pulid/utils.py

import importlib
import os
import random

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from transformers import PretrainedConfig


def seed_everything(seed):
    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


def is_torch2_available():
    return hasattr(F, "scaled_dot_product_attention")


def instantiate_from_config(config):
    if "target" not in config:
        if config == '__is_first_stage__' or config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", {}))


def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def drop_seq_token(seq, drop_rate=0.5):
    idx = torch.randperm(seq.size(1))
    num_keep_tokens = int(len(idx) * (1 - drop_rate))
    idx = idx[:num_keep_tokens]
    seq = seq[:, idx]
    return seq


def import_model_class_from_model_name_or_path(
    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
    text_encoder_config = PretrainedConfig.from_pretrained(
        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
    )
    model_class = text_encoder_config.architectures[0]

    if model_class == "CLIPTextModel":
        from transformers import CLIPTextModel

        return CLIPTextModel
    elif model_class == "CLIPTextModelWithProjection":  # noqa RET505
        from transformers import CLIPTextModelWithProjection

        return CLIPTextModelWithProjection
    else:
        raise ValueError(f"{model_class} is not supported.")


def resize_numpy_image_long(image, resize_long_edge=768):
    h, w = image.shape[:2]
    if max(h, w) <= resize_long_edge:
        return image
    k = resize_long_edge / max(h, w)
    h = int(h * k)
    w = int(w * k)
    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
    return image