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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torch.nn as nn
import torchvision
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError
from transformers import CLIPModel, is_torch_npu_available, is_torch_xpu_available
class MLP(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(768, 1024),
nn.Dropout(0.2),
nn.Linear(1024, 128),
nn.Dropout(0.2),
nn.Linear(128, 64),
nn.Dropout(0.1),
nn.Linear(64, 16),
nn.Linear(16, 1),
)
def forward(self, embed):
return self.layers(embed)
class AestheticScorer(torch.nn.Module):
"""
This model attempts to predict the aesthetic score of an image. The aesthetic score
is a numerical approximation of how much a specific image is liked by humans on average.
This is from https://github.com/christophschuhmann/improved-aesthetic-predictor
"""
def __init__(self, *, dtype, model_id, model_filename):
super().__init__()
self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
self.normalize = torchvision.transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]
)
self.target_size = 224
self.mlp = MLP()
try:
cached_path = hf_hub_download(model_id, model_filename)
except EntryNotFoundError:
cached_path = os.path.join(model_id, model_filename)
state_dict = torch.load(cached_path, map_location=torch.device("cpu"), weights_only=True)
self.mlp.load_state_dict(state_dict)
self.dtype = dtype
self.eval()
def __call__(self, images):
device = next(self.parameters()).device
images = torchvision.transforms.Resize(self.target_size)(images)
images = self.normalize(images).to(self.dtype).to(device)
embed = self.clip.get_image_features(pixel_values=images)
# normalize embedding
embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
reward = self.mlp(embed).squeeze(1)
return reward
def aesthetic_scorer(hub_model_id, model_filename):
scorer = AestheticScorer(
model_id=hub_model_id,
model_filename=model_filename,
dtype=torch.float32,
)
if is_torch_npu_available():
scorer = scorer.npu()
elif is_torch_xpu_available():
scorer = scorer.xpu()
else:
scorer = scorer.cuda()
def _fn(images, prompts, metadata):
images = (images).clamp(0, 1)
scores = scorer(images)
return scores, {}
return _fn
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