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Merge pull request #62 from outis1one/claude/focused-maxwell-3kgh3x

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Auto-download U2Net model on startup; remove dead PyTorch U2Net module
This commit is contained in:
Outis
2026-06-18 09:29:16 -04:00
committed by GitHub
parent 34642161f4 a4a9dbc33a
commit 668728348b
5 changed files with 55 additions and 502 deletions
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.env.example
+5
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@@ -166,6 +166,11 @@ DATABASE_URL=sqlite:///./data/ai_photo_edit.db
# When false: Skips download, Smart Select uses Replicate API (requires REPLICATE_API_KEY)
AUTO_DOWNLOAD_SAM=true
# Auto-download U2Net model on startup (true/false)
# When true (default): Downloads U2Net model (~176MB) on first startup for offline Remove Background
# When false: Skips download, Remove Background falls back to rembg (if installed)
AUTO_DOWNLOAD_U2NET=true
# Allow users to select model per-edit
ALLOW_MODEL_OVERRIDE=true
README.md
+16
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@@ -201,6 +201,22 @@ docker compose -f docker-compose.gpu.yml logs | grep -i sam
If Docker created `./data/` as root and you can't write there without `sudo`, you can also use root's curl as above — the container reads the file regardless of owner.
**Remove Background fails ("Install u2net or rembg")**
The U2Net model auto-downloads (~176MB) from GitHub on first use, same as SAM. If that download fails (DNS/firewall, see above) and `rembg` isn't installed either, you'll see this error. Fix it the same way — download directly on the host:
```bash
mkdir -p ./data/models
sudo curl -L -o ./data/models/u2net.onnx \
https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx
```
The file is ~176 MB. Once it exists at `./data/models/u2net.onnx`, the next "Remove Background" click picks it up — no rebuild or restart needed. Verify with:
```bash
docker compose logs -f | grep -i u2net
# Should show: "U2Net model loaded successfully with OpenCV DNN"
```
**AI models not downloading (container DNS blocked)**
If you ran `./install-local-gpu.sh`, this is already permanently fixed. Otherwise, the container's host firewall is blocking outbound DNS from the Docker bridge — apply the fix manually (does **not** affect container isolation):
backend/app/services/u2net_model.py
-500
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@@ -1,500 +0,0 @@
"""
U2Net Model Definition for Background Removal
Based on: https://github.com/xuebinqin/U-2-Net
This is a simplified implementation that works with the standard U2Net weights.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class REBNCONV(nn.Module):
def __init__(self, in_ch=3, out_ch=3, dirate=1):
super(REBNCONV, self).__init__()
self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1*dirate, dilation=1*dirate)
self.bn_s1 = nn.BatchNorm2d(out_ch)
self.relu_s1 = nn.ReLU(inplace=True)
def forward(self, x):
hx = x
xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
return xout
def _upsample_like(src, tar):
src = F.interpolate(src, size=tar.shape[2:], mode='bilinear', align_corners=False)
return src
class RSU7(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU7, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv6d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv5d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx = self.pool5(hx5)
hx6 = self.rebnconv6(hx)
hx7 = self.rebnconv7(hx6)
hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
hx6dup = _upsample_like(hx6d, hx5)
hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
return hx1d + hxin
class RSU6(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU6, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv5d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx6 = self.rebnconv6(hx5)
hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
return hx1d + hxin
class RSU5(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU5, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx5 = self.rebnconv5(hx4)
hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
return hx1d + hxin
class RSU4(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
return hx1d + hxin
class RSU4F(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4F, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=4)
self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=2)
self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx2 = self.rebnconv2(hx1)
hx3 = self.rebnconv3(hx2)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
return hx1d + hxin
class U2NET(nn.Module):
def __init__(self, in_ch=3, out_ch=1):
super(U2NET, self).__init__()
self.stage1 = RSU7(in_ch, 32, 64)
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage2 = RSU6(64, 32, 128)
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage3 = RSU5(128, 64, 256)
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage4 = RSU4(256, 128, 512)
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage5 = RSU4F(512, 256, 512)
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage6 = RSU4F(512, 256, 512)
# decoder
self.stage5d = RSU4F(1024, 256, 512)
self.stage4d = RSU4(1024, 128, 256)
self.stage3d = RSU5(512, 64, 128)
self.stage2d = RSU6(256, 32, 64)
self.stage1d = RSU7(128, 16, 64)
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
self.outconv = nn.Conv2d(6*out_ch, out_ch, 1)
def forward(self, x):
hx = x
# stage 1
hx1 = self.stage1(hx)
hx = self.pool12(hx1)
# stage 2
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
# stage 3
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
# stage 4
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
# stage 5
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
# stage 6
hx6 = self.stage6(hx)
hx6up = _upsample_like(hx6, hx5)
# decoder
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
# side output
d1 = self.side1(hx1d)
d2 = self.side2(hx2d)
d2 = _upsample_like(d2, d1)
d3 = self.side3(hx3d)
d3 = _upsample_like(d3, d1)
d4 = self.side4(hx4d)
d4 = _upsample_like(d4, d1)
d5 = self.side5(hx5d)
d5 = _upsample_like(d5, d1)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, d1)
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)
class U2NETP(nn.Module):
"""Smaller/faster U2Net variant (u2netp)"""
def __init__(self, in_ch=3, out_ch=1):
super(U2NETP, self).__init__()
self.stage1 = RSU7(in_ch, 16, 64)
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage2 = RSU6(64, 16, 64)
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage3 = RSU5(64, 16, 64)
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage4 = RSU4(64, 16, 64)
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage5 = RSU4F(64, 16, 64)
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage6 = RSU4F(64, 16, 64)
# decoder
self.stage5d = RSU4F(128, 16, 64)
self.stage4d = RSU4(128, 16, 64)
self.stage3d = RSU5(128, 16, 64)
self.stage2d = RSU6(128, 16, 64)
self.stage1d = RSU7(128, 16, 64)
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side3 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side4 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side5 = nn.Conv2d(64, out_ch, 3, padding=1)
self.side6 = nn.Conv2d(64, out_ch, 3, padding=1)
self.outconv = nn.Conv2d(6*out_ch, out_ch, 1)
def forward(self, x):
hx = x
hx1 = self.stage1(hx)
hx = self.pool12(hx1)
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
hx6 = self.stage6(hx)
hx6up = _upsample_like(hx6, hx5)
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
d1 = self.side1(hx1d)
d2 = self.side2(hx2d)
d2 = _upsample_like(d2, d1)
d3 = self.side3(hx3d)
d3 = _upsample_like(d3, d1)
d4 = self.side4(hx4d)
d4 = _upsample_like(d4, d1)
d5 = self.side5(hx5d)
d5 = _upsample_like(d5, d1)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, d1)
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)
backend/entrypoint.sh
+33 -2
View File
@@ -5,8 +5,9 @@
# This script runs when the container starts. It:
# 1. Initializes the database
# 2. Downloads SAM model automatically (can be disabled with AUTO_DOWNLOAD_SAM=false)
# 3. Downloads sample eye images if the catalog is empty
# 4. Starts the FastAPI server
# 3. Downloads U2Net model automatically (can be disabled with AUTO_DOWNLOAD_U2NET=false)
# 4. Downloads sample eye images if the catalog is empty
# 5. Starts the FastAPI server
# =============================================================================
set -e
@@ -61,6 +62,36 @@ else
fi
fi
echo ""
echo "Checking U2Net model (Remove Background)..."
echo "------------------------------------------"
if [ -f "/app/data/models/u2net.onnx" ] || [ -f "/app/data/models/u2netp.onnx" ]; then
echo "✓ U2Net model found - Remove Background will use local AI (free, offline)"
else
# Auto-download U2Net unless explicitly disabled
AUTO_DOWNLOAD_U2NET="${AUTO_DOWNLOAD_U2NET:-true}"
if [ "$AUTO_DOWNLOAD_U2NET" = "true" ]; then
echo "U2Net model not found. Downloading automatically..."
echo "(This is a one-time ~176MB download that persists across rebuilds)"
echo ""
python /scripts/download_u2net_model.py u2net || {
echo ""
echo "⚠ U2Net download failed (non-fatal)"
echo " Remove Background will fall back to rembg (if installed)"
echo " To retry later: docker exec -it ai-photo-edit-backend python /scripts/download_u2net_model.py u2net"
}
else
echo ""
echo "⚠ U2Net model not found (AUTO_DOWNLOAD_U2NET=false)"
echo ""
echo " Remove Background will fall back to rembg (if installed)"
echo ""
echo " To enable FREE offline Remove Background, run:"
echo " docker exec -it ai-photo-edit-backend python /scripts/download_u2net_model.py u2net"
echo ""
fi
fi
echo ""
echo "Checking GPU capabilities..."
echo "------------------------------------------"
docker-compose.gpu.yml
+1
View File
@@ -120,6 +120,7 @@ services:
- SECRET_KEY=${SECRET_KEY:-change-this-secret-key-in-production}
- CORS_ORIGINS=*
- AUTO_DOWNLOAD_SAM=${AUTO_DOWNLOAD_SAM:-true}
- AUTO_DOWNLOAD_U2NET=${AUTO_DOWNLOAD_U2NET:-true}
# ── NVIDIA GPU passthrough ────────────────────────────────────────────────
# Requires nvidia-container-toolkit; see prerequisites at top of this file.