iopaint, ai-gpu: interactive model selection, auto-pull, fixes
iopaint:
- Add model selection menu (10 choices) with CPU/GPU/SD tiers and
size/use-case descriptions shown at install time
- Fix volume mount: ./models:/root/.cache (was only /root/.cache/iopaint)
— now persists both torch hub cache (LaMa) and HuggingFace cache (SD/PowerPaint)
- Refactor compose to use ${MODEL} and ${DEVICE} env vars so switching
models only requires editing .env + restart, no compose file edit needed
- Add PowerPaint-V2-filling and SD 1.5 inpainting as explicit menu choices
for text-guided object replacement
- Update header and README to document all three use cases (erase, fill, replace)
and note that IOPaint is local-only (cannot use a remote GPU)
ai-gpu:
- Add Ollama model selection menu (8 models, multi-select with sizes/descriptions)
defaulting to llama3.2:3b + nomic-embed-text
- Auto-pull selected Ollama models immediately after LLM stack starts
- Add InvokeAI starter model selection (SD 1.5 / SDXL Turbo / SDXL Base / skip)
- Queue InvokeAI model download via REST API (POST /api/v2/models/install)
with fallback instructions if the API is unavailable
- Add HuggingFace token prompt; stored as HUGGING_FACE_HUB_TOKEN in image-gen .env
- Wire SearXNG into Open WebUI via ENABLE_RAG_WEB_SEARCH + SEARXNG_QUERY_URL in llm .env
- Default start choice is now 2 (portal + LLM + Ollama pull) so the stack
is ready to use immediately after install
https://claude.ai/code/session_01JEu7LgCWXKhXo18MeYFRZp
This commit is contained in:
Claude
+225
-101
@@ -206,19 +206,94 @@ install_ai_gpu() {
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if [ "$DRY_RUN" = true ]; then
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echo "[DRY-RUN] Would clone $REPO_URL to $REPO_DIR"
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echo "[DRY-RUN] Would create stacks: image-gen (InvokeAI:9090), llm (Ollama:11434 + OpenWebUI:3000), portal (8080)"
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echo "[DRY-RUN] Would write .env files and patch portal volume paths to $ACTUAL_HOME/docker"
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echo "[DRY-RUN] Would configure Caddy for portal (localai) and InvokeAI (images)"
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echo "[DRY-RUN] Would prompt for Ollama and InvokeAI model selection"
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echo "[DRY-RUN] Would auto-pull selected Ollama models after LLM stack starts"
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echo "[DRY-RUN] Would queue InvokeAI starter model via REST API"
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return 0
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fi
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# TZ — repo defaults to America/New_York, replace with user preference
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# ── Timezone ──────────────────────────────────────────────────────────────
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local TZ_VAL="${SITE_TZ:-UTC}"
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prompt_text "Timezone (e.g. America/New_York) [$TZ_VAL]:" "$TZ_VAL" TZ_VAL
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TZ_VAL="${TZ_VAL:-UTC}"
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mkdir -p "$AI_DIR"
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# ── Ollama model selection ────────────────────────────────────────────────
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echo ""
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log_info "Ollama LLM models — select which to download (enter numbers separated by spaces):"
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log_info "All models are quantized (Q4_K_M) and run comfortably on 6 GB VRAM."
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echo ""
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log_info " 1) llama3.2:3b ~2.0 GB Fast general chat. Great all-rounder. ← Recommended"
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log_info " 2) llama3.2:1b ~1.3 GB Ultra-fast. Light tasks, low latency."
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log_info " 3) qwen2.5:7b ~4.7 GB Top code + math model. Strong reasoning."
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log_info " 4) mistral:7b ~4.1 GB Solid all-rounder. Good at instruction follow."
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log_info " 5) phi4-mini ~2.5 GB Microsoft Phi-4 mini. Excellent for coding."
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log_info " 6) gemma3:4b ~2.5 GB Google Gemma 3. Well-rounded, multilingual."
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log_info " 7) deepseek-r1:7b ~4.7 GB Strong reasoning and math. Think-step model."
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log_info " 8) nomic-embed-text ~274 MB Embedding model — enables RAG/doc search."
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log_info " Recommended to add alongside a chat model."
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echo ""
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log_info " Example: '1 8' pulls llama3.2:3b + nomic-embed-text"
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log_info " Enter '0' or leave blank to skip and pull models manually later."
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echo ""
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local OLLAMA_CHOICES=""
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prompt_text "Models to download [1 8]:" "1 8" OLLAMA_CHOICES
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declare -a OLLAMA_MODELS=()
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for _n in $OLLAMA_CHOICES; do
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case "$_n" in
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1) OLLAMA_MODELS+=("llama3.2:3b") ;;
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2) OLLAMA_MODELS+=("llama3.2:1b") ;;
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3) OLLAMA_MODELS+=("qwen2.5:7b") ;;
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4) OLLAMA_MODELS+=("mistral:7b") ;;
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5) OLLAMA_MODELS+=("phi4-mini") ;;
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6) OLLAMA_MODELS+=("gemma3:4b") ;;
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7) OLLAMA_MODELS+=("deepseek-r1:7b") ;;
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8) OLLAMA_MODELS+=("nomic-embed-text") ;;
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esac
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done
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# ── InvokeAI model selection ──────────────────────────────────────────────
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echo ""
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log_info "InvokeAI image generation models (for 6 GB VRAM with partial GPU offload):"
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log_info "InvokeAI uses VRAM=3 GB + 8 GB RAM cache, so all models below work on 6 GB."
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echo ""
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log_info " 1) stabilityai/stable-diffusion-v1-5 ~4 GB SD 1.5 — fast, huge style/LoRA library."
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log_info " Best starting model for most uses."
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log_info " 2) stabilityai/sdxl-turbo ~7 GB SDXL Turbo — 4-step generation."
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log_info " Fast, high quality, slightly slower on 6 GB."
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log_info " 3) stabilityai/stable-diffusion-xl-base-1.0"
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log_info " ~7 GB SDXL base — best quality at 1024px."
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log_info " Slowest due to RAM offload on 6 GB."
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log_info " 4) Skip — install models via the Model Manager at http://localhost:9090"
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echo ""
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log_info " Tip: SD 1.5 (choice 1) is fastest and most compatible. Start here."
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log_info " HuggingFace token: required for some gated models (free at huggingface.co/settings/tokens)"
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echo ""
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local INVOKE_CHOICE=""
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prompt_text "InvokeAI starter model [1]:" "1" INVOKE_CHOICE
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local INVOKE_MODEL_SOURCE=""
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local INVOKE_MODEL_NAME=""
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case "$INVOKE_CHOICE" in
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2) INVOKE_MODEL_SOURCE="stabilityai/sdxl-turbo"
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INVOKE_MODEL_NAME="SDXL Turbo" ;;
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3) INVOKE_MODEL_SOURCE="stabilityai/stable-diffusion-xl-base-1.0"
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INVOKE_MODEL_NAME="SDXL Base" ;;
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4) INVOKE_MODEL_SOURCE=""
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INVOKE_MODEL_NAME="" ;;
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*) INVOKE_MODEL_SOURCE="stabilityai/stable-diffusion-v1-5"
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INVOKE_MODEL_NAME="SD 1.5" ;;
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esac
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local HF_TOKEN=""
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if [ -n "$INVOKE_MODEL_SOURCE" ]; then
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prompt_text "HuggingFace token (optional — needed for gated models, enter to skip):" "" HF_TOKEN
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fi
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# ── Clone / update repo ───────────────────────────────────────────────────
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mkdir -p "$AI_DIR"
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if [ -d "$REPO_DIR/.git" ]; then
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log_info "Updating ai-6gb-gpu repo..."
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git -C "$REPO_DIR" pull --ff-only 2>/dev/null \
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@@ -235,7 +310,6 @@ install_ai_gpu() {
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mkdir -p "$IMAGE_GEN_DIR"
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if [ -d "$REPO_DIR/ai-image-gen" ]; then
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cp -rn "$REPO_DIR/ai-image-gen/." "$IMAGE_GEN_DIR/" 2>/dev/null || true
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# Replace any hardcoded timezone
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find "$IMAGE_GEN_DIR" -name "docker-compose.yml" -exec \
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sed -i "s|America/New_York|$TZ_VAL|g" {} \;
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fi
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@@ -243,10 +317,11 @@ install_ai_gpu() {
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cat > "$IMAGE_GEN_DIR/.env" << IMGENV
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# InvokeAI — image generation
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TZ=${TZ_VAL}
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# VRAM cap: 3 GB leaves headroom on a 6 GB card
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# VRAM cap: 3 GB leaves headroom on a 6 GB card; remaining model layers go to RAM
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INVOKEAI_vram=3
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# RAM cache for model layers
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# RAM cache size for model layer offload
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INVOKEAI_ram=8
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${HF_TOKEN:+HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}}
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CADDY_NET=${SITE_CADDY_NET}
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IMGENV
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chmod 600 "$IMAGE_GEN_DIR/.env"
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@@ -266,8 +341,11 @@ IMGENV
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cat > "$LLM_DIR/.env" << LLMENV
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# Ollama + Open WebUI + SearXNG
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TZ=${TZ_VAL}
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# Open WebUI session secret
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WEBUI_SECRET_KEY=${WEBUI_SECRET}
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# Open WebUI: enable SearXNG for web search in chats
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ENABLE_RAG_WEB_SEARCH=true
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RAG_WEB_SEARCH_ENGINE=searxng
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SEARXNG_QUERY_URL=http://searxng:8080/search?q=<query>&format=json
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CADDY_NET=${SITE_CADDY_NET}
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LLMENV
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chmod 600 "$LLM_DIR/.env"
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@@ -277,7 +355,6 @@ LLMENV
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mkdir -p "$PORTAL_DIR"
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if [ -d "$REPO_DIR/ai-portal" ]; then
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cp -rn "$REPO_DIR/ai-portal/." "$PORTAL_DIR/" 2>/dev/null || true
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# Fix hardcoded home path in docker-compose.yml volume mounts
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if [ -f "$PORTAL_DIR/docker-compose.yml" ]; then
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sed -i \
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"s|/home/[^/]*/docker:|${ACTUAL_HOME}/docker:|g" \
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@@ -289,25 +366,20 @@ LLMENV
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cat > "$PORTAL_DIR/.env" << PORTALENV
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# AI Portal — GPU stack swap controller
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TZ=${TZ_VAL}
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# Paths inside the container (Docker socket mount maps ACTUAL_HOME/docker → /docker)
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# Paths inside the container (/docker maps to ${ACTUAL_HOME}/docker via volume mount)
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IMAGE_STACK=/docker/ai-gpu/image-gen
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LLM_STACK=/docker/ai-gpu/llm
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CADDY_NET=${SITE_CADDY_NET}
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PORTALENV
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chmod 600 "$PORTAL_DIR/.env"
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# Set ownership across everything
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ensure_docker_dir_ownership "$AI_DIR"
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echo ""
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log_success "AI GPU stacks configured under $AI_DIR"
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log_info "Stack layout:"
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log_info " image-gen/ — InvokeAI (port 9090, nvidia GPU)"
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log_info " llm/ — Ollama (11434) + Open WebUI (3000) + SearXNG (internal)"
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log_info " portal/ — GPU swap portal (port 8080)"
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echo ""
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log_warning "Only ONE GPU stack can run at a time on a 6 GB card."
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log_warning "Use the portal to switch, or manually: docker compose -f <stack>/docker-compose.yml down/up."
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log_info "Use the portal (port 8080) to hot-swap between image-gen and llm."
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echo ""
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# ── Caddy ─────────────────────────────────────────────────────────────────
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configure_caddy_for_service "AI Portal" "ai-portal:8080" "localai"
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@@ -324,125 +396,177 @@ Source: https://github.com/outis1one/ai-6gb-gpu
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| Stack | Service | Port | Notes |
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|-------|---------|------|-------|
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| \`image-gen/\` | InvokeAI | 9090 | Image generation (SDXL, Flux, etc.) |
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| \`image-gen/\` | InvokeAI | 9090 | Image generation (SD 1.5, SDXL, Flux…) |
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| \`llm/\` | Ollama | 11434 | LLM inference engine |
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| \`llm/\` | Open WebUI | 3000 | Chat UI for Ollama |
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| \`llm/\` | SearXNG | — | Internal web search for RAG |
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| \`portal/\` | AI Portal | 8080 | GPU swap controller UI |
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| \`llm/\` | Open WebUI | 3000 | Chat UI — models, RAG, web search |
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| \`llm/\` | SearXNG | internal | Web search backend for RAG in OpenWebUI |
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| \`portal/\` | AI Portal | 8080 | GPU swap controller — start/stop stacks |
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## Important: GPU time-sharing
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## GPU time-sharing (important)
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A 6 GB GPU can only run one AI stack at a time. Use the portal at
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http://localhost:8080 to swap between image-gen and llm — it stops the
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active stack before starting the requested one.
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A 6 GB GPU can only run one AI stack at a time.
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Use the portal at http://localhost:8080 to swap.
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Manual swap:
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\`\`\`bash
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# Stop image-gen, start llm
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docker compose -f $AI_DIR/image-gen/docker-compose.yml down
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docker compose -f $AI_DIR/llm/docker-compose.yml up -d
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# Stop llm, start image-gen
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docker compose -f $AI_DIR/llm/docker-compose.yml down
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docker compose -f $AI_DIR/image-gen/docker-compose.yml up -d
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\`\`\`
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## First-run setup
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## InvokeAI — add more models
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### InvokeAI
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1. Open http://localhost:9090
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2. Install models via the Model Manager (HuggingFace token may be needed)
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3. Recommended for 6 GB: SDXL-Turbo, Flux-Schnell-quantised
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Models installed at setup are in the Model Manager. To add more:
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1. Open http://localhost:9090 → Model Manager → Add Model
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2. Paste a HuggingFace repo ID (e.g. \`stabilityai/stable-diffusion-2-1\`)
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3. Or import a local .safetensors file
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For gated models, add \`HUGGING_FACE_HUB_TOKEN=xxx\` to \`image-gen/.env\`.
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Recommended models for 6 GB (with partial GPU offload):
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| Model | Source | Notes |
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|-------|--------|-------|
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| SD 1.5 | \`stabilityai/stable-diffusion-v1-5\` | Fast, huge LoRA library |
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| SDXL Turbo | \`stabilityai/sdxl-turbo\` | 4-step, good quality |
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| SDXL Base | \`stabilityai/stable-diffusion-xl-base-1.0\` | Best quality, slower |
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| SD 2.1 | \`stabilityai/stable-diffusion-2-1\` | Good mid-size choice |
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## Ollama — add more models
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### Ollama
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\`\`\`bash
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# Pull a model (while llm stack is running)
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docker exec ollama ollama pull llama3.2
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docker exec ollama ollama pull nomic-embed-text # for RAG embeddings
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# Pull any model while llm stack is running
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docker exec ollama ollama pull llama3.2:3b
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docker exec ollama ollama pull nomic-embed-text # RAG embeddings
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docker exec ollama ollama list # see installed models
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\`\`\`
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### Open WebUI
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Open http://localhost:3000 — create admin account on first visit.
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Browse models at: https://ollama.com/library
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For 6 GB cards, stick to 7B or smaller with Q4_K_M quantisation (~4.5 GB).
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## Open WebUI — first login
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Open http://localhost:3000 and create your admin account on first visit.
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Models pulled into Ollama appear automatically in the model dropdown.
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Enable web search: Settings → Admin → Web Search (SearXNG is pre-configured).
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## Manage individual stacks
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\`\`\`bash
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# Image generation
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cd $AI_DIR/image-gen
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docker compose up -d
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docker compose down
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docker compose logs -f invokeai
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# LLM + chat
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cd $AI_DIR/llm
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docker compose up -d
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docker compose down
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docker compose logs -f openwebui
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# Portal
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cd $AI_DIR/portal
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docker compose up -d
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docker compose down
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cd $AI_DIR/image-gen && docker compose up -d # start InvokeAI
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cd $AI_DIR/llm && docker compose up -d # start Ollama + OpenWebUI
|
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cd $AI_DIR/portal && docker compose up -d # start portal
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docker compose -f $AI_DIR/image-gen/docker-compose.yml logs -f invokeai
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docker compose -f $AI_DIR/llm/docker-compose.yml logs -f openwebui
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\`\`\`
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## Update
|
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\`\`\`bash
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# Pull latest repo changes and rebuild
|
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cd $REPO_DIR && git pull
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cd $AI_DIR/image-gen && docker compose pull && docker compose up -d
|
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cd $AI_DIR/llm && docker compose pull && docker compose up -d
|
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cd $AI_DIR/portal && docker compose build --pull && docker compose up -d
|
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\`\`\`
|
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|
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## Files
|
||||
- image-gen/.env — InvokeAI VRAM/RAM limits and TZ
|
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- llm/.env — Open WebUI secret key and TZ
|
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- portal/.env — stack paths and TZ
|
||||
MD
|
||||
|
||||
# ── Start prompt ──────────────────────────────────────────────────────────
|
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# ── Start stacks + pull models ────────────────────────────────────────────
|
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echo ""
|
||||
log_info "What would you like to start now?"
|
||||
log_info " 1) Portal only — start the swap controller, configure the rest later"
|
||||
log_info " 2) Portal + LLM stack — start Ollama/OpenWebUI and pull selected models"
|
||||
log_info " 3) Portal + image-gen — start InvokeAI and queue the starter model download"
|
||||
log_info " 4) None — start manually later"
|
||||
echo ""
|
||||
log_info "Which stack do you want to start now?"
|
||||
log_info " 1) Portal only (recommended first — lets you manage the others)"
|
||||
log_info " 2) Portal + image-gen (InvokeAI)"
|
||||
log_info " 3) Portal + llm (Ollama/OpenWebUI)"
|
||||
log_info " 4) None — start manually later"
|
||||
|
||||
local START_CHOICE=""
|
||||
prompt_text "Choice [1]:" "1" START_CHOICE
|
||||
prompt_text "Choice [2]:" "2" START_CHOICE
|
||||
|
||||
case "$START_CHOICE" in
|
||||
1)
|
||||
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 $PORTAL_DIR/docker-compose.yml logs"
|
||||
;;
|
||||
2)
|
||||
docker compose -f "$PORTAL_DIR/docker-compose.yml" up -d \
|
||||
&& log_success "Portal started" \
|
||||
|| log_warning "Portal start failed"
|
||||
docker compose -f "$IMAGE_GEN_DIR/docker-compose.yml" up -d \
|
||||
&& log_success "InvokeAI started — http://localhost:9090" \
|
||||
|| log_warning "InvokeAI start failed — check: docker compose -f $IMAGE_GEN_DIR/docker-compose.yml logs"
|
||||
;;
|
||||
3)
|
||||
docker compose -f "$PORTAL_DIR/docker-compose.yml" up -d \
|
||||
&& log_success "Portal started" \
|
||||
|| log_warning "Portal start failed"
|
||||
docker compose -f "$LLM_DIR/docker-compose.yml" up -d \
|
||||
&& log_success "LLM stack started — Open WebUI: http://localhost:3000" \
|
||||
|| log_warning "LLM start failed — check: docker compose -f $LLM_DIR/docker-compose.yml logs"
|
||||
;;
|
||||
*)
|
||||
log_info "Skipped. Start when ready:"
|
||||
log_info " docker compose -f $PORTAL_DIR/docker-compose.yml up -d"
|
||||
;;
|
||||
esac
|
||||
# Always start portal if any stack is starting
|
||||
if [[ "$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"
|
||||
echo " InvokeAI: http://localhost:9090 (image-gen stack)"
|
||||
echo " OpenWebUI: http://localhost:3000 (llm stack)"
|
||||
echo " Source: $REPO_DIR"
|
||||
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 ""
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user