Generate and edit images with OpenAI GPT Image 2 (ChatGPT Images 2.0) on RunComfy. Documents GPT Image 2's strengths (embedded text, logos, multilingual typography, instruction precision), its 3 fixed sizes, edit-with-preservation language, and when to route to a sibling (Flux 2 / Nano Banana Pro /

GPT Image 2 — Pro Pack on RunComfy [runcomfy.com](https://www.runcomfy.com/?utm source=skills.sh&utm medium=skill&utm campaign=gpt image 2) · [Text to image](https://www.runcomfy.com/models/openai/gpt image 2/text to image?utm source=skills.sh&utm medium=skill&utm campaign=gpt image 2) · [Edit](https://www.runcomfy.com/models/openai/gpt image 2/edit?utm source=skills.sh&utm medium=skill&utm campaign=gpt image 2) · [GitHub](https://github.com/agentspace so/runcomfy skills/tree/main/gpt image 2) OpenAI GPT Image 2 (ChatGPT Images 2.0) hosted on the RunComfy Model API — no OpenAI key, async REST. When to pick this model (vs siblings) GPT Image 2's distinct strength is directive precision : it follows multi element prompts, layout cues, and embedded text instructions more reliably than its peers. Pick it when what's on the canvas matters more than how stylized it looks . You want Use Embedded text, logos, signage, multilingual typography GPT Image 2 Brand safe, e commerce / ad / UI mockup imagery GPT Image 2 Iterative refinement that holds composition stable GPT Image 2 Heavy stylization, painterly look Flux 2 Hyperrealistic portrait Nano Banana Pro Cinematic / aesthetic first hero shots Seedream 5 If the user explicitly asked for GPT Image 2 / ChatGPT Image 2 / Image 2, route here regardless — don't second guess the model choice. Prerequisites 1. RunComfy CLI — npm i g @runcomfy/cli 2. RunComfy account — runcomfy login opens a browser device code flow. 3. CI / containers — set RUNCOMFY TOKEN=<token instead of runcomfy login . Endpoints + input schema Two endpoints, same model. openai/gpt image 2/text to image Field Type Required Default Notes prompt string yes — The positive prompt size enum no 1024 1024 1024 1024 (1:1), 1024 1536 (2:3 portrait), 1536 1024 (3:2 landscape) — only these three openai/gpt image 2/edit Field Type Required Default Notes prompt string yes — Natural language edit instruction images string[] yes — Up to 10 reference image URLs (publicly fetchable HTTPS) size enum no auto auto (preserve input ratio), or one of the three fixed sizes above size=auto on edit preserves the input aspect ratio — strongly recommended unless the edit explicitly changes framing. How to invoke Text to image: Edit (single ref): Edit (multi ref, up to 10): The CLI submits, polls every 2s until terminal, then downloads any .runcomfy.net / .runcomfy.com URL from the result into output dir . Stdout is the result JSON. Stderr is progress. For pipe friendly usage: Prompting — what actually works These are model specific patterns that empirically improve output quality. Apply to text to image and edit alike. Be explicit on subject + setting + mood. "A close up of a matte ceramic water bottle on warm linen, soft window light, neutral background" — three concrete directives — beats "nice product photo of a bottle". Quote embedded text exactly. Keep it short. GPT Image 2 is the strongest text rendering model in this class, but only when you put the literal characters in quotes . Long blocks of text degrade. For multilingual text, name the script: "Japanese kana", "Cyrillic", "Arabic right to left". Use compositional cues directly. "rule of thirds", "close up", "aerial view", "centered subject", "shallow depth of field" — these have learned meaning to the model. Iterate one attribute at a time. When refining, change one thing per iteration (lighting OR background OR pose OR text) and keep the rest of the prompt verbatim. The model holds composition stable across iterations when only one knob moves. Don't conflict instructions. "no text" + "the word 'AQUA+' on the label" is incoherent — the model will pick one and you don't control which. Don't pile up styles. "ukiyo e + watercolor + 8K + cinematic + minimalist" cancels out. Pick one or two style anchors max. For the edit endpoint specifically: State preservation goals. " keep the person's pose and face identity unchanged", " keep the brand mark and typography on the package", " keep the overall framing". The model needs to know what NOT to change. Use directional language for spatial edits. "Move the headline from top right to bottom center", not "reposition the headline". Multi ref : number the images in the prompt — "subject from image 1, lighting and background from image 2" — and the model will route the cues correctly. Where it shines Use case Why GPT Image 2 E commerce product photography Reliable text on labels, brand safe lighting, consistent across SKUs High conversion ads Headline + visual integration in one pass Brand asset localization One source asset → many language variants of the same headline Signage, posters, packaging mock ups Text rendering accuracy at multiple scales UI mockups, scientific illustrations Layout precision and label legibility Sample prompts (verified to produce strong results) Text to image — product hero: Text to image — multilingual signage: Edit — background swap with preservation: Limitations Only 3 fixed sizes on text to image (and the same 3 + auto on edit). Extreme aspect ratios are auto resized to the nearest supported one. Prompt length ~ a few thousand tokens. Long blocks of embedded text degrade output. Edit's multi image support is "guidance from up to 10 refs", not ControlNet style stacks. The first image is treated as the primary; the rest provide auxiliary cues. Photorealism on portraits is not its strongest suit — Nano Banana Pro wins that head to head. Exit codes The runcomfy CLI uses sysexits style codes: code meaning 0 success 64 bad CLI args 65 bad input JSON / schema mismatch (e.g. size: "2048 2048" would 422) 69 upstream 5xx 75 retryable: timeout / 429 77 not signed in or token rejected Full reference: [docs.runcomfy.com/cli/troubleshooting](https://docs.runcomfy.com/cli/troubleshooting?utm source=skills.sh&utm medium=skill&utm campaign=gpt image 2). How it works 1. The skill invokes runcomfy run openai/gpt image 2/<endpoint with a JSON body matching the schema above. 2. The CLI POSTs to https://model api.runcomfy.net/v1/models/openai/gpt image 2/<endpoint with the user's bearer token. 3. The Model API returns a request id ; the CLI polls GET .../requests/<id /status every 2 seconds. 4. On terminal status, the CLI fetches GET .../requests/<id /result and downloads any URL whose host ends with .runcomfy.net or .runcomfy.com into output dir . Other URLs are listed but not fetched. 5. Ctrl C while polling sends POST .../requests/<id /cancel so you don't get billed for GPU you stopped. What this skill is not Not a direct OpenAI API client. Not a capability grant — depends on a working RunComfy account. Not multi tenant. Security & Privacy Token storage : runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner only read/write). Set RUNCOMFY TOKEN env var to bypass the file entirely in CI / containers. Input boundary : the user prompt is passed as a JSON string to the CLI via input . The CLI does NOT shell expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content. Third party content : image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image based prompt injection is a known risk for any image edit / video edit model. Outbound endpoints : only model api.runcomfy.net (request submission) and .runcomfy.net / .runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks. Generated file size cap : the CLI aborts any single download 2 GiB to prevent disk fill from a malicious or runaway model output.

gpt-image-2