image quality metric

examples/QualityMetric/README.md

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+# Image Quality Metric
+
+The image quality assessment functionality has now been integrated into Diffsynth.
+
+## Usage
+
+### Step 1: Download pretrained reward models 
+
+```
+modelscope download --model 'DiffSynth-Studio/QualityMetric_reward_pretrained' 
+```
+
+The file directory is shown below.
+
+```
+DiffSynth-Studio/
+└── diffsynth/
+    └── extensions/
+        └── QualityMetric/
+            ├── __init__.py
+            ├── hps.py
+            ├── reward_pretrained/
+            │   ├── HPS_v2/
+            │   │   ├── HPS_v2_compressed.safetensors
+            │   │   ├── HPS_v2.1_compressed.safetensors
+            │   └── ...
+            └── ...
+```
+
+### Step 2: Test image quality metric
+
+Prompt: "a painting of an ocean with clouds and birds, day time, low depth field effect"
+
+|1.webp|2.webp|3.webp|4.webp|
+|-|-|-|-|
+|![0](images/1.webp)|![1](images/2.webp)|![2](images/3.webp)|![3](images/4.webp)|
+
+
+
+```
+CUDA_VISIBLE_DEVICES=0 python testreward.py
+```
+
+### Output:
+
+```
+ImageReward: [0.5811904668807983, 0.2745198607444763, -1.4158903360366821, -2.032487154006958]
+Aesthetic [5.900862693786621, 5.776571273803711, 5.799864292144775, 5.05204963684082]
+PickScore: [0.20737126469612122, 0.20443597435951233, 0.20660750567913055, 0.19426065683364868]
+CLIPScore: [0.3894640803337097, 0.3544551134109497, 0.33861416578292847, 0.32878392934799194]
+HPScorev2: [0.2672519087791443, 0.25495243072509766, 0.24888549745082855, 0.24302822351455688]
+HPScorev21: [0.2321144938468933, 0.20233657956123352, 0.1978294551372528, 0.19230154156684875]
+MPS_score: [10.921875, 10.71875, 10.578125, 9.25]
+```