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import gradio as gr
import torch
from PIL import Image
from src.open_clip import create_model_and_transforms, get_tokenizer
import warnings
import argparse
warnings.filterwarnings("ignore", category=UserWarning)
# Create an argument parser
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default='HPS_v2.pt', help='Path to the model checkpoint')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model, preprocess_train, preprocess_val = create_model_and_transforms(
'ViT-H-14',
'laion2B-s32B-b79K',
precision='amp',
device=device,
jit=False,
force_quick_gelu=False,
force_custom_text=False,
force_patch_dropout=False,
force_image_size=None,
pretrained_image=False,
image_mean=None,
image_std=None,
light_augmentation=True,
aug_cfg={},
output_dict=True,
with_score_predictor=False,
with_region_predictor=False
)
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['state_dict'])
tokenizer = get_tokenizer('ViT-H-14')
model.eval()
intro = """
<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
HPS v2
</h1>
<h3 style="font-weight: 600; text-align: center;">
evaluating human preference for generated images
</h3>
<h4 style="text-align: center; margin-bottom: 7px;">
<a href="https://github.com/tgxs002/HPSv2" style="text-decoration: underline;" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2306.09341" style="text-decoration: underline;" target="_blank">ArXiv</a>
</h4>
<p style="font-size: 0.9rem; margin: 0rem; line-height: 1.2em; margin-top:1em">
<p/>"""
def inference(image, prompt):
# Load your image and prompt
with torch.no_grad():
# Process the image
image = preprocess_val(image).unsqueeze(0).to(device=device, non_blocking=True)
# Process the prompt
text = tokenizer([prompt]).to(device=device, non_blocking=True)
# Calculate the HPS
with torch.cuda.amp.autocast():
outputs = model(image, text)
image_features, text_features = outputs["image_features"], outputs["text_features"]
logits_per_image = image_features @ text_features.T
hps_score = torch.diagonal(logits_per_image).cpu().numpy()
output = 'HPSv2 score: ' + str(hps_score[0])
return output
with gr.Blocks(css="style.css") as demo:
gr.HTML(intro)
with gr.Column():
image = gr.Image(label="Image", type="pil")
prompt = gr.Textbox(lines=1, label="Prompt")
button = gr.Button("Compute HPS v2")
score = gr.Textbox(label="output", lines=1, interactive=False, elem_id="output")
button.click(inference, inputs=[image, prompt], outputs=score)
demo.queue(concurrency_count=1)
demo.launch() |