Files
ubuntu-post-install/vendor/paintplus/docker-compose.gpu.yml
Claude b2d064573f Move ai-stack and paintplus vendored source under vendor/
Matches the existing vendor/easy-asterisk convention (used by
services/asterisk.sh) instead of two one-off top-level directories that
cluttered the repo root and didn't look like anything else next to
setup.sh, lib/, services/, extras/. Only the two services' own SRC_DIR
path resolution and header comments needed updating — nothing else in
the repo referenced the old ./ai-stack / ./paintplus paths.

Also documents vendor/ in README.md's Layout section.
2026-08-04 12:55:09 +00:00

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# =============================================================================
# EditmaskwithAI — GPU Docker Compose (NVIDIA CUDA)
#
# ── PREREQUISITES ─────────────────────────────────────────────────────────────
#
# 1. NVIDIA driver ≥ 525 installed on the host
# Check: nvidia-smi
#
# 2. nvidia-container-toolkit installed and configured:
# (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
#
# (RHEL/Fedora/Rocky)
# sudo dnf install -y nvidia-container-toolkit
# sudo nvidia-ctk runtime configure --runtime=docker
# sudo systemctl restart docker
#
# 3. Verify GPU access in Docker:
# docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
#
# ── QUICK START ───────────────────────────────────────────────────────────────
#
# docker compose -f docker-compose.gpu.yml up --build
# Then open: http://localhost:3080
#
# ── OLDER DOCKER SETUPS (docker-compose v1 / nvidia-docker2) ─────────────────
#
# If you installed nvidia-docker2 (older approach) instead of nvidia-container-toolkit,
# replace the 'deploy:' block below with:
#
# runtime: nvidia
# environment:
# - NVIDIA_VISIBLE_DEVICES=all
# - NVIDIA_DRIVER_CAPABILITIES=compute,utility
#
# ── GPU TIER AUTO-SELECTION ───────────────────────────────────────────────────
#
# ≥16 GB VRAM → SDXL (best quality)
# 816 GB → SDXL
# 48 GB → Stable Diffusion 2.x
# 24 GB → Stable Diffusion 1.5 (older GPUs: GTX 970/1060/RX 580)
# <2 GB → SD 1.5 + CPU offload (very slow — consider a remote provider)
#
# ── AMD ROCm ──────────────────────────────────────────────────────────────────
#
# Swap the base image in Dockerfile.gpu:
# FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
# → FROM rocm/pytorch:rocm6.0_ubuntu22.04_py3.9_pytorch_2.1.0
# Remove the 'driver: nvidia' line and add: device_ids: ['0']
#
# =============================================================================
services:
app:
build:
context: .
dockerfile: Dockerfile.gpu
args:
# Increment BUILDID to force pip layers to re-run without full --no-cache:
# BUILDID=$(date +%s) docker compose -f docker-compose.gpu.yml up --build
BUILDID: ${BUILDID:-1}
container_name: paintplus
ports:
- "${PORT:-3080}:8000"
volumes:
# Persistent project data
- ./data:/app/data
# HuggingFace model cache — bind mount so models can be pre-downloaded on the host.
# If container DNS is blocked, run ./prefetch-models.sh on the host first —
# it downloads BEN2/BiRefNet-HR (and optionally SDXL with --sdxl) straight
# into this directory, and the container picks them up on next start.
# To free disk space: rm -rf ./data/hf_cache
- ./data/hf_cache:/root/.cache/huggingface
# Scripts (for exec access)
- ./scripts:/scripts
environment:
# ── Local GPU (default for this compose) ────────────────────────────────
- AI_PROVIDER=${AI_PROVIDER:-local_gpu}
- AUTO_DOWNLOAD_MODELS=${AUTO_DOWNLOAD_MODELS:-true}
# ── Per-operation overrides (optional) ──────────────────────────────────
# Leave blank to use AI_PROVIDER for all operations.
# Example: use InvokeAI for inpaint, local GPU for everything else:
# AI_PROVIDER_INPAINT=invokeai
- AI_PROVIDER_INPAINT=${AI_PROVIDER_INPAINT:-}
- AI_PROVIDER_TXT2IMG=${AI_PROVIDER_TXT2IMG:-}
- AI_PROVIDER_IMG2IMG=${AI_PROVIDER_IMG2IMG:-}
- AI_PROVIDER_OUTPAINT=${AI_PROVIDER_OUTPAINT:-}
# ── Remote/cloud providers (all optional) ────────────────────────────────
- OPENAI_API_KEY=${OPENAI_API_KEY:-}
- OPENAI_MODEL=${OPENAI_MODEL:-gpt-image-2}
- OPENAI_EDIT_MODEL=${OPENAI_EDIT_MODEL:-gpt-image-2}
- REPLICATE_API_KEY=${REPLICATE_API_KEY:-}
- STABILITY_API_KEY=${STABILITY_API_KEY:-}
# ── InvokeAI / ComfyUI (running on another machine or container) ────────
- INVOKEAI_URL=${INVOKEAI_URL:-}
- INVOKEAI_DEFAULT_MODEL=${INVOKEAI_DEFAULT_MODEL:-flux-dev}
- COMFYUI_URL=${COMFYUI_URL:-}
- COMFYUI_DEFAULT_MODEL=${COMFYUI_DEFAULT_MODEL:-v1-5-pruned-emaonly.ckpt}
# ── HuggingFace model overrides (optional) ───────────────────────────────
# Override the auto-selected model for any operation:
# HF_MODEL_INPAINT=your-org/your-model
- HF_MODEL_INPAINT=${HF_MODEL_INPAINT:-}
- HF_MODEL_TXT2IMG=${HF_MODEL_TXT2IMG:-}
- HF_MODEL_IMG2IMG=${HF_MODEL_IMG2IMG:-}
- HF_TOKEN=${HF_TOKEN:-}
# ── App settings ─────────────────────────────────────────────────────────
- DATABASE_URL=sqlite:///./data/ai_photo_edit.db
- SECRET_KEY=${SECRET_KEY:-change-this-secret-key-in-production}
- CORS_ORIGINS=*
- AUTO_DOWNLOAD_SAM=${AUTO_DOWNLOAD_SAM:-true}
- AUTO_DOWNLOAD_U2NET=${AUTO_DOWNLOAD_U2NET:-true}
- BG_REMOVAL_MODEL=${BG_REMOVAL_MODEL:-ben2}
# ── NVIDIA GPU passthrough ────────────────────────────────────────────────
# Requires nvidia-container-toolkit; see prerequisites at top of this file.
# For older nvidia-docker2 setups, replace this block with:
# runtime: nvidia
# environment:
# - NVIDIA_VISIBLE_DEVICES=all
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
# DNS: try host resolver first (works on most networks including corporate/VPN),
# fall back to Cloudflare then Google public resolvers.
# If all three fail (Errno -3), your firewall is blocking port 53 UDP from Docker.
# Fix on the host: sudo iptables -I DOCKER-USER -p udp --dport 53 -j ACCEPT
dns:
- 1.1.1.1
- 8.8.8.8
- 8.8.4.4
restart: unless-stopped