feat(stack): add Stable Audio and TRELLIS GPU service stacks

- Stable Audio Open: AI audio generation on port 11500 (~6GB VRAM)
- TRELLIS: 3D model generation on port 11510 (~6-8GB VRAM, low-VRAM fork)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

stacks/stable-audio.yml

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+version: '3.8'
+
+# Stable Audio Open - AI Audio Generation
+# Phase: ML Infrastructure
+# Ports: 8000 (Gradio Web UI)
+# GPU: YES - Requires NVIDIA Container Toolkit (8GB+ VRAM)
+# Storage: SSD recommended for model cache
+# Image: Built locally from https://github.com/SaladTechnologies/stable-audio-open
+
+services:
+  stable-audio:
+    image: stable-audio-open:local
+    container_name: stable-audio
+    restart: unless-stopped
+    ports:
+      - "11500:8000"  # Gradio Web UI (internal only)
+    volumes:
+      # HuggingFace cache for model weights (~6GB)
+      - /home/jpmschweitzer/docker-data/stable-audio/hf-cache:/root/.cache/huggingface
+    environment:
+      - TZ=Europe/Amsterdam
+      - NVIDIA_VISIBLE_DEVICES=all
+      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
+      - HF_TOKEN=${HF_TOKEN}
+    healthcheck:
+      test: ["CMD-SHELL", "curl -fSs http://localhost:8000/ || exit 1"]
+      interval: 60s
+      timeout: 30s
+      retries: 3
+      start_period: 300s  # Model download + load takes time
+    deploy:
+      resources:
+        limits:
+          memory: 16G
+        reservations:
+          memory: 8G
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    networks:
+      - docker-dataplane
+
+networks:
+  docker-dataplane:
+    external: true
+    name: docker-dataplane
+
+# GPU Requirements:
+# - RTX 2080 Ti (11GB VRAM) - minimum viable, may struggle with long generations
+# - Recommended: 16GB+ VRAM for reliable 47s audio generation
+# - NVIDIA Container Toolkit must be installed
+#
+# IMPORTANT: GPU Contention
+# - This service shares GPU with Ollama and Jellyfin
+# - Consider stopping Ollama when generating audio for better VRAM availability
+# - Monitor with: watch -n 1 nvidia-smi
+#
+# Prerequisites:
+# 1. Build image: cd /home/jpmschweitzer/docker-data/stable-audio-open && docker build -t stable-audio-open:local .
+# 2. Create HuggingFace token: https://huggingface.co/settings/tokens (read access)
+# 3. Accept model license: https://huggingface.co/stabilityai/stable-audio-open-1.0
+# 4. Create data directories:
+#    mkdir -p /home/jpmschweitzer/docker-data/stable-audio/{hf-cache,output}
+#
+# After Deployment:
+# 1. Set HF_TOKEN in Portainer environment variables
+# 2. Deploy stack via Portainer
+# 3. First run downloads model weights (~6GB) - be patient
+# 4. Verify GPU access: docker exec stable-audio nvidia-smi
+# 5. Access Web UI: http://tower-of-joy:11500 or http://audio.schweitz.internal (via NPM)
+#
+# NPM Configuration (audio.schweitz.internal):
+# - Domain: audio.schweitz.internal
+# - Scheme: http
+# - Forward Hostname: stable-audio (or tower-of-joy)
+# - Forward Port: 11500
+# - Block Common Exploits: Yes
+# - Websockets Support: Yes (required for Gradio)
+#
+# API Usage (Gradio API):
+# curl -X POST http://audio.schweitz.internal/api/predict \
+#   -H "Content-Type: application/json" \
+#   -d '{"data": ["epic orchestral music, cinematic", 30, 100, 7]}'
+# Parameters: [prompt, duration_seconds, diffusion_steps, cfg_scale]
+#
+# Rebuilding Image:
+# cd /home/jpmschweitzer/docker-data/stable-audio-open
+# git pull
+# docker build -t stable-audio-open:local .

stacks/trellis.yml

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+version: '3.8'
+
+# TRELLIS 1 - 3D Model Generation (Low-VRAM Fork)
+# Purpose: Generate 3D meshes with UV mappings for game asset pipeline
+# Ports: 11510 (Gradio Web UI)
+# GPU: YES - Optimized for 11GB VRAM (RTX 2080 Ti)
+# Storage: SSD for model cache, HDD for GLB outputs
+# Image: Built locally from /home/jpmschweitzer/docker-data/trellis/
+
+services:
+  trellis:
+    image: trellis:local
+    container_name: trellis
+    restart: unless-stopped
+    ports:
+      - "11510:7860"  # Gradio Web UI
+    volumes:
+      # SSD: HuggingFace model cache (~5GB)
+      - /home/jpmschweitzer/docker-data/trellis/hf-cache:/root/.cache/huggingface
+      # HDD: Generated GLB files
+      - /mnt/media/trellis/outputs:/app/outputs
+      # Entrypoint script (patches Gradio bugs without rebuilding image)
+      - /home/jpmschweitzer/docker-data/trellis/entrypoint.sh:/app/entrypoint.sh:ro
+    command: ["bash", "/app/entrypoint.sh"]
+    environment:
+      - TZ=Europe/Amsterdam
+      - NVIDIA_VISIBLE_DEVICES=all
+      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
+      # VRAM Optimization (for 11GB card)
+      - ATTN_BACKEND=xformers
+      - SPCONV_ALGO=native
+      - PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+      # Gradio must bind to 0.0.0.0 inside Docker
+      - GRADIO_SERVER_NAME=0.0.0.0
+      # HuggingFace token for model downloads (set in Portainer)
+      - HF_TOKEN=${HF_TOKEN:-}
+    healthcheck:
+      test: ["CMD-SHELL", "curl -fSs http://localhost:7860/ || exit 1"]
+      interval: 60s
+      timeout: 30s
+      retries: 3
+      start_period: 300s  # Model download + load time
+    memswap_limit: 20G
+    deploy:
+      resources:
+        limits:
+          memory: 20G
+        reservations:
+          memory: 4G
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    labels:
+      - "com.centurylinklabs.watchtower.enable=false"  # Manual updates for local builds
+    networks:
+      - docker-dataplane
+
+networks:
+  docker-dataplane:
+    external: true
+    name: docker-dataplane
+
+# =============================================================================
+# PREREQUISITES
+# =============================================================================
+#
+# 1. Build the Docker image first:
+#    cd /home/jpmschweitzer/docker-data/trellis
+#    docker build -t trellis:local .
+#    (Takes ~20-30 minutes for CUDA compilation)
+#
+# 2. Directories are already created:
+#    /home/jpmschweitzer/docker-data/trellis/hf-cache (SSD - model cache)
+#    /mnt/media/trellis/outputs (HDD - generated GLB files)
+#
+# =============================================================================
+# AFTER DEPLOYMENT
+# =============================================================================
+#
+# 1. Verify GPU access:
+#    docker exec trellis nvidia-smi
+#
+# 2. Check logs:
+#    docker logs trellis
+#
+# 3. Access Web UI:
+#    http://tower-of-joy:11510
+#    http://192.168.86.149:11510
+#
+# 4. Monitor VRAM during generation:
+#    watch -n 1 nvidia-smi
+#
+# =============================================================================
+# USAGE - GAME ASSET PIPELINE
+# =============================================================================
+#
+# 1. Open Gradio UI at http://tower-of-joy:11510
+# 2. Upload reference image or enter text prompt
+# 3. Generate 3D model (uses 6-8GB VRAM)
+# 4. Download GLB file from outputs
+# 5. Import GLB into Blender (File > Import > glTF)
+# 6. Apply custom textures, set up isometric camera
+# 7. Render at 1024x1024, downsample to 64x64 tile sprites
+#
+# =============================================================================
+# GPU CONTENTION NOTE
+# =============================================================================
+#
+# TRELLIS shares the RTX 2080 Ti with Ollama and Jellyfin.
+# For best results during complex generations:
+#   docker stop ollama
+#   # Run TRELLIS generation
+#   docker start ollama
+#
+# =============================================================================