Claude
3a977c7438
Lets SAM, U2Net, BEN2, and BiRefNet-HR (optionally SDXL via --sdxl) be downloaded outside Docker into ./data/, which is already bind-mounted into the GPU container — so a blocked container network no longer blocks first-run setup. Reuses the existing dual-mode download_sam_model.py and download_u2net_model.py as-is. For the HuggingFace Hub models, sets HF_HOME (rather than --cache-dir) so the host-side cache layout matches the container's default ~/.cache/huggingface resolution exactly, avoiding a path-nesting mismatch between the two. Wired into install-local-gpu.sh's completion banner and bring-up-local-gpu.sh's header, and referenced from the relevant README troubleshooting sections and the hf_cache bind-mount comment in docker-compose.gpu.yml.
149 lines
7.2 KiB
YAML
149 lines
7.2 KiB
YAML
# =============================================================================
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# EditmaskwithAI — GPU Docker Compose (NVIDIA CUDA)
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#
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# ── PREREQUISITES ─────────────────────────────────────────────────────────────
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#
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# 1. NVIDIA driver ≥ 525 installed on the host
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# Check: nvidia-smi
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#
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# 2. nvidia-container-toolkit installed and configured:
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# (Ubuntu/Debian)
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# curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
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# | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-ctk.gpg
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# curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
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# | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-ctk.gpg] https://#g' \
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# | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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# sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
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# sudo nvidia-ctk runtime configure --runtime=docker
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# sudo systemctl restart docker
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#
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# (RHEL/Fedora/Rocky)
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# sudo dnf install -y nvidia-container-toolkit
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# sudo nvidia-ctk runtime configure --runtime=docker
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# sudo systemctl restart docker
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#
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# 3. Verify GPU access in Docker:
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# docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
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#
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# ── QUICK START ───────────────────────────────────────────────────────────────
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#
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# docker compose -f docker-compose.gpu.yml up --build
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# Then open: http://localhost:3080
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#
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# ── OLDER DOCKER SETUPS (docker-compose v1 / nvidia-docker2) ─────────────────
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#
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# If you installed nvidia-docker2 (older approach) instead of nvidia-container-toolkit,
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# replace the 'deploy:' block below with:
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#
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# runtime: nvidia
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# environment:
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# - NVIDIA_VISIBLE_DEVICES=all
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# - NVIDIA_DRIVER_CAPABILITIES=compute,utility
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#
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# ── GPU TIER AUTO-SELECTION ───────────────────────────────────────────────────
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#
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# ≥16 GB VRAM → SDXL (best quality)
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# 8–16 GB → SDXL
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# 4–8 GB → Stable Diffusion 2.x
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# 2–4 GB → Stable Diffusion 1.5 (older GPUs: GTX 970/1060/RX 580)
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# <2 GB → SD 1.5 + CPU offload (very slow — consider a remote provider)
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#
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# ── AMD ROCm ──────────────────────────────────────────────────────────────────
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#
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# Swap the base image in Dockerfile.gpu:
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# FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
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# → FROM rocm/pytorch:rocm6.0_ubuntu22.04_py3.9_pytorch_2.1.0
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# Remove the 'driver: nvidia' line and add: device_ids: ['0']
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#
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# =============================================================================
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services:
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app:
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build:
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context: .
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dockerfile: Dockerfile.gpu
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args:
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# Increment BUILDID to force pip layers to re-run without full --no-cache:
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# BUILDID=$(date +%s) docker compose -f docker-compose.gpu.yml up --build
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BUILDID: ${BUILDID:-1}
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container_name: editmaskwithai-gpu
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ports:
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- "${PORT:-3080}:8000"
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volumes:
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# Persistent project data
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- ./data:/app/data
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# HuggingFace model cache — bind mount so models can be pre-downloaded on the host.
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# If container DNS is blocked, run ./prefetch-models.sh on the host first —
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# it downloads BEN2/BiRefNet-HR (and optionally SDXL with --sdxl) straight
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# into this directory, and the container picks them up on next start.
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# To free disk space: rm -rf ./data/hf_cache
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- ./data/hf_cache:/root/.cache/huggingface
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# Scripts (for exec access)
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- ./scripts:/scripts
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environment:
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# ── Local GPU (default for this compose) ────────────────────────────────
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- AI_PROVIDER=${AI_PROVIDER:-local_gpu}
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- AUTO_DOWNLOAD_MODELS=${AUTO_DOWNLOAD_MODELS:-true}
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# ── Per-operation overrides (optional) ──────────────────────────────────
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# Leave blank to use AI_PROVIDER for all operations.
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# Example: use InvokeAI for inpaint, local GPU for everything else:
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# AI_PROVIDER_INPAINT=invokeai
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- AI_PROVIDER_INPAINT=${AI_PROVIDER_INPAINT:-}
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- AI_PROVIDER_TXT2IMG=${AI_PROVIDER_TXT2IMG:-}
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- AI_PROVIDER_IMG2IMG=${AI_PROVIDER_IMG2IMG:-}
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- AI_PROVIDER_OUTPAINT=${AI_PROVIDER_OUTPAINT:-}
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# ── Remote/cloud providers (all optional) ────────────────────────────────
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- OPENAI_API_KEY=${OPENAI_API_KEY:-}
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- OPENAI_MODEL=${OPENAI_MODEL:-dall-e-3}
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- REPLICATE_API_KEY=${REPLICATE_API_KEY:-}
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- STABILITY_API_KEY=${STABILITY_API_KEY:-}
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# ── InvokeAI / ComfyUI (running on another machine or container) ────────
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- INVOKEAI_URL=${INVOKEAI_URL:-}
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- INVOKEAI_DEFAULT_MODEL=${INVOKEAI_DEFAULT_MODEL:-flux-dev}
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- COMFYUI_URL=${COMFYUI_URL:-}
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- COMFYUI_DEFAULT_MODEL=${COMFYUI_DEFAULT_MODEL:-v1-5-pruned-emaonly.ckpt}
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# ── HuggingFace model overrides (optional) ───────────────────────────────
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# Override the auto-selected model for any operation:
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# HF_MODEL_INPAINT=your-org/your-model
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- HF_MODEL_INPAINT=${HF_MODEL_INPAINT:-}
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- HF_MODEL_TXT2IMG=${HF_MODEL_TXT2IMG:-}
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- HF_MODEL_IMG2IMG=${HF_MODEL_IMG2IMG:-}
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- HF_TOKEN=${HF_TOKEN:-}
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# ── App settings ─────────────────────────────────────────────────────────
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- DATABASE_URL=sqlite:///./data/ai_photo_edit.db
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- SECRET_KEY=${SECRET_KEY:-change-this-secret-key-in-production}
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- CORS_ORIGINS=*
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- AUTO_DOWNLOAD_SAM=${AUTO_DOWNLOAD_SAM:-true}
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- AUTO_DOWNLOAD_U2NET=${AUTO_DOWNLOAD_U2NET:-true}
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- BG_REMOVAL_MODEL=${BG_REMOVAL_MODEL:-ben2}
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# ── NVIDIA GPU passthrough ────────────────────────────────────────────────
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# Requires nvidia-container-toolkit; see prerequisites at top of this file.
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# For older nvidia-docker2 setups, replace this block with:
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# runtime: nvidia
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# environment:
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# - NVIDIA_VISIBLE_DEVICES=all
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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# DNS: try host resolver first (works on most networks including corporate/VPN),
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# fall back to Cloudflare then Google public resolvers.
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# If all three fail (Errno -3), your firewall is blocking port 53 UDP from Docker.
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# Fix on the host: sudo iptables -I DOCKER-USER -p udp --dport 53 -j ACCEPT
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dns:
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- 1.1.1.1
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- 8.8.8.8
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- 8.8.4.4
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restart: unless-stopped
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