InvokeAI

Run InvokeAI professional Stable Diffusion toolkit with node-based canvas on Clore.ai GPUs

InvokeAI is a professional-grade Stable Diffusion toolkit featuring an advanced node-based canvas editor, full SDXL/SD1.5/SD2.x support, ControlNet, IP-Adapter, LoRA management, and a polished web UI. It's designed for artists and creative professionals who need precise control over their image generation workflow. CLORE.AI's high-VRAM GPUs let you run SDXL at full resolution with multiple ControlNets simultaneously.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Server Requirements

Parameter	Minimum	Recommended
RAM	12 GB	32 GB+
VRAM	6 GB	12 GB+
Disk	40 GB	200 GB+
GPU	NVIDIA GTX 1060 6GB+	RTX 3090, RTX 4090, A100

For SDXL (1024×1024) without compromises, 12 GB VRAM is recommended. For SD1.5 (512×512 or 768×768), 6 GB VRAM is sufficient. More VRAM = higher resolution, faster generation, and more ControlNets simultaneously.

Quick Deploy on CLORE.AI

Docker Image: ghcr.io/invoke-ai/invokeai:latest

Ports: 22/tcp, 9090/http

Environment Variables:

Variable	Example	Description
INVOKEAI_ROOT	/invokeai	Root directory for models and outputs

Step-by-Step Setup

1. Rent a GPU Server on CLORE.AI

Visit CLORE.AI Marketplace and look for:

• Budget creative work: RTX 3080/3090 (10–24 GB VRAM)

• Professional SDXL: RTX 4090 (24 GB VRAM)

• Maximum quality: A100 80GB — run multiple models simultaneously

2. SSH into Your Server

ssh -p <PORT> root@<SERVER_IP>

3. Create the InvokeAI Directory Structure

mkdir -p /root/invokeai

4. Pull the InvokeAI Docker Image

docker pull ghcr.io/invoke-ai/invokeai:latest

5. Launch InvokeAI

Basic launch:

docker run -d \
  --name invokeai \
  --gpus all \
  -p 9090:9090 \
  -v /root/invokeai:/invokeai \
  -e INVOKEAI_ROOT=/invokeai \
  ghcr.io/invoke-ai/invokeai:latest \
  invokeai-web --host 0.0.0.0 --port 9090

With custom root and increased resources:

docker run -d \
  --name invokeai \
  --gpus all \
  --shm-size 8g \
  -p 9090:9090 \
  -v /root/invokeai:/invokeai \
  -v /root/models:/root/models \
  -e INVOKEAI_ROOT=/invokeai \
  ghcr.io/invoke-ai/invokeai:latest \
  invokeai-web --host 0.0.0.0 --port 9090

With specific GPU (multi-GPU server):

docker run -d \
  --name invokeai \
  --gpus '"device=0"' \
  -p 9090:9090 \
  -v /root/invokeai:/invokeai \
  -e INVOKEAI_ROOT=/invokeai \
  -e CUDA_VISIBLE_DEVICES=0 \
  ghcr.io/invoke-ai/invokeai:latest \
  invokeai-web --host 0.0.0.0 --port 9090

6. Wait for Initialization

docker logs -f invokeai

Look for: Uvicorn running on http://0.0.0.0:9090

7. Access via CLORE.AI HTTP Proxy

Open your CLORE.AI dashboard and find the http_pub URL for port 9090:

https://<order-id>-9090.clore.ai/

This opens the full InvokeAI web interface in your browser.

8. Download Your First Model

In the InvokeAI UI:

1. Click Model Manager (cube icon in left sidebar)

2. Click Add Model → HuggingFace

3. Enter model ID (e.g., stabilityai/stable-diffusion-xl-base-1.0)

4. Click Add Model

Or download from CivitAI directly:

1. Go to Model Manager → Add Model → URL

2. Paste the CivitAI download URL

3. Set model type (Checkpoint, LoRA, etc.)

---

Usage Examples

Example 1: Basic Image Generation via Web UI

1. Open InvokeAI at your CLORE.AI http_pub URL

2. Click Text to Image in the workflow selector

3. Enter a prompt: "a majestic dragon perched on a crystal mountain, digital art, 4k"

4. Set negative prompt: "blurry, low quality, watermark"

5. Set resolution to 1024x1024 (SDXL) or 512x512 (SD1.5)

6. Click Invoke

Example 2: Using the Node-Based Canvas

The Workflow editor is InvokeAI's signature feature:

1. Click Workflows in the top nav

2. Click New Workflow

3. Add nodes: Text → Image, connect to Save Image

4. Add a ControlNet node for guided generation:

   • Right-click → Add Node → ControlNet

   • Connect your reference image

   • Select processor: Canny, Depth, Pose, etc.

5. Click Invoke to run the full pipeline

Example 3: LoRA Usage

1. Download a LoRA from CivitAI (via Model Manager → URL import)

2. In the generation panel, find LoRA section

3. Click + and select your LoRA

4. Set weight (0.5–1.0 typical)

5. Add trigger word to prompt (listed on CivitAI model page)

Example prompt with LoRA trigger:

portrait of a woman, <lora:detail-tweaker:0.8>, hyperrealistic, studio lighting

Example 4: Using IP-Adapter for Style Transfer

1. Enable IP-Adapter in the generation panel

2. Upload a reference style image

3. Set weight (0.5 = subtle influence, 1.0 = strong influence)

4. Generate with any prompt — output will match the reference style

Example 5: API Usage (Headless)

InvokeAI exposes a REST API for programmatic use:

import requests
import time
import base64

BASE_URL = "http://localhost:9090"  # or your CLORE.AI http_pub URL

# List available models
models = requests.get(f"{BASE_URL}/api/v1/models").json()
print("Available models:", [m["name"] for m in models.get("items", [])])

# Queue a generation
payload = {
    "batch": {
        "graph": {
            "nodes": {
                "text_encoder": {
                    "type": "compel",
                    "id": "text_encoder",
                    "prompt": "a futuristic city at sunset, photorealistic",
                },
                "noise": {
                    "type": "noise",
                    "id": "noise",
                    "width": 1024,
                    "height": 1024,
                    "seed": 42,
                },
                "denoise_latents": {
                    "type": "denoise_latents",
                    "id": "denoise_latents",
                    "steps": 30,
                    "cfg_scale": 7.5,
                    "scheduler": "dpmpp_2m",
                },
                "l2i": {
                    "type": "l2i",
                    "id": "l2i",
                },
            },
            "edges": [],
        }
    }
}

# Simpler: use the queue API
response = requests.post(f"{BASE_URL}/api/v1/queue/default/enqueue_batch", json=payload)
print(response.status_code)

---

Configuration

invokeai.yaml Configuration File

Located at /root/invokeai/invokeai.yaml:

InvokeAI:
  Web Server:
    host: 0.0.0.0
    port: 9090
    allow_origins: []
    
  Features:
    esrgan: true          # ESRGAN upscaling
    internet_available: true
    
  Memory/Performance:
    ram: 12.0             # Max RAM for model cache (GB)
    vram: 0.25            # Fraction of VRAM for model cache
    lazy_offload: true    # Offload models to CPU when not in use
    
  Paths:
    models_path: /invokeai/models
    db_path: /invokeai/databases/invokeai.db
    outdir: /invokeai/outputs

Recommended Settings by GPU

RTX 3090 / 4090 (24 GB VRAM):

Memory/Performance:
  ram: 24.0
  vram: 0.5   # Keep 50% for active model
  lazy_offload: false  # Don't offload — enough VRAM

RTX 3080 (10 GB VRAM):

Memory/Performance:
  ram: 16.0
  vram: 0.25
  lazy_offload: true

Smaller GPUs (6-8 GB VRAM):

Memory/Performance:
  ram: 8.0
  vram: 0.1
  lazy_offload: true

---

Performance Tips

1. Use SDXL-Turbo or SDXL-Lightning for Fast Generation

Instead of SDXL base (25–50 steps), use:

• SDXL-Turbo: 1–4 steps, realtime generation

• SDXL-Lightning: 4–8 steps, near-SDXL quality

Download via Model Manager → HuggingFace:

• stabilityai/sdxl-turbo

• ByteDance/SDXL-Lightning

2. Choose the Right Scheduler

Scheduler	Quality	Speed	Best For
euler_a	Good	Fast	General use
dpmpp_2m	Excellent	Fast	Photorealistic
dpmpp_2m_sde	Excellent	Medium	High detail
ddim	Good	Fast	Inpainting
lms	Good	Fast	Artistic styles

3. Enable xFormers Memory Optimization

InvokeAI enables this automatically when available. Verify in logs:

xFormers is available

4. Use Model Caching

Keep your most-used models in the cache. In invokeai.yaml:

ram: 32.0  # Larger = more models cached

5. Tile for Large Resolutions

For images larger than your VRAM allows (e.g., 2048×2048 on 12 GB GPU):

• Use Tiled VAE in the workflow editor

• Or generate at 1024×1024 then upscale with ESRGAN

---

Troubleshooting

Problem: "CUDA out of memory"

RuntimeError: CUDA out of memory

Solutions:

1. Lower resolution (1024→768 or 512)

2. Reduce batch size to 1

3. Enable lazy offloading in invokeai.yaml

4. Use a smaller model (SD1.5 instead of SDXL)

Problem: Web UI not accessible

# Check if the container is running
docker ps | grep invokeai

# Check logs for errors
docker logs invokeai 2>&1 | tail -50

# Verify port mapping
docker port invokeai

Make sure port 9090 is listed in your CLORE.AI order's port configuration.

Problem: Model download fails inside container

# Download manually via exec
docker exec -it invokeai bash
cd /invokeai/models/main
wget "https://civitai.com/api/download/models/XXX" -O mymodel.safetensors

Problem: Slow generation (< 1 it/s)

• Check GPU utilization: docker exec -it invokeai nvidia-smi

• Make sure xFormers is enabled in logs

• Try euler_a scheduler (fastest)

Problem: Black/broken images

Usually a VAE issue. Try:

1. Model Manager → Edit model → Change VAE to sdxl-vae-fp16-fix

2. Or add --fp32-vae flag

Problem: Container won't start

docker logs invokeai
# Common: port 9090 already in use
# Fix:
docker stop $(docker ps -q --filter "publish=9090")
docker start invokeai

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Links

• GitHub

• Documentation

• Docker Hub / GHCR

• CivitAI (Models)

• CLORE.AI Marketplace

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Clore.ai GPU Recommendations

Use Case	Recommended GPU	Est. Cost on Clore.ai
Development/Testing	RTX 3090 (24GB)	~$0.12/gpu/hr
Production	RTX 4090 (24GB)	~$0.70/gpu/hr
Large Scale	A100 80GB	~$1.20/gpu/hr

💡 All examples in this guide can be deployed on Clore.ai GPU servers. Browse available GPUs and rent by the hour — no commitments, full root access.