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