#!/bin/bash # services/ai-gpu.sh — GPU AI stack: InvokeAI image gen + Ollama/OpenWebUI LLM + swap portal. # Part of the modular post-install system (sourced by setup.sh). # # Can also be run standalone on any machine: # sudo bash ai-gpu.sh # (Docker + nvidia-container-toolkit must already be installed) # # Clones https://github.com/outis1one/ai-6gb-gpu and installs three stacks: # image-gen/ — InvokeAI (port 9090), nvidia GPU, optimised for 6 GB VRAM # llm/ — Ollama (11434) + Open WebUI (3000) + SearXNG (internal) # portal/ — Flask app (port 8080), mounts Docker socket, hot-swaps GPU between stacks # # Because a 6 GB GPU can only run ONE stack at a time, the portal handles the swap: # stop the active stack, start the requested one. Stop image-gen before starting llm, and vice versa. # ── Standalone bootstrap ────────────────────────────────────────────────────── if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then [[ "$(id -u)" == "0" ]] || { echo "Run with sudo: sudo bash $0"; exit 1; } _SELF_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" _COMMON="$_SELF_DIR/../lib/common.sh" if [[ -f "$_COMMON" ]]; then source "$_COMMON" else log_info() { echo -e "\033[0;34m[INFO]\033[0m $*"; } log_success() { echo -e "\033[0;32m[OK]\033[0m $*"; } log_warning() { echo -e "\033[1;33m[WARN]\033[0m $*"; } log_error() { echo -e "\033[0;31m[ERROR]\033[0m $*" >&2; } require_docker() { command -v docker &>/dev/null || { log_error "Docker not found. Install it first:" log_error " curl -fsSL https://get.docker.com | sudo sh" return 1 } docker compose version &>/dev/null || { log_error "Docker Compose plugin missing:" log_error " sudo apt-get install -y docker-compose-plugin" return 1 } } ensure_docker_dir_ownership() { chown -R "$ACTUAL_USER:$ACTUAL_USER" "$@" 2>/dev/null || true } prompt_text() { local _q="$1" _def="$2" _var="$3" _r [[ "${UNATTENDED:-false}" == "true" ]] && { eval "$_var='$_def'"; return; } read -r -p " $_q " _r eval "$_var='${_r:-$_def}'" } prompt_yn() { local _q="$1" _def="$2" _var="$3" _r [[ "${UNATTENDED:-false}" == "true" ]] && { eval "$_var='$_def'"; return; } read -r -p " $_q " _r eval "$_var='${_r:-$_def}'" } configure_caddy_for_service() { local _name="$1" _upstream="$2" _subdomain="$3" _extra="${4:-}" local _caddy_dir="$DOCKER_DIR/caddy" local _caddyfile="$_caddy_dir/Caddyfile" local _display_port="${_upstream##*:}" local _mode="none" [[ -d "$_caddy_dir" ]] && _mode="local" [[ -n "${CADDY_REMOTE_HOST:-}" ]] && [[ "$_mode" != "local" ]] && _mode="remote" [[ "$_mode" == "none" ]] && { log_info "Access $_name directly on port $_display_port." return 0 } echo "" local _do_caddy="" if [[ "$_mode" == "remote" ]]; then log_info "Remote Caddy configured (${CADDY_REMOTE_HOST})." log_info "A snippet file will be saved to ~/docker/caddy-snippets/." fi read -r -p " Configure Caddy reverse proxy for $_name? [y/N]: " _do_caddy [[ "${_do_caddy,,}" == "y" ]] || { log_info "Skipping — access at: http://localhost:$_display_port" return 0 } local _default_domain="" if [[ -n "${SITE_DOMAIN:-}" ]] && [[ "$SITE_DOMAIN" != "example.com" ]]; then _default_domain="${_subdomain}.${SITE_DOMAIN}" log_info "Default: $_default_domain" fi local _domain="" read -r -p " Domain [${_default_domain:-required}]: " _domain _domain="${_domain:-$_default_domain}" [[ -n "$_domain" ]] || { log_warning "No domain entered — skipping Caddy."; return 0; } local _block_upstream="$_upstream" if [[ "$_mode" == "remote" ]]; then _block_upstream="${CADDY_REMOTE_HOST}:${_display_port}" fi local _site_block _site_block="$(cat << CBLOCK # $_name ${_domain} { reverse_proxy ${_block_upstream} header { Strict-Transport-Security "max-age=31536000; includeSubDomains; preload" X-Content-Type-Options "nosniff" X-Frame-Options "SAMEORIGIN" Referrer-Policy "strict-origin-when-cross-origin" } log { output file /var/log/caddy/${_domain}.log format json } ${_extra} } CBLOCK )" if [[ "$_mode" == "local" ]]; then if [[ -f "$_caddyfile" ]]; then local _bk="$_caddy_dir/Caddyfile.backup.$(date +%Y%m%d-%H%M%S)" cp "$_caddyfile" "$_bk" log_info "Backed up Caddyfile to $(basename "$_bk")" else touch "$_caddyfile" fi if grep -q "^${_domain}" "$_caddyfile" 2>/dev/null; then log_warning "$_domain already in Caddyfile" local _ow="" read -r -p " Overwrite? [y/N]: " _ow [[ "${_ow,,}" == "y" ]] || { log_info "Keeping existing entry."; return 0; } sed -i "/^${_domain}/,/^}/d" "$_caddyfile" fi printf '%s\n' "$_site_block" >> "$_caddyfile" log_success "Added $_domain to Caddyfile" docker exec caddy caddy fmt --overwrite /etc/caddy/Caddyfile 2>/dev/null || true if docker exec caddy caddy reload --config /etc/caddy/Caddyfile 2>/dev/null; then log_success "$_name accessible at: https://$_domain" else log_warning "Reload failed — check: docker logs caddy" log_info "Manual reload: docker exec caddy caddy reload --config /etc/caddy/Caddyfile" fi else local _snippet_dir="$DOCKER_DIR/caddy-snippets" local _snippet_file="$_snippet_dir/${_subdomain}.caddy" mkdir -p "$_snippet_dir" printf '%s\n' "$_site_block" > "$_snippet_file" chown "$ACTUAL_USER:$ACTUAL_USER" "$_snippet_file" 2>/dev/null || true log_success "Snippet saved: $_snippet_file" log_info "Copy to Caddy machine:" log_info " scp $_snippet_file caddy-host:~/caddy-snippets/" log_info " rsync -av $_snippet_dir/ caddy-host:~/caddy-snippets/ (all at once)" fi } write_readme() { local _dir="$1" mkdir -p "$_dir" [[ "${DRY_RUN:-false}" == "true" ]] && return 0 cat > "$_dir/README.md" } generate_password() { local _len="${1:-32}" tr -dc 'A-Za-z0-9' < /dev/urandom | head -c "$_len" echo } fi ACTUAL_USER="${ACTUAL_USER:-${SUDO_USER:-$USER}}" ACTUAL_HOME="$(getent passwd "$ACTUAL_USER" 2>/dev/null | cut -d: -f6 || echo "${HOME:-/root}")" DOCKER_DIR="${DOCKER_DIR:-$ACTUAL_HOME/docker}" DRY_RUN="${DRY_RUN:-false}" UNATTENDED="${UNATTENDED:-false}" SITE_TZ="${SITE_TZ:-$(cat /etc/timezone 2>/dev/null || echo UTC)}" SITE_DOMAIN="${SITE_DOMAIN:-example.com}" SITE_CADDY_NET="${SITE_CADDY_NET:-caddy_net}" CADDY_REMOTE_HOST="${CADDY_REMOTE_HOST:-}" register_service() { :; } _RUN_STANDALONE=1 fi # ───────────────────────────────────────────────────────────────────────────── register_service ai-gpu utilities "GPU AI stack — InvokeAI image gen + Ollama/OpenWebUI LLM (6 GB VRAM)" 9090 install_ai_gpu() { require_docker || return 1 log_info "Installing AI GPU stack (InvokeAI + Ollama/OpenWebUI + portal)..." log_info "Requires: nvidia GPU with 6 GB+ VRAM, nvidia-container-toolkit installed." local AI_DIR="$DOCKER_DIR/ai-gpu" local REPO_URL="https://github.com/outis1one/ai-6gb-gpu.git" local REPO_DIR="$AI_DIR/src" if [ "$DRY_RUN" = true ]; then echo "[DRY-RUN] Would clone $REPO_URL to $REPO_DIR" echo "[DRY-RUN] Would create stacks: image-gen (InvokeAI:9090), llm (Ollama:11434 + OpenWebUI:3000), portal (8080)" echo "[DRY-RUN] Would prompt for Ollama and InvokeAI model selection" echo "[DRY-RUN] Would auto-pull selected Ollama models after LLM stack starts" echo "[DRY-RUN] Would queue InvokeAI starter model via REST API" return 0 fi # ── Timezone ────────────────────────────────────────────────────────────── local TZ_VAL="${SITE_TZ:-UTC}" prompt_text "Timezone (e.g. America/New_York) [$TZ_VAL]:" "$TZ_VAL" TZ_VAL TZ_VAL="${TZ_VAL:-UTC}" # ── Ollama model selection ──────────────────────────────────────────────── echo "" log_info "Ollama LLM models — select which to download (enter numbers separated by spaces):" log_info "All models are quantized (Q4_K_M) and run comfortably on 6 GB VRAM." echo "" log_info " 1) llama3.2:3b ~2.0 GB Fast general chat. Great all-rounder. ← Recommended" log_info " 2) llama3.2:1b ~1.3 GB Ultra-fast. Light tasks, low latency." log_info " 3) qwen2.5:7b ~4.7 GB Top code + math model. Strong reasoning." log_info " 4) mistral:7b ~4.1 GB Solid all-rounder. Good at instruction follow." log_info " 5) phi4-mini ~2.5 GB Microsoft Phi-4 mini. Excellent for coding." log_info " 6) gemma3:4b ~2.5 GB Google Gemma 3. Well-rounded, multilingual." log_info " 7) deepseek-r1:7b ~4.7 GB Strong reasoning and math. Think-step model." log_info " 8) nomic-embed-text ~274 MB Embedding model — enables RAG/doc search." log_info " Recommended to add alongside a chat model." echo "" log_info " Example: '1 8' pulls llama3.2:3b + nomic-embed-text" log_info " Enter '0' or leave blank to skip and pull models manually later." echo "" local OLLAMA_CHOICES="" prompt_text "Models to download [1 8]:" "1 8" OLLAMA_CHOICES declare -a OLLAMA_MODELS=() for _n in $OLLAMA_CHOICES; do case "$_n" in 1) OLLAMA_MODELS+=("llama3.2:3b") ;; 2) OLLAMA_MODELS+=("llama3.2:1b") ;; 3) OLLAMA_MODELS+=("qwen2.5:7b") ;; 4) OLLAMA_MODELS+=("mistral:7b") ;; 5) OLLAMA_MODELS+=("phi4-mini") ;; 6) OLLAMA_MODELS+=("gemma3:4b") ;; 7) OLLAMA_MODELS+=("deepseek-r1:7b") ;; 8) OLLAMA_MODELS+=("nomic-embed-text") ;; esac done # ── InvokeAI model selection ────────────────────────────────────────────── echo "" log_info "InvokeAI image generation models (for 6 GB VRAM with partial GPU offload):" log_info "InvokeAI uses VRAM=3 GB + 8 GB RAM cache, so all models below work on 6 GB." echo "" log_info " 1) stabilityai/stable-diffusion-v1-5 ~4 GB SD 1.5 — fast, huge style/LoRA library." log_info " Best starting model for most uses." log_info " 2) stabilityai/sdxl-turbo ~7 GB SDXL Turbo — 4-step generation." log_info " Fast, high quality, slightly slower on 6 GB." log_info " 3) stabilityai/stable-diffusion-xl-base-1.0" log_info " ~7 GB SDXL base — best quality at 1024px." log_info " Slowest due to RAM offload on 6 GB." log_info " 4) Skip — install models via the Model Manager at http://localhost:9090" echo "" log_info " Tip: SD 1.5 (choice 1) is fastest and most compatible. Start here." log_info " HuggingFace token: required for some gated models (free at huggingface.co/settings/tokens)" echo "" local INVOKE_CHOICE="" prompt_text "InvokeAI starter model [1]:" "1" INVOKE_CHOICE local INVOKE_MODEL_SOURCE="" local INVOKE_MODEL_NAME="" case "$INVOKE_CHOICE" in 2) INVOKE_MODEL_SOURCE="stabilityai/sdxl-turbo" INVOKE_MODEL_NAME="SDXL Turbo" ;; 3) INVOKE_MODEL_SOURCE="stabilityai/stable-diffusion-xl-base-1.0" INVOKE_MODEL_NAME="SDXL Base" ;; 4) INVOKE_MODEL_SOURCE="" INVOKE_MODEL_NAME="" ;; *) INVOKE_MODEL_SOURCE="stabilityai/stable-diffusion-v1-5" INVOKE_MODEL_NAME="SD 1.5" ;; esac local HF_TOKEN="" if [ -n "$INVOKE_MODEL_SOURCE" ]; then prompt_text "HuggingFace token (optional — needed for gated models, enter to skip):" "" HF_TOKEN fi # ── Clone / update repo ─────────────────────────────────────────────────── mkdir -p "$AI_DIR" if [ -d "$REPO_DIR/.git" ]; then log_info "Updating ai-6gb-gpu repo..." git -C "$REPO_DIR" pull --ff-only 2>/dev/null \ && log_success "Repo updated" \ || log_warning "Could not pull latest — using existing version" else log_info "Cloning ai-6gb-gpu repo..." git clone --depth 1 "$REPO_URL" "$REPO_DIR" \ || { log_error "Clone failed — check network and git access"; return 1; } fi # ── Image-gen stack (InvokeAI) ──────────────────────────────────────────── local IMAGE_GEN_DIR="$AI_DIR/image-gen" mkdir -p "$IMAGE_GEN_DIR" if [ -d "$REPO_DIR/ai-image-gen" ]; then cp -rn "$REPO_DIR/ai-image-gen/." "$IMAGE_GEN_DIR/" 2>/dev/null || true find "$IMAGE_GEN_DIR" -name "docker-compose.yml" -exec \ sed -i "s|America/New_York|$TZ_VAL|g" {} \; fi cat > "$IMAGE_GEN_DIR/.env" << IMGENV # InvokeAI — image generation TZ=${TZ_VAL} # VRAM cap: 3 GB leaves headroom on a 6 GB card; remaining model layers go to RAM INVOKEAI_vram=3 # RAM cache size for model layer offload INVOKEAI_ram=8 ${HF_TOKEN:+HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}} CADDY_NET=${SITE_CADDY_NET} IMGENV chmod 600 "$IMAGE_GEN_DIR/.env" # ── LLM stack (Ollama + Open WebUI + SearXNG) ───────────────────────────── local LLM_DIR="$AI_DIR/llm" mkdir -p "$LLM_DIR" if [ -d "$REPO_DIR/ai-llm" ]; then cp -rn "$REPO_DIR/ai-llm/." "$LLM_DIR/" 2>/dev/null || true find "$LLM_DIR" -name "docker-compose.yml" -exec \ sed -i "s|America/New_York|$TZ_VAL|g" {} \; fi local WEBUI_SECRET WEBUI_SECRET="$(generate_password 32)" cat > "$LLM_DIR/.env" << LLMENV # Ollama + Open WebUI + SearXNG TZ=${TZ_VAL} WEBUI_SECRET_KEY=${WEBUI_SECRET} # Open WebUI: enable SearXNG for web search in chats ENABLE_RAG_WEB_SEARCH=true RAG_WEB_SEARCH_ENGINE=searxng SEARXNG_QUERY_URL=http://searxng:8080/search?q=&format=json CADDY_NET=${SITE_CADDY_NET} LLMENV chmod 600 "$LLM_DIR/.env" # ── Portal stack (Flask GPU swap controller) ────────────────────────────── local PORTAL_DIR="$AI_DIR/portal" mkdir -p "$PORTAL_DIR" if [ -d "$REPO_DIR/ai-portal" ]; then cp -rn "$REPO_DIR/ai-portal/." "$PORTAL_DIR/" 2>/dev/null || true if [ -f "$PORTAL_DIR/docker-compose.yml" ]; then sed -i \ "s|/home/[^/]*/docker:|${ACTUAL_HOME}/docker:|g" \ "$PORTAL_DIR/docker-compose.yml" sed -i "s|America/New_York|$TZ_VAL|g" "$PORTAL_DIR/docker-compose.yml" fi fi cat > "$PORTAL_DIR/.env" << PORTALENV # AI Portal — GPU stack swap controller TZ=${TZ_VAL} # Paths inside the container (/docker maps to ${ACTUAL_HOME}/docker via volume mount) IMAGE_STACK=/docker/ai-gpu/image-gen LLM_STACK=/docker/ai-gpu/llm CADDY_NET=${SITE_CADDY_NET} PORTALENV chmod 600 "$PORTAL_DIR/.env" ensure_docker_dir_ownership "$AI_DIR" echo "" log_success "AI GPU stacks configured under $AI_DIR" log_warning "Only ONE GPU stack can run at a time on a 6 GB card." log_info "Use the portal (port 8080) to hot-swap between image-gen and llm." echo "" # ── Caddy ───────────────────────────────────────────────────────────────── configure_caddy_for_service "AI Portal" "ai-portal:8080" "localai" configure_caddy_for_service "InvokeAI" "invokeai:9090" "images" # ── README ──────────────────────────────────────────────────────────────── write_readme "$AI_DIR" << MD # AI GPU Stack Three Docker stacks optimised for a 6 GB VRAM nvidia GPU. Source: https://github.com/outis1one/ai-6gb-gpu ## Stacks | Stack | Service | Port | Notes | |-------|---------|------|-------| | \`image-gen/\` | InvokeAI | 9090 | Image generation (SD 1.5, SDXL, Flux…) | | \`llm/\` | Ollama | 11434 | LLM inference engine | | \`llm/\` | Open WebUI | 3000 | Chat UI — models, RAG, web search | | \`llm/\` | SearXNG | internal | Web search backend for RAG in OpenWebUI | | \`portal/\` | AI Portal | 8080 | GPU swap controller — start/stop stacks | ## GPU time-sharing (important) A 6 GB GPU can only run one AI stack at a time. Use the portal at http://localhost:8080 to swap. Manual swap: \`\`\`bash docker compose -f $AI_DIR/llm/docker-compose.yml down docker compose -f $AI_DIR/image-gen/docker-compose.yml up -d \`\`\` ## InvokeAI — add more models Models installed at setup are in the Model Manager. To add more: 1. Open http://localhost:9090 → Model Manager → Add Model 2. Paste a HuggingFace repo ID (e.g. \`stabilityai/stable-diffusion-2-1\`) 3. Or import a local .safetensors file For gated models, add \`HUGGING_FACE_HUB_TOKEN=xxx\` to \`image-gen/.env\`. Recommended models for 6 GB (with partial GPU offload): | Model | Source | Notes | |-------|--------|-------| | SD 1.5 | \`stabilityai/stable-diffusion-v1-5\` | Fast, huge LoRA library | | SDXL Turbo | \`stabilityai/sdxl-turbo\` | 4-step, good quality | | SDXL Base | \`stabilityai/stable-diffusion-xl-base-1.0\` | Best quality, slower | | SD 2.1 | \`stabilityai/stable-diffusion-2-1\` | Good mid-size choice | ## Ollama — add more models \`\`\`bash # Pull any model while llm stack is running docker exec ollama ollama pull llama3.2:3b docker exec ollama ollama pull nomic-embed-text # RAG embeddings docker exec ollama ollama list # see installed models \`\`\` Browse models at: https://ollama.com/library For 6 GB cards, stick to 7B or smaller with Q4_K_M quantisation (~4.5 GB). ## Open WebUI — first login Open http://localhost:3000 and create your admin account on first visit. Models pulled into Ollama appear automatically in the model dropdown. Enable web search: Settings → Admin → Web Search (SearXNG is pre-configured). ## Manage individual stacks \`\`\`bash cd $AI_DIR/image-gen && docker compose up -d # start InvokeAI cd $AI_DIR/llm && docker compose up -d # start Ollama + OpenWebUI cd $AI_DIR/portal && docker compose up -d # start portal docker compose -f $AI_DIR/image-gen/docker-compose.yml logs -f invokeai docker compose -f $AI_DIR/llm/docker-compose.yml logs -f openwebui \`\`\` ## Update \`\`\`bash cd $REPO_DIR && git pull cd $AI_DIR/image-gen && docker compose pull && docker compose up -d cd $AI_DIR/llm && docker compose pull && docker compose up -d cd $AI_DIR/portal && docker compose build --pull && docker compose up -d \`\`\` MD # ── Start stacks + pull models ──────────────────────────────────────────── echo "" log_info "What would you like to start now?" log_info " 1) Portal only — start the swap controller, configure the rest later" log_info " 2) Portal + LLM stack — start Ollama/OpenWebUI and pull selected models" log_info " 3) Portal + image-gen — start InvokeAI and queue the starter model download" log_info " 4) None — start manually later" echo "" local START_CHOICE="" prompt_text "Choice [2]:" "2" START_CHOICE # Always start portal if any stack is starting if [[ "$START_CHOICE" =~ ^[123]$ ]]; then docker compose -f "$PORTAL_DIR/docker-compose.yml" up -d \ && log_success "Portal started — http://localhost:8080" \ || log_warning "Portal start failed — check: docker compose -f $PORTAL_DIR/docker-compose.yml logs" fi if [[ "$START_CHOICE" == "2" ]]; then # Start LLM stack docker compose -f "$LLM_DIR/docker-compose.yml" up -d \ && log_success "LLM stack started" \ || { log_warning "LLM stack start failed"; START_CHOICE="0"; } # Pull Ollama models if any were selected if [ ${#OLLAMA_MODELS[@]} -gt 0 ] && [[ "$START_CHOICE" == "2" ]]; then log_info "Waiting for Ollama to be ready..." local _w=0 while ! curl -sf "http://localhost:11434/api/version" &>/dev/null; do sleep 3; _w=$((_w+3)) [[ $_w -ge 90 ]] && { log_warning "Ollama not responding after 90s — pull models manually later"; break; } done if curl -sf "http://localhost:11434/api/version" &>/dev/null; then for _m in "${OLLAMA_MODELS[@]}"; do log_info "Pulling $_m (this may take a while)..." docker exec ollama ollama pull "$_m" \ && log_success "$_m ready" \ || log_warning "Pull failed for $_m — retry: docker exec ollama ollama pull $_m" done log_success "Open WebUI ready at: http://localhost:3000" log_info "Create your admin account on the first visit." fi fi fi if [[ "$START_CHOICE" == "3" ]]; then # Start image-gen stack docker compose -f "$IMAGE_GEN_DIR/docker-compose.yml" up -d \ && log_success "InvokeAI started" \ || { log_warning "InvokeAI start failed — check: docker compose -f $IMAGE_GEN_DIR/docker-compose.yml logs"; START_CHOICE="0"; } # Queue starter model via InvokeAI REST API if [ -n "$INVOKE_MODEL_SOURCE" ] && [[ "$START_CHOICE" == "3" ]]; then log_info "Waiting for InvokeAI to be ready (model database initialises on first start)..." local _w=0 while ! curl -sf "http://localhost:9090/api/v1/app/version" &>/dev/null; do sleep 5; _w=$((_w+5)) [[ $_w -ge 180 ]] && { log_warning "InvokeAI not responding after 3 min"; break; } done if curl -sf "http://localhost:9090/api/v1/app/version" &>/dev/null; then log_info "Queuing $INVOKE_MODEL_NAME download..." local _resp _resp=$(curl -s -X POST "http://localhost:9090/api/v2/models/install" \ -H "Content-Type: application/json" \ -d "{\"source\": \"${INVOKE_MODEL_SOURCE}\"}" 2>/dev/null) if echo "$_resp" | grep -q '"id"'; then log_success "$INVOKE_MODEL_NAME queued — downloading in background" log_info "Track progress: http://localhost:9090 → Model Manager → In Progress" else log_warning "Could not queue via API. Install manually:" log_info " Open http://localhost:9090 → Model Manager → Add Model" log_info " Source: $INVOKE_MODEL_SOURCE" fi fi fi fi if [[ "$START_CHOICE" == "4" ]] || [[ "$START_CHOICE" == "0" ]]; then log_info "Start when ready:" log_info " docker compose -f $PORTAL_DIR/docker-compose.yml up -d" log_info " docker compose -f $LLM_DIR/docker-compose.yml up -d" log_info " docker compose -f $IMAGE_GEN_DIR/docker-compose.yml up -d" fi echo "" echo " Portal: http://localhost:8080 (GPU swap controller)" echo " InvokeAI: http://localhost:9090 (image-gen stack)" echo " Open WebUI: http://localhost:3000 (llm stack)" echo " Ollama API: http://localhost:11434 (llm stack)" echo " Source repo: $REPO_DIR" echo "" if [ ${#OLLAMA_MODELS[@]} -gt 0 ]; then echo " Ollama models queued: ${OLLAMA_MODELS[*]}" fi if [ -n "$INVOKE_MODEL_NAME" ]; then echo " InvokeAI starter: $INVOKE_MODEL_NAME ($INVOKE_MODEL_SOURCE)" fi echo "" } [[ "${_RUN_STANDALONE:-0}" == 1 ]] && install_ai_gpu