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clip-guided-binary-autoencoder
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from huggingface_hub import hf_hub_url, cached_download
import streamlit as st
import io
import gc

########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################

MODEL_REPO = 'BlinkDL/clip-guided-binary-autoencoder'

import torch, types
import numpy as np
from PIL import Image
import torch.nn as nn
from torch.nn import functional as F
import torchvision as vision
import torchvision.transforms as transforms
from torchvision.transforms import functional as VF

device = 'cuda' if torch.cuda.is_available() else 'cpu'


class ToBinary(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x):
        return torch.floor(
            x + 0.5)  # no need for noise when we have plenty of data

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output.clone()  # pass-through


class ResBlock(nn.Module):

    def __init__(self, c_x, c_hidden):
        super().__init__()
        self.B0 = nn.BatchNorm2d(c_x)
        self.C0 = nn.Conv2d(c_x, c_hidden, kernel_size=3, padding=1)
        self.C1 = nn.Conv2d(c_hidden, c_x, kernel_size=3, padding=1)
        self.C2 = nn.Conv2d(c_x, c_hidden, kernel_size=3, padding=1)
        self.C3 = nn.Conv2d(c_hidden, c_x, kernel_size=3, padding=1)

    def forward(self, x):
        ACT = F.mish
        x = x + self.C1(ACT(self.C0(ACT(self.B0(x)))))
        x = x + self.C3(ACT(self.C2(x)))
        return x


class REncoderSmall(nn.Module):

    def __init__(self, args):
        super().__init__()
        self.args = args
        dd = 8
        self.Bxx = nn.BatchNorm2d(dd * 64)

        self.CIN = nn.Conv2d(3, dd, kernel_size=3, padding=1)
        self.Cx0 = nn.Conv2d(dd, 32, kernel_size=3, padding=1)
        self.Cx1 = nn.Conv2d(32, dd, kernel_size=3, padding=1)

        self.B00 = nn.BatchNorm2d(dd * 4)
        self.C00 = nn.Conv2d(dd * 4, 256, kernel_size=3, padding=1)
        self.C01 = nn.Conv2d(256, dd * 4, kernel_size=3, padding=1)
        self.C02 = nn.Conv2d(dd * 4, 256, kernel_size=3, padding=1)
        self.C03 = nn.Conv2d(256, dd * 4, kernel_size=3, padding=1)

        self.B10 = nn.BatchNorm2d(dd * 16)
        self.C10 = nn.Conv2d(dd * 16, 256, kernel_size=3, padding=1)
        self.C11 = nn.Conv2d(256, dd * 16, kernel_size=3, padding=1)
        self.C12 = nn.Conv2d(dd * 16, 256, kernel_size=3, padding=1)
        self.C13 = nn.Conv2d(256, dd * 16, kernel_size=3, padding=1)

        self.B20 = nn.BatchNorm2d(dd * 64)
        self.C20 = nn.Conv2d(dd * 64, 256, kernel_size=3, padding=1)
        self.C21 = nn.Conv2d(256, dd * 64, kernel_size=3, padding=1)
        self.C22 = nn.Conv2d(dd * 64, 256, kernel_size=3, padding=1)
        self.C23 = nn.Conv2d(256, dd * 64, kernel_size=3, padding=1)

        self.COUT = nn.Conv2d(dd * 64,
                              args.my_img_bit,
                              kernel_size=3,
                              padding=1)

    def forward(self, img):
        ACT = F.mish

        x = self.CIN(img)
        xx = self.Bxx(F.pixel_unshuffle(x, 8))
        x = x + self.Cx1(ACT(self.Cx0(x)))

        x = F.pixel_unshuffle(x, 2)
        x = x + self.C01(ACT(self.C00(ACT(self.B00(x)))))
        x = x + self.C03(ACT(self.C02(x)))

        x = F.pixel_unshuffle(x, 2)
        x = x + self.C11(ACT(self.C10(ACT(self.B10(x)))))
        x = x + self.C13(ACT(self.C12(x)))

        x = F.pixel_unshuffle(x, 2)
        x = x + self.C21(ACT(self.C20(ACT(self.B20(x)))))
        x = x + self.C23(ACT(self.C22(x)))

        x = self.COUT(x + xx)
        return torch.sigmoid(x)


class RDecoderSmall(nn.Module):

    def __init__(self, args):
        super().__init__()
        self.args = args
        dd = 8
        self.CIN = nn.Conv2d(args.my_img_bit,
                             dd * 64,
                             kernel_size=3,
                             padding=1)

        self.B00 = nn.BatchNorm2d(dd * 64)
        self.C00 = nn.Conv2d(dd * 64, 256, kernel_size=3, padding=1)
        self.C01 = nn.Conv2d(256, dd * 64, kernel_size=3, padding=1)
        self.C02 = nn.Conv2d(dd * 64, 256, kernel_size=3, padding=1)
        self.C03 = nn.Conv2d(256, dd * 64, kernel_size=3, padding=1)

        self.B10 = nn.BatchNorm2d(dd * 16)
        self.C10 = nn.Conv2d(dd * 16, 256, kernel_size=3, padding=1)
        self.C11 = nn.Conv2d(256, dd * 16, kernel_size=3, padding=1)
        self.C12 = nn.Conv2d(dd * 16, 256, kernel_size=3, padding=1)
        self.C13 = nn.Conv2d(256, dd * 16, kernel_size=3, padding=1)

        self.B20 = nn.BatchNorm2d(dd * 4)
        self.C20 = nn.Conv2d(dd * 4, 256, kernel_size=3, padding=1)
        self.C21 = nn.Conv2d(256, dd * 4, kernel_size=3, padding=1)
        self.C22 = nn.Conv2d(dd * 4, 256, kernel_size=3, padding=1)
        self.C23 = nn.Conv2d(256, dd * 4, kernel_size=3, padding=1)

        self.Cx0 = nn.Conv2d(dd, 32, kernel_size=3, padding=1)
        self.Cx1 = nn.Conv2d(32, dd, kernel_size=3, padding=1)
        self.COUT = nn.Conv2d(dd, 3, kernel_size=3, padding=1)

    def forward(self, code):
        ACT = F.mish
        x = self.CIN(code)

        x = x + self.C01(ACT(self.C00(ACT(self.B00(x)))))
        x = x + self.C03(ACT(self.C02(x)))
        x = F.pixel_shuffle(x, 2)

        x = x + self.C11(ACT(self.C10(ACT(self.B10(x)))))
        x = x + self.C13(ACT(self.C12(x)))
        x = F.pixel_shuffle(x, 2)

        x = x + self.C21(ACT(self.C20(ACT(self.B20(x)))))
        x = x + self.C23(ACT(self.C22(x)))
        x = F.pixel_shuffle(x, 2)

        x = x + self.Cx1(ACT(self.Cx0(x)))
        x = self.COUT(x)

        return torch.sigmoid(x)


class REncoderLarge(nn.Module):

    def __init__(self, args, dd, ee, ff):
        super().__init__()
        self.args = args
        self.CXX = nn.Conv2d(3, dd, kernel_size=3, padding=1)
        self.BXX = nn.BatchNorm2d(dd)
        self.CX0 = nn.Conv2d(dd, ee, kernel_size=3, padding=1)
        self.CX1 = nn.Conv2d(ee, dd, kernel_size=3, padding=1)
        self.R0 = ResBlock(dd * 4, ff)
        self.R1 = ResBlock(dd * 16, ff)
        self.R2 = ResBlock(dd * 64, ff)
        self.CZZ = nn.Conv2d(dd * 64,
                             args.my_img_bit,
                             kernel_size=3,
                             padding=1)

    def forward(self, x):
        ACT = F.mish
        x = self.BXX(self.CXX(x))

        x = x + self.CX1(ACT(self.CX0(x)))
        x = F.pixel_unshuffle(x, 2)
        x = self.R0(x)
        x = F.pixel_unshuffle(x, 2)
        x = self.R1(x)
        x = F.pixel_unshuffle(x, 2)
        x = self.R2(x)

        x = self.CZZ(x)
        return torch.sigmoid(x)


class RDecoderLarge(nn.Module):

    def __init__(self, args):
        super().__init__()
        self.args = args
        if 'd16_512' in model_prefix:
            dd, ee, ff = 16, 64, 512
        elif 'd32_1024' in model_prefix:
            dd, ee, ff = 32, 128, 1024
        self.CZZ = nn.Conv2d(args.my_img_bit,
                             dd * 64,
                             kernel_size=3,
                             padding=1)
        self.BZZ = nn.BatchNorm2d(dd * 64)
        self.R0 = ResBlock(dd * 64, ff)
        self.R1 = ResBlock(dd * 16, ff)
        self.R2 = ResBlock(dd * 4, ff)
        self.CX0 = nn.Conv2d(dd, ee, kernel_size=3, padding=1)
        self.CX1 = nn.Conv2d(ee, dd, kernel_size=3, padding=1)
        self.CXX = nn.Conv2d(dd, 3, kernel_size=3, padding=1)

    def forward(self, x):
        ACT = F.mish
        x = self.BZZ(self.CZZ(x))

        x = self.R0(x)
        x = F.pixel_shuffle(x, 2)
        x = self.R1(x)
        x = F.pixel_shuffle(x, 2)
        x = self.R2(x)
        x = F.pixel_shuffle(x, 2)
        x = x + self.CX1(ACT(self.CX0(x)))

        x = self.CXX(x)
        return torch.sigmoid(x)


@st.cache
def prepare_model(model_prefix):
    gc.collect()

    if model_prefix == 'out-v7c_d8_256-224-13bit-OB32x0.5-745':
        R_ENCODER, R_DECODER = REncoderSmall, RDecoderSmall
    else:
        if 'd16_512' in model_prefix:
            dd, ee, ff = 16, 64, 512
        elif 'd32_1024' in model_prefix:
            dd, ee, ff = 32, 128, 1024
        R_ENCODER, R_DECODER = ((lambda args: REncoderLarge(args, dd, ee, ff)),
                                (lambda args: RDecoderLarge(args, dd, ee, ff)))

    args = types.SimpleNamespace()
    args.my_img_bit = 13
    encoder = R_ENCODER(args).eval().to(device)
    decoder = R_DECODER(args).eval().to(device)

    zpow = torch.tensor([2**i for i in range(0, 13)]).reshape(13, 1, 1)
    zpow = zpow.to(device).long()

    encoder.load_state_dict(
        torch.load(
            cached_download(hf_hub_url(MODEL_REPO, f'{model_prefix}-E.pth'))))
    decoder.load_state_dict(
        torch.load(
            cached_download(hf_hub_url(MODEL_REPO, f'{model_prefix}-D.pth'))))

    encoder.eval()
    decoder.eval()

    return encoder, decoder


def encode(model_prefix, img):
    encoder, _ = prepare_model(model_prefix)
    img_transform = transforms.Compose([
        transforms.PILToTensor(),
        transforms.ConvertImageDtype(torch.float),
        transforms.Resize((224, 224))
    ])

    with torch.no_grad():
        img = img_transform(img.convert("RGB")).unsqueeze(0).to(device)
        z = encoder(img)
        z = ToBinary.apply(z)

    return z.cpu().numpy()


def decode(model_prefix, z):
    _, decoder = prepare_model(model_prefix)
    decoded = decoder(torch.Tensor(z).to(device))
    return VF.to_pil_image(decoded[0])


st.title("clip-guided-binary-autoencoder")
model_prefix = st.selectbox('The model to use',
                            ('out-v7c_d8_256-224-13bit-OB32x0.5-745',
                             'out-v7d_d16_512-224-13bit-OB32x0.5-2487',
                             'out-v7d_d32_1024-224-13bit-OB32x0.5-5560'))

encoder_tab, decoder_tab = st.tabs(["Encode", "Decode"])

with encoder_tab:
    col_in, col_out = st.columns(2)
    uploaded_file = col_in.file_uploader('Choose an Image')
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        col_in.image(image, 'Input Image')
        z = encode(model_prefix, image)
        with io.BytesIO() as buffer:
            np.save(buffer, z)
            col_out.download_button(
                label="Download Encoded Data",
                data=buffer,
                file_name=uploaded_file.name + '.npy',
            )
        col_out.image(decode(model_prefix, z), 'Output Image preview')

with decoder_tab:
    col_in, col_out = st.columns(2)
    uploaded_file = col_in.file_uploader('Choose an Encoded Data')
    if uploaded_file is not None:
        z = np.load(uploaded_file)
        image = decode(model_prefix, z)
        col_out.image(image, 'Output Image')