From f9013287d9221cecb0beaa8607ea4bc8dfe5af88 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 9 Jun 2026 03:57:01 +0000 Subject: [PATCH] iopaint, ai-gpu: interactive model selection, auto-pull, fixes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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 ${DEVICE} env vars so switching models only requires editing .env + restart, no compose file edit needed - Add PowerPaint-V2-filling and SD 1.5 inpainting as explicit menu choices for text-guided object replacement - Update header and README to document all three use cases (erase, fill, replace) and note that IOPaint is local-only (cannot use a remote GPU) ai-gpu: - Add Ollama model selection menu (8 models, multi-select with sizes/descriptions) defaulting to llama3.2:3b + nomic-embed-text - Auto-pull selected Ollama models immediately after LLM stack starts - Add InvokeAI starter model selection (SD 1.5 / SDXL Turbo / SDXL Base / skip) - Queue InvokeAI model download via REST API (POST /api/v2/models/install) with fallback instructions if the API is unavailable - Add HuggingFace token prompt; stored as HUGGING_FACE_HUB_TOKEN in image-gen .env - Wire SearXNG into Open WebUI via ENABLE_RAG_WEB_SEARCH + SEARXNG_QUERY_URL in llm .env - Default start choice is now 2 (portal + LLM + Ollama pull) so the stack is ready to use immediately after install https://claude.ai/code/session_01JEu7LgCWXKhXo18MeYFRZp --- services/ai-gpu.sh | 326 ++++++++++++++++++++++++++++++-------------- services/iopaint.sh | 221 ++++++++++++++++++++++-------- 2 files changed, 391 insertions(+), 156 deletions(-) diff --git a/services/ai-gpu.sh b/services/ai-gpu.sh index 5a645c7..edcb780 100644 --- a/services/ai-gpu.sh +++ b/services/ai-gpu.sh @@ -206,19 +206,94 @@ install_ai_gpu() { if [ "$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 write .env files and patch portal volume paths to $ACTUAL_HOME/docker" - echo "[DRY-RUN] Would configure Caddy for portal (localai) and InvokeAI (images)" + echo "[DRY-RUN] Would prompt for Ollama and InvokeAI model selection" + echo "[DRY-RUN] Would auto-pull selected Ollama models after LLM stack starts" + echo "[DRY-RUN] Would queue InvokeAI starter model via REST API" return 0 fi - # TZ — repo defaults to America/New_York, replace with user preference + # ── Timezone ────────────────────────────────────────────────────────────── local TZ_VAL="${SITE_TZ:-UTC}" prompt_text "Timezone (e.g. America/New_York) [$TZ_VAL]:" "$TZ_VAL" TZ_VAL TZ_VAL="${TZ_VAL:-UTC}" - mkdir -p "$AI_DIR" + # ── 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 "$REPO_DIR" pull --ff-only 2>/dev/null \ @@ -235,7 +310,6 @@ install_ai_gpu() { mkdir -p "$IMAGE_GEN_DIR" if [ -d "$REPO_DIR/ai-image-gen" ]; then cp -rn "$REPO_DIR/ai-image-gen/." "$IMAGE_GEN_DIR/" 2>/dev/null || true - # Replace any hardcoded timezone find "$IMAGE_GEN_DIR" -name "docker-compose.yml" -exec \ sed -i "s|America/New_York|$TZ_VAL|g" {} \; fi @@ -243,10 +317,11 @@ install_ai_gpu() { cat > "$IMAGE_GEN_DIR/.env" << IMGENV # InvokeAI — image generation TZ=${TZ_VAL} -# VRAM cap: 3 GB leaves headroom on a 6 GB card +# VRAM cap: 3 GB leaves headroom on a 6 GB card; remaining model layers go to RAM INVOKEAI_vram=3 -# RAM cache for model layers +# RAM cache size for model layer offload INVOKEAI_ram=8 +${HF_TOKEN:+HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}} CADDY_NET=${SITE_CADDY_NET} IMGENV chmod 600 "$IMAGE_GEN_DIR/.env" @@ -266,8 +341,11 @@ IMGENV cat > "$LLM_DIR/.env" << LLMENV # Ollama + Open WebUI + SearXNG TZ=${TZ_VAL} -# Open WebUI session secret WEBUI_SECRET_KEY=${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" @@ -277,7 +355,6 @@ LLMENV mkdir -p "$PORTAL_DIR" if [ -d "$REPO_DIR/ai-portal" ]; then cp -rn "$REPO_DIR/ai-portal/." "$PORTAL_DIR/" 2>/dev/null || true - # Fix hardcoded home path in docker-compose.yml volume mounts if [ -f "$PORTAL_DIR/docker-compose.yml" ]; then sed -i \ "s|/home/[^/]*/docker:|${ACTUAL_HOME}/docker:|g" \ @@ -289,25 +366,20 @@ LLMENV cat > "$PORTAL_DIR/.env" << PORTALENV # AI Portal — GPU stack swap controller TZ=${TZ_VAL} -# Paths inside the container (Docker socket mount maps ACTUAL_HOME/docker → /docker) +# 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 "$PORTAL_DIR/.env" - # Set ownership across everything ensure_docker_dir_ownership "$AI_DIR" echo "" log_success "AI GPU stacks configured under $AI_DIR" - log_info "Stack layout:" - log_info " image-gen/ — InvokeAI (port 9090, nvidia GPU)" - log_info " llm/ — Ollama (11434) + Open WebUI (3000) + SearXNG (internal)" - log_info " portal/ — GPU swap portal (port 8080)" - echo "" log_warning "Only ONE GPU stack can run at a time on a 6 GB card." - log_warning "Use the portal to switch, or manually: docker compose -f /docker-compose.yml down/up." + 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" @@ -324,125 +396,177 @@ Source: https://github.com/outis1one/ai-6gb-gpu | Stack | Service | Port | Notes | |-------|---------|------|-------| -| \`image-gen/\` | InvokeAI | 9090 | Image generation (SDXL, Flux, etc.) | +| \`image-gen/\` | InvokeAI | 9090 | Image generation (SD 1.5, SDXL, Flux…) | | \`llm/\` | Ollama | 11434 | LLM inference engine | -| \`llm/\` | Open WebUI | 3000 | Chat UI for Ollama | -| \`llm/\` | SearXNG | — | Internal web search for RAG | -| \`portal/\` | AI Portal | 8080 | GPU swap controller UI | +| \`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 | -## Important: GPU time-sharing +## 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 between image-gen and llm — it stops the -active stack before starting the requested one. +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 -# Stop image-gen, start llm -docker compose -f $AI_DIR/image-gen/docker-compose.yml down -docker compose -f $AI_DIR/llm/docker-compose.yml up -d - -# Stop llm, start image-gen docker compose -f $AI_DIR/llm/docker-compose.yml down docker compose -f $AI_DIR/image-gen/docker-compose.yml up -d \`\`\` -## First-run setup +## InvokeAI — add more models -### InvokeAI -1. Open http://localhost:9090 -2. Install models via the Model Manager (HuggingFace token may be needed) -3. Recommended for 6 GB: SDXL-Turbo, Flux-Schnell-quantised +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 -### Ollama \`\`\`bash -# Pull a model (while llm stack is running) -docker exec ollama ollama pull llama3.2 -docker exec ollama ollama pull nomic-embed-text # for RAG embeddings +# 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 \`\`\` -### Open WebUI -Open http://localhost:3000 — create admin account on first visit. +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 -# Image generation -cd $AI_DIR/image-gen -docker compose up -d -docker compose down -docker compose logs -f invokeai - -# LLM + chat -cd $AI_DIR/llm -docker compose up -d -docker compose down -docker compose logs -f openwebui - -# Portal -cd $AI_DIR/portal -docker compose up -d -docker compose down +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 -# Pull latest repo changes and rebuild 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 $AI_DIR/portal && docker compose build --pull && docker compose up -d \`\`\` - -## Files -- image-gen/.env — InvokeAI VRAM/RAM limits and TZ -- llm/.env — Open WebUI secret key and TZ -- portal/.env — stack paths and TZ MD - # ── Start prompt ────────────────────────────────────────────────────────── + # ── Start stacks + pull models ──────────────────────────────────────────── + echo "" + log_info "What would you like to start now?" + log_info " 1) Portal only — start the swap controller, configure the rest later" + log_info " 2) Portal + LLM stack — start Ollama/OpenWebUI and pull selected models" + log_info " 3) Portal + image-gen — start InvokeAI and queue the starter model download" + log_info " 4) None — start manually later" echo "" - log_info "Which stack do you want to start now?" - log_info " 1) Portal only (recommended first — lets you manage the others)" - log_info " 2) Portal + image-gen (InvokeAI)" - log_info " 3) Portal + llm (Ollama/OpenWebUI)" - log_info " 4) None — start manually later" local START_CHOICE="" - prompt_text "Choice [1]:" "1" START_CHOICE + prompt_text "Choice [2]:" "2" START_CHOICE - case "$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 "" } diff --git a/services/iopaint.sh b/services/iopaint.sh index 20b0861..b457572 100644 --- a/services/iopaint.sh +++ b/services/iopaint.sh @@ -1,13 +1,18 @@ #!/bin/bash -# services/iopaint.sh — AI image inpainting: object removal, fill, restore (IOPaint + LaMa). +# services/iopaint.sh — AI image editing: erase objects, fill regions, replace with text prompt. # Part of the modular post-install system (sourced by setup.sh). # # Can also be run standalone on any machine: # sudo bash iopaint.sh # (Docker must already be installed when run standalone) # -# Runs the LaMa model (erase/fill objects) by default on CPU. -# For GPU inference set DEVICE=cuda in .env and install nvidia-container-toolkit. +# Three use cases: +# Erase/remove — mask an object, AI fills the gap (LaMa, CPU-safe) +# Inpaint/fill — restore damaged areas, remove watermarks (multiple models) +# Replace — mask + text prompt → AI draws new content (PowerPaint, GPU required) +# +# IOPaint is local-only: it cannot call a remote GPU or InvokeAI on another machine. +# For text-guided replacement the GPU must be on this same machine. # IOPaint has no built-in auth — protect with Authelia via Caddy. # ── Standalone bootstrap ────────────────────────────────────────────────────── @@ -192,8 +197,9 @@ install_iopaint() { if [ "$DRY_RUN" = true ]; then echo "[DRY-RUN] Would create $IOPAINT_DIR" + echo "[DRY-RUN] Would prompt for model and GPU (CUDA) support" echo "[DRY-RUN] Would write docker-compose.yml and .env" - echo "[DRY-RUN] Would offer GPU (CUDA) support and Authelia SSO" + echo "[DRY-RUN] Would offer Authelia SSO" return 0 fi @@ -201,10 +207,67 @@ install_iopaint() { ensure_docker_dir_ownership "$IOPAINT_DIR" cd "$IOPAINT_DIR" || return 1 - # Ask about GPU + # ── GPU ────────────────────────────────────────────────────────────────── + log_info "IOPaint can:" + log_info " • Erase / remove — mask an object, AI fills the gap (works CPU-only)" + log_info " • Inpaint / fill — restore damaged areas, remove watermarks" + log_info " • Replace — mask + type what goes there → AI draws it (needs GPU)" + log_info " Note: inference is always local — IOPaint cannot use a GPU on another machine." + echo "" local USE_GPU="" prompt_yn "Enable CUDA GPU support? Requires nvidia-container-toolkit (y/n):" "n" USE_GPU + # ── Model selection ─────────────────────────────────────────────────────── + echo "" + log_info "Select default model (you can switch models in the UI without restarting):" + echo "" + log_info " ── CPU-safe — work on any machine ─────────────────────────────────────────" + log_info " 1) lama Erase / removal. Intelligent gap fill. ~200 MB ← Recommended" + log_info " 2) cv2 OpenCV fill. No download, instant. Rough quality." + log_info " 3) zits Portrait & face restoration. ~200 MB." + log_info " 4) manga Comic/manga text bubble removal. ~100 MB." + echo "" + if [[ "$USE_GPU" =~ ^[Yy]$ ]]; then + log_info " ── GPU-accelerated fill ───────────────────────────────────────────────────" + log_info " 5) migan MiGAN: fast GPU inpainting. ~50 MB." + log_info " 6) fcf FcF: high-quality contextual fill. ~600 MB." + log_info " 7) mat MAT: large missing region fill. ~300 MB." + log_info " 8) ldm LDM: latent diffusion texture fill. ~1.2 GB." + echo "" + log_info " ── Text-guided REPLACEMENT (GPU + Stable Diffusion) ───────────────────────" + log_info " 9) Sanster/PowerPaint-V2-filling" + log_info " Mask + type 'a red barn' → AI draws it. ~4 GB." + log_info " Modes: text-guided, shape-guided, erase, outpaint." + log_info " 10) runwayml/stable-diffusion-inpainting" + log_info " Classic SD 1.5 inpaint. Huge LoRA/style library. ~4 GB." + echo "" + fi + + local MODEL_NUM="" + prompt_text "Model choice [1=lama]:" "1" MODEL_NUM + + local IOPAINT_MODEL="lama" + case "$MODEL_NUM" in + 2) IOPAINT_MODEL="cv2" ;; + 3) IOPAINT_MODEL="zits" ;; + 4) IOPAINT_MODEL="manga" ;; + 5) IOPAINT_MODEL="migan" ;; + 6) IOPAINT_MODEL="fcf" ;; + 7) IOPAINT_MODEL="mat" ;; + 8) IOPAINT_MODEL="ldm" ;; + 9) IOPAINT_MODEL="Sanster/PowerPaint-V2-filling" ;; + 10) IOPAINT_MODEL="runwayml/stable-diffusion-inpainting" ;; + *) IOPAINT_MODEL="lama" ;; + esac + + local DEVICE_VAL="cpu" + [[ "$USE_GPU" =~ ^[Yy]$ ]] && DEVICE_VAL="cuda" + + # ── docker-compose.yml ──────────────────────────────────────────────────── + # MODEL and DEVICE come from .env — change them there and restart to switch. + # Volume ./models:/root/.cache persists ALL model caches: + # /root/.cache/torch/hub/checkpoints/ (LaMa, CV2, ZITS, etc.) + # /root/.cache/huggingface/ (SD, PowerPaint, LDM, etc.) if [[ "$USE_GPU" =~ ^[Yy]$ ]]; then cat > docker-compose.yml << 'IOPAINT_GPU' name: iopaint @@ -215,15 +278,19 @@ services: container_name: iopaint hostname: iopaint restart: unless-stopped - command: iopaint start --model=lama --device=cuda --port=8080 --host=0.0.0.0 + command: >- + iopaint start + --model=${MODEL:-lama} + --device=${DEVICE:-cuda} + --port=8080 + --host=0.0.0.0 ports: - "8100:8080" + env_file: .env volumes: - - ./models:/root/.cache/iopaint + - ./models:/root/.cache - ./input:/app/input - ./output:/app/output - environment: - - DEVICE=cuda deploy: resources: reservations: @@ -239,7 +306,6 @@ networks: external: true name: ${CADDY_NET:-caddy_net} IOPAINT_GPU - log_info "CUDA GPU mode enabled." else cat > docker-compose.yml << 'IOPAINT_CPU' name: iopaint @@ -250,11 +316,17 @@ services: container_name: iopaint hostname: iopaint restart: unless-stopped - command: iopaint start --model=lama --device=cpu --port=8080 --host=0.0.0.0 + command: >- + iopaint start + --model=${MODEL:-lama} + --device=${DEVICE:-cpu} + --port=8080 + --host=0.0.0.0 ports: - "8100:8080" + env_file: .env volumes: - - ./models:/root/.cache/iopaint + - ./models:/root/.cache - ./input:/app/input - ./output:/app/output networks: @@ -265,18 +337,18 @@ networks: external: true name: ${CADDY_NET:-caddy_net} IOPAINT_CPU - log_info "CPU mode (default). Change DEVICE to cuda in .env and update the command to use GPU later." fi + # ── .env ───────────────────────────────────────────────────────────────── cat > .env << IOPAINT_ENV -# IOPaint configuration +# IOPaint — change MODEL and restart to switch (no need to edit docker-compose.yml) -# Model to use for inpainting (lama recommended for object removal/erase) -# Other models: ldm, zits, mat, fcf, manga, cv2, migan -MODEL=lama +# Current model (set during install — see README for full model list) +MODEL=${IOPAINT_MODEL} -# Device: cpu or cuda (cuda requires nvidia-container-toolkit + GPU deploy block) -DEVICE=$([ "${USE_GPU,,}" = "y" ] && echo "cuda" || echo "cpu") +# Device: cpu or cuda +# For GPU: also requires the deploy: block in docker-compose.yml +DEVICE=${DEVICE_VAL} # Caddy network CADDY_NET=${SITE_CADDY_NET} @@ -287,9 +359,14 @@ IOPAINT_ENV chown -R "$ACTUAL_USER:$ACTUAL_USER" "$IOPAINT_DIR" echo "" - log_success "IOPaint configured at $IOPAINT_DIR" - log_info "LaMa model downloads on first start (~170 MB). Upload images via the web UI." - log_info "To switch models later: edit the --model= argument in docker-compose.yml and restart." + log_success "IOPaint configured — model: $IOPAINT_MODEL | device: $DEVICE_VAL" + if [[ "$IOPAINT_MODEL" == *"PowerPaint"* ]] || [[ "$IOPAINT_MODEL" == *"stable-diffusion"* ]]; then + log_info "SD-based model selected (~4 GB). It downloads from HuggingFace on first start." + log_info "If the download fails, try: HF_TOKEN=your_token docker compose up -d" + else + log_info "Model downloads automatically on first start (LaMa ~200 MB)." + fi + log_info "Switch models any time by editing MODEL= in .env and restarting." # No built-in auth — offer Authelia SSO protection local EXTRA_BLOCK="" @@ -301,63 +378,97 @@ IOPAINT_ENV configure_caddy_for_service "IOPaint" "iopaint:8080" "inpaint" "$EXTRA_BLOCK" - write_readme "$IOPAINT_DIR" << MD + write_readme "$IOPAINT_DIR" << 'MD' # IOPaint -AI-powered image inpainting — erase objects, fill regions, restore photos. -Uses the LaMa (Large Mask) model for high-quality object removal by default. +AI-powered image editing: +- **Erase / remove** — mask an object, AI fills the background (LaMa, works CPU-only) +- **Inpaint / restore** — fix damaged areas, remove watermarks +- **Replace with AI** — mask something + type what goes there → AI draws it (PowerPaint, GPU) + +Note: IOPaint is **local only** — all inference runs on this machine. +For text-guided replacement a CUDA GPU on this machine is required. ## Access - URL: http://localhost:8100 -- No built-in login — protect via Authelia SSO if needed +- No built-in login — protect via Authelia SSO if exposed -## Models -The \`--model\` argument in docker-compose.yml selects the AI model: -| Model | Best for | -|--------|----------| -| \`lama\` | Object removal, erase (default) | -| \`ldm\` | Texture-aware fill | -| \`zits\` | Face/portrait restoration | -| \`mat\` | Large missing region fill | -| \`manga\`| Manga/comic text removal | +## Switching models +Edit `MODEL=` in `.env` and restart — no need to touch `docker-compose.yml`: +```bash +cd ~/docker/iopaint +nano .env # change MODEL= line +docker compose restart +``` -Models download automatically on first use. Cached in \`./models/\`. +## Model reference + +| # | Model | Type | Size | Best for | +|---|-------|------|------|---------| +| 1 | `lama` | CPU-safe | ~200 MB | **Object erase/removal** (default) | +| 2 | `cv2` | CPU-safe | built-in | Basic fill, no download | +| 3 | `zits` | CPU-safe | ~200 MB | Portrait & face restoration | +| 4 | `manga` | CPU-safe | ~100 MB | Comic/manga text bubble removal | +| 5 | `migan` | GPU | ~50 MB | Fast GPU inpainting | +| 6 | `fcf` | GPU | ~600 MB | High-quality contextual fill | +| 7 | `mat` | GPU | ~300 MB | Large missing region fill | +| 8 | `ldm` | GPU | ~1.2 GB | Latent diffusion texture fill | +| 9 | `Sanster/PowerPaint-V2-filling` | GPU+SD | ~4 GB | **Text-guided replacement** | +| 10 | `runwayml/stable-diffusion-inpainting` | GPU+SD | ~4 GB | SD 1.5 inpaint, large LoRA library | + +SD-based models (9, 10) download from HuggingFace on first start. +If a gated model needs a token: add `HF_TOKEN=xxx` to `.env`. + +## How to use +1. Open http://localhost:8100 +2. Upload an image (or drag & drop) +3. Paint a mask over the area to change +4. For erase models: click Run → gap fills automatically +5. For SD models (PowerPaint): type a text prompt → AI draws it into the masked area ## GPU acceleration -Requires \`nvidia-container-toolkit\`. To enable: -1. Edit docker-compose.yml: change \`--device=cpu\` → \`--device=cuda\` -2. Uncomment the \`deploy:\` block (or re-run this installer with GPU=y) -3. Restart: \`docker compose down && docker compose up -d\` +Requires `nvidia-container-toolkit`. The GPU compose adds a `deploy:` block. +Re-run the installer with GPU=y to regenerate docker-compose.yml, or manually add: +```yaml + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] +``` +Then change `DEVICE=cuda` in `.env` and restart. ## Manage -\`\`\`bash -cd $IOPAINT_DIR -docker compose up -d # start -docker compose down # stop -docker compose logs -f # logs -docker compose pull && docker compose down && docker compose up -d # update -\`\`\` +```bash +cd ~/docker/iopaint +docker compose up -d +docker compose down +docker compose logs -f +docker compose pull && docker compose down && docker compose up -d +``` ## Files -- docker-compose.yml — stack definition (edit for model/device changes) -- .env — runtime config -- models/ — cached AI model weights -- input/ — optional: place images here -- output/ — processed images written here +- docker-compose.yml — stack (MODEL and DEVICE come from .env) +- .env — model and device config +- models/ — all cached model weights (torch + HuggingFace) +- input/, output/ — optional file staging MD local START_IO="" prompt_yn "Start IOPaint now? (y/n):" "y" START_IO if [ "$START_IO" = "y" ] || [ "$START_IO" = "Y" ]; then docker compose up -d \ - && log_success "IOPaint started — LaMa model will download on first use" \ + && log_success "IOPaint started — model downloads on first use" \ || log_warning "Start failed — check: docker compose logs" fi echo "" echo " URL: http://localhost:8100" - echo " Model: LaMa (erase / object removal)" - echo " Device: $([ "${USE_GPU,,}" = "y" ] && echo "CUDA GPU" || echo "CPU")" + echo " Model: $IOPAINT_MODEL" + echo " Device: $DEVICE_VAL" + echo " Switch: edit MODEL= in $IOPAINT_DIR/.env and restart" echo "" }