image quality metric

examples/QualityMetric/testreward.py

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+import os
+import torch
+from PIL import Image
+from diffsynth.extensions.QualityMetric.imagereward import ImageRewardScore
+from diffsynth.extensions.QualityMetric.pickscore import PickScore
+from diffsynth.extensions.QualityMetric.aesthetic import AestheticScore
+from diffsynth.extensions.QualityMetric.clip import CLIPScore
+from diffsynth.extensions.QualityMetric.hps import HPScore_v2
+from diffsynth.extensions.QualityMetric.mps import MPScore
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# load reward models
+mps_score = MPScore(device)
+image_reward = ImageRewardScore(device)
+aesthetic_score = AestheticScore(device)
+pick_score = PickScore(device)
+clip_score = CLIPScore(device)
+hps_score = HPScore_v2(device, model_version = 'v2')
+hps2_score = HPScore_v2(device, model_version = 'v21')
+
+prompt = "a painting of an ocean with clouds and birds, day time, low depth field effect"
+img_prefix = "images"
+generations = [f"{pic_id}.webp" for pic_id in range(1, 5)]
+
+img_list = [Image.open(os.path.join(img_prefix, img)) for img in generations]
+#img_list = [os.path.join(img_prefix, img) for img in generations]
+
+imre_scores = image_reward.score(img_list, prompt)
+print("ImageReward:", imre_scores)
+
+aes_scores = aesthetic_score.score(img_list)
+print("Aesthetic", aes_scores)
+
+p_scores = pick_score.score(img_list, prompt)
+print("PickScore:", p_scores)
+
+c_scores = clip_score.score(img_list, prompt)
+print("CLIPScore:", c_scores)
+
+h_scores = hps_score.score(img_list,prompt)
+print("HPScorev2:", h_scores)
+
+h2_scores = hps2_score.score(img_list,prompt)
+print("HPScorev21:", h2_scores)
+
+m_scores = mps_score.score(img_list, prompt)
+print("MPS_score:", m_scores)