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ai-pixel-art-image-generation

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포크1

업데이트2026년 5월 10일 03:49

This skill should be used when the user wants to generate images via OpenAI, Azure AI Foundry, or fal.ai OR wants to create pixel-art sprites, tileset sheets, walk-cycle animations, tile maps, or other game graphic assets for 2D games. Triggers on "generate image", "create image", "make picture", "draw", "illustrate", and also on "pixel art sprite", "tileset", "spritesheet", "tiled map", "walk cycle", "generate tiles for my game". Handles Azure auth (CLI → DefaultAzureCredential → API key), Gemini 2.5 Flash Image auth (API key) for frame-consistent animation, fal.ai auth (API key) for Flux/Stable Diffusion models, pixel-art post-processing (nearest-neighbor + palette quantize + optional rembg), and Tiled-compatible TSX/TMJ export.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

ianlintner

ianlintner/ai-pixel-art-image-generation

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

AI Pixel Art & Tile Map Generator

Overview

Generate images via OpenAI/Azure AI Foundry and, via an integrated pixel-art pipeline, produce Tiled-compatible pixel-art sprites, tileset sheets, and short sprite-sheet animations for 2D games. The skill covers two user paths:

General image generation — text-to-image via gpt-image-2 (OpenAI/Azure) or Flux/Stable Diffusion (fal.ai).
Pixel-art game-asset mode — generation + nearest-neighbor downscale + palette quantize + (for animations) Gemini 2.5 Flash Image reference-based frame consistency + TSX/TMJ export for Tiled.

Direct OpenAI uses OPENAI_API_KEY. Azure uses AZURE_OPENAI_ENDPOINT (or --endpoint) plus Azure CLI, DefaultAzureCredential, or AZURE_OPENAI_API_KEY. fal.ai uses FAL_KEY (or --fal-api-key). Provider selection is auto: Azure is used when AZURE_OPENAI_ENDPOINT is set, otherwise direct OpenAI is used. Pass --provider fal explicitly to use fal.ai.

When to Use Which Script

Script	Use when user wants...
`scripts/generate_image.py`	A general-purpose image (any subject, fixed or flexible `gpt-image-2` size).
`scripts/generate_sprite.py`	A single pixel-art sprite (16/32/64 px) with a named palette.
`scripts/generate_tileset.py`	N unique tiles packed into a sheet + TSX + TMJ for Tiled.
`scripts/generate_animation.py`	2–8 frame sprite-sheet animation (walk/idle) + TSX with `<animation>`.
`scripts/pixelize.py`	Post-process an existing image into pixel art (no generation).
`scripts/qa_report.py`	Standalone QA metrics on an existing pixel-art PNG.

All scripts are invokable directly via python3 and print their output paths on completion.

Available Image Models

OpenAI / Azure

Model	Provider	Use case
`gpt-image-2`	OpenAI/Azure	Default — highest quality
`gpt-image-1`	OpenAI/Azure	Older generation compatibility
`gpt-image-1-mini`	OpenAI/Azure	Faster/cheaper drafts
`sora-2`	OpenAI/Azure	Video (not images)

fal.ai — Text-to-Image

Model	Best for
`fal-ai/recraft/v3/text-to-image`	Pixel art tiles & sprites — SOTA vector art, consistent style
`fal-ai/flux/dev`	General purpose, high quality (default)
`fal-ai/flux/schnell`	Fast iteration, 4-step distilled, cheap batch tiles
`fal-ai/nano-banana-pro`	Google's latest — excellent realism + editing combo
`fal-ai/qwen-image`	Complex text rendering (UI/HUD elements)
`fal-ai/flux-pro/v1.1-ultra`	Maximum quality, best composition
`fal-ai/recraft/v4/pro/text-to-image`	Production-ready, refined lighting & materials
`fal-ai/seedream/v4.5/edit`	ByteDance unified generation + editing

fal.ai — Image-to-Image (Sprite Editing)

Model	Best for
`fal-ai/nano-banana-pro/edit`	Best editor — refine generated sprites, restyle
`fal-ai/flux-pro/kontext`	Reference image + text → targeted local edits
`fal-ai/reve/edit`	Simple image-to-image transform via text prompt
`fal-ai/bria/fibo-edit/edit`	JSON + Mask + Image precise editing

fal.ai — Utility Tools

Model	Tool	Use case
`fal-ai/bria/background/remove`	`bg-remove-bria`	Best BG removal — commercially safe
`fal-ai/seedvr/upscale/image`	`upscale-seedvr`	SeedVR2 upscale — $0.001/megapixel
`fal-ai/topaz/upscale/image`	`upscale-topaz`	Professional-grade upscale (highest fidelity)
`fal-ai/ffmpeg-api/images-to-video`	`images-to-video`	Animation export — stitch frames → MP4
`fal-ai/kling-video/v3/pro/image-to-video`	`image-to-video`	Animate a still sprite into video

Gemini (separate provider): gemini-2.5-flash-image ("Nano Banana") — used for multi-frame animation consistency via reference images.

Auth

OpenAI

Use OPENAI_API_KEY or --openai-api-key.

Azure

Azure CLI (az login) — tried first; best for local/interactive.
DefaultAzureCredential — managed identity, env service principal, workload identity.
API key (AZURE_OPENAI_API_KEY or --api-key) — fallback for CI/headless.

Install to enable credential auth: pip install azure-identity

Endpoint gotcha. Azure AI Foundry resources expose two URLs. Use the Azure OpenAI legacy endpoint for gpt-image-*, not the AI Services endpoint:

✅ https://<resource>.openai.azure.com/ — works with scripts/lib/azure_client.py.
❌ https://<resource>.services.ai.azure.com/ — Foundry-native API, not compatible with this skill.

Discover it with:

az cognitiveservices account show --name <resource> --resource-group <rg> \
  --query 'properties.endpoints' -o json

Provider auto-select. Provider resolves to azure when AZURE_OPENAI_ENDPOINT is set in the environment. Some shells/tools (e.g. agent runners) don't source ~/.zshrc; if --provider auto falls back to OpenAI unexpectedly, either prefix the call (AZURE_OPENAI_ENDPOINT=... python scripts/…) or pass --provider azure --endpoint https://… explicitly.

Deploying a model on Azure

Before first use you need a deployment of the image model on your resource. The az cognitiveservices account deployment create extension has a bug where it pins an unsupported api-version. Use az rest directly:

SUB=$(az account show --query id -o tsv)
RG=$(az cognitiveservices account list \
       --query "[?name=='<resource>'].resourceGroup" -o tsv)

az rest --method put \
  --url "https://management.azure.com/subscriptions/${SUB}/resourceGroups/${RG}/providers/Microsoft.CognitiveServices/accounts/<resource>/deployments/gpt-image-2?api-version=2024-10-01" \
  --body '{"sku":{"name":"GlobalStandard","capacity":1},"properties":{"model":{"format":"OpenAI","name":"gpt-image-2","version":"2026-04-21"}}}'

Check what's deployable in your region first:

az cognitiveservices model list --location <region> \
  --query "[?kind=='OpenAI' && starts_with(model.name,'gpt-image')].{name:model.name,version:model.version}" -o table

Regional availability (as of 2026-04). gpt-image-2 (version 2026-04-21) is only published in eastus2. gpt-image-1, gpt-image-1-mini, and gpt-image-1.5 are more broadly available. If your resource lives elsewhere, either deploy gpt-image-1.5 (newest broadly-available) or create a second resource in eastus2.

Gemini (only for `generate_animation.py`)

API key via GEMINI_API_KEY env var (or --gemini-api-key). Google has no usable Entra equivalent for this endpoint.

fal.ai

API key via FAL_KEY env var (or --fal-api-key). Get a key at https://fal.ai/dashboard/keys.

export FAL_KEY="your-key-here"
# or pass per-command:
python3 scripts/generate_image.py --provider fal --fal-api-key "..." --prompt "..." --output ~/img.png

Default model. fal-ai/flux/dev is the default. Override with --model or --deployment, or set FAL_MODEL in the environment.

Quality mapping. When using fal-ai/flux/dev (default):

--quality low or --quality medium → switches to fal-ai/flux/schnell (faster, 4-step distilled)
--quality high → stays on fal-ai/flux/dev (best quality)

Size mapping. fal.ai uses named presets. Standard sizes (1024x1024, 1024x768, etc.) are mapped automatically. Unrecognized sizes fall back to square_hd (1024×1024).

fal.ai Utility Tools

The scripts/lib/fal_tools.py module provides fal-hosted utilities as cloud alternatives to local libraries. Import and use directly:

from lib.fal_tools import remove_background_bria, upscale_seedvr, images_to_video

# Background removal (Bria RMBG 2.0 — commercially safe)
no_bg = remove_background_bria(sprite_bytes)

# Upscale before pixelize downscale (SeedVR2 — $0.001/megapixel)
hi_res = upscale_seedvr(sprite_bytes, scale=2)

# Stitch walk-cycle frames into MP4 animation
video = images_to_video([frame0, frame1, frame2, frame3], fps=8, hold_frames=15)

# Animate a still sprite into video (Kling 3.0 Pro)
animated = image_to_video(sprite_bytes, prompt="idle breathing animation")

Or use the dispatcher:

from lib.fal_tools import run_tool
result = run_tool("bg-remove-bria", image_bytes)
result = run_tool("upscale-seedvr", image_bytes, scale=2)
video = run_tool("images-to-video", [frame0, frame1, frame2, frame3])

Dependencies

pip install openai azure-identity google-genai fal-client pillow rembg onnxruntime

openai, azure-identity → direct OpenAI, Azure auth, and image generation.
google-genai → Gemini reference-image generation (required for generate_animation.py).
fal-client → fal.ai image generation (Flux, Stable Diffusion, etc.) and utility tools (background removal, upscaling).
pillow → post-processing pipeline.
rembg + onnxruntime → optional local background removal (needed only when --transparent-bg is passed; pulls ~100 MB of ONNX runtime).

Workflows

1. General image generation

# OpenAI (default)
python3 scripts/generate_image.py \
  --prompt "a cat on mars, photorealistic" \
  --size 1024x1024 --quality medium \
  --output ~/cat.png

# fal.ai with Flux
python3 scripts/generate_image.py \
  --provider fal --model fal-ai/flux/dev \
  --prompt "a cat on mars, photorealistic" \
  --size 1024x1024 --quality high \
  --output ~/cat_fal.png

Common sizes: 1024x1024, 1536x1024, 1024x1536, 2048x2048, 2048x1152, 3840x2160, 2160x3840, auto. Custom gpt-image-2 sizes are accepted when they satisfy the model constraints. Qualities: low, medium, high.

2. Single pixel-art sprite

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/generate_sprite.py \
  --prompt "orange tabby cat, front view, idle stance" \
  --size 64 --palette db32 --transparent-bg \
  --output ~/cat_64.png

--size is the final pixel dimension (square).
--palette is one of: gameboy, pico8, db16, db32, nes, aap64, or auto.
--palette auto picks a palette from subject keywords — see the Palette Auto-Pick table.
--outline {none,palette-darkest} — default palette-darkest. Adds a 1-px dark ring via alpha dilation → XOR → darkest-luminance neighbour colour before palette quantize.
--transparent-bg runs rembg before downscale.
--qa runs QA metrics after save, writes <output>.qa.json, and exits non-zero on hard-gate failure.

Output: square RGBA PNG at --size × --size, limited to palette colors, hard edges.

3. Tileset for Tiled

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/generate_tileset.py \
  --prompt "grass, dirt, stone, water, sand, path, wood plank, stone brick" \
  --tile-size 32 --count 16 --columns 4 --palette pico8 \
  --name overworld --output-dir ~/tilesets/overworld/

Comma-separated tile concepts in --prompt; repeats if fewer than --count. Emits:

overworld.png — packed sheet at columns*K × rows*K.
overworld.tsx — Tiled tileset XML with per-tile name properties.
overworld.tmj — minimal example map filled with tile 1, openable in Tiled.

Tileset flags:

--palette {gameboy,pico8,db16,db32,nes,aap64,auto} — auto picks from keywords (e.g., "stone" → db32).
--seamless {none,crop,torus,auto} — default auto. Pixelizes source at 3× tile size, centre-crops for edge consistency, then if seam_diff ≥ 12.0 runs a torus-wrap feather blend. See references/seamless.md (if present) or lib/seamless.py.
--qa runs QA metrics (per-tile seam_diff mean, palette fidelity, alpha crispness) and writes a .qa.json sidecar.

4. Sprite-sheet animation

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/generate_animation.py \
  --prompt "knight with blue cape, side view, walking right" \
  --frames 4 --tile-size 32 --palette db16 \
  --duration-ms 120 --action walk \
  --name knight_walk --output-dir ~/sprites/knight/

Frame 0 is generated via OpenAI/Azure (gpt-image-2).
Frames 1..N use Gemini 2.5 Flash Image with frame 0 as a reference image to maintain character consistency.
TSX emits an <animation> block on tile id 0 that cycles all frames at --duration-ms.
Frame prompts come from POSE_LIBRARY in generate_animation.py, keyed by (action, frame_idx, total). Supported actions today: walk (4 frames, contact/passing/contact-mirror/passing-mirror), idle (2 frames), attack (4 frames: wind-up/strike/recoil/recovery). Unknown keys fall back to generic "frame N of M" phrasing.
--qa runs animation QA (palette fidelity, alpha crispness, silhouette_iou between frame 0 and frame 2, bbox_drift_x, bbox_drift_y) and writes a .qa.json sidecar.
--transparent-bg enables rembg on every frame, then applies a shared bbox crop across all frames so the character stays aligned frame-to-frame. Per-frame tight-crop would drift position and break the walk-cycle feel.

Consistency caveat: walk cycles may need rerolls. Rerun for different frames if one drifts.

5. Post-process an existing image

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/pixelize.py \
  --input raw.png --output sprite.png \
  --size 32 --palette pico8 --transparent-bg

Prompt Engineering

See references/pixel_art_prompting.md for detailed guidance. Key rules:

Include "pixel art sprite, clean hard edges, limited palette, plain flat background, no text, no watermark".
Specify view angle explicitly ("side view profile", "top-down").
For tilesets, add "top-down game tile, seamless, centered".
Avoid "photorealistic", "smooth shading", "gradients".

Tiled Format Notes

See references/tiled_format.md. Quick facts:

TSX <image source="..."> is relative to the TSX file — keep PNG + TSX in the same directory.
TMJ data array uses global ids (firstgid + local_id). Tile 0 in TSX → gid 1 in TMJ.
Tile animations declared inline; game engines must re-implement playback at runtime (the <animation> tag is metadata).

Palettes

See references/palettes.md. Available: gameboy (4), pico8 (16), db16 (16), db32 (32), nes (54), aap64 (64).

Palette auto-pick

When --palette auto is passed, lib/palettes.suggest_palette scans the prompt against a keyword table and picks a palette whose colour range matches the subject. Explicit --palette <name> always wins. The chosen palette and matched keyword are logged to stderr.

Keyword pattern	Palette	Why
`metal\|steel\|armor\|stone\|grey\|gray\|silver\|iron\|dungeon\|rock\|brick`	`db32`	Mid-greys present; avoids "stone on pico8" fail
`tropical\|beach\|coral\|jungle\|aquatic\|underwater\|reef`	`aap64`	Wide warm/cool range for saturated scenes
`gameboy\|monochrome\|green only`	`gameboy`	4-shade monochrome
`arcade\|nes\|8-bit\|8bit`	`nes`	Canonical NES palette
`knight\|fantasy\|rpg\|character\|hero\|warrior\|wizard\|mage`	`db16`	DB16 is the canonical RPG-character palette
`terrain\|tile\|overworld\|map\|landscape\|biome`	`db32`	Covers earth/foliage/water tones
`detailed\|portrait\|hi-detail\|high-detail`	`aap64`	64 colours support subtle shading
(no match)	`db32`	Default safe choice

QA metrics and hard gates

When --qa is passed to any generator, or via scripts/qa_report.py <input>, lib/qa_metrics computes the following and writes a <artifact>.qa.json sidecar:

Metric	Scope	Threshold	Gate
`palette_fidelity`	all	== 1.0	hard
`alpha_crispness`	all	≥ 0.999	hard
`tile_seam_diff_mean`	tileset	≤ 12.0 (L2 0–255, per-tile mean)	hard
`silhouette_iou_f0_f2`	animation	≥ 0.85	hard
`bbox_drift_x`	animation	≤ 6 px (walk-cycle leg extension)	hard
`bbox_drift_y`	animation	≤ 3 px (1–2 px bob allowed)	hard
`baseline_alignment`	sprite	≥ 3 contiguous opaque in lowest row	hard
`outline_coverage`	sprite	≥ 0.85	soft
`palette_coverage`	sprite	0.15 ≤ x ≤ 0.60	soft

Hard failures cause non-zero exit. Standalone re-run:

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/qa_report.py \
  --input ~/tilesets/overworld/overworld.png --kind tileset \
  --palette db32 --tile-size 32 --columns 4

Troubleshooting

Azure 401 — CLI token expired (az login) or wrong API key.
Azure 404 DeploymentNotFound — Deployment name mismatch; verify with az cognitiveservices account deployment list -n <resource> -g <rg>. If the model exists in the region but isn't deployed yet, create the deployment (see "Deploying a model on Azure" above).
Azure 404 from AI Services endpoint — you're pointing at https://<resource>.services.ai.azure.com/ instead of https://<resource>.openai.azure.com/. The skill's client needs the legacy OpenAI endpoint.
Azure 429 — deployment rate limit hit; default rate on fresh GlobalStandard image deployments is ~1 req/60s at capacity 1. Bump capacity via the same az rest deploy command (PATCH with higher sku.capacity) or wait between generations.
Azure api-version error on deployment create — the CLI extension pins 2025-09-01 which Azure rejects and ignores --api-version. Use the az rest workaround in "Deploying a model on Azure".
Azure ContentFilterError — Rephrase prompt; Azure content policy violation.
ERROR: OpenAI provider requires OPENAI_API_KEY when you expected Azure — AZURE_OPENAI_ENDPOINT isn't in the process env (shell didn't source ~/.zshrc, or the runner strips it). Prefix inline: AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com/ python scripts/… or pass --provider azure --endpoint ….
Gemini auth error — GEMINI_API_KEY unset; obtain a key from Google AI Studio.
Gemini 429 RESOURCE_EXHAUSTED on gemini-2.5-flash-preview-image — the image-generation model has zero free-tier quota. Upgrade to a paid Google AI Studio plan to use generate_animation.py.
rembg missing — pip install rembg onnxruntime; first run downloads the u2net model (~100 MB).
Tileset renders as magenta in Tiled — image path wrong; ensure PNG and TSX sit side-by-side.
Walk cycle frame breaks — reroll by rerunning with a slightly different action verb ("walking left foot forward").
Mushy output — richer palette, larger --size, tighter prompt. See references/pixel_art_prompting.md.
QA outline_coverage soft-fail at 32px low quality — expected. The model doesn't reliably draw 1-px outlines at small sizes. Either raise --quality medium, increase --size 64, or rely on --outline palette-darkest post-process (default) which adds the ring deterministically.
QA palette_coverage soft-fail < 0.15 — prompt/style is producing a near-monochrome sprite. Loosen prompt colour constraints or switch to a smaller palette (e.g. gameboy/pico8).

Recommendations

Lessons from real runs — read before kicking off a batch.

Model selection

Default to gpt-image-2 when deployed in your region (currently eastus2-only on Azure). Best silhouette fidelity and palette discipline.
gpt-image-1.5 is the best broadly-available fallback. Near-parity quality, wider region coverage.
gpt-image-1-mini for iteration/drafts: ~3× faster, good enough to validate prompt/style/layout before committing. Weaker at 1-px outlines and small-detail fidelity.
Never ask sora-2 for images — it's a video model and will fail or waste quota.

Quality vs size

Goal	Recommended combo
Final 32×32 sprite	`--size 32 --quality medium`, rely on `--outline palette-darkest`
Final 64×64 sprite	`--size 64 --quality medium` or `high`
Tileset (32-px tiles)	`--tile-size 32 --quality medium` + `--seamless auto`
Walk cycle	`--tile-size 32 --quality medium` — frame 0 dictates everything; invest quality there
Iteration / A-B prompts	`--size 32 --quality low --deployment gpt-image-1-mini`

--quality low at 32 px is fine for shape testing but will soft-fail outline/coverage QA. Don't chase hard gates at low.

Palette strategy

When in doubt, use --palette auto and trust the keyword table.
db32 is the default safe choice: covers earth/foliage/water/metal.
pico8 over-saturates dungeons, armour, and stone — its auto-pick rules already steer those to db32, don't override.
For animation consistency, pass the same palette to all frames. Gemini frame-to-frame drift is absorbed by the palette quantize step.

Animation-specific

Frame 0 is generated by gpt-image-2 / Azure. Frames 1..N are Gemini with frame 0 as reference. Spend your prompt-engineering budget on frame 0.
walk with 4 frames is the sweet spot. 2 frames looks janky; 8 frames rarely improves perceived motion and 4× the Gemini cost.
--transparent-bg uses a shared bbox across all frames — do not post-process frames individually or they will jitter.
Expect to reroll ~1-in-4 animations. Budget for it.

Cost / rate

Fresh Azure image deployments default to capacity 1 ≈ 1 req/60s. For batch generation (e.g. 16-tile tileset), either bump capacity (PATCH sku.capacity) or run sequentially with retry.
OpenAI direct has no per-deployment cap but is priced per-image; low quality is roughly 1/4 the cost of high.
rembg (on --transparent-bg) is local CPU — free but slow (~2-4 s/image on first call, cached after).

QA-driven iteration loop

First pass: --quality low, no --qa — just eyeball shape and pose.
Second pass: --quality medium --qa. Read .qa.json. If all hard gates pass, ship.
On soft fails:
- outline_coverage → already handled by --outline palette-darkest default; only worry if you set --outline none.
- palette_coverage low → richer prompt or smaller palette.
- palette_coverage high → subject is too busy for the palette; scale down palette or simplify prompt.
On hard fails, don't tune — regenerate with a different seed/prompt. Hard-fail metrics (palette fidelity, alpha crispness, silhouette IoU) are binary problems, not degrees.

Path / install

Scripts in this doc are shown under ~/.claude/skills/ai-pixel-art-image-generation/ but the skill is agent-agnostic. Substitute your install root:

Claude Code: ~/.claude/skills/ai-pixel-art-image-generation/
OpenCode: ~/.config/opencode/skill/ai-pixel-art-image-generation/
Cloned repo / dev: run from the repo root with python scripts/…

Scope

Supported:

Tiled TSX/TMJ export, uniform grid sheets, per-tile names, simple frame animations.
Seamless tiling post-process (3× crop + torus-blend fallback).
1-px sprite outlines via dilate-XOR-darkest-neighbour.
Explicit per-frame pose library for walk/idle/attack animations using frame 0 as the Gemini reference.
Keyword-based palette auto-pick (--palette auto).
QA metrics with hard-gate exits (--qa on any generator, or scripts/qa_report.py standalone).

Not in this skill:

LDtk export, Aseprite .ase export.
Tile bleed/extrude for runtime texture filtering (planned P1).
Auto-reroll of broken frames, animated tile-scene compositing.
Autotile / Wang 47-tile blob sets.

name

ai-pixel-art-image-generation

description

AI Pixel Art & Tile Map Generator

Overview

General image generation — text-to-image via gpt-image-2 (OpenAI/Azure) or Flux/Stable Diffusion (fal.ai).
Pixel-art game-asset mode — generation + nearest-neighbor downscale + palette quantize + (for animations) Gemini 2.5 Flash Image reference-based frame consistency + TSX/TMJ export for Tiled.

When to Use Which Script

Script	Use when user wants...
`scripts/generate_image.py`	A general-purpose image (any subject, fixed or flexible `gpt-image-2` size).
`scripts/generate_sprite.py`	A single pixel-art sprite (16/32/64 px) with a named palette.
`scripts/generate_tileset.py`	N unique tiles packed into a sheet + TSX + TMJ for Tiled.
`scripts/generate_animation.py`	2–8 frame sprite-sheet animation (walk/idle) + TSX with `<animation>`.
`scripts/pixelize.py`	Post-process an existing image into pixel art (no generation).
`scripts/qa_report.py`	Standalone QA metrics on an existing pixel-art PNG.

All scripts are invokable directly via python3 and print their output paths on completion.

Available Image Models

OpenAI / Azure

Model	Provider	Use case
`gpt-image-2`	OpenAI/Azure	Default — highest quality
`gpt-image-1`	OpenAI/Azure	Older generation compatibility
`gpt-image-1-mini`	OpenAI/Azure	Faster/cheaper drafts
`sora-2`	OpenAI/Azure	Video (not images)

fal.ai — Text-to-Image

Model	Best for
`fal-ai/recraft/v3/text-to-image`	Pixel art tiles & sprites — SOTA vector art, consistent style
`fal-ai/flux/dev`	General purpose, high quality (default)
`fal-ai/flux/schnell`	Fast iteration, 4-step distilled, cheap batch tiles
`fal-ai/nano-banana-pro`	Google's latest — excellent realism + editing combo
`fal-ai/qwen-image`	Complex text rendering (UI/HUD elements)
`fal-ai/flux-pro/v1.1-ultra`	Maximum quality, best composition
`fal-ai/recraft/v4/pro/text-to-image`	Production-ready, refined lighting & materials
`fal-ai/seedream/v4.5/edit`	ByteDance unified generation + editing

fal.ai — Image-to-Image (Sprite Editing)

Model	Best for
`fal-ai/nano-banana-pro/edit`	Best editor — refine generated sprites, restyle
`fal-ai/flux-pro/kontext`	Reference image + text → targeted local edits
`fal-ai/reve/edit`	Simple image-to-image transform via text prompt
`fal-ai/bria/fibo-edit/edit`	JSON + Mask + Image precise editing

fal.ai — Utility Tools

Model	Tool	Use case
`fal-ai/bria/background/remove`	`bg-remove-bria`	Best BG removal — commercially safe
`fal-ai/seedvr/upscale/image`	`upscale-seedvr`	SeedVR2 upscale — $0.001/megapixel
`fal-ai/topaz/upscale/image`	`upscale-topaz`	Professional-grade upscale (highest fidelity)
`fal-ai/ffmpeg-api/images-to-video`	`images-to-video`	Animation export — stitch frames → MP4
`fal-ai/kling-video/v3/pro/image-to-video`	`image-to-video`	Animate a still sprite into video

Gemini (separate provider): gemini-2.5-flash-image ("Nano Banana") — used for multi-frame animation consistency via reference images.

Auth

OpenAI

Use OPENAI_API_KEY or --openai-api-key.

Azure

Azure CLI (az login) — tried first; best for local/interactive.
DefaultAzureCredential — managed identity, env service principal, workload identity.
API key (AZURE_OPENAI_API_KEY or --api-key) — fallback for CI/headless.

Install to enable credential auth: pip install azure-identity

Endpoint gotcha. Azure AI Foundry resources expose two URLs. Use the Azure OpenAI legacy endpoint for gpt-image-*, not the AI Services endpoint:

✅ https://<resource>.openai.azure.com/ — works with scripts/lib/azure_client.py.
❌ https://<resource>.services.ai.azure.com/ — Foundry-native API, not compatible with this skill.

Discover it with:

az cognitiveservices account show --name <resource> --resource-group <rg> \
  --query 'properties.endpoints' -o json

Deploying a model on Azure

SUB=$(az account show --query id -o tsv)
RG=$(az cognitiveservices account list \
       --query "[?name=='<resource>'].resourceGroup" -o tsv)

az rest --method put \
  --url "https://management.azure.com/subscriptions/${SUB}/resourceGroups/${RG}/providers/Microsoft.CognitiveServices/accounts/<resource>/deployments/gpt-image-2?api-version=2024-10-01" \
  --body '{"sku":{"name":"GlobalStandard","capacity":1},"properties":{"model":{"format":"OpenAI","name":"gpt-image-2","version":"2026-04-21"}}}'

Check what's deployable in your region first:

az cognitiveservices model list --location <region> \
  --query "[?kind=='OpenAI' && starts_with(model.name,'gpt-image')].{name:model.name,version:model.version}" -o table

Gemini (only for `generate_animation.py`)

API key via GEMINI_API_KEY env var (or --gemini-api-key). Google has no usable Entra equivalent for this endpoint.

fal.ai

API key via FAL_KEY env var (or --fal-api-key). Get a key at https://fal.ai/dashboard/keys.

export FAL_KEY="your-key-here"
# or pass per-command:
python3 scripts/generate_image.py --provider fal --fal-api-key "..." --prompt "..." --output ~/img.png

Default model. fal-ai/flux/dev is the default. Override with --model or --deployment, or set FAL_MODEL in the environment.

Quality mapping. When using fal-ai/flux/dev (default):

--quality low or --quality medium → switches to fal-ai/flux/schnell (faster, 4-step distilled)
--quality high → stays on fal-ai/flux/dev (best quality)

Size mapping. fal.ai uses named presets. Standard sizes (1024x1024, 1024x768, etc.) are mapped automatically. Unrecognized sizes fall back to square_hd (1024×1024).

fal.ai Utility Tools

The scripts/lib/fal_tools.py module provides fal-hosted utilities as cloud alternatives to local libraries. Import and use directly:

from lib.fal_tools import remove_background_bria, upscale_seedvr, images_to_video

# Background removal (Bria RMBG 2.0 — commercially safe)
no_bg = remove_background_bria(sprite_bytes)

# Upscale before pixelize downscale (SeedVR2 — $0.001/megapixel)
hi_res = upscale_seedvr(sprite_bytes, scale=2)

# Stitch walk-cycle frames into MP4 animation
video = images_to_video([frame0, frame1, frame2, frame3], fps=8, hold_frames=15)

# Animate a still sprite into video (Kling 3.0 Pro)
animated = image_to_video(sprite_bytes, prompt="idle breathing animation")

Or use the dispatcher:

from lib.fal_tools import run_tool
result = run_tool("bg-remove-bria", image_bytes)
result = run_tool("upscale-seedvr", image_bytes, scale=2)
video = run_tool("images-to-video", [frame0, frame1, frame2, frame3])

Dependencies

pip install openai azure-identity google-genai fal-client pillow rembg onnxruntime

openai, azure-identity → direct OpenAI, Azure auth, and image generation.
google-genai → Gemini reference-image generation (required for generate_animation.py).
fal-client → fal.ai image generation (Flux, Stable Diffusion, etc.) and utility tools (background removal, upscaling).
pillow → post-processing pipeline.
rembg + onnxruntime → optional local background removal (needed only when --transparent-bg is passed; pulls ~100 MB of ONNX runtime).

Workflows

1. General image generation

# OpenAI (default)
python3 scripts/generate_image.py \
  --prompt "a cat on mars, photorealistic" \
  --size 1024x1024 --quality medium \
  --output ~/cat.png

# fal.ai with Flux
python3 scripts/generate_image.py \
  --provider fal --model fal-ai/flux/dev \
  --prompt "a cat on mars, photorealistic" \
  --size 1024x1024 --quality high \
  --output ~/cat_fal.png

2. Single pixel-art sprite

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/generate_sprite.py \
  --prompt "orange tabby cat, front view, idle stance" \
  --size 64 --palette db32 --transparent-bg \
  --output ~/cat_64.png

--size is the final pixel dimension (square).
--palette is one of: gameboy, pico8, db16, db32, nes, aap64, or auto.
--palette auto picks a palette from subject keywords — see the Palette Auto-Pick table.
--outline {none,palette-darkest} — default palette-darkest. Adds a 1-px dark ring via alpha dilation → XOR → darkest-luminance neighbour colour before palette quantize.
--transparent-bg runs rembg before downscale.
--qa runs QA metrics after save, writes <output>.qa.json, and exits non-zero on hard-gate failure.

Output: square RGBA PNG at --size × --size, limited to palette colors, hard edges.

3. Tileset for Tiled

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/generate_tileset.py \
  --prompt "grass, dirt, stone, water, sand, path, wood plank, stone brick" \
  --tile-size 32 --count 16 --columns 4 --palette pico8 \
  --name overworld --output-dir ~/tilesets/overworld/

Comma-separated tile concepts in --prompt; repeats if fewer than --count. Emits:

overworld.png — packed sheet at columns*K × rows*K.
overworld.tsx — Tiled tileset XML with per-tile name properties.
overworld.tmj — minimal example map filled with tile 1, openable in Tiled.

Tileset flags:

--palette {gameboy,pico8,db16,db32,nes,aap64,auto} — auto picks from keywords (e.g., "stone" → db32).
--seamless {none,crop,torus,auto} — default auto. Pixelizes source at 3× tile size, centre-crops for edge consistency, then if seam_diff ≥ 12.0 runs a torus-wrap feather blend. See references/seamless.md (if present) or lib/seamless.py.
--qa runs QA metrics (per-tile seam_diff mean, palette fidelity, alpha crispness) and writes a .qa.json sidecar.

4. Sprite-sheet animation

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/generate_animation.py \
  --prompt "knight with blue cape, side view, walking right" \
  --frames 4 --tile-size 32 --palette db16 \
  --duration-ms 120 --action walk \
  --name knight_walk --output-dir ~/sprites/knight/

Frame 0 is generated via OpenAI/Azure (gpt-image-2).
Frames 1..N use Gemini 2.5 Flash Image with frame 0 as a reference image to maintain character consistency.
TSX emits an <animation> block on tile id 0 that cycles all frames at --duration-ms.
Frame prompts come from POSE_LIBRARY in generate_animation.py, keyed by (action, frame_idx, total). Supported actions today: walk (4 frames, contact/passing/contact-mirror/passing-mirror), idle (2 frames), attack (4 frames: wind-up/strike/recoil/recovery). Unknown keys fall back to generic "frame N of M" phrasing.
--qa runs animation QA (palette fidelity, alpha crispness, silhouette_iou between frame 0 and frame 2, bbox_drift_x, bbox_drift_y) and writes a .qa.json sidecar.
--transparent-bg enables rembg on every frame, then applies a shared bbox crop across all frames so the character stays aligned frame-to-frame. Per-frame tight-crop would drift position and break the walk-cycle feel.

Consistency caveat: walk cycles may need rerolls. Rerun for different frames if one drifts.

5. Post-process an existing image

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/pixelize.py \
  --input raw.png --output sprite.png \
  --size 32 --palette pico8 --transparent-bg

Prompt Engineering

See references/pixel_art_prompting.md for detailed guidance. Key rules:

Include "pixel art sprite, clean hard edges, limited palette, plain flat background, no text, no watermark".
Specify view angle explicitly ("side view profile", "top-down").
For tilesets, add "top-down game tile, seamless, centered".
Avoid "photorealistic", "smooth shading", "gradients".

Tiled Format Notes

See references/tiled_format.md. Quick facts:

TSX <image source="..."> is relative to the TSX file — keep PNG + TSX in the same directory.
TMJ data array uses global ids (firstgid + local_id). Tile 0 in TSX → gid 1 in TMJ.
Tile animations declared inline; game engines must re-implement playback at runtime (the <animation> tag is metadata).

Palettes

See references/palettes.md. Available: gameboy (4), pico8 (16), db16 (16), db32 (32), nes (54), aap64 (64).

Palette auto-pick

Keyword pattern	Palette	Why
`metal\|steel\|armor\|stone\|grey\|gray\|silver\|iron\|dungeon\|rock\|brick`	`db32`	Mid-greys present; avoids "stone on pico8" fail
`tropical\|beach\|coral\|jungle\|aquatic\|underwater\|reef`	`aap64`	Wide warm/cool range for saturated scenes
`gameboy\|monochrome\|green only`	`gameboy`	4-shade monochrome
`arcade\|nes\|8-bit\|8bit`	`nes`	Canonical NES palette
`knight\|fantasy\|rpg\|character\|hero\|warrior\|wizard\|mage`	`db16`	DB16 is the canonical RPG-character palette
`terrain\|tile\|overworld\|map\|landscape\|biome`	`db32`	Covers earth/foliage/water tones
`detailed\|portrait\|hi-detail\|high-detail`	`aap64`	64 colours support subtle shading
(no match)	`db32`	Default safe choice

QA metrics and hard gates

When --qa is passed to any generator, or via scripts/qa_report.py <input>, lib/qa_metrics computes the following and writes a <artifact>.qa.json sidecar:

Metric	Scope	Threshold	Gate
`palette_fidelity`	all	== 1.0	hard
`alpha_crispness`	all	≥ 0.999	hard
`tile_seam_diff_mean`	tileset	≤ 12.0 (L2 0–255, per-tile mean)	hard
`silhouette_iou_f0_f2`	animation	≥ 0.85	hard
`bbox_drift_x`	animation	≤ 6 px (walk-cycle leg extension)	hard
`bbox_drift_y`	animation	≤ 3 px (1–2 px bob allowed)	hard
`baseline_alignment`	sprite	≥ 3 contiguous opaque in lowest row	hard
`outline_coverage`	sprite	≥ 0.85	soft
`palette_coverage`	sprite	0.15 ≤ x ≤ 0.60	soft

Hard failures cause non-zero exit. Standalone re-run:

python3 ~/.claude/skills/ai-pixel-art-image-generation/scripts/qa_report.py \
  --input ~/tilesets/overworld/overworld.png --kind tileset \
  --palette db32 --tile-size 32 --columns 4

Troubleshooting

Azure 401 — CLI token expired (az login) or wrong API key.
Azure 404 DeploymentNotFound — Deployment name mismatch; verify with az cognitiveservices account deployment list -n <resource> -g <rg>. If the model exists in the region but isn't deployed yet, create the deployment (see "Deploying a model on Azure" above).
Azure 404 from AI Services endpoint — you're pointing at https://<resource>.services.ai.azure.com/ instead of https://<resource>.openai.azure.com/. The skill's client needs the legacy OpenAI endpoint.
Azure 429 — deployment rate limit hit; default rate on fresh GlobalStandard image deployments is ~1 req/60s at capacity 1. Bump capacity via the same az rest deploy command (PATCH with higher sku.capacity) or wait between generations.
Azure api-version error on deployment create — the CLI extension pins 2025-09-01 which Azure rejects and ignores --api-version. Use the az rest workaround in "Deploying a model on Azure".
Azure ContentFilterError — Rephrase prompt; Azure content policy violation.
ERROR: OpenAI provider requires OPENAI_API_KEY when you expected Azure — AZURE_OPENAI_ENDPOINT isn't in the process env (shell didn't source ~/.zshrc, or the runner strips it). Prefix inline: AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com/ python scripts/… or pass --provider azure --endpoint ….
Gemini auth error — GEMINI_API_KEY unset; obtain a key from Google AI Studio.
Gemini 429 RESOURCE_EXHAUSTED on gemini-2.5-flash-preview-image — the image-generation model has zero free-tier quota. Upgrade to a paid Google AI Studio plan to use generate_animation.py.
rembg missing — pip install rembg onnxruntime; first run downloads the u2net model (~100 MB).
Tileset renders as magenta in Tiled — image path wrong; ensure PNG and TSX sit side-by-side.
Walk cycle frame breaks — reroll by rerunning with a slightly different action verb ("walking left foot forward").
Mushy output — richer palette, larger --size, tighter prompt. See references/pixel_art_prompting.md.
QA outline_coverage soft-fail at 32px low quality — expected. The model doesn't reliably draw 1-px outlines at small sizes. Either raise --quality medium, increase --size 64, or rely on --outline palette-darkest post-process (default) which adds the ring deterministically.
QA palette_coverage soft-fail < 0.15 — prompt/style is producing a near-monochrome sprite. Loosen prompt colour constraints or switch to a smaller palette (e.g. gameboy/pico8).

Recommendations

Lessons from real runs — read before kicking off a batch.

Model selection

Default to gpt-image-2 when deployed in your region (currently eastus2-only on Azure). Best silhouette fidelity and palette discipline.
gpt-image-1.5 is the best broadly-available fallback. Near-parity quality, wider region coverage.
gpt-image-1-mini for iteration/drafts: ~3× faster, good enough to validate prompt/style/layout before committing. Weaker at 1-px outlines and small-detail fidelity.
Never ask sora-2 for images — it's a video model and will fail or waste quota.

Quality vs size

Goal	Recommended combo
Final 32×32 sprite	`--size 32 --quality medium`, rely on `--outline palette-darkest`
Final 64×64 sprite	`--size 64 --quality medium` or `high`
Tileset (32-px tiles)	`--tile-size 32 --quality medium` + `--seamless auto`
Walk cycle	`--tile-size 32 --quality medium` — frame 0 dictates everything; invest quality there
Iteration / A-B prompts	`--size 32 --quality low --deployment gpt-image-1-mini`

--quality low at 32 px is fine for shape testing but will soft-fail outline/coverage QA. Don't chase hard gates at low.

Palette strategy

When in doubt, use --palette auto and trust the keyword table.
db32 is the default safe choice: covers earth/foliage/water/metal.
pico8 over-saturates dungeons, armour, and stone — its auto-pick rules already steer those to db32, don't override.
For animation consistency, pass the same palette to all frames. Gemini frame-to-frame drift is absorbed by the palette quantize step.

Animation-specific

Frame 0 is generated by gpt-image-2 / Azure. Frames 1..N are Gemini with frame 0 as reference. Spend your prompt-engineering budget on frame 0.
walk with 4 frames is the sweet spot. 2 frames looks janky; 8 frames rarely improves perceived motion and 4× the Gemini cost.
--transparent-bg uses a shared bbox across all frames — do not post-process frames individually or they will jitter.
Expect to reroll ~1-in-4 animations. Budget for it.

Cost / rate

Fresh Azure image deployments default to capacity 1 ≈ 1 req/60s. For batch generation (e.g. 16-tile tileset), either bump capacity (PATCH sku.capacity) or run sequentially with retry.
OpenAI direct has no per-deployment cap but is priced per-image; low quality is roughly 1/4 the cost of high.
rembg (on --transparent-bg) is local CPU — free but slow (~2-4 s/image on first call, cached after).

QA-driven iteration loop

First pass: --quality low, no --qa — just eyeball shape and pose.
Second pass: --quality medium --qa. Read .qa.json. If all hard gates pass, ship.
On soft fails:
- outline_coverage → already handled by --outline palette-darkest default; only worry if you set --outline none.
- palette_coverage low → richer prompt or smaller palette.
- palette_coverage high → subject is too busy for the palette; scale down palette or simplify prompt.
On hard fails, don't tune — regenerate with a different seed/prompt. Hard-fail metrics (palette fidelity, alpha crispness, silhouette IoU) are binary problems, not degrees.

Path / install

Scripts in this doc are shown under ~/.claude/skills/ai-pixel-art-image-generation/ but the skill is agent-agnostic. Substitute your install root:

Claude Code: ~/.claude/skills/ai-pixel-art-image-generation/
OpenCode: ~/.config/opencode/skill/ai-pixel-art-image-generation/
Cloned repo / dev: run from the repo root with python scripts/…

Scope

Supported:

Tiled TSX/TMJ export, uniform grid sheets, per-tile names, simple frame animations.
Seamless tiling post-process (3× crop + torus-blend fallback).
1-px sprite outlines via dilate-XOR-darkest-neighbour.
Explicit per-frame pose library for walk/idle/attack animations using frame 0 as the Gemini reference.
Keyword-based palette auto-pick (--palette auto).
QA metrics with hard-gate exits (--qa on any generator, or scripts/qa_report.py standalone).

Not in this skill:

LDtk export, Aseprite .ase export.
Tile bleed/extrude for runtime texture filtering (planned P1).
Auto-reroll of broken frames, animated tile-scene compositing.
Autotile / Wang 47-tile blob sets.

ai-pixel-art-image-generation

AI Pixel Art & Tile Map Generator

Overview

When to Use Which Script

Available Image Models

OpenAI / Azure

fal.ai — Text-to-Image

fal.ai — Image-to-Image (Sprite Editing)

fal.ai — Utility Tools

Auth

OpenAI

Azure

Deploying a model on Azure

Gemini (only for generate_animation.py)

fal.ai

fal.ai Utility Tools

Dependencies

Workflows

1. General image generation

2. Single pixel-art sprite

3. Tileset for Tiled

4. Sprite-sheet animation

5. Post-process an existing image

Prompt Engineering

Tiled Format Notes

Palettes

Palette auto-pick

QA metrics and hard gates

Troubleshooting

Recommendations

Model selection

Quality vs size

Palette strategy

Animation-specific

Cost / rate

QA-driven iteration loop

Path / install

Scope

AI Pixel Art & Tile Map Generator

Overview

When to Use Which Script

Available Image Models

OpenAI / Azure

fal.ai — Text-to-Image

fal.ai — Image-to-Image (Sprite Editing)

fal.ai — Utility Tools

Auth

OpenAI

Azure

Deploying a model on Azure

Gemini (only for generate_animation.py)

fal.ai

fal.ai Utility Tools

Dependencies

Workflows

1. General image generation

2. Single pixel-art sprite

3. Tileset for Tiled

4. Sprite-sheet animation

5. Post-process an existing image

Prompt Engineering

Tiled Format Notes

Palettes

Palette auto-pick

QA metrics and hard gates

Troubleshooting

Recommendations

Model selection

Quality vs size

Palette strategy

Animation-specific

Cost / rate

QA-driven iteration loop

Path / install

Scope

Gemini (only for `generate_animation.py`)

Gemini (only for `generate_animation.py`)