Smoke-testing your artwork!
A simple, artist-friendly interface for Amazon Bedrock's image and video generation models. ArtSmoker helps creative teams use Bedrock efficiently — without needing to learn the API, CLI, or prompt engineering.
Creative teams and game studios want to use AI for asset generation, but face real barriers:
- No simple interface — artists shouldn't need to log into the Bedrock console or write API calls to generate images
- Prompt engineering is hard — composing effective prompts with proper negative prompts, style directives, and model-specific formatting takes expertise most artists don't have
- Teams don't build/train their own models — they need access to the many models already available on Bedrock, through something they can actually use
- Image editing is inaccessible — inpainting, outpainting, search & replace, and style transfer all require API knowledge
ArtSmoker is a self-hosted web application that wraps Amazon Bedrock in a clean creative interface — purpose-built for game asset production, with applicability across other creative industries such as advertising, e-commerce, publishing, and digital media where AI-generated visual content is valuable.
- Artists describe what they need in plain language — ArtSmoker handles prompt composition, negative prompt extraction, model-specific formatting, and style application behind the scenes
- Style-aware generation — upload your game's existing art, and ArtSmoker's vision models learn your visual identity. Every generated asset matches your game's look and feel
- All Bedrock models, all regions — fully configurable. Choose your text-to-image models, video models, and regions. The system discovers available models dynamically via the Bedrock API
- Self-deployed, self-billed — runs on your own infrastructure, uses your own AWS account. No shared endpoints, no third-party data access, no surprise bills from external services
Built on Amazon Bedrock: Claude Sonnet/Opus (prompt engineering & chat), Nova Canvas, Titan Image, Stable Diffusion 3.5 Large, Stable Image Ultra, Stability AI (image editing), Nova Reel, Luma AI Ray (video generation), plus 80+ LLMs from 16 providers for Chat Studio.
Get started now — jump to Prerequisites & Installation ▸
ArtSmoker supports 6 languages. Switch the UI language using the language buttons in the top navigation bar (EN | JA | ZH | KO | FR | ES). Your selection is saved automatically.
| Language | README |
|---|---|
| English | This document |
| 日本語 (Japanese) | README.ja.md |
| 中文 (Chinese) | README.zh.md |
| 한국어 (Korean) | README.ko.md |
| Français (French) | README.fr.md |
| Español (Spanish) | README.es.md |
Multi-lingual prompt support:
- Non-English prompts are automatically detected (Japanese, Chinese, Korean, French, Spanish) and translated to English before generation
- A bilingual preview appears in the prompt area: toggle between your original text and the English translation to see exactly what the model will receive
- The original prompt, detected language, and English translation are all preserved in the asset metadata
- File names are generated from the translated English prompt (so "病院の建物" →
hospital-building_opt1_var1.png) - Chat Studio passes prompts directly to the LLM (no translation) since models like Claude are natively multilingual
- Type Studio text stays in your language (it's rendered on the image as-is)
- All moderation pre-checks and content screening work on the translated English prompt for consistency
ArtSmoker works in two modes — standalone (no art style or theme setup needed, just describe and generate) and style-guided (upload your existing art, and every generation matches your visual identity). Both modes use the same studios and generation pipeline.
No style or theme setup needed — open the 2D Image Studio, Video Studio, or Type Studio and start creating immediately.
- Describe what you need — type a prompt like "hospital building" or "fire mage character", or use voice input. The AI automatically enhances your prompt with proper composition directives, negative prompts, and model-specific formatting.
- Choose your model and settings — select from all available text-to-image models on Amazon Bedrock, pick dimensions, quality tier, and region. Or use "All Available Models" to generate across every enabled model simultaneously for a side-by-side comparison.
- Get multiple options — the system generates up to 5 distinctly different creative concepts, each with up to 5 seed variations (25 images total). Pick the one you like.
- Edit and refine — use inpainting, outpainting, erase, search & replace, or recolor directly in the Asset Viewer. Each edit creates a new version — the original is always preserved.
- Download game-ready files — PNG with transparent background + SVG, named descriptively (e.g.
hospital-building_opt2_var3.png). Videos export as MP4.
For teams that want every generated asset to match an existing art style — upload reference images and let the AI learn your visual identity first.
- Upload your game's art — import reference images from local directories (recursive scan, symlinked to avoid duplication) or S3 buckets (recursive listing with pagination). Smart deduplication runs automatically — removes rotation variants (barrel_N/E/S/W.png keeps only barrel_S.png) and animation frames (Idle0-Idle8 keeps only Idle). For example, a 747-file isometric asset pack deduplicates to ~99 unique objects. Supports: .png, .jpg, .jpeg, .gif, .bmp, .webp, .tiff, .tif, .tga, .ico, .svg, plus automatic texture extraction from 3D models (.glb, .gltf).
- AI learns your style — two-phase cohesion-aware analysis: first, a quick check determines whether your collection is unified, structurally consistent, or diverse. Then a deep analysis of the full reference set produces a metadata-rich style profile — colour palettes, line weights, lighting patterns, composition rules, and production conventions. If you provide generation hints, the AI receives them as "Artist's Guidance" so the analysis understands your intent, not just what's visible.
- Generate with style applied — when you select a style in the Image Studio, every prompt is automatically enhanced with your style's visual directives. A prompt like "hospital building" becomes a detailed generation instruction that includes your game's colour palette, perspective conventions, and rendering style.
- Everything from Standalone mode applies — multiple options, model comparison, editing, versioning, and game-ready downloads all work the same way, now guided by your art style.
Note
All generated content is produced by AI models and depends on the prompts and references you provide. Please review the Disclaimer regarding content quality, intellectual property, and applicable service terms before using generated assets in production.
- 🎨 Style Library — Upload art, AI learns your visual identity
- 🖼️ 2D Image Studio — Generate images with options x variations, guided 3-step prompt workflow
- 🎨 Prompt Designer — AI decomposes your prompt into editable visual components (subject, scene, lighting, colors) with smart asset type classification
- 🎬 Video Studio — Text-to-video with Nova Reel & Luma Ray, multi-shot, image-to-video
- ✍️ Type Studio — AI-designed text overlays with font picker
- 💬 Chat Studio — Multi-model LLM chat with streaming, markdown, code highlighting, vision, sessions, context compaction
- 📁 Unified Gallery — Browse images + videos, media filter, search, download, delete
- ✏️ Image Editing — Inpainting, outpainting, erase, search & replace, recolor (in AssetViewer)
- 🔄 Real-time progress — SSE streaming with retry/throttle visibility
- 🛡️ Smart moderation — Canary testing, auto model switching, AI-assisted rewriting
- ⚙️ Model Registry — Admin UI organized by studio (Image, Video, Chat, Type, Shared), Bedrock discovery, custom model support
- 📝 Prompt Templates — 19 editable LLM directive prompts, AI-assisted refinement, variable validation with auto-fix
- 📦 Asset Versioning — Edit-in-place with version history (v1, v2, ...) and version navigation
- 💰 Cost Tracking — Estimated AWS spend per request, per session, per asset — sent to PulseBoard telemetry
- 🌐 6-Language i18n — Full UI translation (EN, JA, ZH, KO, FR, ES), auto-detect non-English prompts, bilingual preview
- 🔍 Custom Model Support — Discover fine-tuned, imported, and deployed custom Bedrock models automatically
- 🔧 Self-Hosted Models — Deploy open-source models on Amazon SageMaker from an extensible catalog. Container pulls directly from HuggingFace, CPU offloading for large models, auto-scales to zero ($0 idle), async generation with Pending Jobs panel
- 🔄 Auto-Update — Version-gated git pull on startup, self-restart on update, 24h periodic check (
ARTSMOKER_AUTO_UPDATE=falseto disable)
2D Image Studio — Settings on the left, prompt with AI enhancement on the right, model comparison results below. All Available Models mode generates across every enabled image model simultaneously.
Style Library — Upload your game's existing art, AI analyzes the visual style and produces a metadata-rich prompt guide. Reference images are displayed with the full AI analysis and JSON style profile.
Gallery — Unified view of all generated images and videos with media type filter, style filter, search, and sorting. Click any asset to open the full viewer.
Asset Viewer & Image Editing — Full-size preview with zoom/pan, Edit tab for inpainting (mask paint + prompt), version history, and PNG/SVG download.
Video Studio — Settings on the left (model, generation mode, duration, region, cost estimate), prompt on the right. Supports Nova Reel (single shot, multi-shot auto/manual up to 2 minutes) and Luma AI Ray (aspect ratios, looping).
Video Player — Click a video to play it inline with full metadata (original prompt, AI-enhanced prompt, model, duration, region).
For each prompt, the AI creates Options — fundamentally different design interpretations (e.g. for "a warrior": Viking berserker, Japanese samurai, tribal fighter, cyber-soldier, Greek hoplite). For each option, the image model produces Variations — different random seeds giving subtle visual differences. This gives artists a broad creative palette to choose from.
Select "All Available Models" from the model dropdown to generate your prompt across every enabled image model simultaneously — one image per model. This gives a direct side-by-side comparison of how Nova Canvas, Titan Image, SD 3.5 Large, and Stable Image Ultra each interpret the same prompt. Each model runs independently: if stricter models block the prompt, you still get results from models that accepted it, with clear status labels (success, blocked by moderation, or failed) on each option card.
An optional "Model-optimized prompts" toggle tailors the prompt to each model's strengths instead of sending the same prompt to all — useful when you want the best output from each model rather than a direct comparison.
Generate AI-powered videos and animations from text prompts. Supports Amazon Nova Reel (v1.0, v1.1) and Luma AI Ray (v2.0).
| Feature | Nova Reel | Luma Ray v2 |
|---|---|---|
| Max duration | 120s (2 minutes) | 9 seconds |
| Resolution | 1280x720 | 720p / 540p |
| Aspect ratios | 16:9 only | 7 options (1:1, 16:9, 9:16, etc.) |
| Image-to-video | Yes (start frame) | Yes (start + end frame) |
| Looping video | No | Yes |
| Multi-shot control | Yes (auto + manual) | No |
| Price | ~$0.08/sec | ~$1.50/sec |
How it works:
- Select a video model and configure duration, aspect ratio, region
- Enter a prompt — AI enhances it with cinematic vocabulary, camera movements, and temporal coherence cues
- Click Generate — the job runs asynchronously via
StartAsyncInvoke, output goes to your configured S3 bucket - Poll status every 5 seconds — on completion, thumbnail is extracted (via ffmpeg) and MP4 is downloaded locally (or streamed from S3)
- Videos appear in both the Video Studio's "Recent Videos" section and the unified Gallery
S3 bucket required: Video generation outputs to S3. You can configure via Video Settings in the UI (browse existing buckets or create new), or create one via CLI:
# Create an S3 bucket for video storage (replace REGION and YOUR_ORG)
aws s3api create-bucket --bucket artsmoker-video-YOUR_ORG --region us-east-1
# For regions other than us-east-1, add the LocationConstraint:
aws s3api create-bucket --bucket artsmoker-video-YOUR_ORG --region us-west-2 \
--create-bucket-configuration LocationConstraint=us-west-2Storage mode: download locally (default) or stream from S3 on demand.
Video prompt enhancement: The LLM adds camera movements (pan, zoom, dolly, tracking), lighting details, and temporal cues. Since video models don't support negative prompts, avoidance concepts are woven into the positive prompt naturally.
A full-featured LLM chat interface — like a self-hosted conversational AI, running on your own AWS account with no third-party data access.
80+ models from 16 providers — Claude (Sonnet, Opus, Haiku), Amazon Nova, Meta Llama, Mistral, Cohere, Qwen, DeepSeek, Google Gemma, NVIDIA Nemotron, and more. Plus any custom/imported models in your account. All discovered automatically via Sync from AWS.
Core features:
- Streaming responses — real-time token-by-token rendering via Bedrock ConverseStream
- Markdown rendering — headings, bold/italic, lists, tables, blockquotes, horizontal rules
- Code blocks — syntax highlighting (highlight.js) with language badge + copy button
- Per-message metrics — input/output tokens, latency, estimated cost, model used
- Context window bar — visual fill indicator (green/amber/red) with used/max token count
- Region switching — each model shows all available regions, pick the closest or cheapest
Session management:
- Multiple concurrent sessions with auto-save
- Inline rename, duplicate, delete, search/filter in sidebar
- Export conversations as Markdown
- Session totals: token count, estimated cost, message count
Advanced features:
- System prompt templates — General Assistant, Coding Expert, Creative Writer, Game Designer, Data Analyst, Technical Writer
- Vision/multimodal — drag-drop, file picker, or Ctrl+V paste images for vision-capable models
- Context compaction — AI summarizes older messages to free context window space
- Regenerate — re-run any AI response with the same prompt
- Edit & resend — modify any user message and replay from that point
- Fork — branch a conversation from any message into a new session
Pricing transparency: model picker shows cost per 1K tokens, pricing info bar shows estimated cost for 10K and 100K token conversations.
The selected Asset Type fundamentally changes how the AI interprets your prompt — not just the image model, but every stage of the pipeline. When you type "hospital" and select different asset types, you get completely different outputs:
| Type | Composition | Framing | Technical Approach |
|---|---|---|---|
| Game Asset | Single isolated object on transparent background. No scene, no text, no UI. | Straight-on or isometric, object fills 70-80% of frame. | Clean sharp edges for bg removal, consistent top-left lighting, no ground shadows. Designed to compose with other game assets at various scales. |
| Character | Full-body or 3/4-body figure, isolated on clean background. One character only. | Character fills 60-75% vertical, head-to-toe, slightly off-center. | Strong readable silhouette (identifiable from silhouette alone), expressive pose conveying personality, clear facial features and costume details. |
| Icon | Single bold recognizable symbol, centered with generous padding. Maximum simplicity. | Front-facing or slight 3/4 tilt, breathing room at edges. | Must read clearly at 64x64 pixels. High contrast, 3-5 colors maximum, bold shapes, no thin lines or fine detail. |
| Marketing Banner | Full scenic illustration with dramatic composition. Clean text-safe zone reserved on one side — no rendered text or typography. | Wide cinematic feel, camera pulled back to show a scene. | Rich saturated colors, dramatic lighting (rim light, volumetric rays), depth-of-field. The AI is explicitly instructed NOT to render text; the text-safe zone is left clean for post-production overlay in design tools (Figma, Canva, etc.). |
| Environment | Full landscape with foreground/midground/background depth layers, leading lines. | Wide establishing shot, horizon at upper or lower third. | Atmospheric perspective (distant objects lighter/hazier), environmental storytelling through details, mood-setting lighting. |
This matters at every stage:
- "Preview Enhanced Prompt" button — When you click Compose, the AI uses the asset type to reshape your brief into a detailed generation prompt, combining your words with style guidelines and asset type directives. Your explicit intent always overrides style defaults. You can review the composed version before generating.
- Concept generation — When generating multiple options, the AI creates N different design interpretations that all respect the asset type's structural rules. A Character option always has a readable silhouette; a Marketing Banner option always has a text-safe zone with no rendered text.
- The result — Two images from the same prompt but different asset types will look nothing alike. A Game Asset "warrior" is a single centered character sprite. A Marketing Banner "warrior" is an epic battle scene with a clean zone for headline overlay.
- Python 3.11+ (3.12, 3.13, 3.14 all work)
- AWS CLI configured with working credentials
- IAM permissions for Bedrock access (see below)
ArtSmoker uses boto3's standard credential resolution, so any of the following methods work:
| Method | Best for | How |
|---|---|---|
| Environment variables | CI/CD, containers | AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY |
| Shared credentials file | Local development | ~/.aws/credentials via aws configure |
| Named profile | Multiple accounts | Set ARTSMOKER_AWS_PROFILE=myprofile or AWS_PROFILE |
| AWS SSO | Enterprise SSO | aws configure sso |
| IAM Instance Profile | EC2, ECS, App Runner | Attach an IAM role to the instance — no credentials needed on the machine |
| ECS Task Role | ECS/Fargate containers | Assign a task execution role with the required permissions |
Quick check that credentials are working:
aws sts get-caller-identityNote
On EC2 and other AWS compute services, you don't need to configure explicit credentials. Attach an IAM Instance Profile with the required permissions, and boto3 picks it up automatically via the instance metadata service.
Confirming credentials work (sts:GetCallerIdentity) only verifies identity — it does not confirm you have Bedrock permissions. ArtSmoker uses multiple Bedrock APIs, so a quick listing test alone is not sufficient. The most reliable check:
# Test 1: Can you list models? (requires bedrock:ListFoundationModels)
aws bedrock list-foundation-models --region us-east-1 --query "modelSummaries[0].modelId" --output text
# Test 2: Can you invoke a model? (requires bedrock:InvokeModel)
aws bedrock-runtime invoke-model --region us-east-1 \
--model-id amazon.titan-image-generator-v2:0 \
--content-type application/json --accept application/json \
--body '{"textToImageParams":{"text":"test"},"imageGenerationConfig":{"numberOfImages":1,"width":512,"height":512}}' \
/dev/null 2>&1 && echo "InvokeModel: OK" || echo "InvokeModel: FAILED"
# Test 3: Can you use the Converse API? (requires bedrock:Converse)
aws bedrock-runtime converse --region us-east-1 \
--model-id amazon.titan-text-lite-v1 \
--messages '[{"role":"user","content":[{"text":"hi"}]}]' \
--inference-config '{"maxTokens":1}' \
--query "output.message.content[0].text" --output text 2>&1 && echo "Converse: OK" || echo "Converse: FAILED"
# Test 4: Can you list custom models? (requires bedrock:ListCustomModels)
aws bedrock list-custom-models --region us-east-1 \
--query "modelSummaries[0].modelName" --output text 2>&1 && echo "ListCustomModels: OK" || echo "ListCustomModels: no custom models (or permission denied)"If Tests 1-3 pass, your core permissions are set. Test 4 is needed only for custom model discovery. If Test 1 passes but Tests 2-3 fail, your IAM policy allows listing but not invoking — update it using the permissions table below.
Your IAM user, role, or instance profile needs these permissions:
| Permission | Used for |
|---|---|
bedrock:InvokeModel |
Image generation, image editing, post-processing (all image models) |
bedrock:Converse |
LLM calls — prompt refinement, style analysis, concept generation |
bedrock:InvokeModelWithBidirectionalStream |
Voice transcription (optional — app works without it) |
bedrock:StartAsyncInvoke |
Video generation (async invocation) |
bedrock:GetAsyncInvoke |
Poll video generation job status |
bedrock:ListAsyncInvokes |
List video generation jobs |
bedrock:ListFoundationModels |
Foundation model discovery (Sync from AWS) |
bedrock:ListCustomModels |
Discover fine-tuned custom models in your account |
bedrock:ListImportedModels |
Discover imported models in your account |
bedrock:GetCustomModel |
Read custom model details (base model, status) |
bedrock:GetImportedModel |
Read imported model details (architecture, status) |
bedrock:ListProvisionedModelThroughputs |
Find invocable custom models with provisioned throughput |
bedrock:ListCustomModelDeployments |
Find custom models with on-demand deployments |
s3:CreateBucket |
Create S3 bucket for video storage (optional, via UI) |
s3:PutObject / s3:GetObject / s3:DeleteObject / s3:ListBucket |
Video output storage and retrieval |
aws-marketplace:Subscribe |
Auto-subscription on first use of third-party models |
aws-marketplace:ViewSubscriptions |
Check existing model subscriptions |
sts:GetCallerIdentity |
Startup credential validation |
pricing:GetProducts |
Fetch model pricing during Sync from AWS (optional) |
sagemaker:* |
Self-hosted custom models on Amazon SageMaker (optional — only if using Custom Models) |
iam:PassRole |
Allow Amazon SageMaker to use your role (optional — only for Custom Models) |
iam:CreateRole / iam:AttachRolePolicy |
Auto-create Amazon SageMaker execution role on first deploy (optional — only for Custom Models) |
iam:GetRole / iam:UpdateAssumeRolePolicy |
Auto-configure existing role for Amazon SageMaker trust (optional) |
secretsmanager:CreateSecret / secretsmanager:GetSecretValue / secretsmanager:DeleteSecret |
Encrypted storage for HuggingFace tokens on gated models (optional — auto-cleaned on teardown) |
Quickest setup (managed policies — broadest access):
# Option A: Attach managed policies to your IAM user (simplest for local development)
aws iam attach-user-policy --user-name YOUR_USERNAME \
--policy-arn arn:aws:iam::aws:policy/AmazonBedrockFullAccess
# Add S3 access for video storage
aws iam attach-user-policy --user-name YOUR_USERNAME \
--policy-arn arn:aws:iam::aws:policy/AmazonS3FullAccessScoped setup (tighter permissions — recommended for production):
# Create a scoped IAM policy with only the permissions ArtSmoker needs
aws iam create-policy --policy-name ArtSmokerAccess --policy-document '{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Bedrock",
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel",
"bedrock:Converse",
"bedrock:InvokeModelWithBidirectionalStream",
"bedrock:StartAsyncInvoke",
"bedrock:GetAsyncInvoke",
"bedrock:ListAsyncInvokes",
"bedrock:ListFoundationModels",
"bedrock:ListCustomModels",
"bedrock:ListImportedModels",
"bedrock:GetCustomModel",
"bedrock:GetImportedModel",
"bedrock:ListProvisionedModelThroughputs",
"bedrock:ListCustomModelDeployments"
],
"Resource": "*"
},
{
"Sid": "S3VideoStorage",
"Effect": "Allow",
"Action": ["s3:CreateBucket", "s3:PutObject", "s3:GetObject", "s3:ListBucket", "s3:DeleteObject", "s3:HeadBucket"],
"Resource": ["arn:aws:s3:::artsmoker-*", "arn:aws:s3:::artsmoker-*/*"]
},
{
"Sid": "Marketplace",
"Effect": "Allow",
"Action": ["aws-marketplace:Subscribe", "aws-marketplace:ViewSubscriptions"],
"Resource": "*"
},
{
"Sid": "Utility",
"Effect": "Allow",
"Action": ["sts:GetCallerIdentity", "pricing:GetProducts"],
"Resource": "*"
},
{
"Sid": "SageMakerCustomModels",
"Effect": "Allow",
"Action": [
"sagemaker:CreateModel", "sagemaker:CreateEndpointConfig", "sagemaker:CreateEndpoint",
"sagemaker:DeleteModel", "sagemaker:DeleteEndpointConfig", "sagemaker:DeleteEndpoint",
"sagemaker:DescribeEndpoint", "sagemaker:InvokeEndpoint", "sagemaker:InvokeEndpointAsync"
],
"Resource": "arn:aws:sagemaker:*:*:*artsmoker*"
},
{
"Sid": "SageMakerRoleManagement",
"Effect": "Allow",
"Action": ["iam:CreateRole", "iam:AttachRolePolicy", "iam:GetRole", "iam:UpdateAssumeRolePolicy", "iam:PassRole"],
"Resource": ["arn:aws:iam::*:role/ArtSmoker*"]
},
{
"Sid": "SecretsManagerHFTokens",
"Effect": "Allow",
"Action": ["secretsmanager:CreateSecret", "secretsmanager:UpdateSecret", "secretsmanager:GetSecretValue", "secretsmanager:DeleteSecret"],
"Resource": "arn:aws:secretsmanager:*:*:secret:artsmoker/*"
}
]
}'
# Attach to your IAM user (replace YOUR_ACCOUNT_ID and YOUR_USERNAME)
aws iam attach-user-policy --user-name YOUR_USERNAME \
--policy-arn arn:aws:iam::YOUR_ACCOUNT_ID:policy/ArtSmokerAccessTip
For EC2/ECS/App Runner — create an IAM role instead of attaching to a user. See the EC2 Deployment section for the complete role creation commands. No access keys needed — boto3 auto-discovers the role from the instance metadata service.
Note
Bedrock models are available by default in all commercial AWS regions — no manual enablement step is needed. On first invocation of a third-party model (Anthropic, Stability AI), AWS automatically initiates a marketplace subscription in the background (requires the aws-marketplace permissions above). Anthropic models require a one-time First Time Use form completion.
SVG conversion uses external CLI tools (not Python packages). Without them, SVG output falls back to a Pillow-based raster-in-SVG wrapper — functional but not true vector output.
| Tool | Purpose | macOS | Linux (Debian/Ubuntu) | Windows |
|---|---|---|---|---|
| vtracer | Primary SVG (color vector tracing) | pip install vtracer or cargo install vtracer |
pip install vtracer or cargo install vtracer |
pip install vtracer or cargo install vtracer or pre-built binaries |
| potrace | Fallback SVG (monochrome tracing) | brew install potrace |
sudo apt install potrace |
Download from potrace.sourceforge.net |
Verify installation:
# Check SVG conversion tools
which vtracer && echo "vtracer: OK" || echo "vtracer: not installed (optional)"
which potrace && echo "potrace: OK" || echo "potrace: not installed (optional)"Video Studio generates MP4 videos via Amazon Nova Reel and Luma AI Ray. To extract thumbnails (first frame as JPEG) and video metadata (duration, resolution, FPS), ffmpeg and ffprobe must be installed on the machine running the ArtSmoker backend.
Without ffmpeg:
- Videos still generate and play correctly (streamed from S3 or downloaded as MP4)
- Thumbnails will be missing — the Gallery and Video Studio show a black placeholder instead of a preview image
- Video metadata (duration, resolution) won't be displayed
| Tool | Purpose | macOS | Linux (Debian/Ubuntu) | Windows |
|---|---|---|---|---|
| ffmpeg | Thumbnail extraction + video metadata | brew install ffmpeg |
sudo apt install ffmpeg |
Download from ffmpeg.org/download or winget install ffmpeg |
Note
ffprobe is included with ffmpeg — no separate install needed. ArtSmoker checks for ffmpeg at runtime and falls back gracefully if it's not found — video generation works either way, you just won't get thumbnails.
Verify installation:
ffmpeg -version 2>&1 | head -1 && echo "ffmpeg: OK" || echo "ffmpeg: not installed (optional)"
ffprobe -version 2>&1 | head -1 && echo "ffprobe: OK" || echo "ffprobe: not installed (optional)"git clone <repo-url> && cd ArtSmoker
# Option A: With virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate
pip install -r backend/requirements.txt
# Option B: Without virtual environment (system-wide install)
pip3 install -r backend/requirements.txtNote
On macOS, python3 and pip3 are available via Homebrew (brew install python) or the Xcode command-line tools. If you see "command not found", install Python from python.org or via brew install python@3.12.
# Install Python if needed
sudo apt update && sudo apt install python3 python3-pip python3-venv
git clone <repo-url> && cd ArtSmoker
# Option A: With virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate
pip install -r backend/requirements.txt
# Option B: Without virtual environment
pip3 install --user -r backend/requirements.txtNote
On some Linux distros, pip install outside a venv requires the --user flag or --break-system-packages (PEP 668). Using a venv avoids this entirely.
git clone <repo-url>
cd ArtSmoker
# Option A: With virtual environment (recommended)
python -m venv .venv
.venv\Scripts\activate
pip install -r backend\requirements.txt
# Option B: Without virtual environment
pip install -r backend\requirements.txtNote
On Windows, use python (not python3). Install Python from python.org — check "Add to PATH" during installation. The Type Studio font picker detects fonts from C:\Windows\Fonts (system font detection is currently macOS/Linux only — Windows users can use global or style-specific custom fonts).
Single-process with auto-reload on file changes — ideal for one developer working locally:
# With venv (activate first)
source .venv/bin/activate # macOS / Linux
.venv\Scripts\activate # Windows
uvicorn backend.main:app --reload# Without venv (if installed system-wide)
uvicorn backend.main:app --reload
# Or if uvicorn isn't on PATH
python3 -m uvicorn backend.main:app --reload # macOS / Linux
python -m uvicorn backend.main:app --reload # WindowsOpen http://localhost:8000 — the frontend is served by FastAPI, no separate web server needed.
On startup, the console shows AWS credential validation results. If something's wrong, you'll see a clear error box. You can also check http://localhost:8000/api/health for the status.
For any environment with more than one concurrent user — whether a shared dev/test box, staging, or production — use gunicorn with multiple workers:
# Install gunicorn (one-time, in addition to requirements.txt)
pip install gunicorn
# Run with gunicorn (multi-worker, handles concurrent users)
gunicorn backend.main:app \
-w 2 \
-k uvicorn.workers.UvicornWorker \
--bind 0.0.0.0:8000 \
--timeout 300| Flag | Purpose |
|---|---|
-w 2 |
2 worker processes (increase for heavier load) |
-k uvicorn.workers.UvicornWorker |
Use uvicorn's async worker class |
--bind 0.0.0.0:8000 |
Listen on all interfaces (not just localhost) |
--timeout 300 |
5-minute timeout for large batch generations with retries |
Tip
gunicorn is Linux/macOS only. On Windows, use uvicorn backend.main:app --host 0.0.0.0 --port 8000 --workers 2 for multi-worker serving.
Recommended: t3.small (~$15/month) for 1-2 concurrent users.
Step 1: Create an IAM role for the EC2 instance (run from your local machine):
# Create the IAM role with EC2 trust policy
aws iam create-role --role-name ArtSmokerEC2Role \
--assume-role-policy-document '{
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Principal": {"Service": "ec2.amazonaws.com"},
"Action": "sts:AssumeRole"
}]
}'
# Attach the ArtSmoker policy (use the scoped policy from section 2.2, or the managed policy)
aws iam attach-role-policy --role-name ArtSmokerEC2Role \
--policy-arn arn:aws:iam::aws:policy/AmazonBedrockFullAccess
aws iam attach-role-policy --role-name ArtSmokerEC2Role \
--policy-arn arn:aws:iam::aws:policy/AmazonS3FullAccess
# Create an instance profile and attach the role
aws iam create-instance-profile --instance-profile-name ArtSmokerEC2Profile
aws iam add-role-to-instance-profile \
--instance-profile-name ArtSmokerEC2Profile \
--role-name ArtSmokerEC2RoleStep 2: Launch an EC2 instance (or attach the profile to an existing one):
# Attach to an existing running instance
aws ec2 associate-iam-instance-profile \
--instance-id i-YOUR_INSTANCE_ID \
--iam-instance-profile Name=ArtSmokerEC2ProfileStep 3: Install and run on the instance (SSH into the instance):
# Install (one-time)
sudo yum install -y python3 python3-pip git # Amazon Linux
# sudo apt install -y python3 python3-pip python3-venv git # Ubuntu
git clone <repo-url> && cd ArtSmoker
python3 -m venv .venv
source .venv/bin/activate
pip install -r backend/requirements.txt
pip install gunicorn
# Optional: install ffmpeg for video thumbnails
sudo yum install -y ffmpeg # Amazon Linux
# sudo apt install -y ffmpeg # UbuntuStep 4: Run as a systemd service (persistent, auto-restarts):
# Create the service file
sudo tee /etc/systemd/system/artsmoker.service > /dev/null << 'EOF'
[Unit]
Description=ArtSmoker
After=network.target
[Service]
WorkingDirectory=/home/ec2-user/ArtSmoker
ExecStart=/home/ec2-user/ArtSmoker/.venv/bin/gunicorn backend.main:app \
-w 2 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000 --timeout 300
Restart=always
User=ec2-user
[Install]
WantedBy=multi-user.target
EOF
# Enable and start
sudo systemctl daemon-reload
sudo systemctl enable artsmoker
sudo systemctl start artsmoker
# Verify it's running
sudo systemctl status artsmoker
# View logs
sudo journalctl -u artsmoker -fOpen http://YOUR_INSTANCE_IP:8000 — ensure your EC2 security group allows inbound TCP 8000.
After ArtSmoker is running, complete these steps to get the best results:
1. Sync models from AWS — Open Model Settings (gear icon in any studio) → click Sync from AWS. This discovers all available image, video, and chat models across all Bedrock regions. Takes 30-60 seconds. Only needed once, or when AWS adds new models.
2. Review and customize prompt templates — This is the most impactful configuration you can do. Open Model Settings → Prompt Templates tab. ArtSmoker uses 19 editable directive prompts that control how the AI behaves:
| Template | What it controls |
|---|---|
| Image Prompt Refinement | How your text descriptions are turned into detailed image generation prompts |
| Multi-Concept Generation | How multiple creative options are generated from a single idea |
| Style Analysis | How reference images are analyzed to learn your art style |
| Content Moderation | How strict the pre-check and rewrite system is |
| Video Enhancement | How video prompts are enriched with camera movements and lighting |
| Text Layout | How Type Studio designs text positioning on images |
Each template can be:
- Edited directly — modify the instructions to match your team's needs
- Enhanced with AI — select any LLM model, optionally add instructions (e.g., "optimize for pixel art"), and click "Enhance with AI". Review the suggestion, then Accept or Dismiss
- Reset to default — restore the original at any time
Templates are organized by studio (Image Studio, Style Library, Content Safety, Video Studio, Type Studio, Chat Studio, Translation) with friendly descriptions of what each one controls.
Variable safety: Templates use {curly_brace} variables (e.g., {user_prompt}, {model_name}) that are substituted at runtime. If you accidentally remove a required variable, ArtSmoker will:
- Block the save and show which variables are missing
- Offer "Fix & Save" — an LLM automatically inserts the missing variables back into your edited text in the right places
- Verify the fix before saving
All templates are stored in backend/prompt_templates.json and self-heal from code defaults if the file is deleted or corrupted.
Tip
Start by reviewing the Image Prompt Refinement and Creative Options templates. These have the biggest impact on output quality. If your team specializes in a particular art style (e.g., pixel art, watercolor, isometric), add those preferences directly into the templates so every generation benefits.
3. Set up a style profile (optional) — Go to Style Library, create a new style, upload reference images, and click Analyze. This teaches ArtSmoker your visual identity.
4. Choose your language — Click a language button in the nav bar (EN | JA | ZH | KO | FR | ES) if you prefer a non-English interface.
┌─────────────────────────────────────────────┐
│ Browser (SPA) │
│ Vanilla JS + Tailwind CSS │
└──────────────────────┬──────────────────────┘
│ HTTP / SSE
▼
┌─────────────────────────────────────────────┐
│ FastAPI Backend (Python) │
│ │
│ /api/styles Style CRUD + import │
│ /api/generate Two-level generation │
│ /api/type-studio Text overlay + fonts │
│ /api/video Video generation + jobs │
│ /api/chat LLM chat + sessions │
│ /api/gallery Asset browsing + export │
│ /api/browse File/S3 browser │
│ /api/admin Model registry + templates│
│ /api/refine-prompt Prompt + translate │
│ /api/transcribe Voice-to-text │
└────────────┬────────────────────┬───────────┘
│ │
▼ ▼
┌──────────────────────┐ ┌──────────────────────────┐
│ us-west-2 │ │ us-east-1 │
│ │ │ │
│ Claude Sonnet 4.6 │ │ Nova Canvas │
│ Claude Opus 4.6 │ │ Titan Image v2 │
│ SD 3.5 Large │ │ Nova Sonic │
│ Stable Image Ultra │ │ │
│ Stability AI (post) │ │ │
└──────────────────────┘ └──────────────────────────┘ ... (other regions)
│
▼
┌──────────────────────┐
│ Local Storage │
│ data/styles/ │
│ data/generated/ │
│ data/video/ │
│ data/chat/ │
└──────────────────────┘
┌─────────────────┐
│ ArtSmoker │
└────────┬────────┘
│
┌───────────┼───────────┼───────────┼───────────┐
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
┌──────────┐ ┌────────┐ ┌────────┐ ┌──────────┐ ┌────────┐
│ Style │ │ 2D │ │ Video │ │ Type │ │ Chat │
│ Library │ │ Image │ │ Studio │ │ Studio │ │ Studio │
│ │ │ Studio │ │ │ │ │ │ │
│ Upload │ │Generate│ │Generate│ │ Add text │ │ Multi- │
│ Analyze │ │ images │ │ videos │ │ to imgs │ │ model │
│ Set fonts│ │ │ │ │ │ │ │ LLM │
│ │ │ │ │ │ │ │ │ chat │
└────┬─────┘ └───┬────┘ └───┬────┘ └────┬─────┘ └────────┘
│ │ │ │
│ ┌─────────┴────────────┴─────────┐ │
│ │ Style selected? (optional) │ │
└───►│ Enhances output │◄────┘
└─────────┬──────────────────────┘
│
▼
┌─────────────────┐
│ Gallery │
│ │
│ Browse all │
│ Search/filter │
│ Select & delete │
└────────┬────────┘
│
┌──────────────┼──────────────┐
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────┐ ┌──────────────┐
│ Download │ │ Reload │ │ Add Text │
│ PNG / SVG │ │ in 2D │ │ in Type │
│ │ │ Image │ │ Studio │
│ │ │ Studio │ │ │
│ │ │ (refine &│ │ (overlay │
│ │ │ regen) │ │ text) │
└──────────────┘ └──────────┘ └──────────────┘
Three entry points, one unified gallery:
- Start with a style — upload reference art in the Style Library, let AI analyze it, then generate in any studio. The style guides all output.
- Start without a style — jump straight into 2D Image Studio, Video Studio, or Type Studio. AI uses its best judgement.
- Start from the Gallery — pick any previously generated asset and reload it in the appropriate studio for refinement, add text to it, play a video, or download as PNG/SVG/MP4.
All generated assets (images, videos, text overlays, standalone text) land in the unified Gallery. Nothing is overwritten — each generation creates new assets.
User prompt: "hospital building"
│
▼
┌────────────────────────────────────────────────────────┐
│ 1. Prompt Composition Claude Sonnet (1 opt) │
│ (optional "Compose" button) or Opus (2-5 options) │
│ + style + asset type │
└────────────────────────┬───────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 2. Canary Test │
│ Single image tests moderation │
│ Pass? ──► Full batch Fail? ──► Model switch │
│ or rewrite suggestion │
└────────────────────────┬───────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 3. Parallel Image Generation │
│ Up to 5 options × 5 variations = 25 images │
│ ThreadPool (3-5 workers) │
│ Retry with exponential backoff (3 attempts) │
│ SSE progress streaming to browser │
│ Cooperative cancellation on moderation block │
└────────────────────────┬───────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 4. Post-Processing (per image, optional) │
│ Remove Background ──► Stability AI ($0.07/img) │
│ Upscale ──► Stability AI Creative Upscale ($0.60) │
│ SVG ──► vtracer / potrace / Pillow (free, local) │
└────────────────────────┬───────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 5. Storage │
│ data/generated/{asset_id}/ │
│ ├── asset.png (transparent background) │
│ ├── asset.svg (optional) │
│ └── metadata.json (full prompt lineage) │
│ Smart filenames: prompt-slug_opt1_var2.png │
└────────────────────────────────────────────────────────┘
User clicks Generate
│
▼
┌──────────────────────┐
│ Pre-Check enabled? │
│ (Prompt Pre-Check │
│ toggle, on by │
│ default) │
└───┬────────────┬─────┘
Yes No
│ │
▼ │
┌──────────┐ │
│ Claude │ │
│ Sonnet │ │
│ screens │ │
│ prompt │ │
└───┬────┬─┘ │
Issues? No │
│ └──────►│
▼ │
┌──────────────┐ │
│ Indigo │ │
│ dialog: │ │
│ • Switch │ │
│ • Rewrite │ │
│ • Proceed │ │
│ • Cancel │ │
└──┬───────────┘ │
│◄────────────┘
▼
┌──────────────────────┐
│ Canary test │
│ (1 image to model) │
└───┬────────────┬─────┘
Blocked Pass
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│ Try alt │ │ Full │
│ models │ │ batch │
└───┬────┬─┘ │ runs │
Works? No └──────────┘
│ │
▼ ▼
Emerald Amber
dialog dialog
(switch (rewrite →
or enhanced
rewrite) prompt area)
The 2D Image Studio uses a guided 3-step workflow:
Step 1 — Describe your idea: Type a prompt in the textarea. The placeholder shows a realistic example that changes based on your selected Asset Type (e.g., "A young female warrior in ornate silver armor..." for Character, or "A misty Japanese garden at dawn..." for Environment). Use voice input (mic button) to dictate instead of typing.
Step 2 — Prompt Designer (optional): Click 🎨 Prompt Designer to decompose your prompt into structured visual components. The AI analyzes your prompt and breaks it into editable sections:
- Subject — character description, clothing, accessories, pose, expression
- Scene — setting, background, props, time of day
- Composition — camera angle, framing, depth of field
- Lighting — key light, fill/rim light, mood
- Style & Colors — art style, quality level, and a named color palette with hex swatches
Each field can be individually edited. Generate Enhanced Prompt recomposes your edits into a flat recomposed prompt (shown read-only in Step 2) and then automatically generates the Enhanced AI Prompt for Step 3.
Before the Prompt Designer opens, an AI asset type classification runs — if your prompt describes a scene but you selected "Game Asset", a dialog suggests switching to "Environment" or "Character". This ensures the Prompt Designer decomposes with the correct context.
Step 3 — Enhanced prompt preview (optional): Click Generate Enhanced Prompt to see the model-optimized prompt before generating. The AI takes the recomposed prompt from Step 2 and enhances it with model-specific guidance (anatomy, materials, lighting, prompt structure). You can edit the enhanced prompt before generating. If you used the Prompt Designer in Step 2, this is auto-populated.
Prompt pipeline: User Prompt → Decompose → Recompose (recomposed_prompt) → Enhance with model guidance (enhanced_prompt) → Image Model. For multiple options, the enhancement step generates N distinct interpretations from the same recomposed base. All three levels are stored in metadata.
Generate: Click Generate at any point — Steps 2 and 3 are optional. If you skip them, Generate auto-decomposes, recomposes, and enhances your prompt before proceeding. Prompt Pre-Check (on by default) screens the prompt for moderation issues before generation.
Additional controls:
- Asset Type — select in the sidebar. Changes the prompt placeholder and affects how the AI interprets your prompt. The system suggests switching if it detects a mismatch.
- Art Style — select a style profile to guide generation with your visual identity.
- Dimensions, Options, Variations — configure output size and how many creative concepts to generate.
- Post-Processing — Remove Background, Upscale, SVG conversion (applied after generation).
- IP Declaration — assert ownership or licensing for strict model compatibility.
- Model Settings — view/edit model configuration, discover available Amazon Bedrock models.
Generation progress is streamed in real time via SSE — the UI shows which image is being generated (e.g. "Generating images... 12/25"), elapsed time, and current pipeline stage. If the API is throttled, you'll see "API throttled — waiting to retry..." with the delay, then "Retrying... (attempt 2/3)" — each image retries up to 3 times with exponential backoff so large batches don't lose variants to transient throttling.
Generated results survive navigation — switching tabs and back preserves the 2D Image Studio's DOM state. Only the reset button clears it.
Smart content moderation: When your prompt is blocked by a model's content moderation filters, ArtSmoker handles it progressively through three colour-coded dialogs:
- Indigo (Pre-Check) — before generation, an AI pre-screens your prompt against the selected model's known sensitivity. If issues are detected, you see the specific concerns and can: switch to a recommended model, rewrite the prompt for the current model, proceed anyway, or cancel.
- Emerald (Model Switch) — after a generation block, if an alternative model accepts your prompt as-is, ArtSmoker shows which model works and why. One-click to switch. Full attempt log available ("View N model tests").
- Amber (Rewrite) — when all models reject, an AI-generated rewrite is offered in an editable textarea with specific issues listed. A verified/unverified badge indicates whether the rewrite passed canary testing.
Prompt rewrite behaviour: In all three dialogs, choosing "Rewrite" never overwrites your original prompt. The rewritten version appears in the enhanced prompt area below your original text, with a persistent amber disclaimer: "This rewrite is an attempt to make the prompt compatible — it is still subject to the model's own moderation assessment and may be rejected." You review and edit the enhanced prompt, then click Generate when satisfied. Your original prompt is always preserved in history and metadata.
Common triggers include copyrighted IP names and character references, violence/weapon language, and adult content references. Tip: the "Preview Enhanced Prompt" button often produces prompts that pass moderation naturally, since the AI rephrases in descriptive terms.
Smart canary testing: Before generating the full batch, ArtSmoker sends a single "canary" image request to test the prompt against the model's moderation filters. If the canary is blocked, the batch stops immediately (1 wasted API call instead of N×M×3). If the canary passes, remaining tasks run in parallel with cooperative cancellation — if any task hits a moderation block, the rest skip their API calls automatically.
- Go to the Style Library tab.
- Click Create New Style — enter a name and optionally add generation hints. In the create modal, use the "Import References From" section with Local and S3 browse buttons to select a source directory or bucket path. Browsing opens a server-side file/directory browser modal (single-click selects an item, double-click navigates into directories). Imported references are auto-analyzed on creation.
- Local directory imports scan recursively through all subdirectories for images (.png, .jpg, .jpeg, .gif, .bmp, .webp, .tiff, .tif, .tga, .ico, .svg) and 3D models (.glb, .gltf). Image files are symlinked using relative symlinks (no duplication, portable across machines). 3D model files (.glb/.gltf) have their embedded textures automatically extracted — base64 data URIs, binary buffer chunks, and external texture references are all handled. Extracted textures are saved as copies (prefixed with the model name to avoid collisions). S3 imports list recursively with pagination and download files locally. Up to 100 reference images are imported per style. Supported extensions are centralized in
backend/config.py(IMAGE_EXTENSIONSandMODEL_EXTENSIONS_WITH_TEXTURES). - Two-phase cohesion-aware analysis: Phase 1 sends 8 images to Claude Sonnet to determine cohesion level (high/medium/low) — high means unified style, medium means shared structure with different themes, low means diverse styles. Phase 2 feeds the cohesion assessment to Claude Opus alongside the reference images, guiding it to analyze appropriately for the collection type. When a style has more than 20 references, the analyzer selects a diverse representative subset of 20 for the Opus vision call — ensuring coverage across filename groups and file-size diversity. The AI is told how many total images exist vs. how many it is seeing. The analysis prompt is specifically designed for game assets on transparent backgrounds — asks for material-specific rendering details, proportion system, and shadow/lighting specifics. Extracts 9 style attributes including
materials(how stone, wood, metal are rendered) anddetail_level(what surface details are visible vs simplified). Generation hints are expanded to 200 words covering 8 dimensions: perspective, rendering, materials, color palette, proportions, edge treatment, shadow/lighting, detail level, and background — specific enough that generated assets visually blend with existing references. - In the style detail view, use "Import & Analyze" to add more references and trigger analysis in one step. Drag-and-drop upload is also supported and auto re-analyzes when new images are added.
- "Re-Analyze Style" appears after the initial analysis, letting you manually re-run analysis at any time.
- Generation hints are part of the analysis context — the AI receives both reference images and your hints as "Artist's Guidance" when analyzing, so the style profile understands intent, not just visual appearance. Editing generation hints also triggers automatic re-analysis.
- Back in the 2D Image Studio, select your style from the dropdown — all generated assets will match its visual identity (palette, perspective, rendering style, mood).
┌──────────────────────────────────────────┐
│ Create / Import style │
│ (reference images uploaded or imported) │
└────────────────────┬─────────────────────┘
│
▼
┌──────────────────────────────────────────┐
│ Phase 1: Cohesion Check │
│ Claude Sonnet — 8 images — ~$0.01 │
│ Determines: high / medium / low │
│ high = unified style │
│ medium = shared structure, diff themes │
│ low = diverse collection │
└────────────────────┬─────────────────────┘
│
▼
┌──────────────────────────────────────────┐
│ Phase 2: Full Analysis │
│ Claude Opus — up to 20 images │
│ Guided by cohesion level │
│ + Artist's Guidance (user hints) │
│ Extracts 9 style attributes │
└────────────────────┬─────────────────────┘
│
▼
┌──────────────────────────────────────────┐
│ Phase 3: Hint Generation │
│ Claude Sonnet — 200-word hints │
│ 8 dimensions: perspective, rendering, │
│ materials, palette, proportions, edges, │
│ shadow/lighting, detail level │
└────────────────────┬─────────────────────┘
│
▼
┌──────────────────────────────────────────┐
│ Stored in profile.json │
│ ~$0.14 total per style analysis │
│ Used in all future generation │
└──────────────────────────────────────────┘
Add text to images or generate standalone text assets with AI-designed typography.
- Two modes: "On Image" composites text onto a gallery image; "Standalone" renders text on a transparent background.
- Multi-line text editor with per-line font selection, positioning controls, and voice input (mic button per line — dictate text via Nova Sonic transcription).
- AI-designed layouts — the AI suggests colors, sizes, positions, and effects (shadow, outline, glow). Request 1–5 layout options for different creative directions. The LLM model used for layout is configurable (Complex LLM for best quality, Fast LLM for cheaper) — reads from the registry categories.
- Font picker with live preview — style fonts, 8 bundled fonts (Roboto, Open Sans, Lato, Montserrat, Playfair Display, Oswald, Raleway, Source Code Pro), system fonts, and client-side detected fonts (via Local Font Access API or canvas probing).
- Pre-Processing / Post-Processing — same workflow as 2D Image Studio, with an "Apply" button for post-processing. SVG conversion is on by default.
- Click to zoom — clicking the result preview opens the AssetViewer with full zoom/pan, metadata, download, and image editing tools.
- Results are saved as new gallery assets (originals are never overwritten).
- Unified view of all generated images and videos with a Media filter (All / 2D Artwork / Video).
- Search bar for instant filtering across all assets (prompts, styles, models).
- Multi-select with checkboxes for bulk delete (handles both image and video assets). Deletions are batch-aware — surviving siblings track how many variants were removed, so reloading a partial batch in the Image Studio shows "X of Y images remaining (Z deleted)".
- Assets load immediately with an in-memory metadata cache. Sorted newest-first.
- Pagination support (limit/offset) for large collections.
- Gallery auto-refreshes when you navigate back to it, and after any edit or video generation completes.
- Video cards display a thumbnail with a play overlay, VIDEO badge, and duration indicator. Click to open the video player modal.
- Contextual action buttons per asset based on type: "2D Studio" (indigo) to reload in the image studio, "Add Text" (emerald) to open in Type Studio, "Edit in Type Studio" (purple) for text assets.
- Click any image to open the AssetViewer modal with:
- Zoom/pan — mouse wheel to zoom, drag to pan, Fit/1:1 buttons with active mode highlighting.
- Edit tab — inpaint, erase, or outpaint the image directly. Paint a mask with the brush tool, enter a prompt, choose an editing model, and apply. Default replaces the original image; uncheck "Replace original" to save as a new asset.
- Previous / Next — arrow buttons and keyboard left/right to navigate through the list without closing the viewer.
- Full metadata: original prompt, AI-improved prompt, generation prompt, negative prompt, style, asset type, image model (friendly names), dimensions, seed, batch ID, option/variation index, IP declaration status, filename, and creation date.
- Style snapshot: Each asset stores a snapshot of the style used at generation time (name, description, hints, analysis). If the original style is later deleted, the asset retains the full context. Backward compatible — older assets without snapshots display normally.
Click the microphone button next to the prompt editor to dictate your prompt. The audio is sent to Nova Sonic for transcription.
Note
Voice transcription requires Nova Sonic's bidirectional streaming API, which depends on a compatible boto3 version and model access enabled in us-east-1. If the streaming API is not available, the service returns a placeholder acknowledgment. Full real-time transcription works when Nova Sonic streaming is properly configured.
Navigation order: Style Library → 2D Image Studio → Type Studio → Video Studio → Gallery. Switching between views preserves each view's DOM state. Generated results, form inputs, and scroll positions survive navigation. The amber reset button in 2D Image Studio and Video Studio is the only way to clear their state.
All AI model configuration is centralized in backend/model_registry.json — the single source of truth. Models, regions, pricing, quality tiers, and format templates are all stored here and managed through the UI or API:
- Click "Model Settings" in the sidebar of any studio to open the admin modal — it opens to the relevant tab for that studio.
- 7 tabs organized by studio:
- Image Studio — Image generation models (Nova Canvas, Titan, SD 3.5, Ultra), regions, quality tiers, prompt limits, moderation strictness
- Video Studio — Video models (Nova Reel, Luma Ray), S3 bucket settings, regions, pricing
- Chat Studio — Discovered chat/LLM models (80+ from 16 providers), context windows, vision capability, pricing per 1K tokens
- Type Studio — LLM model for text layout generation (Complex or Fast LLM)
- Shared Studio — Cross-studio LLM categories (Fast LLM, Complex LLM, Fallback LLM, Voice), post-processing models (Remove Background, Upscale)
- Prompt Templates — 19 editable LLM directive prompts organized by studio (see section 4.4)
- Registry JSON — Raw JSON editor for the full model registry
- All sections are collapsible with Show All / Hide All toggles for quick navigation.
- LLM categories and post-processing use dropdown model pickers (populated from discovered models) — not raw text fields.
- Sync from AWS: Scans all Bedrock-supported AWS regions (discovered dynamically), auto-registers new image, video, and chat models, updates regional availability, fetches per-model pricing from the AWS Pricing API, and disables models no longer available. This is the only action that calls AWS discovery APIs — all other operations read from the cached registry.
- Custom model discovery: Sync also discovers fine-tuned custom models (
ListCustomModels), imported models (ListImportedModels), and models with on-demand deployments (ListCustomModelDeployments) or provisioned throughput (ListProvisionedModelThroughputs). Custom models inherit their format family from the base model automatically. - Auto-discovery: New foundation models are registered with
enabled=true— the admin can disable them. Existing models get theiravailable_regionsand Bedrock metadata (modalities, lifecycle, ARN) updated automatically. - Styled confirmation dialogs: All destructive actions (Sync, delete, reset) use custom styled modals — no browser
confirm()popups. - Changes are persisted immediately to
model_registry.jsonvia the Admin API. - The registry is backward compatible — existing assets reference model keys (e.g.
nova_canvas), not raw Bedrock model IDs.
ArtSmoker can deploy open-source AI models on Amazon SageMaker in your own AWS account, extending your capabilities beyond what Amazon Bedrock offers. These run alongside Bedrock models and appear in the same studio dropdowns.
Extensible model catalog: Ships with a built-in catalog of open-source models spanning image generation, upscaling, background removal, depth estimation, segmentation, and video. Adding a new model requires only a catalog entry — no code changes. You can also add custom models via the UI (+ Add Model). The catalog and available models evolve over time.
Deployment options:
- Async (scale-to-zero) — pay only when generating. Scales to zero when idle ($0 cost), scales up automatically on new request. Cold start ~5-10 min.
- Always-On — instant responses, ~$1.41/hr (ml.g5.xlarge)
How to deploy: Model Settings → Custom Models tab → click Deploy. The SageMaker container pulls model weights directly from HuggingFace at startup — no multi-GB local download required.
CPU offloading: Large diffusion models use intelligent CPU offloading to fit on smaller GPU instances. Each model's catalog entry specifies the strategy — model_cpu_offload (keeps active layers on GPU) or sequential_cpu_offload (aggressive per-layer offload for very large models). Applied automatically by the inference handler.
Async generation with Pending Jobs: Self-hosted models generate asynchronously. A Pending Jobs panel appears in the 2D Image Studio showing active jobs with progress indicators. Completed images arrive in the Gallery automatically — no polling or page refresh needed.
HuggingFace token management: Gated models require a Read-only HuggingFace token. The token is stored encrypted in AWS Secrets Manager in your account, managed via the UI (set/update/delete), and shared across all models that need it. Tokens are automatically cleaned up when you tear down all models.
Setup: Add Amazon SageMaker and Secrets Manager permissions to the same IAM role you already use for Bedrock — no separate role or environment variable needed. ArtSmoker auto-discovers your role on EC2/ECS, or auto-creates an ArtSmokerSageMakerRole if needed.
# Add Amazon SageMaker permissions to your existing ArtSmoker role (one command)
aws iam attach-role-policy --role-name ArtSmokerEC2Role \
--policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccessPython dependency: huggingface_hub>=0.23 (install with pip install huggingface_hub)
All models are discovered dynamically from the registry — not hardcoded. The Image Studio dropdown is populated from GET /api/admin/models/image-options and the Video Studio dropdown from GET /api/admin/models/video-options on page load. Any model registered and enabled in the registry appears automatically.
The Image Model dropdown is the primary selection. Below it, a smart summary line shows the active region, quality tier, and per-image cost. An expandable Advanced section lets you override:
- Quality — models that support quality tiers (e.g. Nova Canvas: Standard $0.04/img vs Premium $0.06/img) show a dropdown. Models without tiers show "Default".
- Region — shows regions where the selected model is available, sorted cheapest-first with pricing. "Auto" selects the cheapest region.
A cost estimate updates dynamically based on all selections (model × quality × region × options × variations).
Format families: Models are invoked through a generic invoker (invoke_image_model) that reads request templates from the registry (format_families). Currently 15 families covering image generation (2), image editing (8), post-processing (2), and video generation (2):
- Image generation:
amazon_text_to_image(Nova Canvas, Titan Image),stability_text_to_image(SD 3.5 Large, Stable Image Ultra) - Image editing:
amazon_inpainting,amazon_outpainting,stability_inpaint,stability_outpaint,stability_erase,stability_search_replace,stability_search_recolor,stability_control,stability_style_transfer - Post-processing:
stability_remove_bg,stability_upscale - Video:
nova_reel,luma_ray
Adding a new Bedrock image model requires zero code changes — just register it via the admin API or auto-discovery with the correct format family.
Model-optimized prompt engineering: Prompts are automatically structured as descriptive captions (not commands) following AWS documentation. Negation words are removed from the main prompt and exclusion terms are sent as a separate negative prompt. The prompt is truncated to each model's specific prompt_limit from the registry.
Note
Moderation sensitivity varies by model and is tracked in the registry (moderation_strictness). Nova Canvas is the strictest — it rejects prompts with copyrighted names, weapons, and combat language more aggressively. Stable Diffusion 3.5 Large is more relaxed for action/combat themes. ArtSmoker handles this automatically — when a prompt is blocked, the system tries alternative models ordered by strictness before suggesting a rewrite.
| Layer | Technology |
|---|---|
| Backend | FastAPI (Python 3.11+), boto3, Pydantic |
| Frontend | Vanilla JS, Tailwind CSS (CDN) |
| AI (LLM) | Claude Sonnet 4.6 (fast tasks), Claude Opus 4.6 (complex tasks) |
| AI (Image) | Nova Canvas, Titan Image v2, Stable Diffusion 3.5 Large, Stable Image Ultra |
| AI (Post-processing) | Stability AI (Remove Background, Creative Upscale) |
| AI (Chat) | 80+ LLMs from 16 providers via Bedrock ConverseStream (Claude, Nova, Llama, Mistral, etc.) |
| AI (Video) | Nova Reel v1.0/v1.1 (up to 2min), Luma AI Ray v2 (up to 9s) |
| AI (Voice) | Nova Sonic (speech-to-text via bidirectional streaming) |
| i18n | Custom t() function, 817 keys × 6 languages, reverse-lookup DOM translation |
| SVG Conversion | vtracer (primary), potrace (fallback), Pillow (last resort) |
| Text Rendering | Pillow (shadow, outline, glow effects) |
| Storage | Local filesystem (S3-ready interface) |
| Dev | No-cache middleware for static files; client-side error logging via POST /api/log |
No build step required for the frontend.
ArtSmoker is designed as a local/trusted-network development tool — it runs on the developer's own machine or a private EC2 instance. The security model reflects this:
- No authentication — all API endpoints are open. Appropriate for local development and private team deployments.
- Filesystem browser — the
GET /api/browse/localendpoint allows browsing any directory the server process can access. This is intentional for importing reference art from your machine. - Font serving — path traversal protection validates that font file requests stay within expected directories.
- S3 access — S3 browsing and imports use the server's AWS credentials. The user can access any S3 bucket their IAM role permits.
Warning
Do not expose ArtSmoker to untrusted networks without adding authentication and path restrictions. See the Deployment Roadmap in SPEC.md for production hardening guidance (Phase 4 adds Cognito authentication).
Interactive docs at http://localhost:8000/docs (Swagger UI).
Key endpoints:
| Endpoint | Purpose |
|---|---|
| Generation | |
POST /api/generate/ |
Generate assets (options × variations) with SSE streaming |
POST /api/generate/post-process |
Apply processing to existing assets |
POST /api/generate/edit |
Image editing: inpaint, outpaint, erase, search-replace, etc. Accepts source image, mask, prompt, model. |
POST /api/generate/analyze-moderation |
Analyze a moderation-blocked prompt and suggest a safe rewrite |
| Styles | |
POST /api/styles/ |
Create a style profile |
POST /api/styles/{id}/import |
Bulk-import references from a local folder or S3 URI |
POST /api/styles/{id}/analyze |
Trigger AI style analysis |
| Prompt | |
POST /api/refine-prompt/ |
Preview a refined prompt |
POST /api/transcribe/ |
Voice-to-text (Nova Sonic) |
| Gallery | |
GET /api/gallery/ |
Browse generated assets (supports limit/offset pagination) |
GET /api/gallery/batch/{batch_id} |
Reconstruct full options × variations structure for a batch |
DELETE /api/gallery/ |
Bulk delete assets |
| Type Studio | |
POST /api/type-studio/preview |
Render text overlay preview |
POST /api/type-studio/suggest |
AI layout suggestion for text |
GET /api/type-studio/fonts |
List available fonts |
| Browse | |
GET /api/browse/local?path=~ |
Browse local directory contents |
GET /api/browse/s3/buckets |
List available S3 buckets |
GET /api/browse/s3?bucket=name&prefix=path |
Browse S3 bucket contents |
| Chat | |
POST /api/chat/stream |
Stream LLM response via SSE (Bedrock ConverseStream) |
GET /api/chat/models |
List all available chat models (foundation + custom + imported) |
POST /api/chat/sessions |
Create a new chat session |
GET /api/chat/sessions |
List chat sessions |
GET /api/chat/sessions/{id} |
Load a full session (messages + metadata) |
PUT /api/chat/sessions/{id} |
Update session (title, messages, model, temperature) |
DELETE /api/chat/sessions/{id} |
Delete a session |
POST /api/chat/sessions/{id}/duplicate |
Duplicate a session |
GET /api/chat/sessions/{id}/export |
Export session as Markdown |
GET /api/chat/sessions/{id}/search?q= |
Search within a session's messages |
POST /api/chat/compact |
Compact older messages via LLM summarization |
POST /api/chat/generate-title |
Auto-generate a session title from first exchange |
| Video | |
POST /api/video/generate |
Start async video generation job |
GET /api/video/status/{job_id} |
Poll video generation job status |
GET /api/video/jobs |
List all video generation jobs |
GET /api/video/{id}/mp4 |
Serve video MP4 file |
GET /api/video/{id}/thumbnail |
Serve video thumbnail |
DELETE /api/video/{id} |
Delete a video |
| Admin | |
GET /api/admin/models |
Get full model registry (LLMs, image models, post-processing) |
GET /api/admin/models/image-options |
Enabled text-to-image models for the dropdown (with pricing, quality tiers, regions). Accepts ?region= filter. |
GET /api/admin/regions |
Cached list of Bedrock-supported AWS regions (no AWS calls) |
PATCH /api/admin/models/category/{name} |
Update an LLM category config |
PATCH /api/admin/models/image/{key} |
Update an image model config |
POST /api/admin/models/image |
Add a new image model |
POST /api/admin/discover/refresh-all |
Full refresh: discover regions + scan models + fetch pricing + prune stale data. The ONLY endpoint that calls AWS discovery APIs. |
POST /api/admin/discover/{region}/auto-register |
Scan a single region for models, register new ones, update regions for existing |
GET /api/admin/discover/{region} |
Discover available Bedrock models in a region (raw listing) |
GET /api/admin/templates |
Get all 19 editable prompt templates |
PATCH /api/admin/templates/{name} |
Update a template (validates required variables) |
POST /api/admin/templates/{name}/reset |
Reset a template to default |
POST /api/admin/templates/{name}/enhance |
Enhance a template with AI |
| System | |
POST /api/log |
Client-side error/warning logging (recorded as [CLIENT] in server console) |
GET /api/health |
Health check + AWS credential/Bedrock validation |
ArtSmoker/
├── backend/
│ ├── main.py # FastAPI app, startup validation, static mount
│ ├── config.py # Settings (AWS regions, model IDs, paths, limits)
│ ├── model_registry.json # Single source of truth: models, regions, pricing, format families, quality tiers
│ ├── requirements.txt
│ ├── prompt_templates.json # Persisted editable LLM directive prompts (19 templates)
│ ├── routers/
│ │ ├── generate.py # Two-level asset generation + SSE streaming
│ │ ├── styles.py # Style profile CRUD + directory/S3 import + analysis
│ │ ├── gallery.py # Asset browsing + file serving + bulk delete
│ │ ├── typestudio.py # Type Studio: text overlay, font serving, AI layout
│ │ ├── video.py # Video generation (async), job polling, MP4/thumbnail serving
│ │ ├── chat.py # Chat Studio: LLM streaming, sessions, export, context compaction
│ │ ├── browse.py # Server-side file/S3 browser for reference import
│ │ ├── refine.py # Prompt refinement preview + translation preview
│ │ ├── transcribe.py # Voice transcription
│ │ └── admin.py # Model registry management + Bedrock discovery + prompt templates
│ ├── services/
│ │ ├── bedrock_client.py # Shared Bedrock client with connection pooling
│ │ ├── model_registry.py # Model registry: loads/saves model_registry.json
│ │ ├── prompt_engineer.py # Claude: prompt refinement + concept generation
│ │ ├── image_generator.py # Nova Canvas / Titan / SD 3.5 / Ultra: image gen
│ │ ├── style_analyzer.py # Two-phase style analysis (cohesion + full)
│ │ ├── post_processor.py # Stability AI: bg removal, upscale; vtracer: SVG
│ │ ├── transcriber.py # Nova Sonic: streaming speech-to-text
│ │ ├── import_dedup.py # Smart deduplication (rotations, animations, folders)
│ │ ├── texture_extractor.py # glTF/GLB texture extraction
│ │ ├── prompt_translator.py # Auto-detect language + translate to English
│ │ ├── prompt_templates.py # Editable LLM directive prompts (load/save/validate)
│ │ ├── video_generator.py # Video: async Bedrock invoke, S3 download, ffmpeg thumbnails
│ │ ├── cost_tracker.py # Request-scoped cost accumulator
│ │ ├── telemetry.py # PulseBoard SDK wrapper: tracks server events
│ │ ├── custom_models.py # Self-hosted model catalog (extensible)
│ │ ├── async_jobs.py # Async generation job queue (Pending Jobs panel)
│ │ ├── sagemaker_deployer.py # Amazon SageMaker endpoint management (direct HF pull for HF models)
│ │ └── sagemaker_invoker.py # Routes inference to Amazon SageMaker endpoints
│ ├── models/
│ │ ├── style_profile.py # StyleProfile, AnalyzedStyle, Create/Update
│ │ ├── generation_request.py # GenerationRequest, AssetType, ImageModel enums
│ │ └── generation_result.py # GenerationResult, OptionResult, VariantResult
│ └── storage/
│ └── local_store.py # Local filesystem (S3-compatible interface)
├── frontend/
│ ├── index.html # SPA entry point
│ ├── css/styles.css # Dark theme + animations
│ └── js/
│ ├── app.js # SPA router + DOM caching + navigation + showConfirm()
│ ├── i18n/
│ │ ├── i18n.js # Core: t() function, language switching, reverse lookup
│ │ ├── en.json # English (base) — 817 keys
│ │ ├── ja.json # Japanese
│ │ ├── zh.json # Simplified Chinese
│ │ ├── ko.json # Korean
│ │ ├── fr.json # French
│ │ └── es.json # Spanish
│ ├── services/api.js # Backend API client
│ └── components/
│ ├── ImageStudio.js # 2D Image Studio (options × variations)
│ ├── TypeStudio.js # Type Studio (text overlay)
│ ├── VideoStudio.js # Video Studio (text-to-video generation)
│ ├── ChatStudio.js # Chat Studio (multi-model LLM chat)
│ ├── Gallery.js # Gallery grid + search + bulk ops
│ ├── StyleLibrary.js # Style management + file browser
│ ├── AssetViewer.js # Full-size preview + metadata + download
│ ├── ModelSettings.js # Model registry admin UI (modal)
│ ├── PromptEditor.js # Two-area prompt editor + compose
│ └── VoiceInput.js # MediaRecorder + transcription
├── data/
│ ├── styles/ # Style profiles + reference images (symlinked)
│ ├── generated/ # Output assets (PNG + SVG + metadata.json)
│ ├── video/ # Video assets (MP4 + thumbnails + job metadata)
│ └── chat/ # Chat sessions (JSON per session)
├── SPEC.md # Full technical specification (rebuild blueprint)
└── README.md # This file
Settings in backend/config.py can be overridden via environment variables (prefix ARTSMOKER_):
| Setting | Env Variable | Default | Purpose |
|---|---|---|---|
max_reference_images |
ARTSMOKER_MAX_REFERENCE_IMAGES |
100 | Max images imported per style |
max_analysis_images |
ARTSMOKER_MAX_ANALYSIS_IMAGES |
20 | Max images sent to AI per analysis call |
aws_region_models |
ARTSMOKER_AWS_REGION_MODELS |
us-west-2 | Region for Claude + Stability AI models |
aws_region_images |
ARTSMOKER_AWS_REGION_IMAGES |
us-east-1 | Region for Nova Canvas + Titan + Nova Sonic |
aws_profile |
ARTSMOKER_AWS_PROFILE |
None | AWS profile name (uses default chain if unset) |
auto_update |
ARTSMOKER_AUTO_UPDATE |
true | Git pull on startup + 24h periodic check, self-restart on update |
Reducing max_analysis_images reduces AI vision costs per analysis. Reducing max_reference_images limits storage. Both can be tuned based on budget.
Note
The tables below are reference pricing for planning purposes. The app itself shows live per-model pricing in the Image Studio sidebar — fetched from the AWS Pricing API during registry refresh and stored in model_registry.json. The in-app cost estimate updates dynamically based on selected model, quality tier, region, and batch size.
All pricing from the official Amazon Bedrock Pricing page for US regions. See also SPEC.md for monthly team projections and deployment cost estimates.
| Service | Model | Cost | Unit |
|---|---|---|---|
| Claude Sonnet 4.6 | us.anthropic.claude-sonnet-4-6 |
$3.00 input / $15.00 output | per 1M tokens |
| Claude Opus 4.6 | us.anthropic.claude-opus-4-6-v1 |
$5.00 input / $25.00 output | per 1M tokens |
| Claude Opus (vision) | same | ~$0.008 | per 1024×1024 image input |
| Nova Canvas | amazon.nova-canvas-v1:0 |
$0.06 | per image (1024×1024 premium) |
| Titan Image v2 | amazon.titan-image-generator-v2:0 |
$0.01 | per image |
| Stable Diffusion 3.5 Large | stability.sd3-5-large-v1:0 |
$0.08 | per image |
| Stable Image Ultra | stability.stable-image-ultra-v1:1 |
$0.14 | per image |
| Remove Background | Stability AI | $0.07 | per image |
| Creative Upscale | Stability AI | $0.60 | per image |
| SVG Conversion | Local (vtracer/potrace) | $0.00 | free |
Note
Prices from the official Amazon Bedrock Pricing page as of March 2026. Prices may change — always verify against the official source before budgeting.
These LLM calls are included in the generation workflow but not separately itemized in the batch cost tables below:
| Call | Model | When | Approx. Cost |
|---|---|---|---|
| Prompt Pre-Check | Claude Sonnet 4.6 | Before generation (if toggle enabled) | ~$0.005 |
| Moderation Rewrite | Claude Sonnet 4.6 | Only when all models reject a prompt | ~$0.005 |
| Type Studio Layout | Claude Opus 4.6 | Each AI layout suggestion request | ~$0.02–$0.05 |
These are small — pre-check and moderation rewrite are a fraction of a cent each. Type Studio layout is comparable to a single-option prompt refinement.
~$0.14 per style (20 images sent to Claude Opus + 8 images cohesion check at Claude Sonnet). The cohesion check adds ~$0.01 (Sonnet with 8 images is very cheap).
Includes prompt refinement/concept generation + image generation:
| Scenario | Nova Canvas | Titan Image v2 | Stable Diffusion 3.5 Large | Stable Image Ultra |
|---|---|---|---|---|
| 1 option × 1 variation | ~$0.07 | ~$0.02 | ~$0.09 | ~$0.15 |
| 1 option × 5 variations | ~$0.31 | ~$0.06 | ~$0.41 | ~$0.71 |
| 5 options × 5 variations | ~$1.55 | ~$0.30 | ~$2.05 | ~$3.55 |
| Add-on | Per image | 1 image | 5 images | 25 images |
|---|---|---|---|---|
| Remove Background | $0.07 | $0.07 | $0.35 | $1.75 |
| Creative Upscale | $0.60 | $0.60 | $3.00 | $15.00 |
| Convert to SVG | $0.00 | $0.00 | $0.00 | $0.00 |
Tip
Creative Upscale note: Handles Stability AI's 16MB response payload limit automatically by using JPEG output format internally, then converting back to PNG. Includes retry with exponential backoff for API throttling.
| Example | Configuration | Total Cost |
|---|---|---|
| Cheapest | 1×1, Titan Image, no processing | ~$0.02 |
| Standard | 1×5, Nova Canvas, Remove BG | ~$0.66 |
| Full exploration | 5×5, Stable Diffusion 3.5 Large, Remove BG + SVG | ~$3.80 |
| Premium | 5×5, Stable Image Ultra, Remove BG + Upscale + SVG | ~$20.30 |
Tip
Key takeaway: Image generation itself is cheap ($0.01–$0.14/image). Creative Upscale at $0.60/image is the dominant cost — use it selectively on your final chosen assets, not the full batch. Remove Background at $0.07/image is reasonable. SVG conversion is free (runs locally).
Important
Generated Content Quality: All images, videos, and other assets generated by ArtSmoker are produced by AI models available through Amazon Bedrock, including both first-party AWS models and third-party models. The quality, accuracy, and appropriateness of generated content depend entirely on the prompts provided, the models selected, and the style references uploaded by the user. The authors and contributors of ArtSmoker make no guarantees regarding the quality, suitability, or fitness for purpose of any generated content.
Intellectual Property: Users are solely responsible for ensuring that their prompts, reference images, and generated outputs do not infringe on any third-party intellectual property rights, including but not limited to copyrights, trademarks, and personality rights. ArtSmoker is a tool — it does not filter, validate, or assess the IP status of inputs or outputs. The tool authors and contributors bear no responsibility for any IP infringement arising from the use of this software.
AI Model and Service Terms: Generated content is subject to the terms of service and acceptable use policies of the underlying AI model providers accessible through Amazon Bedrock. Users should review the AWS Service Terms, the Amazon Bedrock SLA, and the individual model provider terms before using generated assets in production or commercial contexts.
No Warranty: This software is provided "as is" without warranty of any kind. See LICENSE for full terms.
See SPEC.md for the complete technical specification — architecture, component design, model configuration, API reference, security model, pricing, deployment roadmap, and enough detail to rebuild the project from scratch.