Claude
549a1a4e82
- Fix My Library: Add CSS styling for library browser, items now visible - Integrate My Library into Shapes tool with tabbed interface - Improve AI Inpaint: Add transform mode for scaling/sizing selections - Add helpful guidance explaining inpaint vs transform modes - Add U2net as alternative background removal (avoids rembg issues) - Create U2net model definition and download script - Improve Caddyfile with multiple options and troubleshooting guide Note: Brush Select (AI Paint) tool was already implemented and working. https://claude.ai/code/session_01CLedz6CanT9t46KBvng3vz
501 lines
15 KiB
Python
501 lines
15 KiB
Python
"""
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U2Net Model Definition for Background Removal
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Based on: https://github.com/xuebinqin/U-2-Net
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This is a simplified implementation that works with the standard U2Net weights.
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class REBNCONV(nn.Module):
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def __init__(self, in_ch=3, out_ch=3, dirate=1):
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super(REBNCONV, self).__init__()
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self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1*dirate, dilation=1*dirate)
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self.bn_s1 = nn.BatchNorm2d(out_ch)
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self.relu_s1 = nn.ReLU(inplace=True)
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def forward(self, x):
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hx = x
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xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
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return xout
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def _upsample_like(src, tar):
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src = F.interpolate(src, size=tar.shape[2:], mode='bilinear', align_corners=False)
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return src
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class RSU7(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU7, self).__init__()
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
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self.rebnconv6d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv5d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
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def forward(self, x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx = self.pool4(hx4)
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hx5 = self.rebnconv5(hx)
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hx = self.pool5(hx5)
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hx6 = self.rebnconv6(hx)
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hx7 = self.rebnconv7(hx6)
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hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
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hx6dup = _upsample_like(hx6d, hx5)
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hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
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hx5dup = _upsample_like(hx5d, hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
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hx4dup = _upsample_like(hx4d, hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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return hx1d + hxin
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class RSU6(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU6, self).__init__()
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
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self.rebnconv5d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
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def forward(self, x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx = self.pool4(hx4)
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hx5 = self.rebnconv5(hx)
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hx6 = self.rebnconv6(hx5)
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hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
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hx5dup = _upsample_like(hx5d, hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
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hx4dup = _upsample_like(hx4d, hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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return hx1d + hxin
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class RSU5(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU5, self).__init__()
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
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self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
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def forward(self, x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx5 = self.rebnconv5(hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
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hx4dup = _upsample_like(hx4d, hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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return hx1d + hxin
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class RSU4(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU4, self).__init__()
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
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self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
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def forward(self, x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx4 = self.rebnconv4(hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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return hx1d + hxin
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class RSU4F(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU4F, self).__init__()
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
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self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=4)
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self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=2)
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self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
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def forward(self, x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx2 = self.rebnconv2(hx1)
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hx3 = self.rebnconv3(hx2)
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hx4 = self.rebnconv4(hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
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hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
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hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
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return hx1d + hxin
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class U2NET(nn.Module):
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def __init__(self, in_ch=3, out_ch=1):
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super(U2NET, self).__init__()
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self.stage1 = RSU7(in_ch, 32, 64)
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self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage2 = RSU6(64, 32, 128)
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self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage3 = RSU5(128, 64, 256)
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self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage4 = RSU4(256, 128, 512)
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self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage5 = RSU4F(512, 256, 512)
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self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage6 = RSU4F(512, 256, 512)
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# decoder
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self.stage5d = RSU4F(1024, 256, 512)
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self.stage4d = RSU4(1024, 128, 256)
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self.stage3d = RSU5(512, 64, 128)
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self.stage2d = RSU6(256, 32, 64)
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self.stage1d = RSU7(128, 16, 64)
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self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
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self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
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self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
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self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
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self.outconv = nn.Conv2d(6*out_ch, out_ch, 1)
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def forward(self, x):
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hx = x
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# stage 1
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hx1 = self.stage1(hx)
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hx = self.pool12(hx1)
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# stage 2
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hx2 = self.stage2(hx)
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hx = self.pool23(hx2)
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# stage 3
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hx3 = self.stage3(hx)
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hx = self.pool34(hx3)
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# stage 4
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hx4 = self.stage4(hx)
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hx = self.pool45(hx4)
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# stage 5
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hx5 = self.stage5(hx)
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hx = self.pool56(hx5)
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# stage 6
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hx6 = self.stage6(hx)
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hx6up = _upsample_like(hx6, hx5)
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# decoder
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hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
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hx5dup = _upsample_like(hx5d, hx4)
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hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
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hx4dup = _upsample_like(hx4d, hx3)
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hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
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# side output
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d1 = self.side1(hx1d)
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d2 = self.side2(hx2d)
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d2 = _upsample_like(d2, d1)
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d3 = self.side3(hx3d)
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d3 = _upsample_like(d3, d1)
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d4 = self.side4(hx4d)
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d4 = _upsample_like(d4, d1)
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d5 = self.side5(hx5d)
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d5 = _upsample_like(d5, d1)
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d6 = self.side6(hx6)
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d6 = _upsample_like(d6, d1)
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d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
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return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)
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class U2NETP(nn.Module):
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"""Smaller/faster U2Net variant (u2netp)"""
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def __init__(self, in_ch=3, out_ch=1):
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super(U2NETP, self).__init__()
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self.stage1 = RSU7(in_ch, 16, 64)
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self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage2 = RSU6(64, 16, 64)
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self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage3 = RSU5(64, 16, 64)
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self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage4 = RSU4(64, 16, 64)
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self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage5 = RSU4F(64, 16, 64)
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self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.stage6 = RSU4F(64, 16, 64)
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# decoder
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self.stage5d = RSU4F(128, 16, 64)
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self.stage4d = RSU4(128, 16, 64)
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self.stage3d = RSU5(128, 16, 64)
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self.stage2d = RSU6(128, 16, 64)
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self.stage1d = RSU7(128, 16, 64)
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self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side3 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side4 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side5 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.side6 = nn.Conv2d(64, out_ch, 3, padding=1)
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self.outconv = nn.Conv2d(6*out_ch, out_ch, 1)
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def forward(self, x):
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hx = x
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hx1 = self.stage1(hx)
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hx = self.pool12(hx1)
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hx2 = self.stage2(hx)
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hx = self.pool23(hx2)
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hx3 = self.stage3(hx)
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hx = self.pool34(hx3)
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hx4 = self.stage4(hx)
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hx = self.pool45(hx4)
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hx5 = self.stage5(hx)
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hx = self.pool56(hx5)
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hx6 = self.stage6(hx)
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hx6up = _upsample_like(hx6, hx5)
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hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
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hx5dup = _upsample_like(hx5d, hx4)
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hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
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hx4dup = _upsample_like(hx4d, hx3)
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hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
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d1 = self.side1(hx1d)
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d2 = self.side2(hx2d)
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d2 = _upsample_like(d2, d1)
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d3 = self.side3(hx3d)
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d3 = _upsample_like(d3, d1)
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d4 = self.side4(hx4d)
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d4 = _upsample_like(d4, d1)
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d5 = self.side5(hx5d)
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d5 = _upsample_like(d5, d1)
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d6 = self.side6(hx6)
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d6 = _upsample_like(d6, d1)
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d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
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return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)
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