Update imagegen system skill for gpt-image-2

Author: vb-openai

codex-rs/skills/src/assets/samples/imagegen/SKILL.md

@@ -12,17 +12,19 @@ Generates or edits images for the current project (for example website assets, g
 This skill has exactly two top-level modes:
 
 - **Default built-in tool mode (preferred):** built-in `image_gen` tool for normal image generation and editing. Does not require `OPENAI_API_KEY`.
-- **Fallback CLI mode (explicit-only):** `scripts/image_gen.py` CLI. Use only when the user explicitly asks for the CLI path. Requires `OPENAI_API_KEY`.
+- **Fallback CLI mode:** `scripts/image_gen.py` CLI. Use when the user explicitly asks for the CLI/API/model path, or when they explicitly require a transparent background that needs `gpt-image-1.5`. Requires `OPENAI_API_KEY`.
 
-Within the explicit CLI fallback only, the CLI exposes three subcommands:
+Within CLI fallback, the CLI exposes three subcommands:
 
 - `generate`
 - `edit`
 - `generate-batch`
 
 Rules:
-- Use the built-in `image_gen` tool by default for all normal image generation and editing requests.
-- Never switch to CLI fallback automatically.
+- Use the built-in `image_gen` tool by default for normal image generation and editing requests.
+- Do not switch to CLI fallback for ordinary quality, size, or file-path control.
+- If the user explicitly asks for a transparent image/background, use CLI fallback with `gpt-image-1.5` and explain briefly that transparent backgrounds are not supported in `gpt-image-2`, the latest model.
+- The word `batch` by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant.
 - If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires `OPENAI_API_KEY`. Proceed only if the user explicitly asks for that fallback.
 - If the user explicitly asks for CLI mode, use the bundled `scripts/image_gen.py` workflow. Do not create one-off SDK runners.
 - Never modify `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.
@@ -79,12 +81,13 @@ Built-in edit semantics:
 
 Execution strategy:
 - In the built-in default path, produce many assets or variants by issuing one `image_gen` call per requested asset or variant.
-- In the explicit CLI fallback path, use the CLI `generate-batch` subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.
+- In the CLI fallback path, use the CLI `generate-batch` subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.
+- For many distinct assets, do not use `n` as a substitute for separate prompts. `n` is for variants of one prompt; distinct assets need distinct built-in calls or distinct CLI `generate-batch` jobs.
 
 Assume the user wants a new image unless they clearly ask to change an existing one.
 
 ## Workflow
-1. Decide the top-level mode: built-in by default, fallback CLI only if explicitly requested.
+1. Decide the top-level mode: built-in by default; fallback CLI if explicitly requested or if the user explicitly needs transparent output.
 2. Decide the intent: `generate` or `edit`.
 3. Decide whether the output is preview-only or meant to be consumed by the current project.
 4. Decide the execution strategy: single asset vs repeated built-in calls vs CLI `generate-batch`.
@@ -99,13 +102,13 @@ Assume the user wants a new image unless they clearly ask to change an existing
    - If the user's prompt is already specific and detailed, normalize it into a clear spec without adding creative requirements.
    - If the user's prompt is generic, add tasteful augmentation only when it materially improves output quality.
 10. Use the built-in `image_gen` tool by default.
-11. If the user explicitly chooses the CLI fallback, then and only then use the fallback-only docs for quality, `input_fidelity`, masks, output format, output paths, and network setup.
+11. If the user explicitly chooses the CLI fallback, or explicitly asks for transparent output, then use the fallback-only docs for model, quality, size, `input_fidelity`, masks, output format, output paths, and network setup.
 12. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items.
 13. Iterate with a single targeted change, then re-check.
 14. For preview-only work, render the image inline; the underlying file may remain at the default `$CODEX_HOME/generated_images/...` path.
 15. For project-bound work, move or copy the selected artifact into the workspace and update any consuming code or references. Never leave a project-referenced asset only at the default `$CODEX_HOME/generated_images/...` path.
-16. For batches, persist only the selected finals in the workspace unless the user explicitly asked to keep discarded variants.
-17. Always report the final saved path for any workspace-bound asset, plus the final prompt and whether the built-in tool or fallback CLI mode was used.
+16. For batches or multi-asset requests, persist every requested deliverable final in the workspace unless the user explicitly asked to keep outputs preview-only. Discarded variants do not need to be kept unless requested.
+17. Always report the final saved path(s) for any workspace-bound asset(s), plus the final prompt or prompt set and whether the built-in tool or fallback CLI mode was used.
 
 ## Prompt augmentation
 
@@ -140,6 +143,9 @@ Generate:
 - product-mockup — product/packaging shots, catalog imagery, merch concepts.
 - ui-mockup — app/web interface mockups and wireframes; specify the desired fidelity.
 - infographic-diagram — diagrams/infographics with structured layout and text.
+- scientific-educational — classroom explainers, scientific diagrams, and learning visuals with required labels and accuracy constraints.
+- ads-marketing — campaign concepts and ad creatives with audience, brand position, scene, and exact tagline/copy.
+- productivity-visual — slide, chart, workflow, and data-heavy business visuals.
 - logo-brand — logo/mark exploration, vector-friendly.
 - illustration-story — comics, children’s book art, narrative scenes.
 - stylized-concept — style-driven concept art, 3D/stylized renders.
@@ -179,7 +185,7 @@ Avoid: <negative constraints>
 Notes:
 - `Asset type` and `Input images` are prompt scaffolding, not dedicated CLI flags.
 - `Scene/backdrop` refers to the visual setting. It is not the same as the fallback CLI `background` parameter, which controls output transparency behavior.
-- Fallback-only execution notes such as `Quality:`, `Input fidelity:`, masks, output format, and output paths belong in the explicit CLI path only. Do not treat them as built-in `image_gen` tool arguments.
+- Fallback-only execution notes such as `Quality:`, `Input fidelity:`, masks, output format, and output paths belong in the CLI path only. Do not treat them as built-in `image_gen` tool arguments.
 
 Augmentation rules:
 - Keep it short.
@@ -220,18 +226,41 @@ Constraints: change only the background; keep the product and its edges unchange
 - Iterate with single-change follow-ups.
 - If the prompt is generic, add only the extra detail that will materially help.
 - If the prompt is already detailed, normalize it instead of expanding it.
-- For explicit CLI fallback only, see `references/cli.md` and `references/image-api.md` for `quality`, `input_fidelity`, masks, output format, and output-path guidance.
+- For CLI fallback only, see `references/cli.md` and `references/image-api.md` for model, `quality`, `input_fidelity`, masks, output format, and output-path guidance.
 
 More principles shared by both modes: `references/prompting.md`.
 Copy/paste specs shared by both modes: `references/sample-prompts.md`.
 
 ## Guidance by asset type
 Asset-type templates (website assets, game assets, wireframes, logo) are consolidated in `references/sample-prompts.md`.
 
+## gpt-image-2 guidance for CLI fallback
+
+The fallback CLI defaults to `gpt-image-2`.
+
+- Use `gpt-image-2` for new CLI/API workflows unless the request needs transparent output.
+- If the user explicitly asks for transparent output, use `gpt-image-1.5` and explain that transparent backgrounds are not supported in `gpt-image-2`, the latest model.
+- `gpt-image-2` always uses high fidelity for image inputs; do not set `input_fidelity` with this model.
+- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.
+- Use `quality low` for fast drafts, thumbnails, and quick iterations. Use `medium`, `high`, or `auto` for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
+- Square images are typically fastest to generate. Use `1024x1024` for fast square drafts.
+- If the user asks for 4K-style output, use `3824x2160` for landscape or `2160x3824` for portrait. Do not use `3840x2160`, because the maximum edge length must be less than `3840px`.
+- `gpt-image-2` size may be `auto` or `WIDTHxHEIGHT` if all constraints hold: max edge `< 3840px`, both edges multiples of `16px`, long-to-short ratio `<= 3:1`, total pixels between `655,360` and `8,294,400`.
+
+Popular `gpt-image-2` sizes:
+- `1024x1024` square
+- `1536x1024` landscape
+- `1024x1536` portrait
+- `2048x2048` 2K square
+- `2048x1152` 2K landscape
+- `3824x2160` near-4K landscape
+- `2160x3824` near-4K portrait
+- `auto`
+
 ## Fallback CLI mode only
 
 ### Temp and output conventions
-These conventions apply only to the explicit CLI fallback. They do not describe built-in `image_gen` output behavior.
+These conventions apply only to the CLI fallback. They do not describe built-in `image_gen` output behavior.
 - Use `tmp/imagegen/` for intermediate files (for example JSONL batches); delete them when done.
 - Write final artifacts under `output/imagegen/`.
 - Use `--out` or `--out-dir` to control output paths; keep filenames stable and descriptive.
@@ -276,4 +305,4 @@ If installation is not possible in this environment, tell the user which depende
 - `references/cli.md`: fallback-only CLI usage via `scripts/image_gen.py`.
 - `references/image-api.md`: fallback-only API/CLI parameter reference.
 - `references/codex-network.md`: fallback-only network/sandbox troubleshooting for CLI mode.
-- `scripts/image_gen.py`: fallback-only CLI implementation. Do not load or use it unless the user explicitly chooses CLI mode.
+- `scripts/image_gen.py`: fallback-only CLI implementation. Do not load or use it unless the user explicitly chooses CLI mode or explicitly asks for transparent output.

codex-rs/skills/src/assets/samples/imagegen/agents/openai.yaml

@@ -3,4 +3,4 @@ interface:
   short_description: "Generate or edit images for websites, games, and more"
   icon_small: "./assets/imagegen-small.svg"
   icon_large: "./assets/imagegen.png"
-  default_prompt: "Generate or edit the visual assets for this task with the built-in `image_gen` tool by default. First confirm that the task actually calls for a raster image; if the project already has SVG/vector/code-native assets and the user wants to extend or match those, do not use this skill. If the task includes reference images, treat them as references unless the user clearly wants an existing image modified. For multi-asset requests, loop built-in calls rather than treating batch as a separate top-level mode. Only use the fallback CLI if the user explicitly asks for it, and keep CLI-only controls such as `generate-batch`, `quality`, `input_fidelity`, masks, and output paths on that fallback path."
+  default_prompt: "Generate or edit the visual assets for this task with the built-in `image_gen` tool by default. First confirm that the task actually calls for a raster image; if the project already has SVG/vector/code-native assets and the user wants to extend or match those, do not use this skill. If the task includes reference images, treat them as references unless the user clearly wants an existing image modified. For multi-asset requests, loop built-in calls; the word `batch` alone is not CLI opt-in. Use the fallback CLI only if the user explicitly asks for CLI/API/model controls or explicitly needs transparent output; for transparent output use `gpt-image-1.5` and explain that transparent backgrounds are not supported in `gpt-image-2`, the latest model. Keep CLI-only controls such as `generate-batch`, `quality`, `input_fidelity`, masks, and output paths on that fallback path."

codex-rs/skills/src/assets/samples/imagegen/references/cli.md

@@ -1,13 +1,14 @@
 # CLI reference (`scripts/image_gen.py`)
 
-This file is for the fallback CLI mode only. Read it only after the user explicitly asks to use `scripts/image_gen.py` instead of the built-in `image_gen` tool.
+This file is for the fallback CLI mode only. Read it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or when the user explicitly asks for transparent output that requires the `gpt-image-1.5` fallback path.
 
 `generate-batch` is a CLI subcommand in this fallback path. It is not a top-level mode of the skill.
+The word `batch` in a user request is not CLI opt-in by itself.
 
 ## What this CLI does
 - `generate`: generate a new image from a prompt
 - `edit`: edit one or more existing images
-- `generate-batch`: run many generation jobs from a JSONL file
+- `generate-batch`: run many generation jobs from a JSONL file after the user explicitly chooses CLI/API/model controls
 
 Real API calls require **network access** + `OPENAI_API_KEY`. `--dry-run` does not.
 
@@ -16,7 +17,7 @@ Set a stable path to the skill CLI (default `CODEX_HOME` is `~/.codex`):
 
 ```
 export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
-export IMAGE_GEN="$CODEX_HOME/skills/imagegen/scripts/image_gen.py"
+export IMAGE_GEN="$CODEX_HOME/skills/.system/imagegen/scripts/image_gen.py"
 ```
 
 Install dependencies into that environment with its package manager. In uv-managed environments, `uv pip install ...` remains the preferred path.
@@ -60,25 +61,96 @@ python "$IMAGE_GEN" edit \
 - **Never modify** `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.
 
 ## Defaults
-- Model: `gpt-image-1.5`
+- Model: `gpt-image-2`
 - Supported model family for this CLI: GPT Image models (`gpt-image-*`)
-- Size: `1024x1024`
-- Quality: `auto`
+- Size: `auto`
+- Quality: `medium`
 - Output format: `png`
 - Default one-off output path: `output/imagegen/output.png`
 - Background: unspecified unless `--background` is set
 
+## gpt-image-2 size and model guidance
+
+`gpt-image-2` is the default model for new CLI fallback work.
+
+- Use `--quality low` for fast drafts, thumbnails, and quick iterations.
+- Use `--quality medium`, `--quality high`, or `--quality auto` for final assets, dense text, diagrams, identity-sensitive edits, and high-resolution outputs.
+- Square images are typically fastest. Use `--size 1024x1024` for quick square drafts.
+- If the user asks for 4K-style output, use `--size 3824x2160` for landscape or `--size 2160x3824` for portrait.
+- Do not pass `--input-fidelity` with `gpt-image-2`; this model always uses high fidelity for image inputs.
+- Do not use `--background transparent` with `gpt-image-2`; use `gpt-image-1.5` for transparent output.
+
+Popular `gpt-image-2` sizes:
+- `1024x1024`
+- `1536x1024`
+- `1024x1536`
+- `2048x2048`
+- `2048x1152`
+- `3824x2160`
+- `2160x3824`
+- `auto`
+
+`gpt-image-2` size constraints:
+- max edge `< 3840px`
+- both edges multiples of `16px`
+- long edge to short edge ratio `<= 3:1`
+- total pixels between `655,360` and `8,294,400`
+
+Fast draft:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A product thumbnail of a matte ceramic mug on a stone surface" \
+  --quality low \
+  --size 1024x1024 \
+  --out output/imagegen/mug-draft.png
+```
+
+Final 2K landscape:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A polished landing-page hero image of a matte ceramic mug on a stone surface" \
+  --quality high \
+  --size 2048x1152 \
+  --out output/imagegen/mug-hero.png
+```
+
+Near-4K landscape:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A detailed architectural visualization at golden hour" \
+  --size 3824x2160 \
+  --quality high \
+  --out output/imagegen/architecture-near-4k.png
+```
+
+Transparent background request:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --model gpt-image-1.5 \
+  --prompt "A clean product cutout on a transparent background" \
+  --background transparent \
+  --output-format png \
+  --out output/imagegen/product-cutout.png
+```
+
+When using this path, explain briefly that transparent backgrounds are not supported in `gpt-image-2`, the latest model, so `gpt-image-1.5` is required.
+
 ## Quality, input fidelity, and masks (CLI fallback only)
 These are explicit CLI controls. They are not built-in `image_gen` tool arguments.
 
 - `--quality` works for `generate`, `edit`, and `generate-batch`: `low|medium|high|auto`
-- `--input-fidelity` is **edit-only** and validated as `low|high`
+- `--input-fidelity` is **edit-only** and validated as `low|high`; it is not supported for `gpt-image-2`
 - `--mask` is **edit-only**
 
 Example:
 
 ```bash
 python "$IMAGE_GEN" edit \
+  --model gpt-image-1.5 \
   --image input.png \
   --prompt "Change only the background" \
   --quality high \
@@ -147,10 +219,11 @@ Notes:
 - Per-job overrides are supported in JSONL (for example `size`, `quality`, `background`, `output_format`, `output_compression`, `moderation`, `n`, `model`, `out`, and prompt-augmentation fields).
 - `--n` generates multiple variants for a single prompt; `generate-batch` is for many different prompts.
 - In batch mode, per-job `out` is treated as a filename under `--out-dir`.
+- For many requested deliverable assets, provide one prompt/job per distinct asset and use semantic filenames when possible.
 
 ## CLI notes
-- Supported sizes: `1024x1024`, `1536x1024`, `1024x1536`, or `auto`.
-- Transparent backgrounds require `output_format` to be `png` or `webp`.
+- Supported sizes depend on the model. `gpt-image-2` supports flexible constrained sizes; older GPT Image models support `1024x1024`, `1536x1024`, `1024x1536`, or `auto`.
+- Transparent backgrounds require `output_format` to be `png` or `webp` and are not supported by `gpt-image-2`.
 - `--prompt-file`, `--output-compression`, `--moderation`, `--max-attempts`, `--fail-fast`, `--force`, and `--no-augment` are supported.
 - This CLI is intended for GPT Image models. Do not assume older non-GPT image-model behavior applies here.
 

codex-rs/skills/src/assets/samples/imagegen/references/codex-network.md

@@ -1,6 +1,6 @@
 # Codex network approvals / sandbox notes
 
-This file is for the fallback CLI mode only. Read it only after the user explicitly asks to use `scripts/image_gen.py`.
+This file is for the fallback CLI mode only. Read it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or when the user explicitly asks for transparent output that requires the `gpt-image-1.5` fallback path.
 
 This guidance is intentionally isolated from `SKILL.md` because it can vary by environment and may become stale. Prefer the defaults in your environment when in doubt.
 

codex-rs/skills/src/assets/samples/imagegen/references/image-api.md

@@ -1,13 +1,46 @@
 # Image API quick reference
 
-This file is for the fallback CLI mode only. Use it only after the user explicitly asks to use `scripts/image_gen.py` instead of the built-in `image_gen` tool.
+This file is for the fallback CLI mode only. Use it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or when the user explicitly asks for transparent output that requires the `gpt-image-1.5` fallback path.
 
 These parameters describe the Image API and bundled CLI fallback surface. Do not assume they are normal arguments on the built-in `image_gen` tool.
 
 ## Scope
-- This fallback CLI is intended for GPT Image models (`gpt-image-1.5`, `gpt-image-1`, and `gpt-image-1-mini`).
+- This fallback CLI is intended for GPT Image models (`gpt-image-2`, `gpt-image-1.5`, `gpt-image-1`, and `gpt-image-1-mini`).
 - The built-in `image_gen` tool and the fallback CLI do not expose the same controls.
 
+## Model summary
+
+| Model | Quality | Input fidelity | Resolutions | Recommended use |
+| --- | --- | --- | --- | --- |
+| `gpt-image-2` | `low`, `medium`, `high`, `auto` | Always high fidelity for image inputs; do not set `input_fidelity` | `auto` or flexible sizes that satisfy the constraints below | Default for new CLI/API workflows: high-quality generation and editing, text-heavy images, photorealism, compositing, identity-sensitive edits, and workflows where fewer retries matter |
+| `gpt-image-1.5` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Transparent backgrounds and backward-compatible workflows |
+| `gpt-image-1` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Legacy compatibility |
+| `gpt-image-1-mini` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Cost-sensitive draft batches and lower-stakes previews |
+
+## gpt-image-2 sizes
+
+`gpt-image-2` accepts `auto` or any `WIDTHxHEIGHT` size that satisfies all constraints:
+
+- Maximum edge length must be less than `3840px`.
+- Both edges must be multiples of `16px`.
+- Long edge to short edge ratio must not exceed `3:1`.
+- Total pixels must be at least `655,360` and no more than `8,294,400`.
+
+Popular sizes:
+
+| Label | Size | Notes |
+| --- | --- | --- |
+| Square | `1024x1024` | Typical fast default |
+| Landscape | `1536x1024` | Standard landscape |
+| Portrait | `1024x1536` | Standard portrait |
+| 2K square | `2048x2048` | Larger square output |
+| 2K landscape | `2048x1152` | Widescreen output |
+| Near-4K landscape | `3824x2160` | Use instead of `3840x2160` |
+| Near-4K portrait | `2160x3824` | Use instead of `2160x3840` |
+| Auto | `auto` | Default size |
+
+Square images are typically fastest to generate. For 4K-style output, use `3824x2160` or `2160x3824`, not `3840x2160`, because the maximum edge length must be less than `3840px`.
+
 ## Endpoints
 - Generate: `POST /v1/images/generations` (`client.images.generate(...)`)
 - Edit: `POST /v1/images/edits` (`client.images.edit(...)`)
@@ -16,7 +49,7 @@ These parameters describe the Image API and bundled CLI fallback surface. Do not
 - `prompt`: text prompt
 - `model`: image model
 - `n`: number of images (1-10)
-- `size`: `1024x1024`, `1536x1024`, `1024x1536`, or `auto`
+- `size`: `auto` by default for `gpt-image-2`; flexible `WIDTHxHEIGHT` sizes are allowed only for `gpt-image-2`; older GPT Image models use `1024x1024`, `1536x1024`, `1024x1536`, or `auto`
 - `quality`: `low`, `medium`, `high`, or `auto`
 - `background`: output transparency behavior (`transparent`, `opaque`, or `auto`) for generated output; this is not the same thing as the prompt's visual scene/backdrop
 - `output_format`: `png` (default), `jpeg`, `webp`
@@ -26,12 +59,17 @@ These parameters describe the Image API and bundled CLI fallback surface. Do not
 ## Edit-specific parameters
 - `image`: one or more input images. For GPT Image models, you can provide up to 16 images.
 - `mask`: optional mask image
-- `input_fidelity`: `low` (default) or `high`
+- `input_fidelity`: `low` or `high` only for models that support it; do not set this for `gpt-image-2`
 
 Model-specific note for `input_fidelity`:
+- `gpt-image-2` always uses high fidelity for image inputs and does not support setting `input_fidelity`.
 - `gpt-image-1` and `gpt-image-1-mini` preserve all input images, but the first image gets richer textures and finer details.
 - `gpt-image-1.5` preserves the first 5 input images with higher fidelity.
 
+## Transparent backgrounds
+
+`gpt-image-2` does not currently support transparent backgrounds. If the user explicitly asks for a transparent image or transparent background, use `gpt-image-1.5` with `background=transparent` and a transparent-capable output format such as `png` or `webp`.
+
 ## Output
 - `data[]` list with `b64_json` per image
 - The bundled `scripts/image_gen.py` CLI decodes `b64_json` and writes output files for you.
@@ -41,8 +79,9 @@ Model-specific note for `input_fidelity`:
 - Use the edits endpoint when the user requests changes to an existing image.
 - Masking is prompt-guided; exact shapes are not guaranteed.
 - Large sizes and high quality increase latency and cost.
-- High `input_fidelity` can materially increase input token usage.
-- If a request fails because a specific option is unsupported by the selected GPT Image model, retry manually without that option.
+- Use `quality=low` for fast drafts, thumbnails, and quick iterations. Use `medium` or `high` for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
+- High `input_fidelity` can materially increase input token usage on models that support it.
+- If a request fails because a specific option is unsupported by the selected GPT Image model, retry manually without that option only when the option is not required by the user. If transparent output is required, switch to `gpt-image-1.5` instead of dropping `background=transparent`.
 
 ## Important boundary
 - `quality`, `input_fidelity`, explicit masks, `background`, `output_format`, and related parameters are fallback-only execution controls.

codex-rs/skills/src/assets/samples/imagegen/references/prompting.md

@@ -28,6 +28,7 @@ This file is about prompt structure, specificity, and iteration. Fallback-only e
 - If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.
 - If the prompt is generic, you may add tasteful detail when it materially improves the output.
 - Treat examples in `sample-prompts.md` as fully-authored recipes, not as the default amount of augmentation to add to every request.
+- For photorealism, include `photorealistic` directly when that is the goal, plus concrete real-world texture such as pores, wrinkles, fabric wear, material grain, or imperfect everyday detail.
 
 ## Allowed and disallowed augmentation
 
@@ -46,6 +47,7 @@ Do not add:
 - Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.
 - Call out negative space if the asset clearly needs room for UI or copy.
 - Avoid making left/right layout decisions unless the user or surrounding layout supports them.
+- For people, describe body framing, scale, gaze, and object interactions when they matter (`full body visible`, `looking down at the book`, `hands naturally gripping the handlebars`).
 
 ## Constraints and invariants
 - State what must not change (`keep background unchanged`).
@@ -55,6 +57,7 @@ Do not add:
 - Put literal text in quotes or ALL CAPS and specify typography (font style, size, color, placement).
 - Spell uncommon words letter-by-letter if accuracy matters.
 - For in-image copy, require verbatim rendering and no extra characters.
+- In CLI fallback mode, use `medium` or `high` quality for small text, dense infographics, data-heavy slides, multi-font layouts, legends, axes, and footnotes.
 
 ## Input images and references
 - Do not assume that every provided image is an edit target.
@@ -71,15 +74,22 @@ Do not add:
 ## Fallback-only execution controls
 - `quality`, `input_fidelity`, explicit masks, output format, and output paths are fallback-only execution controls.
 - Do not assume they are built-in `image_gen` tool arguments.
-- If the user explicitly chooses CLI fallback, see `references/cli.md` and `references/image-api.md` for those controls.
+- If the user explicitly chooses CLI fallback or explicitly asks for transparent output, see `references/cli.md` and `references/image-api.md` for those controls.
+- In CLI fallback mode, `gpt-image-2` is the default. It supports `quality=low|medium|high|auto`; use `low` for fast drafts and thumbnails, and move to `medium`, `high`, or `auto` for final assets.
+- `gpt-image-2` always uses high fidelity for image inputs, so do not set `input_fidelity` with that model.
+- If the user explicitly asks for transparent output, use `gpt-image-1.5` and explain that transparent backgrounds are not supported in `gpt-image-2`, the latest model.
+- If the user asks for 4K-style output with `gpt-image-2`, use `3824x2160` for landscape or `2160x3824` for portrait.
 
 ## Use-case tips
 Generate:
 - photorealistic-natural: Prompt as if a real photo is captured in the moment; use photography language (lens, lighting, framing); call for real texture; avoid over-stylized polish unless requested.
 - product-mockup: Describe the product/packaging and materials; ensure clean silhouette and label clarity; if in-image text is needed, require verbatim rendering and specify typography.
 - ui-mockup: Describe the target fidelity first (shippable mockup or low-fi wireframe), then focus on layout, hierarchy, and practical UI elements; avoid concept-art language.
-- infographic-diagram: Define the audience and layout flow; label parts explicitly; require verbatim text.
+- infographic-diagram: Define the audience and layout flow; label parts explicitly; require verbatim text; prefer higher quality in CLI mode for dense labels.
 - logo-brand: Keep it simple and scalable; ask for a strong silhouette and balanced negative space; avoid decorative flourishes unless requested.
+- ads-marketing: Write like a creative brief; include brand positioning, audience, desired vibe, scene, and exact tagline if text must appear.
+- productivity-visual: Name the exact artifact (slide, chart, workflow diagram), define the canvas and hierarchy, provide real labels/data, and ask for readable typography and polished spacing.
+- scientific-educational: Define audience, lesson objective, required labels, scientific constraints, arrows, and scan-friendly whitespace.
 - illustration-story: Define panels or scene beats; keep each action concrete.
 - stylized-concept: Specify style cues, material finish, and rendering approach (3D, painterly, clay) without inventing new story elements.
 - historical-scene: State the location/date and required period accuracy; constrain clothing, props, and environment to match the era.

codex-rs/skills/src/assets/samples/imagegen/references/sample-prompts.md

@@ -2,7 +2,7 @@
 
 These prompt recipes are shared across both top-level modes of the skill:
 - built-in `image_gen` tool (default)
-- explicit `scripts/image_gen.py` CLI fallback
+- `scripts/image_gen.py` CLI fallback for explicit CLI/API/model requests or explicit transparent-output requests
 
 Use these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.
 
@@ -13,7 +13,14 @@
 
 The labeled lines are prompt scaffolding, not a closed schema. `Asset type` and `Input images` are prompt-only scaffolding; the CLI does not expose them as dedicated flags.
 
-Execution details such as explicit CLI flags, `quality`, `input_fidelity`, masks, output formats, and local output paths depend on mode. Use the built-in tool by default; only apply CLI-specific controls after the user explicitly opts into fallback mode.
+Execution details such as explicit CLI flags, `quality`, `input_fidelity`, masks, output formats, and local output paths depend on mode. Use the built-in tool by default; only apply CLI-specific controls when the user explicitly opts into fallback mode or explicitly asks for transparent output.
+
+CLI model notes:
+- `gpt-image-2` is the fallback CLI default for new workflows.
+- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.
+- For 4K-style `gpt-image-2` output, use `3824x2160` or `2160x3824` instead of `3840x2160`.
+- If transparent output is explicitly required, use `gpt-image-1.5` and explain that transparent backgrounds are not supported in `gpt-image-2`, the latest model.
+- Do not set `input_fidelity` with `gpt-image-2`; image inputs already use high fidelity.
 
 For prompting principles (structure, specificity, invariants, iteration), see `references/prompting.md`.
 
@@ -68,6 +75,18 @@
 Constraints: clear labels, strong contrast, no logos or trademarks, no watermark
 ```
 
+### scientific-educational
+```
+Use case: scientific-educational
+Primary request: biology diagram titled "Cellular Respiration at a Glance" for high school students
+Scene/backdrop: clean white classroom handout background
+Subject: glucose turns into energy inside a cell; include glycolysis, Krebs cycle, and electron transport chain
+Style/medium: flat scientific diagram with consistent icons, arrows, and readable labels
+Composition/framing: landscape slide-style layout with clear hierarchy and generous whitespace
+Text (verbatim): "Cellular Respiration at a Glance", "Glucose", "Pyruvate", "ATP", "NADH", "FADH2", "CO2", "O2", "H2O"
+Constraints: scientifically plausible; avoid tiny text; no extra decoration; no watermark
+```
+
 ### logo-brand
 ```
 Use case: logo-brand
@@ -100,6 +119,30 @@
 Constraints: no logos or trademarks; no watermark
 ```
 
+### ads-marketing
+```
+Use case: ads-marketing
+Primary request: campaign image for a streetwear brand called Thread
+Subject: group of friends hanging out together in a stylish urban setting
+Style/medium: polished youth streetwear campaign photography
+Composition/framing: vertical ad layout with natural poses and integrated headline space
+Lighting/mood: contemporary, energetic, tasteful
+Text (verbatim): "Yours to Create."
+Constraints: render the tagline exactly once; clean legible typography; no extra text; no watermarks; no unrelated logos
+```
+
+### productivity-visual
+```
+Use case: productivity-visual
+Primary request: one pitch-deck slide titled "Market Opportunity"
+Asset type: fundraising slide image
+Style/medium: clean modern deck slide, white background, crisp sans-serif typography
+Subject: TAM/SAM/SOM concentric-circle diagram plus a small growth bar chart from 2021 to 2026
+Composition/framing: 16:9 landscape slide, clear data hierarchy, polished spacing
+Text (verbatim): "Market Opportunity", "TAM: $42B", "SAM: $8.7B", "SOM: $340M", "AGI Research, 2024", "Internal analysis"
+Constraints: readable labels, no clip art, no stock photography, no decorative clutter, no watermark
+```
+
 ### historical-scene
 ```
 Use case: historical-scene
@@ -351,6 +394,8 @@
 Constraints: crisp silhouette; no halos or fringing; preserve label text exactly; no restyling
 ```
 
+CLI note: if transparent output is explicitly required, use `gpt-image-1.5` because `gpt-image-2` does not currently support transparent backgrounds.
+
 ### style-transfer
 ```
 Use case: style-transfer
@@ -367,6 +412,17 @@
 Constraints: match lighting, perspective, and scale; keep the base framing unchanged; no extra elements
 ```
 
+### character consistency workflow
+```
+Use case: identity-preserve
+Input images: Image 1: previous character anchor illustration
+Primary request: continue the story with the same character in a new scene and action
+Scene/backdrop: snowy forest after a winter storm
+Subject: same young forest hero gently helping a frightened squirrel out of a fallen tree
+Style/medium: same children's book watercolor illustration style as Image 1
+Constraints: do not redesign the character; preserve facial features, proportions, outfit, color palette, and personality; no text; no watermark
+```
+
 ### sketch-to-render
 ```
 Use case: sketch-to-render

codex-rs/skills/src/assets/samples/imagegen/scripts/image_gen.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python3
 """Fallback CLI for explicit image generation or editing with GPT Image models.
 
-Used only when the user explicitly opts into CLI fallback mode.
+Used only when the user explicitly opts into CLI fallback mode, or when explicit
+transparent output requires the `gpt-image-1.5` fallback path.
 
-Defaults to gpt-image-1.5 and a structured prompt augmentation workflow.
+Defaults to gpt-image-2 and a structured prompt augmentation workflow.
 """
 
 from __future__ import annotations
@@ -21,20 +22,28 @@
 
 from io import BytesIO
 
-DEFAULT_MODEL = "gpt-image-1.5"
-DEFAULT_SIZE = "1024x1024"
-DEFAULT_QUALITY = "auto"
+DEFAULT_MODEL = "gpt-image-2"
+DEFAULT_SIZE = "auto"
+DEFAULT_QUALITY = "medium"
 DEFAULT_OUTPUT_FORMAT = "png"
 DEFAULT_CONCURRENCY = 5
 DEFAULT_DOWNSCALE_SUFFIX = "-web"
 DEFAULT_OUTPUT_PATH = "output/imagegen/output.png"
 GPT_IMAGE_MODEL_PREFIX = "gpt-image-"
 
-ALLOWED_SIZES = {"1024x1024", "1536x1024", "1024x1536", "auto"}
+ALLOWED_LEGACY_SIZES = {"1024x1024", "1536x1024", "1024x1536", "auto"}
 ALLOWED_QUALITIES = {"low", "medium", "high", "auto"}
 ALLOWED_BACKGROUNDS = {"transparent", "opaque", "auto", None}
 ALLOWED_INPUT_FIDELITIES = {"low", "high", None}
 
+GPT_IMAGE_2_MODEL = "gpt-image-2"
+GPT_IMAGE_2_MIN_PIXELS = 655_360
+GPT_IMAGE_2_MAX_PIXELS = 8_294_400
+GPT_IMAGE_2_MAX_EDGE_EXCLUSIVE = 3840
+GPT_IMAGE_2_MAX_RATIO = 3.0
+GPT_IMAGE_2_NEAR_4K_LANDSCAPE = "3824x2160"
+GPT_IMAGE_2_NEAR_4K_PORTRAIT = "2160x3824"
+
 MAX_IMAGE_BYTES = 50 * 1024 * 1024
 MAX_BATCH_JOBS = 500
 
@@ -104,10 +113,52 @@ def _normalize_output_format(fmt: Optional[str]) -> str:
     return "jpeg" if fmt == "jpg" else fmt
 
 
-def _validate_size(size: str) -> None:
-    if size not in ALLOWED_SIZES:
+def _parse_size(size: str) -> Optional[Tuple[int, int]]:
+    match = re.fullmatch(r"([1-9][0-9]*)x([1-9][0-9]*)", size)
+    if not match:
+        return None
+    return int(match.group(1)), int(match.group(2))
+
+
+def _validate_gpt_image_2_size(size: str) -> None:
+    if size == "auto":
+        return
+
+    parsed = _parse_size(size)
+    if parsed is None:
+        _die("size must be auto or WIDTHxHEIGHT, for example 1024x1024.")
+
+    width, height = parsed
+    max_edge = max(width, height)
+    min_edge = min(width, height)
+    total_pixels = width * height
+
+    if max_edge >= GPT_IMAGE_2_MAX_EDGE_EXCLUSIVE:
+        hint = GPT_IMAGE_2_NEAR_4K_LANDSCAPE
+        if height > width:
+            hint = GPT_IMAGE_2_NEAR_4K_PORTRAIT
+        _die(
+            "gpt-image-2 size maximum edge length must be less than 3840px. "
+            f"For 4K-style output, use {hint} instead of {size}."
+        )
+    if width % 16 != 0 or height % 16 != 0:
+        _die("gpt-image-2 size width and height must be multiples of 16px.")
+    if max_edge / min_edge > GPT_IMAGE_2_MAX_RATIO:
+        _die("gpt-image-2 size long edge to short edge ratio must not exceed 3:1.")
+    if total_pixels < GPT_IMAGE_2_MIN_PIXELS or total_pixels > GPT_IMAGE_2_MAX_PIXELS:
+        _die(
+            "gpt-image-2 size total pixels must be at least 655,360 and no more than 8,294,400."
+        )
+
+
+def _validate_size(size: str, model: str) -> None:
+    if model == GPT_IMAGE_2_MODEL:
+        _validate_gpt_image_2_size(size)
+        return
+
+    if size not in ALLOWED_LEGACY_SIZES:
         _die(
-            "size must be one of 1024x1024, 1536x1024, 1024x1536, or auto for GPT image models."
+            "size must be one of 1024x1024, 1536x1024, 1024x1536, or auto for this GPT Image model."
         )
 
 
@@ -138,17 +189,38 @@ def _validate_transparency(background: Optional[str], output_format: str) -> Non
         _die("transparent background requires output-format png or webp.")
 
 
+def _validate_model_specific_options(
+    *,
+    model: str,
+    background: Optional[str],
+    input_fidelity: Optional[str] = None,
+) -> None:
+    if model != GPT_IMAGE_2_MODEL:
+        return
+    if background == "transparent":
+        _die(
+            "transparent backgrounds are not supported in gpt-image-2, the latest model. "
+            "Use --model gpt-image-1.5 --background transparent --output-format png instead."
+        )
+    if input_fidelity is not None:
+        _die(
+            "input_fidelity is not supported in gpt-image-2 because image inputs always use high fidelity for this model."
+        )
+
+
 def _validate_generate_payload(payload: Dict[str, Any]) -> None:
-    _validate_model(str(payload.get("model", DEFAULT_MODEL)))
+    model = str(payload.get("model", DEFAULT_MODEL))
+    _validate_model(model)
     n = int(payload.get("n", 1))
     if n < 1 or n > 10:
         _die("n must be between 1 and 10")
     size = str(payload.get("size", DEFAULT_SIZE))
     quality = str(payload.get("quality", DEFAULT_QUALITY))
     background = payload.get("background")
-    _validate_size(size)
+    _validate_size(size, model)
     _validate_quality(quality)
     _validate_background(background)
+    _validate_model_specific_options(model=model, background=background)
     oc = payload.get("output_compression")
     if oc is not None and not (0 <= int(oc) <= 100):
         _die("output_compression must be between 0 and 100")
@@ -912,10 +984,15 @@ def main() -> int:
     if getattr(args, "downscale_max_dim", None) is not None and args.downscale_max_dim < 1:
         _die("--downscale-max-dim must be >= 1")
 
-    _validate_size(args.size)
+    _validate_model(args.model)
+    _validate_size(args.size, args.model)
     _validate_quality(args.quality)
     _validate_background(args.background)
-    _validate_model(args.model)
+    _validate_model_specific_options(
+        model=args.model,
+        background=args.background,
+        input_fidelity=getattr(args, "input_fidelity", None),
+    )
     _ensure_api_key(args.dry_run)
 
     args.func(args)