Datasets:

zer0int
/

Thatcher_Effect_Faces

# CLIP vs SigLIP 2 Large vs DINOv2 Large on FFHQ and SFHQ.
# Install: pip install -U torch transformers datasets pillow
from itertools import islice
from io import BytesIO
from math import acos, pi
from pathlib import Path
from statistics import fmean, stdev

import torch
import torch.nn.functional as F
from datasets import Image as HFImage, load_dataset
from PIL import Image
from transformers import AutoImageProcessor, AutoProcessor, CLIPModel, Dinov2Model, SiglipVisionModel

REPO = "zer0int/Thatcher_Effect_Faces"  # HF ID by default; also accepts a local export folder.
N = 49  # 49 = Full-Thatcher ('both') quartets from each dataset's original root.
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODELS = [
    ("CLIP ViT-L/14", "openai/clip-vit-large-patch14", "clip"),
    ("SigLIP 2 Large", "google/siglip2-large-patch16-256", "siglip2"),
    ("DINOv2 Large", "facebook/dinov2-large", "dino"),
]


class SiglipImageOnly(SiglipVisionModel):
    # ignore unused text tower and contrastive logits that are unexpected
    # when loading the vision tower from a joint SigLIP checkpoint.
    _keys_to_ignore_on_load_unexpected = set(
        SiglipVisionModel._keys_to_ignore_on_load_unexpected or ()
    ) | {r"^text_model\.", r"^logit_scale$", r"^logit_bias$"}


def decode_rgb(image_value):
    if isinstance(image_value, Image.Image):
        return image_value.convert("RGB")
    if isinstance(image_value, dict):
        if image_value.get("bytes") is not None:
            with Image.open(BytesIO(image_value["bytes"])) as image:
                return image.convert("RGB")
        if image_value.get("path") and Path(image_value["path"]).is_file():
            with Image.open(image_value["path"]) as image:
                return image.convert("RGB")
    raise TypeError(f"Unsupported image format: {type(image_value).__name__}")


def summarize(upright, inverted):
    """Summarize paired faces, keeping model distances distinct from human accuracy."""
    excesses = [i - u for u, i in zip(upright, inverted)]
    n = len(excesses)
    if not n:
        raise ValueError("Cannot summarize an empty set of Thatcher quartets")
    # angular distance / 180 degrees as a genuine bounded percentage.
    # report the *paired* upright-minus-inverted angular distance in pp.
    angular_excess_pp = fmean(
        100.0 / pi * (acos(max(-1., min(1., u))) - acos(max(-1., min(1., i))))
        for u, i in zip(upright, inverted)
    )
    # % of face quartets where inversion makes normal and Thatcher MORE similar.
    favorable_pct = 100.0 * sum(e > 0 for e in excesses) / n
    # face-to-face consistency
    spread = stdev(excesses) if n > 1 else 0.0
    d_faces = fmean(excesses) / spread if spread > 0 else float("nan")
    return fmean(upright), fmean(inverted), fmean(excesses), angular_excess_pp, favorable_pct, d_faces, n


rows_by_source = {}
for source in ("ffhq", "sfhq"):
    if Path(REPO).is_dir():
        shards = sorted((Path(REPO) / "data" / source / "original").glob("*.parquet"))
        if not shards:
            raise FileNotFoundError(f"No {source} Parquet files in {REPO}")
        ds = load_dataset("parquet", data_files={"train": [str(p) for p in shards]},
                          split="train", streaming=False)
    else:
        ds = load_dataset(REPO, f"{source}_original", split="train", streaming=False)
    # .decode(False) is for IterableDataset, not a cached Dataset.
    # Disable automatic image decoding using the Dataset.cast_column API.
    for column in ("normal_u", "normal_i", "thatcher_u", "thatcher_i"):
        if isinstance(ds.features[column], HFImage):
            ds = ds.cast_column(column, HFImage(decode=False))
    rows_by_source[source] = list(islice((r for r in ds if r["face_variant"] == "both"), N))
    if not rows_by_source[source]:
        raise RuntimeError(f"Empty dataset config: {source}_original")

scores = {source: {} for source in rows_by_source}
for label, checkpoint, family in MODELS:
    # SigLIP's vision-only class bypasses the upstream text-config
    processor_cls = AutoProcessor if family == "clip" else AutoImageProcessor
    model_cls = {"clip": CLIPModel, "siglip2": SiglipImageOnly, "dino": Dinov2Model}[family]
    processor = processor_cls.from_pretrained(checkpoint)
    model = model_cls.from_pretrained(checkpoint).to(DEVICE).eval().float()
    for source, rows in rows_by_source.items():
        upright, inverted = [], []
        for row in rows:
            images = [decode_rgb(row[k]) for k in ("normal_u", "normal_i", "thatcher_u", "thatcher_i")]
            batch = {k: v.to(DEVICE) for k, v in processor(images=images, return_tensors="pt").items()}
            with torch.inference_mode():
                if family == "dino":
                    z = model(**batch).last_hidden_state[:, 0, :]
                elif family == "siglip2":
                    z = model(**batch).pooler_output
                else:
                    z = model.get_image_features(**batch)
                    if not isinstance(z, torch.Tensor):
                        z = (getattr(z, "image_embeds", None)
                             if getattr(z, "image_embeds", None) is not None
                             else z.pooler_output)
                z = F.normalize(z.float(), dim=-1)
                upright.append((z[0] * z[2]).sum().item())
                inverted.append((z[1] * z[3]).sum().item())
        scores[source][label] = summarize(upright, inverted)
    del model
    if DEVICE == "cuda":
        torch.cuda.empty_cache()

for source in ("ffhq", "sfhq"):
    print(f"\n{source.upper()} — original root, full Thatcher, {len(rows_by_source[source])} quartets")
    print(f"{'Model':20} {'N':>3} {'upright':>9} {'inverted':>9} {'cos excess':>10} {'ang pp':>8} {'% faces':>8} {'d_faces':>8}")
    for label, _, _ in MODELS:
        u, i, excess, ang_pp, favorable, d_faces, n = scores[source][label]
        print(f"{label:20} {n:3d} {u:9.4f} {i:9.4f} {excess:+10.4f} {ang_pp:+8.2f} {favorable:8.1f} {d_faces:8.2f}")

	# CLIP vs SigLIP 2 Large vs DINOv2 Large on FFHQ and SFHQ.
	# Install: pip install -U torch transformers datasets pillow
	from itertools import islice
	from io import BytesIO
	from math import acos, pi
	from pathlib import Path
	from statistics import fmean, stdev

	import torch
	import torch.nn.functional as F
	from datasets import Image as HFImage, load_dataset
	from PIL import Image
	from transformers import AutoImageProcessor, AutoProcessor, CLIPModel, Dinov2Model, SiglipVisionModel

	REPO = "zer0int/Thatcher_Effect_Faces" # HF ID by default; also accepts a local export folder.
	N = 49 # 49 = Full-Thatcher ('both') quartets from each dataset's original root.
	DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
	MODELS = [
	("CLIP ViT-L/14", "openai/clip-vit-large-patch14", "clip"),
	("SigLIP 2 Large", "google/siglip2-large-patch16-256", "siglip2"),
	("DINOv2 Large", "facebook/dinov2-large", "dino"),
	]


	class SiglipImageOnly(SiglipVisionModel):
	# ignore unused text tower and contrastive logits that are unexpected
	# when loading the vision tower from a joint SigLIP checkpoint.
	_keys_to_ignore_on_load_unexpected = set(
	SiglipVisionModel._keys_to_ignore_on_load_unexpected or ()
	) \| {r"^text_model\.", r"^logit_scale$", r"^logit_bias$"}


	def decode_rgb(image_value):
	if isinstance(image_value, Image.Image):
	return image_value.convert("RGB")
	if isinstance(image_value, dict):
	if image_value.get("bytes") is not None:
	with Image.open(BytesIO(image_value["bytes"])) as image:
	return image.convert("RGB")
	if image_value.get("path") and Path(image_value["path"]).is_file():
	with Image.open(image_value["path"]) as image:
	return image.convert("RGB")
	raise TypeError(f"Unsupported image format: {type(image_value).__name__}")


	def summarize(upright, inverted):
	"""Summarize paired faces, keeping model distances distinct from human accuracy."""
	excesses = [i - u for u, i in zip(upright, inverted)]
	n = len(excesses)
	if not n:
	raise ValueError("Cannot summarize an empty set of Thatcher quartets")
	# angular distance / 180 degrees as a genuine bounded percentage.
	# report the paired upright-minus-inverted angular distance in pp.
	angular_excess_pp = fmean(
	100.0 / pi * (acos(max(-1., min(1., u))) - acos(max(-1., min(1., i))))
	for u, i in zip(upright, inverted)
	)
	# % of face quartets where inversion makes normal and Thatcher MORE similar.
	favorable_pct = 100.0 * sum(e > 0 for e in excesses) / n
	# face-to-face consistency
	spread = stdev(excesses) if n > 1 else 0.0
	d_faces = fmean(excesses) / spread if spread > 0 else float("nan")
	return fmean(upright), fmean(inverted), fmean(excesses), angular_excess_pp, favorable_pct, d_faces, n


	rows_by_source = {}
	for source in ("ffhq", "sfhq"):
	if Path(REPO).is_dir():
	shards = sorted((Path(REPO) / "data" / source / "original").glob("*.parquet"))
	if not shards:
	raise FileNotFoundError(f"No {source} Parquet files in {REPO}")
	ds = load_dataset("parquet", data_files={"train": [str(p) for p in shards]},
	split="train", streaming=False)
	else:
	ds = load_dataset(REPO, f"{source}_original", split="train", streaming=False)
	# .decode(False) is for IterableDataset, not a cached Dataset.
	# Disable automatic image decoding using the Dataset.cast_column API.
	for column in ("normal_u", "normal_i", "thatcher_u", "thatcher_i"):
	if isinstance(ds.features[column], HFImage):
	ds = ds.cast_column(column, HFImage(decode=False))
	rows_by_source[source] = list(islice((r for r in ds if r["face_variant"] == "both"), N))
	if not rows_by_source[source]:
	raise RuntimeError(f"Empty dataset config: {source}_original")

	scores = {source: {} for source in rows_by_source}
	for label, checkpoint, family in MODELS:
	# SigLIP's vision-only class bypasses the upstream text-config
	processor_cls = AutoProcessor if family == "clip" else AutoImageProcessor
	model_cls = {"clip": CLIPModel, "siglip2": SiglipImageOnly, "dino": Dinov2Model}[family]
	processor = processor_cls.from_pretrained(checkpoint)
	model = model_cls.from_pretrained(checkpoint).to(DEVICE).eval().float()
	for source, rows in rows_by_source.items():
	upright, inverted = [], []
	for row in rows:
	images = [decode_rgb(row[k]) for k in ("normal_u", "normal_i", "thatcher_u", "thatcher_i")]
	batch = {k: v.to(DEVICE) for k, v in processor(images=images, return_tensors="pt").items()}
	with torch.inference_mode():
	if family == "dino":
	z = model(**batch).last_hidden_state[:, 0, :]
	elif family == "siglip2":
	z = model(**batch).pooler_output
	else:
	z = model.get_image_features(**batch)
	if not isinstance(z, torch.Tensor):
	z = (getattr(z, "image_embeds", None)
	if getattr(z, "image_embeds", None) is not None
	else z.pooler_output)
	z = F.normalize(z.float(), dim=-1)
	upright.append((z[0] * z[2]).sum().item())
	inverted.append((z[1] * z[3]).sum().item())
	scores[source][label] = summarize(upright, inverted)
	del model
	if DEVICE == "cuda":
	torch.cuda.empty_cache()

	for source in ("ffhq", "sfhq"):
	print(f"\n{source.upper()} — original root, full Thatcher, {len(rows_by_source[source])} quartets")
	print(f"{'Model':20} {'N':>3} {'upright':>9} {'inverted':>9} {'cos excess':>10} {'ang pp':>8} {'% faces':>8} {'d_faces':>8}")
	for label, _, _ in MODELS:
	u, i, excess, ang_pp, favorable, d_faces, n = scores[source][label]
	print(f"{label:20} {n:3d} {u:9.4f} {i:9.4f} {excess:+10.4f} {ang_pp:+8.2f} {favorable:8.1f} {d_faces:8.2f}")