
repo = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if repo not in sys.path:
sys.path.insert(0, repo)
from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore
from diffsynth.extensions.ImageQualityMetric.config import MODEL_PATHS
import torch
model = CLIPScore(device=torch.device(device), path=MODEL_PATHS)
model.model.eval()
feats = []
for pil in pil_list:
x = model.preprocess_val(pil).unsqueeze(0).to(device=model.device)
with torch.no_grad():
f = model.model.encode_image(x, normalize=True)
feats.append(f.cpu().numpy().squeeze(0))
return np.stack(feats, axis=0)
except Exception:
return None
def clip_consistency_with_clip(pil_list, device: str = "cuda") -> dict[str, float] | None:
"""CLIP-based consistency. Returns None if CLIP not available."""
emb = _try_clip_embeddings(pil_list, device)
if emb is None or emb.shape[0] < 2:
return None
first = emb[0]
consec_sims = [float(np.dot(emb[i], emb[i + 1])) for i in range(emb.shape[0] - 1)]
to_first_sims = [float(np.dot(emb[i], first)) for i in range(1, emb.shape[0])]
return {
"mean_consecutive_sim": float(np.mean(consec_sims)),
"min_consecutive_sim": float(np.min(consec_sims)),
"mean_to_first_sim": float(np.mean(to_first_sims)),
"min_to_first_sim": float(np.min(to_first_sims)),
"embedding": "clip",
}
def run_identity_preservation(
evals_root: str,
use_clip: bool = False,
device: str = "cuda",
video_paths: list[tuple[str, str]] | None = None,
max_frames_per_video: int | None = 100,
) -> dict[str, Any]:
"""
Compute identity preservation (CLIP consistency) over all gen_only videos.
When use_clip=False uses simple embedding; when use_clip=True tries diffsynth CLIP.
"""
if video_paths is None:
video_paths = discover_evals_videos(evals_root)
per_video = []
agg_consec = []
agg_to_first = []
for rel, absp in video_paths:
if not os.path.isfile(absp):
continue
if use_clip:
pil_list = load_video_frames_pil(absp, max_frames=max_frames_per_video)
if not pil_list:
per_video.append({"rel": rel, "mean_consecutive_sim": None, "mean_to_first_sim": None, "embedding": None})
continue
res = clip_consistency_with_clip(pil_list, device)
if res is None:
frames = load_video_frames(absp, max_frames=max_frames_per_video)
res = clip_consistency_simple(frames)
else:
frames = load_video_frames(absp, max_frames=max_frames_per_video)
res = clip_consistency_simple(frames)
agg_consec.append(res["mean_consecutive_sim"])
agg_to_first.append(res["mean_to_first_sim"])
per_video.append({"rel": rel, **res})
aggregate = {}
if agg_consec:
aggregate["mean_consecutive_sim"] = float(np.mean(agg_consec))
aggregate["min_mean_to_first_sim"] = float(np.min(agg_to_first))
aggregate["mean_to_first_sim"] = float(np.mean(agg_to_first))
aggregate["face_embedding_note"] = "Optional: install insightface/torchreid for Face Embedding / character ID retention."
return {
"dimension": "identity_preservation",
"params": {"use_clip": use_clip, "device": device},
"per_video": per_video,
"aggregate": aggregate,
"num_videos": len(per_video),
}
def main():
p = argparse.ArgumentParser(description="Identity Preservation (CLIP consistency)")
p.add_argument("--evals_root", type=str, required=True)
p.add_argument("--use_clip", action="store_true", help="Use CLIP image encoder when available")
p.add_argument("--device", type=str, default="cuda")
p.add_argument("--max_frames", type=int, default=100)
p.add_argument("--output", type=str, default=None)
args = p.parse_args()
result = run_identity_preservation(
args.evals_root,
use_clip=args.use_clip,
device=args.device,
max_frames_per_video=args.max_frames,
)
out = json.dumps(result, indent=2)
print(out)
if args.output:
with open(args.output, "w") as f:
f.write(out)
if __name__ == "__main__":
main()
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