GPT Image 2.6 (via DroidProxy Codex)

POST http://localhost:8317/v1/images/generations (or /edits) with Authorization: Bearer dummy-not-used. Default to gpt-image-2.6-flare; choose gpt-image-2.6-sunburst for detail and edit consistency. No dated 2.6 snapshots are documented. Never use an OPENAI_API_KEY with this skill. These are Images API models, not chat models: do not register them in the Factory chat-model picker or send them to /v1/responses or chat completions.

Compatibility, checked November 4, 2026: the installed CLIProxyAPI route rejects both 2.6 IDs with HTTP 400 ("Model ... is not supported"). Upstream's image allowlist also lacks them. This skill prepares the documented requests, but does not upgrade that backend or claim working 2.6 OAuth access. OpenAI's all-tier ChatGPT/Codex availability announcement does not establish whether this OAuth backend accepts these IDs or new fields. Keep the model and new features explicit/configurable. Never silently rename 2.6 to 2.5, drop markup, or return a raster image as an SVG/GIF.

Default to Flare for fast, high-quality everyday generation. Use Sunburst when the user prioritizes demanding quality, editing precision, or subject preservation over latency. If Sunburst meets the requirements, prefer Flare only after it produces acceptable results on the same prompt, references, dimensions, and quality setting.

Use this for generating or editing visual assets, not for understanding an image the user already attached.

Run the bundled helper (Bun, no dependencies)

Resolve scripts/image.ts relative to this installed skill, not the user's working directory. Write request JSON to a file, then run:

SKILL="$HOME/.factory/skills/gpt-image"
bun "$SKILL/scripts/image.ts" generate /absolute/request.json /absolute/workspace/asset

The last argument is an output prefix, without an extension. The helper decodes all data[].b64_json results, inspects their bytes, saves .png, .jpeg, .webp, .svg, or .gif (numbered when n>1), and prints file paths and response metadata. Embed the resulting absolute path in Markdown. It never retries, substitutes a model, reads OAuth tokens, or logs image data.

For a backend that rejects 2.6, explicitly select a known available older model for a raster-only request, without new unsupported fields:

GPT_IMAGE_MODEL=gpt-image-2.5-flare \
  bun "$SKILL/scripts/image.ts" generate /absolute/request.json /absolute/workspace/asset

Preserve older API model IDs: gpt-image-1-mini, gpt-image-1.5, gpt-image-2, gpt-image-2-2026-04-21, gpt-image-2.5-flare, gpt-image-2.5-flare-2026-09-08, gpt-image-2.5-sunburst, gpt-image-2.5-sunburst-2026-09-08, and chatgpt-image-latest. The existing backend alias gpt-image-2.5 is also passed through. OpenAI API support is not a promise of OAuth backend support; older models may have different parameter limits. gpt-image-1 shut down October 23, 2026.

Generate

Requests often take 15–70s. Use --max-time 180. Default quality to auto; select a higher explicit setting only to meet a quality requirement.

RESP=$(curl -sS --max-time 180 http://localhost:8317/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer dummy-not-used" \
  -d "$(jq -n --arg p "A red balloon on a wooden table, soft natural light" \
    '{model:"gpt-image-2.6-flare", prompt:$p, size:"1024x1024",
      quality:"auto"}')")

if B64=$(printf '%s' "$RESP" | jq -er '.data[0].b64_json' 2>/dev/null); then
  printf '%s' "$B64" | base64 -d > out.png
else
  printf '%s\n' "$RESP" >&2
  exit 1
fi

Check the HTTP status and .data[0].b64_json before decoding. Codex errors are JSON (usage_limit_reached, auth_not_found, moderation_blocked). Report the error code/message, not request headers, credentials or image data.

Edit

Same auth. Prefer JSON with a data URL, with no upload step. Public API file_id inputs require access to that file and are not verified on OAuth. Do not pass the data URL through jq --arg: a typical generated PNG is larger than ARG_MAX (~1MB on macOS) and jq dies with Argument list too long. Write the data URL to a file and use --rawfile, then POST with --data-binary. Decode the same way as Generate.

SRC_FILE=$(mktemp)
REQ=$(mktemp)
printf 'data:image/png;base64,%s' "$(base64 -i ./photo.png)" > "$SRC_FILE"
jq -n --arg p "Make it blue-tinted studio lighting" --rawfile u "$SRC_FILE" \
  '{model:"gpt-image-2.6-sunburst", prompt:$p, images:[{image_url:$u}],
    quality:"auto"}' > "$REQ"

RESP=$(curl -sS --max-time 180 http://localhost:8317/v1/images/edits \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer dummy-not-used" \
  --data-binary @"$REQ")
rm -f "$SRC_FILE" "$REQ"

if B64=$(printf '%s' "$RESP" | jq -er '.data[0].b64_json' 2>/dev/null); then
  printf '%s' "$B64" | base64 -d > out.png
else
  printf '%s\n' "$RESP" >&2
  exit 1
fi

Pick the data-URL mime from the source (image/jpeg, image/png, image/webp). Optional mask.image_url (PNG with alpha) marks the region to replace. Chain edits by feeding each output back as the next images[0].image_url.

Markup-guided edits (opt-in)

Keep the original as images[0]. Make a separate transparent PNG overlay with circles, arrows, sketches or labels. After orientation, it must match the first input's pixel dimensions and contain transparent pixels. Each may have at most 16,777,216 pixels; the overlay must be at most 50 MB. Do not flatten the overlay onto the source or combine markup with mask or prompt_rewriter. Check these file properties locally before sending; the helper checks JSON shape, not image dimensions or alpha.

JSON uses images and a markup object with exactly one of image_url (HTTPS or base64 PNG data URL) or file_id. Additional images are references. For local files, avoid ARG_MAX by writing data URLs to files:

base64 -i /absolute/photo.png | tr -d '\n' > /tmp/photo.b64
base64 -i /absolute/overlay.png | tr -d '\n' > /tmp/overlay.b64
jq -n --rawfile photo /tmp/photo.b64 --rawfile overlay /tmp/overlay.b64 \
  '{model:"gpt-image-2.6-sunburst",
    prompt:"Make only the circled vase cobalt blue. Preserve everything else. Remove the annotations.",
    images:[{image_url:("data:image/png;base64,"+$photo)}],
    markup:{image_url:("data:image/png;base64,"+$overlay)},quality:"medium"}' > /tmp/edit.json
GPT_IMAGE_FEATURES=markup bun "$SKILL/scripts/image.ts" edit /tmp/edit.json /absolute/workspace/vase

The public API also accepts multipart field markup (PNG file) alongside image and prompt on /v1/images/edits. Prefer the JSON path through DroidProxy: binary multipart forwarding and markup handling by CLIProxyAPI are not verified. Markup has no extra fee.

SVG export (generations only, opt-in)

Example request file:

{"model":"gpt-image-2.6-flare","prompt":"A simple editable vector icon of a red balloon, clean paths and flat colors.","output_format":"svg","n":1,"quality":"medium","complexity":"low"}

GPT_IMAGE_FEATURES=svg,complexity \
  bun "$SKILL/scripts/image.ts" generate /absolute/svg-request.json /absolute/workspace/balloon

SVG requires n=1. Omit streaming, partial_images, output_compression, and prompt_rewriter. Quality and complexity control the reference image used to author the SVG. Base64-decode data[0].b64_json as UTF-8 XML and save .svg, not PNG. Edits accept neither SVG input nor SVG output. Inspect the XML before embedding it; do not treat untrusted SVG as executable HTML. SVG incurs the reference image cost plus the unnamed authoring model's token rate, so there is no documented fixed total SVG price.

Animated GIF (generations only, experimental opt-in)

{"model":"gpt-image-2.6-flare","prompt":"A red balloon gently bobbing up and down in a seamless loop, locked camera.","output_format":"gif","frame_count":9,"frame_duration_ms":200,"size":"auto"}

GPT_IMAGE_FEATURES=gif GPT_IMAGE_MAX_FRAMES=16 \
  bun "$SKILL/scripts/image.ts" generate /absolute/gif-request.json /absolute/workspace/balloon

GIF may need preview access. The guide says frame_count 2–64, but the API reference says 2–16. Do not hard-code either as universal: set an optional local cap and let the API validate. Defaults are 9 frames and 200 ms. frame_duration_ms is 20–2000, rounded by the API to 10 ms. Frame fields are GIF-only. Omit streaming, partial images, compression and prompt_rewriter. Save decoded binary as .gif; never rename a PNG to GIF. If a pixel-budget error occurs, reduce frames, frame size, or n.

Prompting

If the user gives a detailed prompt or asks you to use theirs, use it verbatim. Otherwise define the intended result, then describe the subject, action/pose, setting, style, composition, lighting/mood, visible details, and constraints. For complex work, use labeled sections. Keep requirements easy to read rather than relying on special prompt syntax.

Quote required text exactly, specify its placement and typography, ask for no extra text, and verify spelling and legibility. For edits, say “change only X” and explicitly list what must stay the same. Identify each reference image by number and role (subject, style, clothing, or background). Make one change per edit and restate critical preservation constraints on every turn.

Ground named people, brands, places, and “current/latest” facts with a web search first. For a named real person, edit from a real reference photo — do not generate the likeness from text alone.

Parameters

Response

{"created": 0, "background": "opaque", "data": [{"b64_json": "..."}],
 "output_format": "png", "quality": "low", "size": "1536x1024", "usage": {}}

GPT Image returns base64, so do not send response_format. .data[0] is only b64_json — no url, no mime_type. Pick the file extension from top-level output_format when present, otherwise from the decoded magic bytes (PNG / JFIF / RIFF…WEBP).

Previously observed with 2.5: Codex OAuth often ignores size and sometimes quality. A 1024x1024 / low request may come back 1536x1024 / low, or an off-enum size like 1312x1199 with quality: medium. Trust the response size / quality / background and the decoded IHDR. Do not retry just because they differ from the request.

Transparency

When the user explicitly wants a transparent PNG or alpha background (sticker, cutout, icon), send both:

{"background":"transparent","output_format":"png"}

If they asked for a campaign, slide, or print asset without mentioning transparency, keep opaque or ask. Don't assume.

The prompt beats background. If it describes a backdrop, scene, color, plinth, or shadow, the model paints that instead of alpha. Keep the prompt on an isolated subject and say so explicitly:

• Isolated object, fully visible, generously padded
• Actual fully transparent alpha
• No backdrop, rectangle, plinth, cast shadow, watermark, or readable label text
• Preserve natural transparency, refraction, and fine material edges (glass, ribbon, fibers)

Size by use: icons/stickers 1024x1024, product shots 1024x1536, charts 1536x1024. Compare medium or high when the asset has small text. Expect the returned pixel size to differ; that's not a failure.

Products / campaign cutouts. One object. No scene. Ask for fully transparent alpha around the silhouette. Good for reuse across storefronts and seasonal backgrounds.

Charts for dark slides. Ask for a genuinely transparent background and a transparent plot area — not just a transparent silhouette. No card, frame, filled panel, title, or grid fill. Specify pale/white labels and bright series colors so they read on navy or gradient themes. Keep exact data values in the prompt, then verify labels and proportions against the source before using the chart.

Icons, stickers, decorations. Keep everything outside the tile, die-cut border, or sprig transparent — including gaps between branches and leaves. No text, watermark, or drop shadow.

Print-on-demand. Generate the artwork and any blank garments as separate transparent PNGs. Keep open space inside the design transparent too (no white rectangle or badge). Composite locally; do not bake the print onto the garment in one generation if they need to reuse it.

After decoding, confirm a real alpha channel (RGBA / PNG tRNS) and that some pixels are fully transparent. Also check top-level "background":"transparent". If the file is opaque, strip backdrop language from the prompt and retry once.

If background:transparent 400s, generate on a flat uniform background that contrasts with the subject and key it out locally (PIL, ImageMagick). Recolor the subject for the background they named before you finish — a near-black mark on a dark header is invisible. Say what you did.

API pricing and quota

Both 2.6 variants have the same published rates, per 1M tokens:

These are public API rates, not a subscription charge or guaranteed cache discount on this OAuth route. No fixed per-image price, published rate-limit table, or image-specific subscription quota is available. Limits vary by tier. Do not infer remaining image quota from the Codex text-token gauge. Use returned usage/errors and reset information; do not probe in a loop.

Failures

On moderation_blocked, stop. Don't retry and don't paraphrase the prompt to evade the filter. Report the API's own message. Never invent image content or a URL for a request that failed.

Sources

• GPT Image 2.6 Flare
• GPT Image 2.6 Sunburst
• Image prompting guide
• Image generation guide
• Images generate reference