GPT Image2 Pro
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About this skill
Command-Line Image Generation: From Need to Implementation
In rapid design and development cycles, teams often need to generate high-quality images directly from the command line for prototyping, marketing assets, or content creation. Traditional methods rely on graphical interfaces or complex API calls, making them hard to integrate into automated scripts or batch processes. The GPT-image2 skill addresses this pain point by encapsulating OpenAI's gpt-image-2 model, allowing developers to generate images with simple commands while fine-tuning output parameters.
Core Capabilities and Workflow
The skill offers two primary modes:
- Text-to-Image: Input a text prompt to generate an image from scratch. For example, running python3 imagegen.py "an astronaut cat walking on the moon" produces a PNG image.
- Image-to-Image: Upload a reference image and add a prompt to edit it. Use the --image parameter to specify a local image path (e.g., --image cat.jpg), and control fidelity with --fidelity high (high preserves details, low allows style transfer).
Key steps include:
1. Configure API Key: Set the REDFOX_API_KEY (obtained from redfox.hk) via environment variables, command-line arguments, or a configuration file.
2. Invoke Generation: Control dimensions (--size 1024x1792), format (--format webp), and background transparency (--bg transparent) using command-line parameters. For batch generation, -n 10 generates up to 10 images in one call.
3. Task Management: Support for --no-download to submit tasks without waiting, and --task-id to query status, suitable for asynchronous workflows.
Applicable Boundaries and Considerations
- Dimension Constraints: Only preset resolutions are supported (e.g.,
1024x1024,2048x2048); custom resolutions may cause generation failures or delays. JPEG format does not support transparent backgrounds. - Performance and Cost: Generation typically takes 10-30 seconds, with high-resolution tiers (e.g.,
2048x2048) taking longer. API calls may incur costs, requiring users to manage their own quotas. - File Handling: Reference images are limited to local PNG, JPEG, or WebP files; outputs default to
~/Downloads/QoderImagesand can be redirected with-o. - Model Characteristics: The
gpt-image-2model is OpenAI's latest; prompts are limited to 500 characters, with longer text blocked. Output quality depends on prompt detail.
These features provide developers with a flexible, integrable command-line image generation solution, particularly suited for scenarios requiring automation or parametric control.
Use Cases
- When preparing social media marketing assets, a designer needs to quickly generate multiple size variants (e.g., square, portrait, landscape) of images for the same theme to publish across different platforms.
- While building a web prototype, a front-end engineer needs to batch-generate a set of icons and decorative elements with transparent backgrounds via a script and integrate them directly into the project resource directory.
- While creating online course materials, a digital content creator needs to generate high-quality illustrations based on a text description and ensure the output is in WebP format to optimize webpage loading speed.
- During a product feature demo, a product manager needs to use the image-to-image mode to add details or modify the style of hand-drawn sketches or existing screenshots, rapidly creating high-fidelity prototype visuals.
Best For
- A social media operations specialist who frequently needs to generate differently sized images for posts and wants to automate repetitive image creation tasks using command-line scripts.
- A front-end engineer responsible for managing project UI resources who needs to batch-create and update assets like icons and backgrounds, with precise control over format and dimensions.
- An independent game developer or illustrator who needs to rapidly generate concept art or material drafts and explore styles by adjusting fidelity parameters.
- A technical documentation or online course author who needs to batch-generate accompanying illustrations for articles and requires support for transparent backgrounds to facilitate layout compositing.
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