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Merge pull request #59 from outis1one/claude/fervent-dirac-ldwaki

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Claude/fervent dirac ldwaki
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Outis
2026-06-14 06:03:01 -04:00
committed by GitHub
parent 4cc92c3e40 ea77d95ab0
commit ffe56c99e9
3 changed files with 168 additions and 37 deletions
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README.md
+39 -37
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@@ -7,29 +7,22 @@ A self-hosted, web-based AI photo editor. Paint over any object, describe what y
### GPU machine (recommended — free inference, best quality)
```bash
# Prerequisites: Docker + nvidia-container-toolkit
# Install toolkit once (Ubuntu/Debian):
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-ctk.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-ctk.gpg] https://#g' \
| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
# Verify GPU passes through into Docker:
docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
# Clone and run:
git clone https://github.com/outis1one/editmaskwithai
cd editmaskwithai
docker compose -f docker-compose.gpu.yml up --build
# One-time setup: installs nvidia-container-toolkit, configures Docker,
# and sets up a permanent DNS fix so the container can download models.
chmod +x install-local-gpu.sh
./install-local-gpu.sh
# Start the app (run this each time):
chmod +x bring-up-local-gpu.sh
./bring-up-local-gpu.sh
```
Open **http://localhost:3080**
**First startup downloads the AI model for your GPU (5–20 GB, one time).** Models are cached in a Docker volume and survive rebuilds.
**First startup downloads the AI model for your GPU (~13 GB, one time).** Models are cached in `./data/hf_cache/` and survive rebuilds.
---
@@ -52,15 +45,15 @@ Open **http://localhost:3080**
```bash
git pull
# GPU:
docker compose -f docker-compose.gpu.yml up -d --build
# or cloud:
./bring-up-local-gpu.sh
# or cloud (no GPU):
docker compose up -d --build
```
If pip packages seem stale after a pull (e.g., wrong diffusers version), force a pip layer rebuild without re-downloading the entire PyTorch base image:
```bash
BUILDID=$(date +%s) docker compose -f docker-compose.gpu.yml up -d --build
BUILDID=$(date +%s) ./bring-up-local-gpu.sh
```
---
@@ -208,32 +201,41 @@ 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.
**AI Edit returns "model files not yet downloaded" or "Errno -3 / DNS" error**
**AI models not downloading (container DNS blocked)**
The container's DNS is blocked (common on corporate networks or custom iptables rules), so it can't download SDXL models from HuggingFace. Two options:
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):
*Option A — fix Docker DNS (recommended, one command):*
```bash
sudo iptables -I DOCKER-USER -p udp --dport 53 -j ACCEPT
docker compose -f docker-compose.gpu.yml restart
./bring-up-local-gpu.sh
```
*Option B — pre-download models on the host (if iptables fix isn't possible):*
The container will now resolve hostnames and download models automatically (~13 GB on first run, then cached). Watch progress:
```bash
pip install huggingface-hub
# Download the inpainting model (~6.5 GB, needed for AI Edit):
huggingface-cli download diffusers/stable-diffusion-xl-1.0-inpainting-0.1 \
--cache-dir ./data/hf_cache \
--exclude "*.msgpack" "flax_*" "tf_*"
# Download the text-to-image model (~6.5 GB, needed for Text → Image):
huggingface-cli download stabilityai/stable-diffusion-xl-base-1.0 \
--cache-dir ./data/hf_cache \
--exclude "*.msgpack" "flax_*" "tf_*"
docker compose -f docker-compose.gpu.yml logs -f | grep -E "local_gpu|Cached|failed"
```
The models land in `./data/hf_cache/` which is bind-mounted into the container — no rebuild needed. Restart the container and the first AI Edit request loads from local disk.
**Alternative: download with a Docker helper container** (no iptables, no host Python needed):
```bash
# Inpainting model (~6.5 GB) — needed for AI Edit, Make less symmetrical, etc.
docker run --rm \
-v "$(pwd)/data/hf_cache:/root/.cache/huggingface" \
python:3.11-slim \
bash -c "pip install -q huggingface-hub && \
huggingface-cli download diffusers/stable-diffusion-xl-1.0-inpainting-0.1 \
--exclude '*.msgpack' 'flax_*' 'tf_*'"
# Text-to-image model (~6.5 GB) — needed for Text → Image
docker run --rm \
-v "$(pwd)/data/hf_cache:/root/.cache/huggingface" \
python:3.11-slim \
bash -c "pip install -q huggingface-hub && \
huggingface-cli download stabilityai/stable-diffusion-xl-base-1.0 \
--exclude '*.msgpack' 'flax_*' 'tf_*'"
```
Then restart: `docker compose -f docker-compose.gpu.yml restart`
**Out of VRAM during generation**
- Reduce `LOCAL_GPU_MAX_PIPELINES=1` in `.env` (default 2)
bring-up-local-gpu.sh
Executable
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#!/usr/bin/env bash
# bring-up-local-gpu.sh — start the GPU container.
#
# Run this each time you want to start the app.
# Run ./install-local-gpu.sh once first on a new machine.
#
# Usage:
# ./bring-up-local-gpu.sh # start (detached, rebuild if needed)
# ./bring-up-local-gpu.sh --no-build # start without rebuilding
# ./bring-up-local-gpu.sh down # stop and remove container
# ./bring-up-local-gpu.sh logs -f # tail logs
#
# Force pip layer rebuild (e.g. after requirements change):
# BUILDID=$(date +%s) ./bring-up-local-gpu.sh
set -euo pipefail
if [ $# -eq 0 ]; then
exec docker compose -f docker-compose.gpu.yml up -d --build
else
exec docker compose -f docker-compose.gpu.yml "$@"
fi
install-local-gpu.sh
Executable
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@@ -0,0 +1,107 @@
#!/usr/bin/env bash
# install-local-gpu.sh — one-time setup for local GPU inference.
#
# Run this once on a new machine. It:
# 1. Installs the NVIDIA container toolkit (so Docker can use the GPU)
# 2. Installs a systemd service that permanently fixes Docker container DNS
# (allows containers to resolve hostnames — does not touch ufw)
# 3. Restarts Docker so both changes take effect
# 4. Verifies the GPU is accessible inside Docker
#
# After this, use ./bring-up-local-gpu.sh each time to start the app.
set -euo pipefail
# ── Must run as root (or via sudo) ───────────────────────────────────────────
if [ "$EUID" -ne 0 ]; then
exec sudo bash "$0" "$@"
fi
echo "=================================================="
echo " EditmaskwithAI — Local GPU one-time setup"
echo "=================================================="
echo ""
# ── 1. NVIDIA container toolkit ──────────────────────────────────────────────
if command -v nvidia-ctk &>/dev/null; then
echo "✓ nvidia-container-toolkit already installed — skipping"
else
echo "Installing nvidia-container-toolkit..."
. /etc/os-release
case "$ID" in
ubuntu|debian)
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
| gpg --dearmor -o /usr/share/keyrings/nvidia-ctk.gpg
curl -fsSL "https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list" \
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-ctk.gpg] https://#g' \
| tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
apt-get update -qq
apt-get install -y nvidia-container-toolkit
;;
rhel|fedora|rocky|centos|almalinux)
dnf install -y nvidia-container-toolkit
;;
*)
echo "⚠ Unrecognised distro ($ID). Install nvidia-container-toolkit manually."
echo " See: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html"
;;
esac
fi
nvidia-ctk runtime configure --runtime=docker
# ── 2. Permanent Docker DNS fix via systemd ───────────────────────────────────
# Adds a rule to the DOCKER-USER iptables chain so containers can resolve
# hostnames. Runs after docker.service on every boot. Does NOT touch ufw.
echo ""
echo "Installing docker-dns-fix systemd service..."
cat > /etc/systemd/system/docker-dns-fix.service << 'EOF'
[Unit]
Description=Allow Docker containers to resolve DNS (DOCKER-USER iptables rule)
After=docker.service
Requires=docker.service
BindsTo=docker.service
[Service]
Type=oneshot
ExecStart=/bin/sh -c \
'iptables -C DOCKER-USER -p udp --dport 53 -j ACCEPT 2>/dev/null || \
iptables -I DOCKER-USER -p udp --dport 53 -j ACCEPT'
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target
EOF
systemctl daemon-reload
systemctl enable docker-dns-fix.service
echo "✓ docker-dns-fix.service installed and enabled"
# ── 3. Restart Docker ─────────────────────────────────────────────────────────
echo ""
echo "Restarting Docker..."
systemctl restart docker
sleep 2
echo "✓ Docker restarted"
# ── 4. Apply DNS rule now (don't wait for next boot) ─────────────────────────
systemctl start docker-dns-fix.service
echo "✓ DNS fix applied"
# ── 5. Verify GPU access ─────────────────────────────────────────────────────
echo ""
echo "Verifying GPU access inside Docker..."
if docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi &>/dev/null; then
echo "✓ GPU is accessible inside Docker"
else
echo "⚠ GPU check failed. Is the NVIDIA driver installed on the host?"
echo " Check: nvidia-smi"
echo " Minimum driver version: 525"
fi
echo ""
echo "=================================================="
echo " Setup complete."
echo " Start the app with: ./bring-up-local-gpu.sh"
echo "=================================================="