model.py

from torch import nn
from transformers import PreTrainedModel
from transformers import PretrainedConfig
from torchvision import transforms
from transformers.models.auto.configuration_auto import CONFIG_MAPPING
from transformers.models.auto.modeling_auto import MODEL_MAPPING

class ImageToImageConfig(PretrainedConfig):
    model_type = "upscaleing"

    def __init__(self, in_channels=3, out_channels=3,in_resolution=256,out_resolution=768, **kwargs):
        super().__init__(**kwargs)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.in_resolution = in_resolution
        self.out_resolution = out_resolution

class ImageToImageModel(PreTrainedModel):
    config_class = ImageToImageConfig

    def __init__(self, config):
        super().__init__(config)
        self.model = nn.Sequential(
            nn.Conv2d(in_channels=config.in_channels, out_channels=64, kernel_size=5, padding=2),
            nn.ReLU(),
            nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=3 * 3 * 3, kernel_size=3, padding=1),
            nn.PixelShuffle(3)
        )
        self.transform1 = transforms.Compose(
            transforms=(
                transforms.Resize((config.in_resolution,config.in_resolution)),
                transforms.ToTensor()
            )
        )
        self.transform2 = transforms.Compose(
            transforms=(
                transforms.ToPILImage(),
                transforms.Resize((config.out_resolution,config.out_resolution))
            )
        )

    def forward(self, image):
        x = self.transform1(image)
        x = self.model(x)
        x = self.transform2(x)
        return x

CONFIG_MAPPING.register("upscaleing", ImageToImageConfig)
MODEL_MAPPING.register(ImageToImageConfig, ImageToImageModel)

config = ImageToImageConfig()
model = ImageToImageModel(config)
model.save_pretrained("AIupscaleing")