gpt-image-v2

# GPT Image v2 OpenAI's GPT Image v2 on the EachLabs Predictions API. GPT Image v2 delivers higher-fidelity images than v1.5 with stronger prompt understanding, improved compositional consistency, physically accurate lighting, and enhanced fine-detail rendering — including reliable in-image text. Two model slugs: | Slug | Category | Use | |------|----------|-----| | `gpt-image-v2-text-to-image` | Text to Image | Generate new images from a prompt | | `gpt-image-v2-edit` | Image to Image | Edit an existing image with natural-language instructions | ## When to use - User asks for "GPT Image", "OpenAI image", "gpt-image-2", or the image model behind ChatGPT. - High-fidelity photorealism with accurate in-image text (posters, infographics, packaging, signage). - Brand-consistent product photography with legible labels and logos. - Instruction-following edits that must preserve the subject and layout of a reference image. For a wider model comparison (Flux, Seedream, Imagen, etc.) see `eachlabs-image-generation`. For other edit models see `eachlabs-image-edit`. ## Authentication ``` Header: X-API-Key: <your-api-key> ``` Set the `EACHLABS_API_KEY` environment variable. Get your key at [eachlabs.ai/dashboard/api-keys](https://www.eachlabs.ai/dashboard/api-keys). ## Prediction Flow 1. **(Recommended) Check schema** — `GET https://api.eachlabs.ai/v1/model?slug=gpt-image-v2-text-to-image` to see the current `request_schema`. Do the same for `gpt-image-v2-edit`. 2. **POST** `https://api.eachlabs.ai/v1/prediction` with `model`, `version: "0.0.1"`, and `input`. 3. **Poll** `GET https://api.eachlabs.ai/v1/prediction/{id}` until `status` is `"success"` or `"error"`, or use a webhook. 4. **Extract** the output URLs from `output` (array). ## Quick Start — Text to Image ```bash curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "gpt-image-v2-text-to-image", "version": "0.0.1", "input": { "prompt": "A minimalist poster reading \"BREW LAB\" in bold serif, steam rising from a ceramic mug, warm paper texture, editorial photography" } }' ``` Typical processing time: **~40 seconds**. ## Quick Start — Edit ```bash curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "gpt-image-v2-edit", "version": "0.0.1", "input": { "prompt": "Replace the background with a sunlit loft interior while keeping the product label and angle identical", "image_url": "https://your-cdn.example.com/product.jpg" } }' ``` Typical processing time: **~100 seconds**. Reference images are always processed at high fidelity, so input image tokens (and cost) are higher than other GPT Image models. ## Polling ```bash curl https://api.eachlabs.ai/v1/prediction/{PREDICTION_ID} \ -H "X-API-Key: $EACHLABS_API_KEY" ``` | Status | Meaning | |--------|---------| | `processing` | Still running — poll again | | `success` | Done — read `output` (array of URLs) | | `error` | Failed — read `message` / `details` | ## Webhook (alternative to polling) Pass `"webhook_url": "https://your.host/path"` in the create body. EachLabs POSTs: ```json { "exec_id": "prediction-uuid", "status": "succeeded", "output": "https://...", "error": "" } ``` `status` is `"succeeded"` or `"failed"`. Return 2xx within 30 seconds. ## Pricing Both models use **dynamic token-based pricing**: | Token type | Rate | |------------|------| | Text input | $5 / 1M tokens | | Image input | $10 / 1M tokens | | Text output | $40 / 1M tokens | | Image output | $30 / 1M tokens | If the token breakdown is unavailable, EachLabs falls back to a "medium 1024×1024 equivalent" rate. > `gpt-image-v2-edit` always processes reference images at high fidelity, so image-input tokens (and cost) run noticeably higher than with `gpt-image-v1-5-edit` or Nano Banana. Prefer downscaling references to 1024px on the long edge before upload unless you need high detail. ## Prompt Tips - **In-image text**: wrap the exact copy in double quotes (`"BREW LAB"`) and specify typography ("bold serif", "sans-serif headline"). v2 renders dense paragraphs and multilingual layouts reliably. - **Composition**: describe subject, framing, lens, and lighting separately. v2 respects compositional directives better than v1.5. - **Edits**: be specific about what must stay unchanged ("keep the label, angle, and lighting identical; only replace the background"). - **Photorealism**: add concrete physical cues ("raking 45° sunlight, soft falloff, subtle skin SSS") rather than generic adjectives. ## Rate Limits & Limits | Limit | Value | |-------|-------| | Create requests | 100 / minute per key | | Concurrent predictions | 10 per key | | File inputs | Publicly reachable HTTPS URLs only — no data-URIs, no localhost | ## Errors Error body: `{ "status": "error", "message": "...", "details": "..." }` | Code | Meaning | |------|---------| | 400 | Invalid input | | 401 | Missing / invalid `X-API-Key` | | 404 | Unknown model or prediction id | | 429 | Rate limited — back off | | 5xx | Retry with exponential backoff | ## Security Constraints - **No arbitrary URL loading**: `image_url` must point to your own HTTPS-reachable storage (S3, GCS, CDN). Do not forward user-pasted URLs without validation. - **No third-party API tokens**: never forward OpenAI / Anthropic / HF tokens through `input` — authentication is exclusively via the EachLabs API key. - **Validate before calling**: always resolve the live `request_schema` via `GET /v1/model?slug=<slug>` before constructing `input`. The schema is the source of truth. ## Parameter Reference See [references/MODELS.md](references/MODELS.md) for the full per-slug parameter table.