| import torch |
| from transformers import AutoProcessor |
| from ..core import ModelConfig |
| from ..core.device.npu_compatible_device import get_device_type |
| from ..models.hpsv2 import HPSv2Model |
| from .base import Metric |
|
|
| class HPSv2Metric(Metric): |
| def __init__(self, model: HPSv2Model): |
| super().__init__() |
| self.model = model |
|
|
| @classmethod |
| def from_pretrained( |
| cls, |
| model_config: ModelConfig = ModelConfig(model_id="DiffSynth-Studio/ImageMetrics", origin_file_pattern="HPSv2/model.safetensors"), |
| processor_config: ModelConfig = ModelConfig(model_id="DiffSynth-Studio/ImageMetrics", origin_file_pattern="HPSv2/"), |
| torch_dtype: torch.dtype = None, |
| device: torch.device = get_device_type(), |
| processor_kwargs: dict = None, |
| vram_limit: float = None, |
| ): |
|
|
| processor_kwargs = processor_kwargs or {} |
| model_pool = cls.download_and_load_models([model_config], torch_dtype=torch_dtype, device=device, vram_limit=vram_limit) |
| model = model_pool.fetch_model("image_metrics_hpsv2") |
| processor_config.download_if_necessary() |
| processor = AutoProcessor.from_pretrained(processor_config.path, **processor_kwargs) |
| model = HPSv2Model(model=model, processor=processor).eval() |
| return cls(model) |
|
|
| @torch.no_grad() |
| def score(self, prompt: str | list[str], images): |
| scores = self.model(prompt, images) |
| return self.tensor_to_list(scores) |
|
|
| def compute(self, prompt: str | list[str], images): |
| return self.score(prompt, images) |
|
|
| def forward(self, prompt: str | list[str], images): |
| return self.score(prompt, images) |
|
|
