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Adapted from https://github.com/tgxs002/HPSv2. Originally Apache License, Version 2.0, January 2004.
"""
import torch
from open_clip import create_model_and_transforms, get_tokenizer
from PIL import Image
class HPSv2Scorer():
def __init__(self, clip_pretrained_name_or_path, model_pretrained_name_or_path, device='cuda'):
self.model, _, self.preprocess_val = create_model_and_transforms(
'ViT-H-14',
# 'laion2B-s32B-b79K',
clip_pretrained_name_or_path,
precision='amp',
device=device,
jit=False,
force_quick_gelu=False,
force_custom_text=False,
force_patch_dropout=False,
force_image_size=None,
pretrained_image=False,
image_mean=None,
image_std=None,
light_augmentation=True,
aug_cfg={},
output_dict=True,
with_score_predictor=False,
with_region_predictor=False
)
self.device = device
checkpoint = torch.load(model_pretrained_name_or_path, map_location=device)
self.model.load_state_dict(checkpoint['state_dict'])
self.tokenizer = get_tokenizer('ViT-H-14')
self.model = self.model.to(device)
def score(self, img_path, prompt):
if isinstance(img_path, list):
result = []
for one_img_path in img_path:
# Load your image and prompt
with torch.no_grad():
# Process the image
if isinstance(one_img_path, str):
image = self.preprocess_val(Image.open(one_img_path)).unsqueeze(0).to(device=self.device, non_blocking=True)
elif isinstance(one_img_path, Image.Image):
image = self.preprocess_val(one_img_path).unsqueeze(0).to(device=self.device, non_blocking=True)
else:
raise TypeError('The type of parameter img_path is illegal.')
# Process the prompt
text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
# Calculate the HPS
with torch.cuda.amp.autocast():
outputs = self.model(image, text)
image_features, text_features = outputs["image_features"], outputs["text_features"]
logits_per_image = image_features @ text_features.T
hps_score = torch.diagonal(logits_per_image).cpu().numpy()
result.append(hps_score[0])
return result
elif isinstance(img_path, str):
# Load your image and prompt
with torch.no_grad():
# Process the image
image = self.preprocess_val(Image.open(img_path)).unsqueeze(0).to(device=self.device, non_blocking=True)
# Process the prompt
text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
# Calculate the HPS
with torch.cuda.amp.autocast():
outputs = self.model(image, text)
image_features, text_features = outputs["image_features"], outputs["text_features"]
logits_per_image = image_features @ text_features.T
hps_score = torch.diagonal(logits_per_image).cpu().numpy()
return [hps_score[0]]
elif isinstance(img_path, Image.Image):
# Load your image and prompt
with torch.no_grad():
# Process the image
image = self.preprocess_val(img_path).unsqueeze(0).to(device=self.device, non_blocking=True)
# Process the prompt
text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
# Calculate the HPS
with torch.cuda.amp.autocast():
outputs = self.model(image, text)
image_features, text_features = outputs["image_features"], outputs["text_features"]
logits_per_image = image_features @ text_features.T
hps_score = torch.diagonal(logits_per_image).cpu().numpy()
return [hps_score[0]]
else:
raise TypeError('The type of parameter img_path is illegal.')
if __name__ == "__main__":
from huggingface_hub import hf_hub_download
clip_model_path = hf_hub_download(repo_id="laion/CLIP-ViT-H-14-laion2B-s32B-b79K", filename="open_clip_pytorch_model.bin")
hps_model_path = hf_hub_download(repo_id="xswu/HPSv2", filename="HPS_v2_compressed.pt")
hpsv2_scorer = HPSv2Scorer(clip_pretrained_name_or_path=clip_model_path,
model_pretrained_name_or_path=hps_model_path)
score = hpsv2_scorer.score(img_path=['./image0.png', './image1.png'],
prompt='photorealistic image of a lone painter standing in a gallery, watching an exhibition of paintings made entirely with AI. In the foreground of the image a robot looks proudly at his art')
print(score)
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