diff --git a/lib_layerdiffuse/vae.py b/lib_layerdiffuse/vae.py
index 8b13789..084fa32 100644
--- a/lib_layerdiffuse/vae.py
+++ b/lib_layerdiffuse/vae.py
@@ -1 +1,452 @@
+import torch.nn as nn
+import torch
+import cv2
+import numpy as np
+import safetensors.torch as sf
+from accelerate.logging import get_logger
 
+logger = get_logger(__name__, log_level="INFO")
+
+from tqdm import tqdm
+from typing import Optional, Tuple
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
+from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
+
+import torchvision
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+class LatentTransparencyOffsetEncoder(torch.nn.Module):
+    def __init__(self, latent_c=4, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.blocks = torch.nn.Sequential(
+            torch.nn.Conv2d(4, 32, kernel_size=3, padding=1, stride=1),
+            nn.SiLU(),
+            torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1),
+            nn.SiLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2),
+            nn.SiLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1),
+            nn.SiLU(),
+            torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2),
+            nn.SiLU(),
+            torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1),
+            nn.SiLU(),
+            torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),
+            nn.SiLU(),
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+            nn.SiLU(),
+            zero_module(torch.nn.Conv2d(256, latent_c, kernel_size=3, padding=1, stride=1)),
+        )
+
+    def __call__(self, x):
+        return self.blocks(x)
+
+
+# 1024 * 1024 * 3 -> 16 * 16 * 512 -> 1024 * 1024 * 3
+class UNet1024(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+            self,
+            in_channels: int = 3,
+            out_channels: int = 3,
+            down_block_types: Tuple[str] = (
+            "DownBlock2D", "DownBlock2D", "DownBlock2D", "DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D",
+            "AttnDownBlock2D"),
+            up_block_types: Tuple[str] = (
+            "AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D"),
+            block_out_channels: Tuple[int] = (32, 32, 64, 128, 256, 512, 512),
+            layers_per_block: int = 2,
+            mid_block_scale_factor: float = 1,
+            downsample_padding: int = 1,
+            downsample_type: str = "conv",
+            upsample_type: str = "conv",
+            dropout: float = 0.0,
+            act_fn: str = "silu",
+            attention_head_dim: Optional[int] = 8,
+            norm_num_groups: int = 4,
+            norm_eps: float = 1e-5,
+            latent_c: int = 4,
+    ):
+        super().__init__()
+
+        # input
+        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
+        self.latent_conv_in = zero_module(nn.Conv2d(latent_c, block_out_channels[2], kernel_size=1))
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=None,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
+                downsample_padding=downsample_padding,
+                resnet_time_scale_shift="default",
+                downsample_type=downsample_type,
+                dropout=dropout,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        self.mid_block = UNetMidBlock2D(
+            in_channels=block_out_channels[-1],
+            temb_channels=None,
+            dropout=dropout,
+            resnet_eps=norm_eps,
+            resnet_act_fn=act_fn,
+            output_scale_factor=mid_block_scale_factor,
+            resnet_time_scale_shift="default",
+            attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
+            resnet_groups=norm_num_groups,
+            attn_groups=None,
+            add_attention=True,
+        )
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            is_final_block = i == len(block_out_channels) - 1
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=None,
+                add_upsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
+                resnet_time_scale_shift="default",
+                upsample_type=upsample_type,
+                dropout=dropout,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
+
+    def forward(self, x, latent):
+        sample_latent = self.latent_conv_in(latent)
+        sample = self.conv_in(x)
+        emb = None
+
+        down_block_res_samples = (sample,)
+        for i, downsample_block in enumerate(self.down_blocks):
+            if i == 3:
+                sample = sample + sample_latent
+
+            sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+            down_block_res_samples += res_samples
+
+        sample = self.mid_block(sample, emb)
+
+        for upsample_block in self.up_blocks:
+            res_samples = down_block_res_samples[-len(upsample_block.resnets):]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+            sample = upsample_block(sample, res_samples, emb)
+
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+        return sample
+
+
+def checkerboard(shape):
+    return np.indices(shape).sum(axis=0) % 2
+
+
+def build_alpha_pyramid(color, alpha, dk=1.2):
+    # Written by lvmin at Stanford
+    # Massive iterative Gaussian filters are mathematically consistent to pyramid.
+
+    pyramid = []
+    current_premultiplied_color = color * alpha
+    current_alpha = alpha
+
+    while True:
+        pyramid.append((current_premultiplied_color, current_alpha))
+
+        H, W, C = current_alpha.shape
+        if min(H, W) == 1:
+            break
+
+        current_premultiplied_color = cv2.resize(current_premultiplied_color, (int(W / dk), int(H / dk)),
+                                                 interpolation=cv2.INTER_AREA)
+        current_alpha = cv2.resize(current_alpha, (int(W / dk), int(H / dk)), interpolation=cv2.INTER_AREA)[:, :, None]
+    return pyramid[::-1]
+
+
+def pad_rgb(np_rgba_hwc_uint8):
+    # Written by lvmin at Stanford
+    # Massive iterative Gaussian filters are mathematically consistent to pyramid.
+
+    np_rgba_hwc = np_rgba_hwc_uint8.astype(np.float32)  # / 255.0
+    pyramid = build_alpha_pyramid(color=np_rgba_hwc[..., :3], alpha=np_rgba_hwc[..., 3:])
+
+    top_c, top_a = pyramid[0]
+    fg = np.sum(top_c, axis=(0, 1), keepdims=True) / np.sum(top_a, axis=(0, 1), keepdims=True).clip(1e-8, 1e32)
+
+    for layer_c, layer_a in pyramid:
+        layer_h, layer_w, _ = layer_c.shape
+        fg = cv2.resize(fg, (layer_w, layer_h), interpolation=cv2.INTER_LINEAR)
+        fg = layer_c + fg * (1.0 - layer_a)
+
+    return fg
+
+
+def dist_sample_deterministic(dist: DiagonalGaussianDistribution, perturbation: torch.Tensor):
+    # Modified from diffusers.models.autoencoders.vae.DiagonalGaussianDistribution.sample()
+    x = dist.mean + dist.std * perturbation.to(dist.std)
+    return x
+
+
+class TransparentVAE(torch.nn.Module):
+    def __init__(self, sd_vae, dtype=torch.float16, encoder_file=None, decoder_file=None, alpha=300.0, latent_c=16,
+                 *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.dtype = dtype
+
+        self.sd_vae = sd_vae
+        self.sd_vae.to(dtype=self.dtype)
+        self.sd_vae.requires_grad_(False)
+
+        self.encoder = LatentTransparencyOffsetEncoder(latent_c=latent_c)
+        if encoder_file is not None:
+            temp = sf.load_file(encoder_file)
+            # del temp['blocks.16.weight']
+            # del temp['blocks.16.bias']
+            self.encoder.load_state_dict(temp, strict=True)
+            del temp
+        self.encoder.to(dtype=self.dtype)
+        self.alpha = alpha
+
+        self.decoder = UNet1024(in_channels=3, out_channels=4, latent_c=latent_c)
+        if decoder_file is not None:
+            temp = sf.load_file(decoder_file)
+            # del temp['latent_conv_in.weight']
+            # del temp['latent_conv_in.bias']
+            self.decoder.load_state_dict(temp, strict=True)
+            del temp
+        self.decoder.to(dtype=self.dtype)
+        self.latent_c = latent_c
+
+    def sd_decode(self, latent):
+        return self.sd_vae.decode(latent)
+
+    def decode(self, latent, aug=True):
+        origin_pixel = self.sd_vae.decode(latent).sample
+        origin_pixel = (origin_pixel * 0.5 + 0.5)
+        if not aug:
+            y = self.decoder(origin_pixel.to(self.dtype), latent.to(self.dtype))
+            return origin_pixel, y
+        list_y = []
+        for i in range(int(latent.shape[0])):
+            y = self.estimate_augmented(origin_pixel[i:i + 1].to(self.dtype), latent[i:i + 1].to(self.dtype))
+            list_y.append(y)
+        y = torch.concat(list_y, dim=0)
+        return origin_pixel, y
+
+    def encode(self, img_rgba, img_rgb, padded_img_rgb, use_offset=True):
+        a_bchw_01 = img_rgba[:, 3:, :, :]
+        vae_feed = img_rgb.to(device=self.sd_vae.device, dtype=self.sd_vae.dtype)
+        latent_dist = self.sd_vae.encode(vae_feed).latent_dist
+        offset_feed = torch.cat([padded_img_rgb, a_bchw_01], dim=1).to(device=self.sd_vae.device, dtype=self.dtype)
+        offset = self.encoder(offset_feed) * self.alpha
+        if use_offset:
+            latent = dist_sample_deterministic(dist=latent_dist, perturbation=offset)
+            latent = self.sd_vae.config.scaling_factor * (latent - self.sd_vae.config.shift_factor)
+        else:
+            latent = latent_dist.sample()
+            latent = self.sd_vae.config.scaling_factor * (latent - self.sd_vae.config.shift_factor)
+        return latent
+
+    def forward(self, img_rgba, img_rgb, padded_img_rgb, use_offset=True):
+        return self.decode(self.encode(img_rgba, img_rgb, padded_img_rgb, use_offset))
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @torch.no_grad()
+    def estimate_augmented(self, pixel, latent):
+        args = [
+            [False, 0], [False, 1], [False, 2], [False, 3], [True, 0], [True, 1], [True, 2], [True, 3],
+        ]
+
+        result = []
+
+        for flip, rok in tqdm(args):
+            feed_pixel = pixel.clone()
+            feed_latent = latent.clone()
+
+            if flip:
+                feed_pixel = torch.flip(feed_pixel, dims=(3,))
+                feed_latent = torch.flip(feed_latent, dims=(3,))
+
+            feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3))
+            feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3))
+
+            eps = self.decoder(feed_pixel, feed_latent).clip(0, 1)
+            eps = torch.rot90(eps, k=-rok, dims=(2, 3))
+
+            if flip:
+                eps = torch.flip(eps, dims=(3,))
+
+            result += [eps]
+
+        result = torch.stack(result, dim=0)
+        median = torch.median(result, dim=0).values
+        return median
+
+
+class TransparentVAEDecoder(torch.nn.Module):
+    def __init__(self, filename, dtype=torch.float16, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        sd = sf.load_file(filename)
+        model = UNet1024(in_channels=3, out_channels=4)
+        model.load_state_dict(sd, strict=True)
+        model.to(dtype=dtype)
+        model.eval()
+        self.model = model
+        self.dtype = dtype
+        return
+
+    @torch.no_grad()
+    def estimate_single_pass(self, pixel, latent):
+        y = self.model(pixel, latent)
+        return y
+
+    @torch.no_grad()
+    def estimate_augmented(self, pixel, latent):
+        args = [
+            [False, 0], [False, 1], [False, 2], [False, 3], [True, 0], [True, 1], [True, 2], [True, 3],
+        ]
+
+        result = []
+
+        for flip, rok in tqdm(args):
+            feed_pixel = pixel.clone()
+            feed_latent = latent.clone()
+
+            if flip:
+                feed_pixel = torch.flip(feed_pixel, dims=(3,))
+                feed_latent = torch.flip(feed_latent, dims=(3,))
+
+            feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3))
+            feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3))
+
+            eps = self.estimate_single_pass(feed_pixel, feed_latent).clip(0, 1)
+            eps = torch.rot90(eps, k=-rok, dims=(2, 3))
+
+            if flip:
+                eps = torch.flip(eps, dims=(3,))
+
+            result += [eps]
+
+        result = torch.stack(result, dim=0)
+        median = torch.median(result, dim=0).values
+        return median
+
+    @torch.no_grad()
+    def forward(self, sd_vae, latent):
+        pixel = sd_vae.decode(latent).sample
+        pixel = (pixel * 0.5 + 0.5).clip(0, 1).to(self.dtype)
+        latent = latent.to(self.dtype)
+        result_list = []
+        vis_list = []
+
+        for i in range(int(latent.shape[0])):
+            y = self.estimate_augmented(pixel[i:i + 1], latent[i:i + 1])
+
+            y = y.clip(0, 1).movedim(1, -1)
+            alpha = y[..., :1]
+            fg = y[..., 1:]
+
+            B, H, W, C = fg.shape
+            cb = checkerboard(shape=(H // 64, W // 64))
+            cb = cv2.resize(cb, (W, H), interpolation=cv2.INTER_NEAREST)
+            cb = (0.5 + (cb - 0.5) * 0.1)[None, ..., None]
+            cb = torch.from_numpy(cb).to(fg)
+
+            vis = (fg * alpha + cb * (1 - alpha))[0]
+            vis = (vis * 255.0).detach().cpu().float().numpy().clip(0, 255).astype(np.uint8)
+            vis_list.append(vis)
+
+            png = torch.cat([fg, alpha], dim=3)[0]
+            png = (png * 255.0).detach().cpu().float().numpy().clip(0, 255).astype(np.uint8)
+            result_list.append(png)
+
+        return result_list, vis_list
+
+
+class TransparentVAEEncoder(torch.nn.Module):
+    def __init__(self, filename, dtype=torch.float16, alpha=300.0, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        sd = sf.load_file(filename)
+        self.dtype = dtype
+
+        model = LatentTransparencyOffsetEncoder()
+        model.load_state_dict(sd, strict=True)
+        model.to(dtype=self.dtype)
+        model.eval()
+
+        self.model = model
+
+        # similar to LoRA's alpha to avoid initial zero-initialized outputs being too small
+        self.alpha = alpha
+        return
+
+    @torch.no_grad()
+    def forward(self, sd_vae, list_of_np_rgba_hwc_uint8, use_offset=True):
+        list_of_np_rgb_padded = [pad_rgb(x) for x in list_of_np_rgba_hwc_uint8]
+        rgb_padded_bchw_01 = torch.from_numpy(np.stack(list_of_np_rgb_padded, axis=0)).float().movedim(-1, 1)
+        rgba_bchw_01 = torch.from_numpy(np.stack(list_of_np_rgba_hwc_uint8, axis=0)).float().movedim(-1, 1) / 255.0
+        rgb_bchw_01 = rgba_bchw_01[:, :3, :, :]
+        a_bchw_01 = rgba_bchw_01[:, 3:, :, :]
+        vae_feed = (rgb_bchw_01 * 2.0 - 1.0) * a_bchw_01
+        vae_feed = vae_feed.to(device=sd_vae.device, dtype=sd_vae.dtype)
+        latent_dist = sd_vae.encode(vae_feed).latent_dist
+        offset_feed = torch.cat([a_bchw_01, rgb_padded_bchw_01], dim=1).to(device=sd_vae.device, dtype=self.dtype)
+        offset = self.model(offset_feed) * self.alpha
+        if use_offset:
+            latent = dist_sample_deterministic(dist=latent_dist, perturbation=offset)
+        else:
+            latent = latent_dist.sample()
+        return latent