utils/utils.py

import PIL

from BeamDiffusionModel.models.diffusionModel.configs.config_loader import CONFIG
from BeamDiffusionModel.models.clip.clip import Clip
import torch
import json

clip = Clip()


def read_json(path):
    with open(path, 'r') as f:
        data = json.load(f)
        return data

def get_img(path):
    img = PIL.Image.open(path)
    return img

def clip_score(step, imgs_path):
    imgs = []
    if isinstance(imgs_path, list):
        for img_path in imgs_path:
            img = get_img(img_path)
            imgs.append(img)
    else:
        img = get_img(imgs_path)
        imgs.append(img)

    return clip.similarity(step, imgs)

def gen_img(sd, caption, latent= None, seed=None):
    system = "cuda" if CONFIG.get("diffusion_model", {}).get("use_cuda", True) and torch.cuda.is_available() else "cpu"
    if seed:
        generator = torch.Generator(system).manual_seed(seed)
    else:
        generator = None
    img, latents = sd.generate_image(caption, generator=generator, latent=latent)
    return latents, img