gpt-image-2
Generate Images with GPT Image 2 on RunComfy
Also available from: doany-ai,agentspace-so
Create and edit images using OpenAI GPT Image 2 through the RunComfy cloud platform. This skill provides expert prompting patterns, size constraints, and model selection guidance so you get the best results on every generation.
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Test it
Using "gpt-image-2". Generate a product photo of a ceramic water bottle on warm linen with the text 'AQUA+' on the label
Expected outcome:
High-resolution product image with accurate label text reading AQUA+, soft window lighting, subtle rim highlights, and neutral e-commerce background
Using "gpt-image-2". Edit this cafe photo: turn the background into a bright white studio sweep and add the headline 'OPEN STUDIO' in bold sans-serif, centered
Expected outcome:
Edited image with clean white-to-soft-gray studio background, centered bold OPEN STUDIO headline text, preserved subject identity and composition
Using "gpt-image-2". Create a Tokyo cafe storefront at dusk with the sign reading the Japanese characters for coffee in kana
Expected outcome:
Cinematic storefront image with warm interior glow, accurate Japanese text on wooden plaque, shallow depth of field, rule of thirds composition
Security Audit
Low RiskStatic analysis flagged 77 patterns across SKILL.md (58 shell backticks, 10 hardcoded URLs, 5 filesystem paths, 3 weak-crypto indicators, 1 reconnaissance). All findings evaluated as false positives: backticks contain markdown CLI documentation examples, URLs are legitimate RunComfy service endpoints, filesystem references document CLI config paths (~/.config/runcomfy/token.json), and crypto/reconnaissance patterns are misidentified text ("Exit codes" heading, jq pipe examples). No executable code, no prompt injection, no data exfiltration detected. Minor concerns: user prompts transmitted to RunComfy's external API, third-party CLI dependency, local token file storage.
Low Risk Issues (3)
Risk Factors
⚙️ External commands (13)
🌐 Network access (6)
📁 Filesystem access (2)
Detected Patterns
Quality Score
What You Can Build
E-Commerce Product Photography
Generate product images with accurate label text, brand-safe lighting, and consistent styling across product lines using GPT Image 2's precise text rendering.
Multilingual Brand Asset Creation
Create signage, posters, and packaging mockups with accurate text rendering in multiple languages from a single source asset using the edit endpoint.
Iterative Image Refinement
Edit images step by step, changing one attribute at a time while preserving composition, faces, and brand elements across multiple generations.
Try These Prompts
Generate an image of [describe subject] in [describe setting] with [describe mood or lighting] using GPT Image 2 on RunComfy.
Create a product photo of [product] on [surface or background], the label reads "[exact text]" in [font style], [lighting description], e-commerce ready, neutral background.
Edit this image using GPT Image 2: [describe specific change]. Keep [list elements to preserve] unchanged. Use [size] for output.
Compose a new image using GPT Image 2 edit: subject from image 1 and background from image 2. Match the lighting of image 2. Keep the pose and face identity from image 1 unchanged.
Best Practices
- Quote all embedded text exactly as you want it to appear in the generated image for accurate text rendering
- Change only one attribute per edit iteration such as lighting, background, pose, or text to maintain composition stability
- Use compositional cues like rule of thirds, close-up, aerial view, or shallow depth of field directly in your prompts
Avoid
- Do not combine conflicting instructions such as requesting no text while also specifying text on product labels
- Avoid piling up multiple art styles like ukiyo-e plus watercolor plus cinematic in one prompt, which produces inconsistent results
- Do not use extreme aspect ratios since only three fixed sizes are supported and unsupported ratios are auto-resized