New BEST AI image generator is here
Layered transparency generation and stable multi-turn image iteration allow filmmakers to rapidly prototype graphic assets and compositing plates without manual rotoscoping.
What this lesson covers
This review stress tests OpenAI's GPT Image 2.5 against preceding models like GPT Image 2 and Nano Banana 2 across rigorous image editing and generation challenges. Key capabilities explored include sketch-to-image workflows, multi-turn consistency over iterations, and native layer generation exported directly as layered PSD files.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:00 ↗
GPT Image 2.5 allows users to sketch basic concepts directly or draw annotation instructions over existing images for targeted micro-edits.
- 02:07 ↗
Multi-turn editing preserves spatial and object consistency across dozens of iterative modifications, enabling frame-by-frame stop motion experiments.
- 02:43 ↗
The model can generate sequential transparent layers natively from foreground to background and package them directly into a layered PSD file.
- 03:32 ↗
Existing visual assets such as complex posters can be uploaded and deconstructed into separate, editable transparent layer elements inside a PSD format.
- 05:27 ↗
When generating complex dense multi-element grids like 100 anime posters, GPT Image 2 preserved facial coherency better than GPT Image 2.5, though 2.5 excelled at complex text layout.
Workflow outlined in the video
- Open ChatGPT and select the sketch tool to draw rough compositions or guide drone and lighting perspectives.
- Annotate existing image assets with visual markers and text instructions to perform precise inpainting and background replacements.
- Prompt the model to generate sequential transparent layers and compile them into a downloadable PSD file for UI design or visual effects.
- Supply full flat graphics or posters and instruct the model to separate foreground characters, background plates, and typography into modular transparent layers.
- Chain multi-turn prompts across consecutive turns to refine frame-to-frame rotational consistency for animatics.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
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How to interpret the numbers
195,313 views captured 27 September 2026. ReelStack recommendation score: 44/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 0.55× views vs 4 other channel videos observed at a similar age. View-evidence factor 99% (views / (views + 1,000))
- 25% freshness: 43/100 with a 14-day half-life
- 20% momentum: 555 views/day vs 332 channel baseline (3 comparable uploads), with the same view-evidence factor
- 10% engagement: 47/100; likes + 4× comments, smoothed with a 500-view neutral prior