Added AEMatter ComfyUI node

.gitignore

@@ -19,3 +19,10 @@ pretrain_model/
 **/__pycache__
 /rm.txt
 /waste.txt
+ComfyUI_AEMatter/AEMatter.execute.py
+ComfyUI_AEMatter/__pycache__/__init__.cpython-310.pyc
+ComfyUI_AEMatter/AEMatter.run.sh
+ComfyUI_AEMatter/AEMatter.class.py
+ComfyUI_AEMatter/AEMatter.import.py
+ComfyUI_AEMatter/AEMatter.function.py
+ComfyUI_AEMatter/AEMatter.unify.sh

ComfyUI_AEMatter/AEMatter.py

@@ -0,0 +1,1248 @@
+#!/usr/bin/python3
+import cv2
+import math
+import numpy as np
+import os
+import random
+import wget
+
+import torch
+import torch.nn as nn
+from torch.nn import init
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from collections import OrderedDict
+from einops import rearrange, repeat
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+import folder_paths
+from folder_paths import models_dir
+
+
+#!/usr/bin/python3
+def mkdir_safe(out_path):
+    if type(out_path) == str:
+        if len(out_path) > 0:
+            if not os.path.exists(out_path):
+                os.mkdir(out_path)
+
+
+def get_model_path():
+    import folder_paths
+    from folder_paths import models_dir
+
+    path_file_model = models_dir
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'AEMatter')
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'AEM_RWA.ckpt')
+
+    return path_file_model
+
+
+def download_model(path):
+    if not os.path.exists(path):
+        wget.download(
+            'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/AEMatter/AEM_RWA.ckpt?download=true',
+            out=path)
+
+
+def from_torch_image(image):
+    image = image.cpu().numpy() * 255.0
+    image = np.clip(image, 0, 255).astype(np.uint8)
+    return image
+
+
+def to_torch_image(image):
+    image = image.astype(dtype=np.float32)
+    image /= 255.0
+    image = torch.from_numpy(image)
+    return image
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+               C)
+    windows = x.permute(0, 1, 3, 2, 4,
+                        5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size,
+                     window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+def get_AEMatter_model(path_model_checkpoint):
+
+    download_model(path=path_model_checkpoint)
+
+    matmodel = AEMatter()
+    matmodel.load_state_dict(
+        torch.load(path_model_checkpoint, map_location='cpu')['model'])
+
+    matmodel = matmodel.cuda()
+    matmodel.eval()
+
+    return matmodel
+
+
+def do_infer(rawimg, trimap, matmodel):
+    trimap_nonp = trimap.copy()
+    h, w, c = rawimg.shape
+    nonph, nonpw, _ = rawimg.shape
+    newh = (((h - 1) // 32) + 1) * 32
+    neww = (((w - 1) // 32) + 1) * 32
+    padh = newh - h
+    padh1 = int(padh / 2)
+    padh2 = padh - padh1
+    padw = neww - w
+    padw1 = int(padw / 2)
+    padw2 = padw - padw1
+
+    rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+
+    trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+
+    h_pad, w_pad, _ = rawimg_pad.shape
+    tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
+    tritemp[:, :, 0] = (trimap_pad == 0)
+    tritemp[:, :, 1] = (trimap_pad == 128)
+    tritemp[:, :, 2] = (trimap_pad == 255)
+    tritempimgs = np.transpose(tritemp, (2, 0, 1))
+    tritempimgs = tritempimgs[np.newaxis, :, :, :]
+    img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
+    img = np.array(img, np.float32)
+    img = img / 255.
+    img = torch.from_numpy(img).cuda()
+    tritempimgs = torch.from_numpy(tritempimgs).cuda()
+    with torch.no_grad():
+        pred = matmodel(img, tritempimgs)
+        pred = pred.detach().cpu().numpy()[0]
+        pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
+        preda = pred[
+            0:1,
+        ] * 255
+        preda = np.transpose(preda, (1, 2, 0))
+        preda = preda * (trimap_nonp[:, :, None]
+                         == 128) + (trimap_nonp[:, :, None] == 255) * 255
+    preda = np.array(preda, np.uint8)
+    return preda
+
+
+def main():
+    ptrimap = '/home/asd/Desktop/demo/retriever_trimap.png'
+    pimgs = '/home/asd/Desktop/demo/retriever_rgb.png'
+    p_outs = 'alpha.png'
+
+    matmodel = get_AEMatter_model(
+        path_model_checkpoint='/home/asd/Desktop/AEM_RWA.ckpt')
+
+    # matmodel = AEMatter()
+    # matmodel.load_state_dict(
+    #     torch.load('/home/asd/Desktop/AEM_RWA.ckpt',
+    #                map_location='cpu')['model'])
+
+    # matmodel = matmodel.cuda()
+    # matmodel.eval()
+
+    rawimg = pimgs
+    trimap = ptrimap
+    rawimg = cv2.imread(rawimg, cv2.IMREAD_COLOR)
+    trimap = cv2.imread(trimap, cv2.IMREAD_GRAYSCALE)
+    trimap_nonp = trimap.copy()
+    h, w, c = rawimg.shape
+    nonph, nonpw, _ = rawimg.shape
+    newh = (((h - 1) // 32) + 1) * 32
+    neww = (((w - 1) // 32) + 1) * 32
+    padh = newh - h
+    padh1 = int(padh / 2)
+    padh2 = padh - padh1
+    padw = neww - w
+    padw1 = int(padw / 2)
+    padw2 = padw - padw1
+    rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+    trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+    h_pad, w_pad, _ = rawimg_pad.shape
+    tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
+    tritemp[:, :, 0] = (trimap_pad == 0)
+    tritemp[:, :, 1] = (trimap_pad == 128)
+    tritemp[:, :, 2] = (trimap_pad == 255)
+    tritempimgs = np.transpose(tritemp, (2, 0, 1))
+    tritempimgs = tritempimgs[np.newaxis, :, :, :]
+    img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
+    img = np.array(img, np.float32)
+    img = img / 255.
+    img = torch.from_numpy(img).cuda()
+    tritempimgs = torch.from_numpy(tritempimgs).cuda()
+    with torch.no_grad():
+        pred = matmodel(img, tritempimgs)
+        pred = pred.detach().cpu().numpy()[0]
+        pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
+        preda = pred[
+            0:1,
+        ] * 255
+        preda = np.transpose(preda, (1, 2, 0))
+        preda = preda * (trimap_nonp[:, :, None]
+                         == 128) + (trimap_nonp[:, :, None] == 255) * 255
+    preda = np.array(preda, np.uint8)
+    cv2.imwrite(p_outs, preda)
+
+
+#!/usr/bin/python3
+class WindowAttention(nn.Module):
+    """ Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self,
+                 dim,
+                 window_size,
+                 num_heads,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :,
+                                         None] - coords_flatten[:,
+                                                                None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(
+            1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :,
+                        0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index",
+                             relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """ Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[
+            2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1],
+                self.window_size[0] * self.window_size[1],
+                -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                             N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """ Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size=7,
+                 shift_size=0,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(dim,
+                                    window_size=to_2tuple(self.window_size),
+                                    num_heads=num_heads,
+                                    qkv_bias=qkv_bias,
+                                    qk_scale=qk_scale,
+                                    attn_drop=attn_drop,
+                                    proj_drop=drop)
+
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer,
+                       drop=drop)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x,
+                                   shifts=(-self.shift_size, -self.shift_size),
+                                   dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                   C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size,
+                                         self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                   Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x,
+                           shifts=(self.shift_size, self.shift_size),
+                           dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """ Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 use_checkpoint=False):
+
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim,
+                                 num_heads=num_heads,
+                                 window_size=window_size,
+                                 shift_size=0 if
+                                 (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias,
+                                 qk_scale=qk_scale,
+                                 drop=drop,
+                                 attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(
+                                     drop_path, list) else drop_path,
+                                 norm_layer=norm_layer) for i in range(depth)
+        ])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        # print(x.shape,H,W)
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size)  # nW, window_size, window_size, 1
+
+        mask_windows = mask_windows.view(-1,
+                                         self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(
+            2)  # nW, ww window_size*window_size
+        attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                          float(-100.0)).masked_fill(
+                                              attn_mask == 0, float(0.0))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 norm_layer=None):
+
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x,
+                      (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """ Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.2,
+                 norm_layer=nn.LayerNorm,
+                 ape=False,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 use_checkpoint=False):
+
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1]
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0],
+                            patches_resolution[1]))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2**i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                norm_layer=norm_layer,
+                downsample=PatchMerging if
+                (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in backbone.
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+                                               size=(Wh, Ww),
+                                               mode='bicubic')
+            x = (x + absolute_pos_embed).flatten(2).transpose(1,
+                                                              2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W,
+                                 self.num_features[i]).permute(0, 3, 1,
+                                                               2).contiguous()
+                outs.append(out)
+
+        return tuple(outs)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class ResBlock(nn.Module):
+
+    def __init__(self, inc, midc):
+        super(ResBlock, self).__init__()
+        self.conv1 = nn.Conv2d(inc,
+                               midc,
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               bias=True)
+        self.gn1 = nn.GroupNorm(16, midc)
+        self.conv2 = nn.Conv2d(midc,
+                               midc,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=True)
+        self.gn2 = nn.GroupNorm(16, midc)
+        self.conv3 = nn.Conv2d(midc,
+                               inc,
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               bias=True)
+        self.relu = nn.LeakyReLU(0.1)
+
+    def forward(self, x):
+        x_ = x
+        x = self.conv1(x)
+        x = self.gn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.gn2(x)
+        x = self.relu(x)
+        x = self.conv3(x)
+        x = x + x_
+        x = self.relu(x)
+        return x
+
+
+class AEALblock(nn.Module):
+
+    def __init__(self,
+                 d_model,
+                 nhead,
+                 dim_feedforward=512,
+                 dropout=0.0,
+                 layer_norm_eps=1e-5,
+                 batch_first=True,
+                 norm_first=False,
+                 width=5):
+        super(AEALblock, self).__init__()
+        self.self_attn2 = nn.MultiheadAttention(d_model // 2,
+                                                nhead // 2,
+                                                dropout=dropout,
+                                                batch_first=batch_first)
+        self.self_attn1 = nn.MultiheadAttention(d_model // 2,
+                                                nhead // 2,
+                                                dropout=dropout,
+                                                batch_first=batch_first)
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+        self.norm_first = norm_first
+        self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+        self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.activation = nn.ReLU()
+        self.width = width
+        self.trans = nn.Sequential(
+            nn.Conv2d(d_model + 512, d_model // 2, 1, 1, 0),
+            ResBlock(d_model // 2, d_model // 4),
+            nn.Conv2d(d_model // 2, d_model, 1, 1, 0))
+        self.gamma = nn.Parameter(torch.zeros(1))
+
+    def forward(
+        self,
+        src,
+        feats,
+    ):
+        src = self.gamma * self.trans(torch.cat([src, feats], 1)) + src
+        b, c, h, w = src.shape
+        x1 = src[:, 0:c // 2]
+        x1_ = rearrange(x1, 'b c (h1 h2) w -> b c h1 h2 w', h2=self.width)
+        x1_ = rearrange(x1_, 'b c h1 h2 w -> (b h1) (h2 w) c')
+        x2 = src[:, c // 2:]
+        x2_ = rearrange(x2, 'b c h (w1 w2) -> b c h w1 w2', w2=self.width)
+        x2_ = rearrange(x2_, 'b c h w1 w2 -> (b w1) (h w2) c')
+        x = rearrange(src, 'b c h w-> b (h w) c')
+        x = self.norm1(x + self._sa_block(x1_, x2_, h, w))
+        x = self.norm2(x + self._ff_block(x))
+        x = rearrange(x, 'b (h w) c->b c h w', h=h, w=w)
+        return x
+
+    def _sa_block(self, x1, x2, h, w):
+        x1 = self.self_attn1(x1,
+                             x1,
+                             x1,
+                             attn_mask=None,
+                             key_padding_mask=None,
+                             need_weights=False)[0]
+
+        x2 = self.self_attn2(x2,
+                             x2,
+                             x2,
+                             attn_mask=None,
+                             key_padding_mask=None,
+                             need_weights=False)[0]
+
+        x1 = rearrange(x1,
+                       '(b h1) (h2 w) c-> b (h1 h2 w) c',
+                       h2=self.width,
+                       h1=h // self.width)
+        x2 = rearrange(x2,
+                       ' (b w1) (h w2) c-> b (h w1 w2) c',
+                       w2=self.width,
+                       w1=w // self.width)
+        x = torch.cat([x1, x2], dim=2)
+        return self.dropout1(x)
+
+    def _ff_block(self, x):
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout2(x)
+
+
+class AEMatter(nn.Module):
+
+    def __init__(self):
+        super(AEMatter, self).__init__()
+        trans = SwinTransformer(pretrain_img_size=224,
+                                embed_dim=96,
+                                depths=[2, 2, 6, 2],
+                                num_heads=[3, 6, 12, 24],
+                                window_size=7,
+                                ape=False,
+                                drop_path_rate=0.2,
+                                patch_norm=True,
+                                use_checkpoint=False)
+
+        # trans.load_state_dict(torch.load(
+        #     '/home/asd/Desktop/swin_tiny_patch4_window7_224.pth',
+        #     map_location="cpu")["model"],
+        #                       strict=False)
+
+        trans.patch_embed.proj = nn.Conv2d(64, 96, 3, 2, 1)
+
+        self.start_conv0 = nn.Sequential(nn.Conv2d(6, 48, 3, 1, 1),
+                                         nn.PReLU(48))
+
+        self.start_conv = nn.Sequential(nn.Conv2d(48, 64, 3, 2,
+                                                  1), nn.PReLU(64),
+                                        nn.Conv2d(64, 64, 3, 1, 1),
+                                        nn.PReLU(64))
+
+        self.trans = trans
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=640 + 768,
+                      out_channels=256,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True))
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=256 + 384,
+                      out_channels=256,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True), )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(in_channels=256 + 192,
+                      out_channels=192,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True), )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(in_channels=192 + 96,
+                      out_channels=128,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True), )
+        self.ctran0 = BasicLayer(256, 3, 8, 7, drop_path=0.09)
+        self.ctran1 = BasicLayer(256, 3, 8, 7, drop_path=0.07)
+        self.ctran2 = BasicLayer(192, 3, 6, 7, drop_path=0.05)
+        self.ctran3 = BasicLayer(128, 3, 4, 7, drop_path=0.03)
+        self.conv5 = nn.Sequential(
+            nn.Conv2d(in_channels=192,
+                      out_channels=64,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(64),
+            nn.Conv2d(in_channels=64,
+                      out_channels=64,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(64),
+            nn.Conv2d(in_channels=64,
+                      out_channels=48,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(48))
+        self.convo = nn.Sequential(
+            nn.Conv2d(in_channels=48 + 48 + 6,
+                      out_channels=32,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(32),
+            nn.Conv2d(in_channels=32,
+                      out_channels=32,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(32),
+            nn.Conv2d(in_channels=32,
+                      out_channels=1,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True))
+        self.up = nn.Upsample(scale_factor=2,
+                              mode='bilinear',
+                              align_corners=False)
+        self.upn = nn.Upsample(scale_factor=2, mode='nearest')
+        self.apptrans = nn.Sequential(
+            nn.Conv2d(256 + 384, 256, 1, 1, bias=True), ResBlock(256, 128),
+            ResBlock(256, 128), nn.Conv2d(256, 512, 2, 2, bias=True),
+            ResBlock(512, 128))
+        self.emb = nn.Sequential(nn.Conv2d(768, 640, 1, 1, 0),
+                                 ResBlock(640, 160))
+        self.embdp = nn.Sequential(nn.Conv2d(640, 640, 1, 1, 0))
+        self.h2l = nn.Conv2d(768, 256, 1, 1, 0)
+        self.width = 5
+        self.trans1 = AEALblock(d_model=640,
+                                nhead=20,
+                                dim_feedforward=2048,
+                                dropout=0.2,
+                                width=self.width)
+        self.trans2 = AEALblock(d_model=640,
+                                nhead=20,
+                                dim_feedforward=2048,
+                                dropout=0.2,
+                                width=self.width)
+        self.trans3 = AEALblock(d_model=640,
+                                nhead=20,
+                                dim_feedforward=2048,
+                                dropout=0.2,
+                                width=self.width)
+
+    def aeal(self, x, sem):
+        xe = self.emb(x)
+        x_ = xe
+        x_ = self.embdp(x_)
+        b, c, h1, w1 = x_.shape
+        bnew_ph = int(np.ceil(h1 / self.width) * self.width) - h1
+        bnew_pw = int(np.ceil(w1 / self.width) * self.width) - w1
+        newph1 = bnew_ph // 2
+        newph2 = bnew_ph - newph1
+        newpw1 = bnew_pw // 2
+        newpw2 = bnew_pw - newpw1
+        x_ = F.pad(x_, (newpw1, newpw2, newph1, newph2))
+        sem = F.pad(sem, (newpw1, newpw2, newph1, newph2))
+        x_ = self.trans1(x_, sem)
+        x_ = self.trans2(x_, sem)
+        x_ = self.trans3(x_, sem)
+        x_ = x_[:, :, newph1:h1 + newph1, newpw1:w1 + newpw1]
+        return x_
+
+    def forward(self, x, y):
+        inputs = torch.cat((x, y), 1)
+        x = self.start_conv0(inputs)
+        x_ = self.start_conv(x)
+        x1, x2, x3, x4 = self.trans(x_)
+        x4h = self.h2l(x4)
+        x3s = self.apptrans(torch.cat([x3, self.upn(x4h)], 1))
+        x4_ = self.aeal(x4, x3s)
+        x4 = torch.cat((x4, x4_), 1)
+        X4 = self.conv1(x4)
+        wh, ww = X4.shape[2], X4.shape[3]
+        X4 = rearrange(X4, 'b c h w -> b (h w) c')
+        X4, _, _, _, _, _ = self.ctran0(X4, wh, ww)
+        X4 = rearrange(X4, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X3 = self.up(X4)
+        X3 = torch.cat((x3, X3), 1)
+        X3 = self.conv2(X3)
+        wh, ww = X3.shape[2], X3.shape[3]
+        X3 = rearrange(X3, 'b c h w -> b (h w) c')
+        X3, _, _, _, _, _ = self.ctran1(X3, wh, ww)
+        X3 = rearrange(X3, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X2 = self.up(X3)
+        X2 = torch.cat((x2, X2), 1)
+        X2 = self.conv3(X2)
+        wh, ww = X2.shape[2], X2.shape[3]
+        X2 = rearrange(X2, 'b c h w -> b (h w) c')
+        X2, _, _, _, _, _ = self.ctran2(X2, wh, ww)
+        X2 = rearrange(X2, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X1 = self.up(X2)
+        X1 = torch.cat((x1, X1), 1)
+        X1 = self.conv4(X1)
+        wh, ww = X1.shape[2], X1.shape[3]
+        X1 = rearrange(X1, 'b c h w -> b (h w) c')
+        X1, _, _, _, _, _ = self.ctran3(X1, wh, ww)
+        X1 = rearrange(X1, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X0 = self.up(X1)
+        X0 = torch.cat((x_, X0), 1)
+        X0 = self.conv5(X0)
+        X = self.up(X0)
+        X = torch.cat((inputs, x, X), 1)
+        alpha = self.convo(X)
+        alpha = torch.clamp(alpha, min=0, max=1)
+        return alpha
+
+
+class load_AEMatter_Model:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+        }
+
+    RETURN_TYPES = ("AEMatter_Model", )
+    FUNCTION = "test"
+    CATEGORY = "AEMatter"
+
+    def test(self):
+        return (get_AEMatter_model(get_model_path()), )
+
+
+class run_AEMatter_inference:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "trimap": ("MASK", ),
+                "AEMatter_Model": ("AEMatter_Model", ),
+            },
+        }
+
+    RETURN_TYPES = ("MASK", )
+    FUNCTION = "test"
+    CATEGORY = "AEMatter"
+
+    def test(
+        self,
+        image,
+        trimap,
+        AEMatter_Model,
+    ):
+
+        ret = []
+        batch_size = image.shape[0]
+
+        for i in range(batch_size):
+            tmp_i = from_torch_image(image[i])
+            tmp_m = from_torch_image(trimap[i])
+            tmp = do_infer(tmp_i, tmp_m, AEMatter_Model)
+            ret.append(tmp)
+
+        ret = to_torch_image(np.array(ret))
+        ret = ret.squeeze(-1)
+        print(ret.shape)
+
+        return ret
+
+
+#!/usr/bin/python3
+NODE_CLASS_MAPPINGS = {
+    'load_AEMatter_Model': load_AEMatter_Model,
+    'run_AEMatter_inference': run_AEMatter_inference,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    'load_AEMatter_Model': 'load_AEMatter_Model',
+    'run_AEMatter_inference': 'run_AEMatter_inference',
+}

ComfyUI_AEMatter/README.org

@@ -0,0 +1,1357 @@
+* COMMENT SAMPLE
+
+** AEMatter.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.import.py
+#+end_src
+
+** AEMatter.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+#+end_src
+
+** AEMatter.execute.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.execute.py
+#+end_src
+
+** AEMatter.unify.sh
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.unify.sh
+#+end_src
+
+** AEMatter.run.sh
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.run.sh
+#+end_src
+
+* Code for AEMatter inference
+
+** AEMatter.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.import.py
+  import cv2
+  import math
+  import numpy as np
+  import os
+  import random
+  import wget
+
+  import torch
+  import torch.nn as nn
+  from torch.nn import init
+  import torch.nn.functional as F
+  import torch.utils.checkpoint as checkpoint
+
+  from collections import OrderedDict
+  from einops import rearrange, repeat
+  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+  import folder_paths
+  from folder_paths import models_dir
+#+end_src
+
+** Functions to prepare directory structure and download models
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+  def mkdir_safe(out_path):
+      if type(out_path) == str:
+          if len(out_path) > 0:
+              if not os.path.exists(out_path):
+                  os.mkdir(out_path)
+
+
+  def get_model_path():
+      import folder_paths
+      from folder_paths import models_dir
+
+      path_file_model = models_dir
+      mkdir_safe(out_path=path_file_model)
+
+      path_file_model = os.path.join(path_file_model, 'AEMatter')
+      mkdir_safe(out_path=path_file_model)
+
+      path_file_model = os.path.join(path_file_model, 'AEM_RWA.ckpt')
+
+      return path_file_model
+
+
+  def download_model(path):
+      if not os.path.exists(path):
+          wget.download(
+              'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/AEMatter/AEM_RWA.ckpt?download=true',
+              out=path)
+
+
+  def from_torch_image(image):
+      image = image.cpu().numpy() * 255.0
+      image = np.clip(image, 0, 255).astype(np.uint8)
+      return image
+
+
+  def to_torch_image(image):
+      image = image.astype(dtype=np.float32)
+      image /= 255.0
+      image = torch.from_numpy(image)
+      return image
+#+end_src
+
+** AEMatter.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+  def window_partition(x, window_size):
+      """
+      Args:
+          x: (B, H, W, C)
+          window_size (int): window size
+      Returns:
+          windows: (num_windows*B, window_size, window_size, C)
+      """
+      B, H, W, C = x.shape
+      x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+      windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+      return windows
+#+end_src
+
+** AEMatter.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+  def window_reverse(windows, window_size, H, W):
+      """
+      Args:
+          windows: (num_windows*B, window_size, window_size, C)
+          window_size (int): Window size
+          H (int): Height of image
+          W (int): Width of image
+      Returns:
+          x: (B, H, W, C)
+      """
+      B = int(windows.shape[0] / (H * W / window_size / window_size))
+      x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+      return x
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class WindowAttention(nn.Module):
+      """ Window based multi-head self attention (W-MSA) module with relative position bias.
+      It supports both of shifted and non-shifted window.
+      Args:
+          dim (int): Number of input channels.
+          window_size (tuple[int]): The height and width of the window.
+          num_heads (int): Number of attention heads.
+          qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+          attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+          proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+      """
+
+      def __init__(self,
+                   dim,
+                   window_size,
+                   num_heads,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   attn_drop=0.,
+                   proj_drop=0.):
+
+          super().__init__()
+          self.dim = dim
+          self.window_size = window_size  # Wh, Ww
+          self.num_heads = num_heads
+          head_dim = dim // num_heads
+          self.scale = qk_scale or head_dim**-0.5
+
+          # define a parameter table of relative position bias
+          self.relative_position_bias_table = nn.Parameter(
+              torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                          num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+          # get pair-wise relative position index for each token inside the window
+          coords_h = torch.arange(self.window_size[0])
+          coords_w = torch.arange(self.window_size[1])
+          coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+          coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+          relative_coords = coords_flatten[:, :,
+                                           None] - coords_flatten[:,
+                                                                  None, :]  # 2, Wh*Ww, Wh*Ww
+          relative_coords = relative_coords.permute(
+              1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+          relative_coords[:, :,
+                          0] += self.window_size[0] - 1  # shift to start from 0
+          relative_coords[:, :, 1] += self.window_size[1] - 1
+          relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+          relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+          self.register_buffer("relative_position_index",
+                               relative_position_index)
+
+          self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+          self.attn_drop = nn.Dropout(attn_drop)
+          self.proj = nn.Linear(dim, dim)
+          self.proj_drop = nn.Dropout(proj_drop)
+
+          trunc_normal_(self.relative_position_bias_table, std=.02)
+          self.softmax = nn.Softmax(dim=-1)
+
+      def forward(self, x, mask=None):
+          """ Forward function.
+          Args:
+              x: input features with shape of (num_windows*B, N, C)
+              mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+          """
+          B_, N, C = x.shape
+          qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                    C // self.num_heads).permute(2, 0, 3, 1, 4)
+          q, k, v = qkv[0], qkv[1], qkv[
+              2]  # make torchscript happy (cannot use tensor as tuple)
+
+          q = q * self.scale
+          attn = (q @ k.transpose(-2, -1))
+
+          relative_position_bias = self.relative_position_bias_table[
+              self.relative_position_index.view(-1)].view(
+                  self.window_size[0] * self.window_size[1],
+                  self.window_size[0] * self.window_size[1],
+                  -1)  # Wh*Ww,Wh*Ww,nH
+          relative_position_bias = relative_position_bias.permute(
+              2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+          attn = attn + relative_position_bias.unsqueeze(0)
+
+          if mask is not None:
+              nW = mask.shape[0]
+              attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                               N) + mask.unsqueeze(1).unsqueeze(0)
+              attn = attn.view(-1, self.num_heads, N, N)
+              attn = self.softmax(attn)
+          else:
+              attn = self.softmax(attn)
+
+          attn = self.attn_drop(attn)
+
+          x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+          x = self.proj(x)
+          x = self.proj_drop(x)
+          return x
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class SwinTransformerBlock(nn.Module):
+      """ Swin Transformer Block.
+      Args:
+          dim (int): Number of input channels.
+          num_heads (int): Number of attention heads.
+          window_size (int): Window size.
+          shift_size (int): Shift size for SW-MSA.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+          drop (float, optional): Dropout rate. Default: 0.0
+          attn_drop (float, optional): Attention dropout rate. Default: 0.0
+          drop_path (float, optional): Stochastic depth rate. Default: 0.0
+          act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+      """
+
+      def __init__(self,
+                   dim,
+                   num_heads,
+                   window_size=7,
+                   shift_size=0,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop=0.,
+                   attn_drop=0.,
+                   drop_path=0.,
+                   act_layer=nn.GELU,
+                   norm_layer=nn.LayerNorm):
+          super().__init__()
+          self.dim = dim
+          self.num_heads = num_heads
+          self.window_size = window_size
+          self.shift_size = shift_size
+          self.mlp_ratio = mlp_ratio
+          assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+          self.norm1 = norm_layer(dim)
+          self.attn = WindowAttention(dim,
+                                      window_size=to_2tuple(self.window_size),
+                                      num_heads=num_heads,
+                                      qkv_bias=qkv_bias,
+                                      qk_scale=qk_scale,
+                                      attn_drop=attn_drop,
+                                      proj_drop=drop)
+
+          self.drop_path = DropPath(
+              drop_path) if drop_path > 0. else nn.Identity()
+          self.norm2 = norm_layer(dim)
+          mlp_hidden_dim = int(dim * mlp_ratio)
+          self.mlp = Mlp(in_features=dim,
+                         hidden_features=mlp_hidden_dim,
+                         act_layer=act_layer,
+                         drop=drop)
+
+          self.H = None
+          self.W = None
+
+      def forward(self, x, mask_matrix):
+          """ Forward function.
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+              mask_matrix: Attention mask for cyclic shift.
+          """
+          B, L, C = x.shape
+          H, W = self.H, self.W
+          assert L == H * W, "input feature has wrong size"
+
+          shortcut = x
+          x = self.norm1(x)
+          x = x.view(B, H, W, C)
+
+          # pad feature maps to multiples of window size
+          pad_l = pad_t = 0
+          pad_r = (self.window_size - W % self.window_size) % self.window_size
+          pad_b = (self.window_size - H % self.window_size) % self.window_size
+          x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+          _, Hp, Wp, _ = x.shape
+
+          # cyclic shift
+          if self.shift_size > 0:
+              shifted_x = torch.roll(x,
+                                     shifts=(-self.shift_size, -self.shift_size),
+                                     dims=(1, 2))
+              attn_mask = mask_matrix
+          else:
+              shifted_x = x
+              attn_mask = None
+
+          # partition windows
+          x_windows = window_partition(
+              shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+          x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                     C)  # nW*B, window_size*window_size, C
+
+          # W-MSA/SW-MSA
+          attn_windows = self.attn(
+              x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+          # merge windows
+          attn_windows = attn_windows.view(-1, self.window_size,
+                                           self.window_size, C)
+          shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                     Wp)  # B H' W' C
+
+          # reverse cyclic shift
+          if self.shift_size > 0:
+              x = torch.roll(shifted_x,
+                             shifts=(self.shift_size, self.shift_size),
+                             dims=(1, 2))
+          else:
+              x = shifted_x
+
+          if pad_r > 0 or pad_b > 0:
+              x = x[:, :H, :W, :].contiguous()
+
+          x = x.view(B, H * W, C)
+
+          # FFN
+          x = shortcut + self.drop_path(x)
+          x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+          return x
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class PatchMerging(nn.Module):
+      """ Patch Merging Layer
+      Args:
+          dim (int): Number of input channels.
+          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+      """
+
+      def __init__(self, dim, norm_layer=nn.LayerNorm):
+          super().__init__()
+          self.dim = dim
+          self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+          self.norm = norm_layer(4 * dim)
+
+      def forward(self, x, H, W):
+          """ Forward function.
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+          """
+          B, L, C = x.shape
+          assert L == H * W, "input feature has wrong size"
+
+          x = x.view(B, H, W, C)
+
+          # padding
+          pad_input = (H % 2 == 1) or (W % 2 == 1)
+          if pad_input:
+              x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+          x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+          x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+          x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+          x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+          x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+          x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+          x = self.norm(x)
+          x = self.reduction(x)
+
+          return x
+#+end_src
+
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class BasicLayer(nn.Module):
+      """ A basic Swin Transformer layer for one stage.
+      Args:
+          dim (int): Number of feature channels
+          depth (int): Depths of this stage.
+          num_heads (int): Number of attention head.
+          window_size (int): Local window size. Default: 7.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+          drop (float, optional): Dropout rate. Default: 0.0
+          attn_drop (float, optional): Attention dropout rate. Default: 0.0
+          drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+          norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+          downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+      """
+
+      def __init__(self,
+                   dim,
+                   depth,
+                   num_heads,
+                   window_size=7,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop=0.,
+                   attn_drop=0.,
+                   drop_path=0.,
+                   norm_layer=nn.LayerNorm,
+                   downsample=None,
+                   use_checkpoint=False):
+
+          super().__init__()
+          self.window_size = window_size
+          self.shift_size = window_size // 2
+          self.depth = depth
+          self.use_checkpoint = use_checkpoint
+
+          # build blocks
+          self.blocks = nn.ModuleList([
+              SwinTransformerBlock(dim=dim,
+                                   num_heads=num_heads,
+                                   window_size=window_size,
+                                   shift_size=0 if
+                                   (i % 2 == 0) else window_size // 2,
+                                   mlp_ratio=mlp_ratio,
+                                   qkv_bias=qkv_bias,
+                                   qk_scale=qk_scale,
+                                   drop=drop,
+                                   attn_drop=attn_drop,
+                                   drop_path=drop_path[i] if isinstance(
+                                       drop_path, list) else drop_path,
+                                   norm_layer=norm_layer) for i in range(depth)
+          ])
+
+          # patch merging layer
+          if downsample is not None:
+              self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+          else:
+              self.downsample = None
+
+      def forward(self, x, H, W):
+          """ Forward function.
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+          """
+          # print(x.shape,H,W)
+          # calculate attention mask for SW-MSA
+          Hp = int(np.ceil(H / self.window_size)) * self.window_size
+          Wp = int(np.ceil(W / self.window_size)) * self.window_size
+          img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+          h_slices = (slice(0, -self.window_size),
+                      slice(-self.window_size,
+                            -self.shift_size), slice(-self.shift_size, None))
+          w_slices = (slice(0, -self.window_size),
+                      slice(-self.window_size,
+                            -self.shift_size), slice(-self.shift_size, None))
+          cnt = 0
+          for h in h_slices:
+              for w in w_slices:
+                  img_mask[:, h, w, :] = cnt
+                  cnt += 1
+
+          mask_windows = window_partition(
+              img_mask, self.window_size)  # nW, window_size, window_size, 1
+
+          mask_windows = mask_windows.view(-1,
+                                           self.window_size * self.window_size)
+          attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(
+              2)  # nW, ww window_size*window_size
+          attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                            float(-100.0)).masked_fill(
+                                                attn_mask == 0, float(0.0))
+
+          for blk in self.blocks:
+              blk.H, blk.W = H, W
+              if self.use_checkpoint:
+                  x = checkpoint.checkpoint(blk, x, attn_mask)
+              else:
+                  x = blk(x, attn_mask)
+
+          if self.downsample is not None:
+              x_down = self.downsample(x, H, W)
+              Wh, Ww = (H + 1) // 2, (W + 1) // 2
+              return x, H, W, x_down, Wh, Ww
+          else:
+              return x, H, W, x, H, W
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class PatchEmbed(nn.Module):
+      """ Image to Patch Embedding
+      Args:
+          patch_size (int): Patch token size. Default: 4.
+          in_chans (int): Number of input image channels. Default: 3.
+          embed_dim (int): Number of linear projection output channels. Default: 96.
+          norm_layer (nn.Module, optional): Normalization layer. Default: None
+      """
+
+      def __init__(self,
+                   patch_size=4,
+                   in_chans=3,
+                   embed_dim=96,
+                   norm_layer=None):
+
+          super().__init__()
+          patch_size = to_2tuple(patch_size)
+          self.patch_size = patch_size
+
+          self.in_chans = in_chans
+          self.embed_dim = embed_dim
+
+          self.proj = nn.Conv2d(in_chans,
+                                embed_dim,
+                                kernel_size=patch_size,
+                                stride=patch_size)
+          if norm_layer is not None:
+              self.norm = norm_layer(embed_dim)
+          else:
+              self.norm = None
+
+      def forward(self, x):
+          """Forward function."""
+          # padding
+          _, _, H, W = x.size()
+          if W % self.patch_size[1] != 0:
+              x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+          if H % self.patch_size[0] != 0:
+              x = F.pad(x,
+                        (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+          x = self.proj(x)  # B C Wh Ww
+          if self.norm is not None:
+              Wh, Ww = x.size(2), x.size(3)
+              x = x.flatten(2).transpose(1, 2)
+              x = self.norm(x)
+              x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+          return x
+#+end_src
+
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class SwinTransformer(nn.Module):
+      """ Swin Transformer backbone.
+          A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+            https://arxiv.org/pdf/2103.14030
+      Args:
+          pretrain_img_size (int): Input image size for training the pretrained model,
+              used in absolute postion embedding. Default 224.
+          patch_size (int | tuple(int)): Patch size. Default: 4.
+          in_chans (int): Number of input image channels. Default: 3.
+          embed_dim (int): Number of linear projection output channels. Default: 96.
+          depths (tuple[int]): Depths of each Swin Transformer stage.
+          num_heads (tuple[int]): Number of attention head of each stage.
+          window_size (int): Window size. Default: 7.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+          qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+          drop_rate (float): Dropout rate.
+          attn_drop_rate (float): Attention dropout rate. Default: 0.
+          drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+          norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+          ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+          patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+          out_indices (Sequence[int]): Output from which stages.
+          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+              -1 means not freezing any parameters.
+          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+      """
+
+      def __init__(self,
+                   pretrain_img_size=224,
+                   patch_size=4,
+                   in_chans=3,
+                   embed_dim=96,
+                   depths=[2, 2, 6, 2],
+                   num_heads=[3, 6, 12, 24],
+                   window_size=7,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop_rate=0.,
+                   attn_drop_rate=0.,
+                   drop_path_rate=0.2,
+                   norm_layer=nn.LayerNorm,
+                   ape=False,
+                   patch_norm=True,
+                   out_indices=(0, 1, 2, 3),
+                   frozen_stages=-1,
+                   use_checkpoint=False):
+
+          super().__init__()
+
+          self.pretrain_img_size = pretrain_img_size
+          self.num_layers = len(depths)
+          self.embed_dim = embed_dim
+          self.ape = ape
+          self.patch_norm = patch_norm
+          self.out_indices = out_indices
+          self.frozen_stages = frozen_stages
+
+          # split image into non-overlapping patches
+          self.patch_embed = PatchEmbed(
+              patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+              norm_layer=norm_layer if self.patch_norm else None)
+
+          # absolute position embedding
+          if self.ape:
+              pretrain_img_size = to_2tuple(pretrain_img_size)
+              patch_size = to_2tuple(patch_size)
+              patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
+
+              self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
+              trunc_normal_(self.absolute_pos_embed, std=.02)
+
+          self.pos_drop = nn.Dropout(p=drop_rate)
+
+          # stochastic depth
+          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+          # build layers
+          self.layers = nn.ModuleList()
+          for i_layer in range(self.num_layers):
+              layer = BasicLayer(
+                  dim=int(embed_dim * 2 ** i_layer),
+                  depth=depths[i_layer],
+                  num_heads=num_heads[i_layer],
+                  window_size=window_size,
+                  mlp_ratio=mlp_ratio,
+                  qkv_bias=qkv_bias,
+                  qk_scale=qk_scale,
+                  drop=drop_rate,
+                  attn_drop=attn_drop_rate,
+                  drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                  norm_layer=norm_layer,
+                  downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                  use_checkpoint=use_checkpoint)
+              self.layers.append(layer)
+
+          num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+          self.num_features = num_features
+
+          # add a norm layer for each output
+          for i_layer in out_indices:
+              layer = norm_layer(num_features[i_layer])
+              layer_name = f'norm{i_layer}'
+              self.add_module(layer_name, layer)
+
+          self._freeze_stages()
+
+      def _freeze_stages(self):
+          if self.frozen_stages >= 0:
+              self.patch_embed.eval()
+              for param in self.patch_embed.parameters():
+                  param.requires_grad = False
+
+          if self.frozen_stages >= 1 and self.ape:
+              self.absolute_pos_embed.requires_grad = False
+
+          if self.frozen_stages >= 2:
+              self.pos_drop.eval()
+              for i in range(0, self.frozen_stages - 1):
+                  m = self.layers[i]
+                  m.eval()
+                  for param in m.parameters():
+                      param.requires_grad = False
+
+      def init_weights(self, pretrained=None):
+          """Initialize the weights in backbone.
+          Args:
+              pretrained (str, optional): Path to pre-trained weights.
+                  Defaults to None.
+          """
+
+
+      def forward(self, x):
+          """Forward function."""
+          x = self.patch_embed(x)
+
+          Wh, Ww = x.size(2), x.size(3)
+          if self.ape:
+              # interpolate the position embedding to the corresponding size
+              absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
+              x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+          else:
+              x = x.flatten(2).transpose(1, 2)
+          x = self.pos_drop(x)
+
+          outs = []
+          for i in range(self.num_layers):
+              layer = self.layers[i]
+
+              x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+              if i in self.out_indices:
+                  norm_layer = getattr(self, f'norm{i}')
+                  x_out = norm_layer(x_out)
+
+                  out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                  outs.append(out)
+
+          return tuple(outs)
+
+      def train(self, mode=True):
+          """Convert the model into training mode while keep layers freezed."""
+          super(SwinTransformer, self).train(mode)
+          self._freeze_stages()
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+#+end_src
+
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class ResBlock(nn.Module):
+
+      def __init__(self, inc, midc):
+          super(ResBlock, self).__init__()
+          self.conv1 = nn.Conv2d(inc,
+                                 midc,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0,
+                                 bias=True)
+          self.gn1 = nn.GroupNorm(16, midc)
+          self.conv2 = nn.Conv2d(midc,
+                                 midc,
+                                 kernel_size=3,
+                                 stride=1,
+                                 padding=1,
+                                 bias=True)
+          self.gn2 = nn.GroupNorm(16, midc)
+          self.conv3 = nn.Conv2d(midc,
+                                 inc,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0,
+                                 bias=True)
+          self.relu = nn.LeakyReLU(0.1)
+
+      def forward(self, x):
+          x_ = x
+          x = self.conv1(x)
+          x = self.gn1(x)
+          x = self.relu(x)
+          x = self.conv2(x)
+          x = self.gn2(x)
+          x = self.relu(x)
+          x = self.conv3(x)
+          x = x + x_
+          x = self.relu(x)
+          return x
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class AEALblock(nn.Module):
+
+      def __init__(self,
+                   d_model,
+                   nhead,
+                   dim_feedforward=512,
+                   dropout=0.0,
+                   layer_norm_eps=1e-5,
+                   batch_first=True,
+                   norm_first=False,
+                   width=5):
+          super(AEALblock, self).__init__()
+          self.self_attn2 = nn.MultiheadAttention(d_model // 2,
+                                                  nhead // 2,
+                                                  dropout=dropout,
+                                                  batch_first=batch_first)
+          self.self_attn1 = nn.MultiheadAttention(d_model // 2,
+                                                  nhead // 2,
+                                                  dropout=dropout,
+                                                  batch_first=batch_first)
+          self.linear1 = nn.Linear(d_model, dim_feedforward)
+          self.dropout = nn.Dropout(dropout)
+          self.linear2 = nn.Linear(dim_feedforward, d_model)
+          self.norm_first = norm_first
+          self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+          self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+          self.dropout1 = nn.Dropout(dropout)
+          self.dropout2 = nn.Dropout(dropout)
+          self.activation = nn.ReLU()
+          self.width = width
+          self.trans = nn.Sequential(
+              nn.Conv2d(d_model + 512, d_model // 2, 1, 1, 0),
+              ResBlock(d_model // 2, d_model // 4),
+              nn.Conv2d(d_model // 2, d_model, 1, 1, 0))
+          self.gamma = nn.Parameter(torch.zeros(1))
+
+      def forward(
+          self,
+          src,
+          feats,
+      ):
+          src = self.gamma * self.trans(torch.cat([src, feats], 1)) + src
+          b, c, h, w = src.shape
+          x1 = src[:, 0:c // 2]
+          x1_ = rearrange(x1, 'b c (h1 h2) w -> b c h1 h2 w', h2=self.width)
+          x1_ = rearrange(x1_, 'b c h1 h2 w -> (b h1) (h2 w) c')
+          x2 = src[:, c // 2:]
+          x2_ = rearrange(x2, 'b c h (w1 w2) -> b c h w1 w2', w2=self.width)
+          x2_ = rearrange(x2_, 'b c h w1 w2 -> (b w1) (h w2) c')
+          x = rearrange(src, 'b c h w-> b (h w) c')
+          x = self.norm1(x + self._sa_block(x1_, x2_, h, w))
+          x = self.norm2(x + self._ff_block(x))
+          x = rearrange(x, 'b (h w) c->b c h w', h=h, w=w)
+          return x
+
+      def _sa_block(self, x1, x2, h, w):
+          x1 = self.self_attn1(x1,
+                               x1,
+                               x1,
+                               attn_mask=None,
+                               key_padding_mask=None,
+                               need_weights=False)[0]
+
+          x2 = self.self_attn2(x2,
+                               x2,
+                               x2,
+                               attn_mask=None,
+                               key_padding_mask=None,
+                               need_weights=False)[0]
+
+          x1 = rearrange(x1,
+                         '(b h1) (h2 w) c-> b (h1 h2 w) c',
+                         h2=self.width,
+                         h1=h // self.width)
+          x2 = rearrange(x2,
+                         ' (b w1) (h w2) c-> b (h w1 w2) c',
+                         w2=self.width,
+                         w1=w // self.width)
+          x = torch.cat([x1, x2], dim=2)
+          return self.dropout1(x)
+
+      def _ff_block(self, x):
+          x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+          return self.dropout2(x)
+#+end_src
+
+** AEMatter.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class AEMatter(nn.Module):
+
+      def __init__(self):
+          super(AEMatter, self).__init__()
+          trans = SwinTransformer(pretrain_img_size=224,
+                                  embed_dim=96,
+                                  depths=[2, 2, 6, 2],
+                                  num_heads=[3, 6, 12, 24],
+                                  window_size=7,
+                                  ape=False,
+                                  drop_path_rate=0.2,
+                                  patch_norm=True,
+                                  use_checkpoint=False)
+
+          # trans.load_state_dict(torch.load(
+          #     '/home/asd/Desktop/swin_tiny_patch4_window7_224.pth',
+          #     map_location="cpu")["model"],
+          #                       strict=False)
+
+          trans.patch_embed.proj = nn.Conv2d(64, 96, 3, 2, 1)
+
+          self.start_conv0 = nn.Sequential(nn.Conv2d(6, 48, 3, 1, 1),
+                                           nn.PReLU(48))
+
+          self.start_conv = nn.Sequential(nn.Conv2d(48, 64, 3, 2,
+                                                    1), nn.PReLU(64),
+                                          nn.Conv2d(64, 64, 3, 1, 1),
+                                          nn.PReLU(64))
+
+          self.trans = trans
+          self.conv1 = nn.Sequential(
+              nn.Conv2d(in_channels=640 + 768,
+                        out_channels=256,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        bias=True))
+          self.conv2 = nn.Sequential(
+              nn.Conv2d(in_channels=256 + 384,
+                        out_channels=256,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        bias=True), )
+          self.conv3 = nn.Sequential(
+              nn.Conv2d(in_channels=256 + 192,
+                        out_channels=192,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        bias=True), )
+          self.conv4 = nn.Sequential(
+              nn.Conv2d(in_channels=192 + 96,
+                        out_channels=128,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        bias=True), )
+          self.ctran0 = BasicLayer(256, 3, 8, 7, drop_path=0.09)
+          self.ctran1 = BasicLayer(256, 3, 8, 7, drop_path=0.07)
+          self.ctran2 = BasicLayer(192, 3, 6, 7, drop_path=0.05)
+          self.ctran3 = BasicLayer(128, 3, 4, 7, drop_path=0.03)
+          self.conv5 = nn.Sequential(
+              nn.Conv2d(in_channels=192,
+                        out_channels=64,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        bias=True), nn.PReLU(64),
+              nn.Conv2d(in_channels=64,
+                        out_channels=64,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        bias=True), nn.PReLU(64),
+              nn.Conv2d(in_channels=64,
+                        out_channels=48,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        bias=True), nn.PReLU(48))
+          self.convo = nn.Sequential(
+              nn.Conv2d(in_channels=48 + 48 + 6,
+                        out_channels=32,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        bias=True), nn.PReLU(32),
+              nn.Conv2d(in_channels=32,
+                        out_channels=32,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        bias=True), nn.PReLU(32),
+              nn.Conv2d(in_channels=32,
+                        out_channels=1,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        bias=True))
+          self.up = nn.Upsample(scale_factor=2,
+                                mode='bilinear',
+                                align_corners=False)
+          self.upn = nn.Upsample(scale_factor=2, mode='nearest')
+          self.apptrans = nn.Sequential(
+              nn.Conv2d(256 + 384, 256, 1, 1, bias=True), ResBlock(256, 128),
+              ResBlock(256, 128), nn.Conv2d(256, 512, 2, 2, bias=True),
+              ResBlock(512, 128))
+          self.emb = nn.Sequential(nn.Conv2d(768, 640, 1, 1, 0),
+                                   ResBlock(640, 160))
+          self.embdp = nn.Sequential(nn.Conv2d(640, 640, 1, 1, 0))
+          self.h2l = nn.Conv2d(768, 256, 1, 1, 0)
+          self.width = 5
+          self.trans1 = AEALblock(d_model=640,
+                                  nhead=20,
+                                  dim_feedforward=2048,
+                                  dropout=0.2,
+                                  width=self.width)
+          self.trans2 = AEALblock(d_model=640,
+                                  nhead=20,
+                                  dim_feedforward=2048,
+                                  dropout=0.2,
+                                  width=self.width)
+          self.trans3 = AEALblock(d_model=640,
+                                  nhead=20,
+                                  dim_feedforward=2048,
+                                  dropout=0.2,
+                                  width=self.width)
+
+      def aeal(self, x, sem):
+          xe = self.emb(x)
+          x_ = xe
+          x_ = self.embdp(x_)
+          b, c, h1, w1 = x_.shape
+          bnew_ph = int(np.ceil(h1 / self.width) * self.width) - h1
+          bnew_pw = int(np.ceil(w1 / self.width) * self.width) - w1
+          newph1 = bnew_ph // 2
+          newph2 = bnew_ph - newph1
+          newpw1 = bnew_pw // 2
+          newpw2 = bnew_pw - newpw1
+          x_ = F.pad(x_, (newpw1, newpw2, newph1, newph2))
+          sem = F.pad(sem, (newpw1, newpw2, newph1, newph2))
+          x_ = self.trans1(x_, sem)
+          x_ = self.trans2(x_, sem)
+          x_ = self.trans3(x_, sem)
+          x_ = x_[:, :, newph1:h1 + newph1, newpw1:w1 + newpw1]
+          return x_
+
+      def forward(self, x, y):
+          inputs = torch.cat((x, y), 1)
+          x = self.start_conv0(inputs)
+          x_ = self.start_conv(x)
+          x1, x2, x3, x4 = self.trans(x_)
+          x4h = self.h2l(x4)
+          x3s = self.apptrans(torch.cat([x3, self.upn(x4h)], 1))
+          x4_ = self.aeal(x4, x3s)
+          x4 = torch.cat((x4, x4_), 1)
+          X4 = self.conv1(x4)
+          wh, ww = X4.shape[2], X4.shape[3]
+          X4 = rearrange(X4, 'b c h w -> b (h w) c')
+          X4, _, _, _, _, _ = self.ctran0(X4, wh, ww)
+          X4 = rearrange(X4, 'b (h w) c -> b c h w', h=wh, w=ww)
+          X3 = self.up(X4)
+          X3 = torch.cat((x3, X3), 1)
+          X3 = self.conv2(X3)
+          wh, ww = X3.shape[2], X3.shape[3]
+          X3 = rearrange(X3, 'b c h w -> b (h w) c')
+          X3, _, _, _, _, _ = self.ctran1(X3, wh, ww)
+          X3 = rearrange(X3, 'b (h w) c -> b c h w', h=wh, w=ww)
+          X2 = self.up(X3)
+          X2 = torch.cat((x2, X2), 1)
+          X2 = self.conv3(X2)
+          wh, ww = X2.shape[2], X2.shape[3]
+          X2 = rearrange(X2, 'b c h w -> b (h w) c')
+          X2, _, _, _, _, _ = self.ctran2(X2, wh, ww)
+          X2 = rearrange(X2, 'b (h w) c -> b c h w', h=wh, w=ww)
+          X1 = self.up(X2)
+          X1 = torch.cat((x1, X1), 1)
+          X1 = self.conv4(X1)
+          wh, ww = X1.shape[2], X1.shape[3]
+          X1 = rearrange(X1, 'b c h w -> b (h w) c')
+          X1, _, _, _, _, _ = self.ctran3(X1, wh, ww)
+          X1 = rearrange(X1, 'b (h w) c -> b c h w', h=wh, w=ww)
+          X0 = self.up(X1)
+          X0 = torch.cat((x_, X0), 1)
+          X0 = self.conv5(X0)
+          X = self.up(X0)
+          X = torch.cat((inputs, x, X), 1)
+          alpha = self.convo(X)
+          alpha = torch.clamp(alpha, min=0, max=1)
+          return alpha
+#+end_src
+
+** Function to load model
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+  def get_AEMatter_model(path_model_checkpoint):
+
+      download_model(path=path_model_checkpoint)
+
+      matmodel = AEMatter()
+      matmodel.load_state_dict(
+          torch.load(path_model_checkpoint, map_location='cpu')['model'])
+
+      matmodel = matmodel.cuda()
+      matmodel.eval()
+
+      return matmodel
+#+end_src
+
+** Function to do inference
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+  def do_infer(rawimg, trimap, matmodel):
+      trimap_nonp = trimap.copy()
+      h, w, c = rawimg.shape
+      nonph, nonpw, _ = rawimg.shape
+      newh = (((h - 1) // 32) + 1) * 32
+      neww = (((w - 1) // 32) + 1) * 32
+      padh = newh - h
+      padh1 = int(padh / 2)
+      padh2 = padh - padh1
+      padw = neww - w
+      padw1 = int(padw / 2)
+      padw2 = padw - padw1
+
+      rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
+                                      cv2.BORDER_REFLECT)
+
+      trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
+                                      cv2.BORDER_REFLECT)
+
+      h_pad, w_pad, _ = rawimg_pad.shape
+      tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
+      tritemp[:, :, 0] = (trimap_pad == 0)
+      tritemp[:, :, 1] = (trimap_pad == 128)
+      tritemp[:, :, 2] = (trimap_pad == 255)
+      tritempimgs = np.transpose(tritemp, (2, 0, 1))
+      tritempimgs = tritempimgs[np.newaxis, :, :, :]
+      img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
+      img = np.array(img, np.float32)
+      img = img / 255.
+      img = torch.from_numpy(img).cuda()
+      tritempimgs = torch.from_numpy(tritempimgs).cuda()
+      with torch.no_grad():
+          pred = matmodel(img, tritempimgs)
+          pred = pred.detach().cpu().numpy()[0]
+          pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
+          preda = pred[
+              0:1,
+          ] * 255
+          preda = np.transpose(preda, (1, 2, 0))
+          preda = preda * (trimap_nonp[:, :, None]
+                           == 128) + (trimap_nonp[:, :, None] == 255) * 255
+      preda = np.array(preda, np.uint8)
+      return preda
+#+end_src
+
+** Load ComfyUI AEMatter model
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
+  class load_AEMatter_Model:
+
+      def __init__(self):
+          pass
+
+      @classmethod
+      def INPUT_TYPES(s):
+          return {
+              "required": {},
+          }
+
+      RETURN_TYPES = ("AEMatter_Model", )
+      FUNCTION = "test"
+      CATEGORY = "AEMatter"
+
+      def test(self):
+          return (get_AEMatter_model(get_model_path()), )
+
+
+  class run_AEMatter_inference:
+
+      def __init__(self):
+          pass
+
+      @classmethod
+      def INPUT_TYPES(s):
+          return {
+              "required": {
+                  "image": ("IMAGE", ),
+                  "trimap": ("MASK", ),
+                  "AEMatter_Model": ("AEMatter_Model", ),
+              },
+          }
+
+      RETURN_TYPES = ("MASK", )
+      FUNCTION = "test"
+      CATEGORY = "AEMatter"
+
+      def test(
+          self,
+          image,
+          trimap,
+          AEMatter_Model,
+      ):
+
+          ret = []
+          batch_size = image.shape[0]
+
+          for i in range(batch_size):
+              tmp_i = from_torch_image(image[i])
+              tmp_m = from_torch_image(trimap[i])
+              tmp = do_infer(tmp_i, tmp_m, AEMatter_Model)
+              ret.append(tmp)
+
+          ret = to_torch_image(np.array(ret))
+          ret = ret.squeeze(-1)
+          print(ret.shape)
+
+          return ret
+#+end_src
+
+** Main function
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
+  def main():
+      ptrimap = '/home/asd/Desktop/demo/retriever_trimap.png'
+      pimgs = '/home/asd/Desktop/demo/retriever_rgb.png'
+      p_outs = 'alpha.png'
+
+      matmodel = get_AEMatter_model(
+          path_model_checkpoint='/home/asd/Desktop/AEM_RWA.ckpt')
+
+      # matmodel = AEMatter()
+      # matmodel.load_state_dict(
+      #     torch.load('/home/asd/Desktop/AEM_RWA.ckpt',
+      #                map_location='cpu')['model'])
+
+      # matmodel = matmodel.cuda()
+      # matmodel.eval()
+
+      rawimg = pimgs
+      trimap = ptrimap
+      rawimg = cv2.imread(rawimg, cv2.IMREAD_COLOR)
+      trimap = cv2.imread(trimap, cv2.IMREAD_GRAYSCALE)
+      trimap_nonp = trimap.copy()
+      h, w, c = rawimg.shape
+      nonph, nonpw, _ = rawimg.shape
+      newh = (((h - 1) // 32) + 1) * 32
+      neww = (((w - 1) // 32) + 1) * 32
+      padh = newh - h
+      padh1 = int(padh / 2)
+      padh2 = padh - padh1
+      padw = neww - w
+      padw1 = int(padw / 2)
+      padw2 = padw - padw1
+      rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
+                                      cv2.BORDER_REFLECT)
+      trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
+                                      cv2.BORDER_REFLECT)
+      h_pad, w_pad, _ = rawimg_pad.shape
+      tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
+      tritemp[:, :, 0] = (trimap_pad == 0)
+      tritemp[:, :, 1] = (trimap_pad == 128)
+      tritemp[:, :, 2] = (trimap_pad == 255)
+      tritempimgs = np.transpose(tritemp, (2, 0, 1))
+      tritempimgs = tritempimgs[np.newaxis, :, :, :]
+      img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
+      img = np.array(img, np.float32)
+      img = img / 255.
+      img = torch.from_numpy(img).cuda()
+      tritempimgs = torch.from_numpy(tritempimgs).cuda()
+      with torch.no_grad():
+          pred = matmodel(img, tritempimgs)
+          pred = pred.detach().cpu().numpy()[0]
+          pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
+          preda = pred[
+              0:1,
+          ] * 255
+          preda = np.transpose(preda, (1, 2, 0))
+          preda = preda * (trimap_nonp[:, :, None]
+                           == 128) + (trimap_nonp[:, :, None] == 255) * 255
+      preda = np.array(preda, np.uint8)
+      cv2.imwrite(p_outs, preda)
+
+#+end_src
+
+** Comfyui Dictionary
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.execute.py
+  NODE_CLASS_MAPPINGS = {
+      'load_AEMatter_Model': load_AEMatter_Model,
+      'run_AEMatter_inference': run_AEMatter_inference,
+  }
+
+  NODE_DISPLAY_NAME_MAPPINGS = {
+      'load_AEMatter_Model': 'load_AEMatter_Model',
+      'run_AEMatter_inference': 'run_AEMatter_inference',
+  }
+#+end_src
+
+** COMMENT AEMatter.execute.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.execute.py
+  if __name__ == '__main__':
+      # main()
+
+      rawimg = cv2.imread('/home/asd/Desktop/demo/retriever_rgb.png',
+                          cv2.IMREAD_COLOR)
+
+      trimap = cv2.imread('/home/asd/Desktop/demo/retriever_trimap.png',
+                          cv2.IMREAD_GRAYSCALE)
+
+      do_infer(rawimg, trimap,
+               get_AEMatter_model('/home/asd/Desktop/AEM_RWA.ckpt'))
+#+end_src
+
+** AEMatter.unify.sh
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.unify.sh
+  . "${HOME}/dbnew.sh"
+
+  cat \
+        'AEMatter.import.py' \
+        'AEMatter.function.py' \
+        'AEMatter.class.py' \
+        'AEMatter.execute.py' \
+      | expand | yapf3 \
+      > 'AEMatter.py' \
+  ;
+
+  cp 'AEMatter.py' '__init__.py'
+#+end_src
+
+** AEMatter.run.sh
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.run.sh
+  . "${HOME}/dbnew.sh"
+  python3 './AEMatter.py'
+#+end_src
+
+#+RESULTS:
+
+* COMMENT WORK SPACE
+
+** ESHELL
+#+begin_src elisp
+  (save-buffer)
+  (org-babel-tangle)
+  (shell-command "./AEMatter.unify.sh")
+#+end_src
+
+#+RESULTS:
+: 0
+
+** SHELL
+#+begin_src sh :shebang #!/bin/sh :results output 
+  realpath .
+  cd /home/asd/GITHUB/aravind-h-v/dreambooth_experiments/AEMatter
+#+end_src
+
+#+RESULTS:
+
+** SHELL
+#+begin_src sh :shebang #!/bin/sh :results output 
+  ls
+#+end_src

ComfyUI_AEMatter/__init__.py

@@ -0,0 +1,1248 @@
+#!/usr/bin/python3
+import cv2
+import math
+import numpy as np
+import os
+import random
+import wget
+
+import torch
+import torch.nn as nn
+from torch.nn import init
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from collections import OrderedDict
+from einops import rearrange, repeat
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+import folder_paths
+from folder_paths import models_dir
+
+
+#!/usr/bin/python3
+def mkdir_safe(out_path):
+    if type(out_path) == str:
+        if len(out_path) > 0:
+            if not os.path.exists(out_path):
+                os.mkdir(out_path)
+
+
+def get_model_path():
+    import folder_paths
+    from folder_paths import models_dir
+
+    path_file_model = models_dir
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'AEMatter')
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'AEM_RWA.ckpt')
+
+    return path_file_model
+
+
+def download_model(path):
+    if not os.path.exists(path):
+        wget.download(
+            'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/AEMatter/AEM_RWA.ckpt?download=true',
+            out=path)
+
+
+def from_torch_image(image):
+    image = image.cpu().numpy() * 255.0
+    image = np.clip(image, 0, 255).astype(np.uint8)
+    return image
+
+
+def to_torch_image(image):
+    image = image.astype(dtype=np.float32)
+    image /= 255.0
+    image = torch.from_numpy(image)
+    return image
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+               C)
+    windows = x.permute(0, 1, 3, 2, 4,
+                        5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size,
+                     window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+def get_AEMatter_model(path_model_checkpoint):
+
+    download_model(path=path_model_checkpoint)
+
+    matmodel = AEMatter()
+    matmodel.load_state_dict(
+        torch.load(path_model_checkpoint, map_location='cpu')['model'])
+
+    matmodel = matmodel.cuda()
+    matmodel.eval()
+
+    return matmodel
+
+
+def do_infer(rawimg, trimap, matmodel):
+    trimap_nonp = trimap.copy()
+    h, w, c = rawimg.shape
+    nonph, nonpw, _ = rawimg.shape
+    newh = (((h - 1) // 32) + 1) * 32
+    neww = (((w - 1) // 32) + 1) * 32
+    padh = newh - h
+    padh1 = int(padh / 2)
+    padh2 = padh - padh1
+    padw = neww - w
+    padw1 = int(padw / 2)
+    padw2 = padw - padw1
+
+    rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+
+    trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+
+    h_pad, w_pad, _ = rawimg_pad.shape
+    tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
+    tritemp[:, :, 0] = (trimap_pad == 0)
+    tritemp[:, :, 1] = (trimap_pad == 128)
+    tritemp[:, :, 2] = (trimap_pad == 255)
+    tritempimgs = np.transpose(tritemp, (2, 0, 1))
+    tritempimgs = tritempimgs[np.newaxis, :, :, :]
+    img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
+    img = np.array(img, np.float32)
+    img = img / 255.
+    img = torch.from_numpy(img).cuda()
+    tritempimgs = torch.from_numpy(tritempimgs).cuda()
+    with torch.no_grad():
+        pred = matmodel(img, tritempimgs)
+        pred = pred.detach().cpu().numpy()[0]
+        pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
+        preda = pred[
+            0:1,
+        ] * 255
+        preda = np.transpose(preda, (1, 2, 0))
+        preda = preda * (trimap_nonp[:, :, None]
+                         == 128) + (trimap_nonp[:, :, None] == 255) * 255
+    preda = np.array(preda, np.uint8)
+    return preda
+
+
+def main():
+    ptrimap = '/home/asd/Desktop/demo/retriever_trimap.png'
+    pimgs = '/home/asd/Desktop/demo/retriever_rgb.png'
+    p_outs = 'alpha.png'
+
+    matmodel = get_AEMatter_model(
+        path_model_checkpoint='/home/asd/Desktop/AEM_RWA.ckpt')
+
+    # matmodel = AEMatter()
+    # matmodel.load_state_dict(
+    #     torch.load('/home/asd/Desktop/AEM_RWA.ckpt',
+    #                map_location='cpu')['model'])
+
+    # matmodel = matmodel.cuda()
+    # matmodel.eval()
+
+    rawimg = pimgs
+    trimap = ptrimap
+    rawimg = cv2.imread(rawimg, cv2.IMREAD_COLOR)
+    trimap = cv2.imread(trimap, cv2.IMREAD_GRAYSCALE)
+    trimap_nonp = trimap.copy()
+    h, w, c = rawimg.shape
+    nonph, nonpw, _ = rawimg.shape
+    newh = (((h - 1) // 32) + 1) * 32
+    neww = (((w - 1) // 32) + 1) * 32
+    padh = newh - h
+    padh1 = int(padh / 2)
+    padh2 = padh - padh1
+    padw = neww - w
+    padw1 = int(padw / 2)
+    padw2 = padw - padw1
+    rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+    trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
+                                    cv2.BORDER_REFLECT)
+    h_pad, w_pad, _ = rawimg_pad.shape
+    tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
+    tritemp[:, :, 0] = (trimap_pad == 0)
+    tritemp[:, :, 1] = (trimap_pad == 128)
+    tritemp[:, :, 2] = (trimap_pad == 255)
+    tritempimgs = np.transpose(tritemp, (2, 0, 1))
+    tritempimgs = tritempimgs[np.newaxis, :, :, :]
+    img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
+    img = np.array(img, np.float32)
+    img = img / 255.
+    img = torch.from_numpy(img).cuda()
+    tritempimgs = torch.from_numpy(tritempimgs).cuda()
+    with torch.no_grad():
+        pred = matmodel(img, tritempimgs)
+        pred = pred.detach().cpu().numpy()[0]
+        pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
+        preda = pred[
+            0:1,
+        ] * 255
+        preda = np.transpose(preda, (1, 2, 0))
+        preda = preda * (trimap_nonp[:, :, None]
+                         == 128) + (trimap_nonp[:, :, None] == 255) * 255
+    preda = np.array(preda, np.uint8)
+    cv2.imwrite(p_outs, preda)
+
+
+#!/usr/bin/python3
+class WindowAttention(nn.Module):
+    """ Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self,
+                 dim,
+                 window_size,
+                 num_heads,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :,
+                                         None] - coords_flatten[:,
+                                                                None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(
+            1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :,
+                        0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index",
+                             relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """ Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[
+            2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1],
+                self.window_size[0] * self.window_size[1],
+                -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                             N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """ Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size=7,
+                 shift_size=0,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(dim,
+                                    window_size=to_2tuple(self.window_size),
+                                    num_heads=num_heads,
+                                    qkv_bias=qkv_bias,
+                                    qk_scale=qk_scale,
+                                    attn_drop=attn_drop,
+                                    proj_drop=drop)
+
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer,
+                       drop=drop)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x,
+                                   shifts=(-self.shift_size, -self.shift_size),
+                                   dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                   C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size,
+                                         self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                   Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x,
+                           shifts=(self.shift_size, self.shift_size),
+                           dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """ Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 use_checkpoint=False):
+
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim,
+                                 num_heads=num_heads,
+                                 window_size=window_size,
+                                 shift_size=0 if
+                                 (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias,
+                                 qk_scale=qk_scale,
+                                 drop=drop,
+                                 attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(
+                                     drop_path, list) else drop_path,
+                                 norm_layer=norm_layer) for i in range(depth)
+        ])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        # print(x.shape,H,W)
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size)  # nW, window_size, window_size, 1
+
+        mask_windows = mask_windows.view(-1,
+                                         self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(
+            2)  # nW, ww window_size*window_size
+        attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                          float(-100.0)).masked_fill(
+                                              attn_mask == 0, float(0.0))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 norm_layer=None):
+
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x,
+                      (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """ Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.2,
+                 norm_layer=nn.LayerNorm,
+                 ape=False,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 use_checkpoint=False):
+
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1]
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0],
+                            patches_resolution[1]))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2**i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                norm_layer=norm_layer,
+                downsample=PatchMerging if
+                (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in backbone.
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+                                               size=(Wh, Ww),
+                                               mode='bicubic')
+            x = (x + absolute_pos_embed).flatten(2).transpose(1,
+                                                              2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W,
+                                 self.num_features[i]).permute(0, 3, 1,
+                                                               2).contiguous()
+                outs.append(out)
+
+        return tuple(outs)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class ResBlock(nn.Module):
+
+    def __init__(self, inc, midc):
+        super(ResBlock, self).__init__()
+        self.conv1 = nn.Conv2d(inc,
+                               midc,
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               bias=True)
+        self.gn1 = nn.GroupNorm(16, midc)
+        self.conv2 = nn.Conv2d(midc,
+                               midc,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=True)
+        self.gn2 = nn.GroupNorm(16, midc)
+        self.conv3 = nn.Conv2d(midc,
+                               inc,
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               bias=True)
+        self.relu = nn.LeakyReLU(0.1)
+
+    def forward(self, x):
+        x_ = x
+        x = self.conv1(x)
+        x = self.gn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.gn2(x)
+        x = self.relu(x)
+        x = self.conv3(x)
+        x = x + x_
+        x = self.relu(x)
+        return x
+
+
+class AEALblock(nn.Module):
+
+    def __init__(self,
+                 d_model,
+                 nhead,
+                 dim_feedforward=512,
+                 dropout=0.0,
+                 layer_norm_eps=1e-5,
+                 batch_first=True,
+                 norm_first=False,
+                 width=5):
+        super(AEALblock, self).__init__()
+        self.self_attn2 = nn.MultiheadAttention(d_model // 2,
+                                                nhead // 2,
+                                                dropout=dropout,
+                                                batch_first=batch_first)
+        self.self_attn1 = nn.MultiheadAttention(d_model // 2,
+                                                nhead // 2,
+                                                dropout=dropout,
+                                                batch_first=batch_first)
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+        self.norm_first = norm_first
+        self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+        self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.activation = nn.ReLU()
+        self.width = width
+        self.trans = nn.Sequential(
+            nn.Conv2d(d_model + 512, d_model // 2, 1, 1, 0),
+            ResBlock(d_model // 2, d_model // 4),
+            nn.Conv2d(d_model // 2, d_model, 1, 1, 0))
+        self.gamma = nn.Parameter(torch.zeros(1))
+
+    def forward(
+        self,
+        src,
+        feats,
+    ):
+        src = self.gamma * self.trans(torch.cat([src, feats], 1)) + src
+        b, c, h, w = src.shape
+        x1 = src[:, 0:c // 2]
+        x1_ = rearrange(x1, 'b c (h1 h2) w -> b c h1 h2 w', h2=self.width)
+        x1_ = rearrange(x1_, 'b c h1 h2 w -> (b h1) (h2 w) c')
+        x2 = src[:, c // 2:]
+        x2_ = rearrange(x2, 'b c h (w1 w2) -> b c h w1 w2', w2=self.width)
+        x2_ = rearrange(x2_, 'b c h w1 w2 -> (b w1) (h w2) c')
+        x = rearrange(src, 'b c h w-> b (h w) c')
+        x = self.norm1(x + self._sa_block(x1_, x2_, h, w))
+        x = self.norm2(x + self._ff_block(x))
+        x = rearrange(x, 'b (h w) c->b c h w', h=h, w=w)
+        return x
+
+    def _sa_block(self, x1, x2, h, w):
+        x1 = self.self_attn1(x1,
+                             x1,
+                             x1,
+                             attn_mask=None,
+                             key_padding_mask=None,
+                             need_weights=False)[0]
+
+        x2 = self.self_attn2(x2,
+                             x2,
+                             x2,
+                             attn_mask=None,
+                             key_padding_mask=None,
+                             need_weights=False)[0]
+
+        x1 = rearrange(x1,
+                       '(b h1) (h2 w) c-> b (h1 h2 w) c',
+                       h2=self.width,
+                       h1=h // self.width)
+        x2 = rearrange(x2,
+                       ' (b w1) (h w2) c-> b (h w1 w2) c',
+                       w2=self.width,
+                       w1=w // self.width)
+        x = torch.cat([x1, x2], dim=2)
+        return self.dropout1(x)
+
+    def _ff_block(self, x):
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout2(x)
+
+
+class AEMatter(nn.Module):
+
+    def __init__(self):
+        super(AEMatter, self).__init__()
+        trans = SwinTransformer(pretrain_img_size=224,
+                                embed_dim=96,
+                                depths=[2, 2, 6, 2],
+                                num_heads=[3, 6, 12, 24],
+                                window_size=7,
+                                ape=False,
+                                drop_path_rate=0.2,
+                                patch_norm=True,
+                                use_checkpoint=False)
+
+        # trans.load_state_dict(torch.load(
+        #     '/home/asd/Desktop/swin_tiny_patch4_window7_224.pth',
+        #     map_location="cpu")["model"],
+        #                       strict=False)
+
+        trans.patch_embed.proj = nn.Conv2d(64, 96, 3, 2, 1)
+
+        self.start_conv0 = nn.Sequential(nn.Conv2d(6, 48, 3, 1, 1),
+                                         nn.PReLU(48))
+
+        self.start_conv = nn.Sequential(nn.Conv2d(48, 64, 3, 2,
+                                                  1), nn.PReLU(64),
+                                        nn.Conv2d(64, 64, 3, 1, 1),
+                                        nn.PReLU(64))
+
+        self.trans = trans
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=640 + 768,
+                      out_channels=256,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True))
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=256 + 384,
+                      out_channels=256,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True), )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(in_channels=256 + 192,
+                      out_channels=192,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True), )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(in_channels=192 + 96,
+                      out_channels=128,
+                      kernel_size=1,
+                      stride=1,
+                      padding=0,
+                      bias=True), )
+        self.ctran0 = BasicLayer(256, 3, 8, 7, drop_path=0.09)
+        self.ctran1 = BasicLayer(256, 3, 8, 7, drop_path=0.07)
+        self.ctran2 = BasicLayer(192, 3, 6, 7, drop_path=0.05)
+        self.ctran3 = BasicLayer(128, 3, 4, 7, drop_path=0.03)
+        self.conv5 = nn.Sequential(
+            nn.Conv2d(in_channels=192,
+                      out_channels=64,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(64),
+            nn.Conv2d(in_channels=64,
+                      out_channels=64,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(64),
+            nn.Conv2d(in_channels=64,
+                      out_channels=48,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(48))
+        self.convo = nn.Sequential(
+            nn.Conv2d(in_channels=48 + 48 + 6,
+                      out_channels=32,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(32),
+            nn.Conv2d(in_channels=32,
+                      out_channels=32,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True), nn.PReLU(32),
+            nn.Conv2d(in_channels=32,
+                      out_channels=1,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1,
+                      bias=True))
+        self.up = nn.Upsample(scale_factor=2,
+                              mode='bilinear',
+                              align_corners=False)
+        self.upn = nn.Upsample(scale_factor=2, mode='nearest')
+        self.apptrans = nn.Sequential(
+            nn.Conv2d(256 + 384, 256, 1, 1, bias=True), ResBlock(256, 128),
+            ResBlock(256, 128), nn.Conv2d(256, 512, 2, 2, bias=True),
+            ResBlock(512, 128))
+        self.emb = nn.Sequential(nn.Conv2d(768, 640, 1, 1, 0),
+                                 ResBlock(640, 160))
+        self.embdp = nn.Sequential(nn.Conv2d(640, 640, 1, 1, 0))
+        self.h2l = nn.Conv2d(768, 256, 1, 1, 0)
+        self.width = 5
+        self.trans1 = AEALblock(d_model=640,
+                                nhead=20,
+                                dim_feedforward=2048,
+                                dropout=0.2,
+                                width=self.width)
+        self.trans2 = AEALblock(d_model=640,
+                                nhead=20,
+                                dim_feedforward=2048,
+                                dropout=0.2,
+                                width=self.width)
+        self.trans3 = AEALblock(d_model=640,
+                                nhead=20,
+                                dim_feedforward=2048,
+                                dropout=0.2,
+                                width=self.width)
+
+    def aeal(self, x, sem):
+        xe = self.emb(x)
+        x_ = xe
+        x_ = self.embdp(x_)
+        b, c, h1, w1 = x_.shape
+        bnew_ph = int(np.ceil(h1 / self.width) * self.width) - h1
+        bnew_pw = int(np.ceil(w1 / self.width) * self.width) - w1
+        newph1 = bnew_ph // 2
+        newph2 = bnew_ph - newph1
+        newpw1 = bnew_pw // 2
+        newpw2 = bnew_pw - newpw1
+        x_ = F.pad(x_, (newpw1, newpw2, newph1, newph2))
+        sem = F.pad(sem, (newpw1, newpw2, newph1, newph2))
+        x_ = self.trans1(x_, sem)
+        x_ = self.trans2(x_, sem)
+        x_ = self.trans3(x_, sem)
+        x_ = x_[:, :, newph1:h1 + newph1, newpw1:w1 + newpw1]
+        return x_
+
+    def forward(self, x, y):
+        inputs = torch.cat((x, y), 1)
+        x = self.start_conv0(inputs)
+        x_ = self.start_conv(x)
+        x1, x2, x3, x4 = self.trans(x_)
+        x4h = self.h2l(x4)
+        x3s = self.apptrans(torch.cat([x3, self.upn(x4h)], 1))
+        x4_ = self.aeal(x4, x3s)
+        x4 = torch.cat((x4, x4_), 1)
+        X4 = self.conv1(x4)
+        wh, ww = X4.shape[2], X4.shape[3]
+        X4 = rearrange(X4, 'b c h w -> b (h w) c')
+        X4, _, _, _, _, _ = self.ctran0(X4, wh, ww)
+        X4 = rearrange(X4, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X3 = self.up(X4)
+        X3 = torch.cat((x3, X3), 1)
+        X3 = self.conv2(X3)
+        wh, ww = X3.shape[2], X3.shape[3]
+        X3 = rearrange(X3, 'b c h w -> b (h w) c')
+        X3, _, _, _, _, _ = self.ctran1(X3, wh, ww)
+        X3 = rearrange(X3, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X2 = self.up(X3)
+        X2 = torch.cat((x2, X2), 1)
+        X2 = self.conv3(X2)
+        wh, ww = X2.shape[2], X2.shape[3]
+        X2 = rearrange(X2, 'b c h w -> b (h w) c')
+        X2, _, _, _, _, _ = self.ctran2(X2, wh, ww)
+        X2 = rearrange(X2, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X1 = self.up(X2)
+        X1 = torch.cat((x1, X1), 1)
+        X1 = self.conv4(X1)
+        wh, ww = X1.shape[2], X1.shape[3]
+        X1 = rearrange(X1, 'b c h w -> b (h w) c')
+        X1, _, _, _, _, _ = self.ctran3(X1, wh, ww)
+        X1 = rearrange(X1, 'b (h w) c -> b c h w', h=wh, w=ww)
+        X0 = self.up(X1)
+        X0 = torch.cat((x_, X0), 1)
+        X0 = self.conv5(X0)
+        X = self.up(X0)
+        X = torch.cat((inputs, x, X), 1)
+        alpha = self.convo(X)
+        alpha = torch.clamp(alpha, min=0, max=1)
+        return alpha
+
+
+class load_AEMatter_Model:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+        }
+
+    RETURN_TYPES = ("AEMatter_Model", )
+    FUNCTION = "test"
+    CATEGORY = "AEMatter"
+
+    def test(self):
+        return (get_AEMatter_model(get_model_path()), )
+
+
+class run_AEMatter_inference:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "trimap": ("MASK", ),
+                "AEMatter_Model": ("AEMatter_Model", ),
+            },
+        }
+
+    RETURN_TYPES = ("MASK", )
+    FUNCTION = "test"
+    CATEGORY = "AEMatter"
+
+    def test(
+        self,
+        image,
+        trimap,
+        AEMatter_Model,
+    ):
+
+        ret = []
+        batch_size = image.shape[0]
+
+        for i in range(batch_size):
+            tmp_i = from_torch_image(image[i])
+            tmp_m = from_torch_image(trimap[i])
+            tmp = do_infer(tmp_i, tmp_m, AEMatter_Model)
+            ret.append(tmp)
+
+        ret = to_torch_image(np.array(ret))
+        ret = ret.squeeze(-1)
+        print(ret.shape)
+
+        return ret
+
+
+#!/usr/bin/python3
+NODE_CLASS_MAPPINGS = {
+    'load_AEMatter_Model': load_AEMatter_Model,
+    'run_AEMatter_inference': run_AEMatter_inference,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    'load_AEMatter_Model': 'load_AEMatter_Model',
+    'run_AEMatter_inference': 'run_AEMatter_inference',
+}