Flare vs. Sunburst + Better Image Generation
GPT Image 2.5 is OpenAI’s latest image generation and editing model family. It comes in two versions:
Flare is optimized for speed and high-quality everyday image creation.
Sunburst is optimized for maximum quality, precision, and control, especially when editing existing images or preserving important details.
Both models can generate images from text, work with image references, edit existing images, combine visual references, render text, create transparent assets, and preserve subjects across iterative edits.
GPT Image 2.5 also improves natural lighting, texture, subject preservation, and multi-turn editing consistency compared with earlier GPT Image models.
The simplest rule is:
Start with Flare. Move to Sunburst when precision matters more than speed.
Flare vs. Sunburst
Flare
Best description: Fast, high-quality everyday image model.
Best for: Exploration, iteration, batches, social content, concepting, and workflows where speed matters.
Image generation: Excellent
Image editing: Excellent
Reference preservation: Strong
Multi-turn editing: Strong
Speed: Faster
High-volume workflows: Ideal
Final production assets: Often more than sufficient
Use Flare when you want to move quickly, test several directions, create variations, or produce strong everyday imagery without paying the speed cost of maximum precision.
Sunburst
Best description: Highest-quality precision model.
Best for: Final creative, product imagery, identity preservation, exact edits, complex reference workflows, and demanding compositions.
Image generation: Highest-quality option
Image editing: Most precise
Reference preservation: Best choice when small details matter
Multi-turn editing: Best choice when important details need to survive repeated edits
Speed: Slower
High-volume workflows: Usually unnecessary
Final production assets: Preferred when small errors are unacceptable
Use Sunburst when precision matters more than speed, especially when preserving a person's identity, maintaining product details, combining multiple references, or making surgical edits.
The simplest way to choose
Start with Flare. Move to Sunburst when precision becomes the bottleneck.
A useful production workflow is:
Explore with Flare → identify the winning direction → refine or finish with Sunburst.
Use Flare when...
- Exploring several creative directions
- Making lots of variations
- Creating social content
- Generating moodboards or concepts
- Testing compositions
- Rapidly iterating with the Agent
- Creating everyday photorealistic imagery
- Speed matters more than microscopic accuracy
- You are still figuring out what the final asset should look like
Use Sunburst when...
- Creating final campaign imagery
- Product shape or packaging must remain accurate
- A person’s identity must remain consistent
- You are combining several references
- An edit must change one thing and preserve everything else
- Typography or dense layouts matter
- You are performing repeated edits on the same image
- Fine materials, textures, reflections, or small details matter
- Flare is almost right, but keeps missing an important requirement
A useful production workflow
Explore with Flare → identify the winning direction → refine or finish with Sunburst.
The GPT Image 2.5 instruction formula
You do not need special syntax to work effectively with GPT Image 2.5.
Natural language, paragraphs, labeled sections, and structured instructions can all work.
The important thing is making the visual requirements clear.
For more complicated generations, this structure is especially reliable:
1. Outcome
Start by saying exactly what you want to create.
Create a realistic smartphone photograph for an outdoor apparel social campaign.
2. Subject
Describe who or what needs to appear.
A woman in her late twenties wearing a dark green hiking shell and black trail pants, standing beside a dusty SUV.
3. Action
Describe what is physically happening.
She is leaning against the open passenger door while tightening one of her hiking boots.
4. Composition
Explain where subjects should appear and how the image should be framed.
Vertical composition. Full body visible with both feet inside the frame. Camera positioned approximately six feet away at chest height. The SUV occupies the right third of the image.
5. Visual treatment
Describe the photographic or illustrative language.
Casual smartphone photography with ordinary exposure, realistic dynamic range, slight handheld imperfection, natural skin texture, and no obvious commercial lighting.
6. Lighting and environment
Describe visible conditions instead of relying only on mood words.
Overcast afternoon light with soft shadows. Dry mountain trailhead surrounded by scrub, gravel, and pine-covered hills.
7. Constraints
Say what must not happen.
Do not make this look cinematic or staged. No dramatic color grading, artificial rim lighting, excessive background blur, beauty retouching, text, logos, or watermarks.
Put together, you have a complete visual brief instead of a collection of aesthetic keywords.
The most useful generation template
Copy and adapt
Create:
[type of image]
Subject:
[who or what appears]
Action:
[what is happening]
Composition:
[camera position, framing, placement, scale]
Environment:
[location and surrounding details]
Lighting:
[source, direction, softness, time of day]
Visual treatment:
[photographic style, illustration style, materials, texture]
Important details:
[specific objects, wardrobe, colors, expressions, surfaces]
Constraints:
[things that must remain true or must not appear]
This format is particularly useful for complex instructions because you can change one section without rewriting the whole thing.
Photorealism: describe evidence of reality
Simply saying photorealistic is often not enough.
Describe the imperfections and physical properties that make an image feel photographed.
Instead of:
Photorealistic portrait of a man.
Try:
Create a realistic candid photograph of a man sitting outside a neighborhood coffee shop. Natural skin texture with visible pores, subtle under-eye detail, individual beard hairs, slightly wrinkled clothing, imperfect posture, ordinary daylight, realistic highlights and shadows, and natural background clutter. Avoid beauty retouching, dramatic cinematic lighting, excessive shallow depth of field, or stylized color grading.
When realism is the goal, describe things the camera would actually capture:
- framing
- light
- texture
- materials
- imperfections
- exposure
- physical interaction between objects
Useful realism descriptors
For natural photography
- candid photograph
- ordinary available light
- natural skin texture
- realistic dynamic range
- subtle sensor noise
- minor handheld imperfection
- slightly imperfect framing
- practical lighting
- natural color balance
- realistic fabric wrinkles
- ordinary environmental clutter
- unretouched appearance
To reduce the “AI commercial” look
- not cinematic
- not staged
- no dramatic color grading
- no artificial rim lighting
- no beauty retouching
- no excessive bokeh
- no perfectly symmetrical composition
- no glossy advertising finish
Do not stack every descriptor into every image.
Choose the ones that describe the specific failure you are trying to prevent.
Composition: describe what the camera can actually see
GPT Image responds well to concrete spatial instructions.
Weak:
Cool wide shot of a chef cooking.
Better:
Wide horizontal photograph. The chef stands on the left third of the frame behind a stainless steel prep counter. Show the chef from head to knee. A second cook is visible deeper in the kitchen on the right. Camera at eye level approximately ten feet away. Leave open negative space above the prep counter for later graphic design.
For people, explicitly describe details such as:
- full body visible
- both hands visible
- feet included inside the frame
- looking toward the window
- holding the glass with the right hand
- seated behind the table
- subject occupies approximately one third of the frame
- camera positioned at eye level
- three-quarter profile
Describe body framing, relative scale, gaze, and physical interaction instead of assuming the model will infer them.
Quality and resolution cheat sheet
GPT Image 2.5 supports multiple quality levels, including:
- Auto
- Low
- Medium
- High
- XHigh
- Max
Common output sizes can include:
- 1024 × 1024
- 1536 × 1024
- 1024 × 1536
- 2048 × 2048
- 2048 × 1152
- 3840 × 2160
- 2160 × 3840
Custom resolutions may also be available within supported output constraints.
Very high-resolution outputs should still be treated carefully. Higher resolution does not automatically guarantee better composition or better adherence.
Do not assume Max automatically means best
Higher quality settings can take longer and do not guarantee that every generation improves.
The better strategy is to find the lowest setting that reliably satisfies the visual requirement.
Practical rule
Exploration: Flare + moderate quality
Promising direction: Flare + higher quality
Precision problem: Sunburst
Final demanding asset: Sunburst + higher quality if it visibly helps
A compact instruction stack
For quick everyday generation:
[Deliverable] + [subject] + [action] + [composition] + [environment] + [lighting] + [visual treatment] + [important constraints]
Example
Create a candid vertical smartphone photograph of two friends eating tacos outside a neighborhood food truck at night. One person is laughing while holding a taco and the other is reaching for a napkin. Waist-up framing, slightly off-center composition, handheld perspective from approximately five feet away. Mixed practical light from the truck window and nearby streetlights, realistic skin, ordinary shadows, slight low-light phone noise, natural colors. It should feel like a genuine social-media photo taken by a friend, not a commercial shoot. No cinematic grading, flash photography, beauty retouching, advertising text, logos, or excessive background blur.
Flare or Sunburst? A 10-second decision guide
I need ideas quickly.
Use Flare.
I need twenty variations.
Use Flare.
I'm creating everyday social imagery.
Use Flare.
I'm experimenting with composition or style.
Use Flare.
I'm making the final hero image.
Try Flare, then use Sunburst if greater precision is needed.
The person's identity keeps drifting.
Use Sunburst.
The product keeps changing shape.
Use Sunburst.
I need to edit one tiny thing without touching everything else.
Use Sunburst.
I'm combining several references and every detail matters.
Use Sunburst.
I'm doing a long chain of revisions.
Prefer Sunburst when preservation becomes difficult.
A helpful reminder
GPT Image 2.5 understands natural language well enough that elaborate instruction syntax is usually unnecessary.
The goal is not to discover a secret sequence of keywords.
Think like a creative director giving instructions to another person:
What are we making?
What should it look like?
What information matters most?
What is allowed to change?
What must stay the same?
When something goes wrong, do not automatically make the entire instruction longer.
Identify the specific failure and add the constraint that resolves it.
Key takeaway
Flare is the model to start with. Sunburst is the model to reach for when precision becomes the bottleneck.
Across both models, the most reliable technique is to describe the desired image like a visual brief: define the outcome, subject, composition, visible details, and constraints.
Tell the model what the image needs to contain, rather than relying on vague aesthetic keywords.
References, Identity Preservation, and Precise Editing
GPT Image 2.5 becomes much more powerful when you stop thinking of references as pictures the model should vaguely imitate.
Instead, give each reference a specific responsibility.
Then separate your instructions into two categories:
What should change?
and
What absolutely must stay the same?
This is especially important for:
- people
- products
- branded assets
- characters
- wardrobe changes
- environment swaps
- multi-reference compositions
- repeated edits
Sunburst is particularly useful when small deviations in these areas become unacceptable.
Reference images: give every image a job
When combining multiple references, do not simply upload everything and say:
Combine these.
Tell GPT Image exactly which information comes from each reference.
Example
Use Image 1 as the identity reference. Preserve this person's facial features, hairstyle, proportions, skin tone, and overall likeness.
Use Image 2 only as the wardrobe reference. Transfer the jacket, shirt, pants, and shoes onto the person from Image 1.
Use Image 3 as the environment reference. Place the person in this location and match its lighting and perspective.
Do not copy the person from Images 2 or 3.
Preserve the identity from Image 1 throughout the final image.
This reduces ambiguity between references.
GPT Image 2.5 can also combine subjects and environments from separate images.
The important part is identifying:
- which elements should transfer
- which elements should remain fixed
- which references should not influence certain parts of the result
Think of references as separate ingredients
A useful mental model is to assign references into categories.
Identity reference
Defines who someone is.
Use it for:
- face
- hair
- skin tone
- age
- body proportions
- defining physical characteristics
Product reference
Defines what an object must look like.
Use it for:
- geometry
- packaging
- materials
- logos
- label placement
- colors
- proportions
Style reference
Defines how the image should look.
Use it for:
- palette
- texture
- lighting treatment
- illustration technique
- visual finish
Composition reference
Defines where things should appear.
Use it for:
- subject placement
- framing
- camera position
- negative space
- relative scale
Environment reference
Defines where the scene takes place.
Use it for:
- architecture
- landscape
- interior
- lighting environment
- spatial context
Wardrobe reference
Defines what someone should wear.
Use it for:
- garments
- footwear
- accessories
- colors
- styling
Explicit role assignment becomes increasingly important as you add more references.
Identity preservation
When preserving a person, object, or product matters, do not only say:
Keep them the same.
List what same means.
Identity-preservation pattern
Preserve the person's exact identity.
Do not change their facial structure, eyes, nose, mouth, skin tone, hairstyle, body proportions, age, or defining features.
Keep their expression and pose unchanged.
Change only the clothing.
Match the new clothing naturally to the existing body, lighting, perspective, and shadows.
Do not change the environment, framing, camera position, or image quality.
Separating what changes from what stays fixed makes the task much easier to control.
For products, define different invariants
A person's identity and a product's identity are not preserved through the same details.
For products, specify things like:
Preserve the exact bottle geometry, proportions, cap shape, label placement, typography, colors, logo, and packaging construction. Do not redesign or reinterpret the product.
Useful product invariants include:
- overall silhouette
- dimensions
- geometry
- material
- color
- cap or closure
- label dimensions
- logo placement
- typography
- package construction
- surface finish
- distinctive manufacturing details
If product accuracy is mission-critical, Sunburst is usually the safer starting point.
For characters, repeat the character definition
When creating multiple scenes with the same character, references help, but it is still useful to repeat defining characteristics.
For example:
Same facial features, hairstyle, proportions, wardrobe colors, accessories, and illustration style as the reference.
Do not assume the model will remember which characteristics were important simply because they appeared in the previous generation.
Repeat important invariants when necessary.
Style references
When using another image for aesthetic direction, define which visual properties should transfer.
Weak:
Make it look like this image.
Better:
Use the attached image only as a visual style reference.
Transfer its muted color palette, coarse paper texture, flat graphic shapes, limited shading, and hand-printed appearance.
Do not copy its subject, composition, text, symbols, or specific objects.
Apply those visual characteristics to a new image of a fisherman repairing a net.
This separates style from content.
The best editing formula
For editing, a very useful structure is:
CHANGE
What should be different?
PRESERVE
What must stay exactly the same?
INTEGRATE
How should the new element interact with the original image?
EXCLUDE
What should not be introduced?
Example
CHANGE: Replace the red chair with a tan leather lounge chair.
PRESERVE: Keep the room architecture, camera position, crop, floor, windows, table, plants, lighting direction, and all other furniture unchanged.
INTEGRATE: Match the chair's perspective, contact shadows, reflections, scale, and warm afternoon lighting to the existing scene.
EXCLUDE: Do not add decorations, pillows, people, text, logos, or additional furniture.
This is much safer than:
Make the chair leather.
For precise edits, say “change only”
GPT Image 2.5 is specifically improved at localized editing, but unnecessary changes can still happen.
For surgical edits, explicitly say:
Change only [X].
Then identify the important invariants.
Example
Remove only the flower from the man's hand. Reconstruct the fingers naturally where the flower was removed. Preserve his exact face, expression, hand position, clothing, pose, background, lighting, color, crop, and camera angle. Do not modify anything else.
Generative editing is still generative.
If a region must remain truly pixel-identical for production reasons, traditional compositing can still be the safer workflow.
Multi-turn editing: change one thing at a time
GPT Image 2.5 is designed to retain details better during iterative editing.
The safest workflow is still incremental.
Instead of:
Change the weather, outfit, expression, car, billboard text, camera angle, and lighting.
Break the work into steps.
Turn 1
Change the environment from summer to winter. Preserve everything else.
Turn 2
Replace the black jacket with the attached orange jacket. Preserve the person's identity, pose, framing, and the winter environment.
Turn 3
Change the billboard text to "MADE FOR ANYWHERE." Preserve everything else exactly.
Turn 4
Make the light slightly warmer, as if the sun is beginning to set. Do not modify any objects or subjects.
Start with one output, inspect it, then make narrow follow-up changes.
Constraints that remain important should be repeated during later edits.
Use Sunburst when a long editing chain begins accumulating unwanted changes.
Creating product photography from references
GPT Image 2.5 is particularly useful for placing a supplied product into new advertising environments.
Product workflow
Use the supplied bottle as the exact product reference.
Preserve its shape, proportions, materials, cap design, label dimensions, colors, branding, typography, and graphic layout.
Place it upright on a wet stone beside a mountain stream.
Early morning natural light from camera left creates realistic highlights on the bottle and soft contact shadows underneath.
Commercial product photography, premium but physically realistic.
Do not redesign the packaging, modify the label, add text, change the logo, or alter the bottle proportions.
Notice that the instruction contains three different kinds of information:
What is fixed: the product
What is changing: the environment
How they interact: light, shadows, reflections, perspective, and contact
A compact edit stack
For controlled edits:
CHANGE: [only the thing that changes]
PRESERVE: [everything important that stays fixed]
INTEGRATE: [lighting, geometry, perspective, contact]
EXCLUDE: [things the model should not introduce]
Example
CHANGE: Replace only the woman's white sneakers with the supplied black boots.
PRESERVE: Exact identity, face, hair, body, pose, clothing, environment, framing, camera angle, and lighting.
INTEGRATE: Fit the boots naturally to her feet and pose. Match perspective, shadows, material response, and ground contact.
EXCLUDE: No additional accessories, clothing changes, text, logos, or background modifications.
Common reference and editing problems
The model keeps changing the person's face
Separate the requested change from the identity constraints:
Change only the jacket. Preserve the exact facial structure, eyes, nose, mouth, hairstyle, skin tone, expression, age, body proportions, pose, and identity.
If the problem continues, move the task to Sunburst.
The model redesigns a product
Describe product invariants explicitly:
Preserve exact geometry, proportions, materials, label dimensions, logo placement, typography, colors, cap shape, and packaging construction.
Do not simply say:
Keep the product the same.
The edit changes the whole image
Use:
Change only [specific object].
Then explicitly list what stays fixed.
For example:
Preserve the camera position, crop, subject identity, pose, room architecture, furniture, lighting direction, shadows, color treatment, and background.
Reference images are getting mixed together
Assign roles:
Image 1 = identity
Image 2 = clothing
Image 3 = environment
Image 4 = visual style
Then explain which properties should and should not transfer.
The first edit worked but later edits drift
Repeat preservation constraints during every important edit.
Change one thing per step.
If consistency still degrades, return to the last approved image and branch from that version instead of continuing the compromised editing chain.
A helpful reminder
References work best when you tell the model why each image is there.
Do not leave it to infer whether an uploaded image represents:
- identity
- product design
- wardrobe
- composition
- location
- lighting
- style
For editing, think like a VFX supervisor reviewing a shot.
Specify:
What changes?
What stays locked?
How should the new element physically integrate?
What should not appear?
AI can make mistakes, so review important identity, branding, text, and product details before using generated assets in production.
Key takeaway
The key to reliable reference and editing workflows is separating variables from invariants.
Tell GPT Image 2.5 exactly which parts of the image are allowed to change, then explicitly lock everything that matters.
When preservation becomes the bottleneck, move from Flare to Sunburst.
Text, Transparency, Storyboards, Diagrams, and Troubleshooting
GPT Image 2.5 can do much more than create standalone photographs or illustrations.
It can also handle production-oriented image tasks such as:
- exact text
- posters
- layouts
- diagrams
- slides
- infographics
- transparent assets
- product cutouts
- sketch-to-image
- sequential storyboards
- multi-panel images
These tasks become much more reliable when you give the model structural instructions, not just visual descriptions.
Working with exact text
GPT Image 2.5 is strong at text rendering, but exact wording should still be treated like a specification.
Use quotation marks
Instead of:
Add a sign saying Grand Opening.
Write:
The sign must contain exactly this text:
"GRAND OPENING"
Say how many times it appears
Render the phrase exactly once.
Prevent unwanted copy
No additional words, captions, logos, labels, signatures, or watermarks.
Describe typography separately
Large condensed sans-serif uppercase typography, centered horizontally, strong kerning, white letters against a black background.
Complete example
Create a vertical streetwear campaign photograph.
Include exactly one line of advertising copy:
"BUILT FOR THE CITY"
Render this text exactly once in large uppercase sans-serif typography near the bottom of the image.
Keep it highly legible.
Do not generate any other text, logos, labels, watermarks, or signatures.
For demanding typography, packaging, or dense layouts, Sunburst and higher quality settings are worth testing.
Infographics, diagrams, and slides
When creating information-heavy images, write your instructions like a design brief, not a piece of concept art.
Define:
- intended audience
- exact title
- required information
- hierarchy
- labels
- data
- layout
- visual language
- elements that must not appear
Example
Create a clean 16:9 educational slide titled:
"HOW A HEAT PUMP MOVES HEAT"
Show a simple left-to-right diagram containing four stages:
Use arrows to clearly indicate energy flow.
Large readable labels, flat technical illustrations, white background, generous spacing, and a consistent icon system.
Designed for a beginner audience with no engineering background.
No decorative photography, gradients, tiny labels, unnecessary text, or unrelated technical components.
- Outdoor air
- Evaporator
- Compressor
- Indoor heating
For dense diagrams, small text, axes, legends, or footnotes, higher-quality generation becomes more useful.
The more information an image contains, the more important it becomes to explicitly define its hierarchy.
Creating transparent assets
GPT Image 2.5 supports transparent backgrounds.
When transparency matters, ask for it both through the available output settings and in the natural-language instruction.
Use an output format that preserves alpha transparency, such as PNG or WebP.
Example
Extract the sneaker from the supplied photograph.
Center the complete sneaker on a fully transparent background.
Preserve the exact geometry, colors, materials, stitching, sole shape, logo placement, and proportions.
Create clean alpha edges around laces, fabric, and small details.
No floor, scenery, backdrop, checkerboard, glow, outline, or artificial shadow.
Do not redesign or restyle the product.
Sketch-to-image
GPT Image 2.5 can turn rough sketches or diagrams into finished imagery.
The sketch should be treated as a layout constraint, not merely aesthetic inspiration.
Example
Turn this sketch into a realistic architectural photograph.
Preserve the exact placement, scale relationships, camera perspective, building silhouette, window locations, pathway, and tree positions from the drawing.
Replace the rough lines with believable architecture and materials.
Exterior walls are pale limestone with dark aluminum window frames. Natural late-afternoon daylight.
Do not change the layout or add new buildings, signs, people, or text.
For this workflow, explicitly preserve:
- layout
- proportions
- perspective
- relative position
- silhouette
- important boundaries
Then describe how the rough visual information should be resolved into finished materials and detail.
Storyboards and sequential images
GPT Image 2.5 can also reason about a sequence of visual beats.
Instead of describing the entire story in one paragraph, define each frame separately.
Example
Create a four-panel horizontal storyboard showing a courier arriving at an apartment building.
Panel 1: Wide exterior. Courier approaches the entrance carrying a cardboard package.
Panel 2: Medium shot. Courier checks the apartment number on their phone while standing beside the intercom.
Panel 3: Close shot. The courier presses apartment 408 on the intercom.
Panel 4: Interior lobby. The front door opens and the courier steps inside carrying the same package.
Maintain the same courier, clothing, package, time of day, and building architecture throughout all four panels.
Clear cinematic blocking, but simple storyboard presentation. No captions or text.
The important part is defining one clear visual beat per panel.
Then explicitly identify details that must persist throughout the sequence.
Examples include:
- same character
- same wardrobe
- same object
- same vehicle
- same architecture
- same time of day
- same visual treatment
Common problems and how to fix them
The image looks too cinematic
Add visible photographic constraints:
Ordinary available light, natural dynamic range, neutral colors, casual framing, realistic skin and fabric texture. Avoid cinematic lighting, dramatic grading, artificial rim light, heavy bokeh, glossy retouching, or movie-poster composition.
The text is wrong
Try these fixes:
- quote the exact copy
- reduce the amount of text
- state that the phrase appears exactly once
- specify where it appears
- describe typography independently
- request no additional text
- move to Sunburst if needed
- increase quality when small typography matters
The person keeps getting cropped
Be literal:
Full body visible from the top of the head to the bottom of both shoes. Leave visible space above the head and below the feet.
Do not assume phrases like full-body portrait will always be interpreted exactly the way you expect.
It keeps adding unwanted objects
Add an exclusion section:
Do not add furniture, decorative objects, people, plants, text, signs, logos, or additional props.
When unwanted details consistently appear, naming exclusions can be more effective than repeatedly rewriting the positive description.
Transparent background produces a fake checkerboard
Request:
Fully transparent alpha background. Do not draw a checkerboard or simulated transparency.
Also make sure transparency is enabled in the available output settings.
The composition is wrong
Do not only describe the subject.
Describe the geometry of the frame:
Subject on the left third. Camera at chest height approximately eight feet away. Full body visible. Product on the table in the lower-right quadrant. Leave the upper-right quadrant mostly empty.
Concrete spatial relationships are usually more useful than phrases like:
Make the composition dynamic.
The output feels generic
Add physical specificity.
Instead of:
Beautiful restaurant interior.
Try:
Narrow neighborhood restaurant with scratched dark-wood tables, cream plaster walls, mismatched bentwood chairs, condensation on the front windows, handwritten specials taped beside the kitchen door, and warm practical bulbs hanging from exposed cords.
Visual specificity gives the model more meaningful information than broad quality adjectives.
Too many things are going wrong at once
Simplify the task.
Generate or edit one important element first.
Inspect it.
Then make the next narrow change.
A long complicated instruction is not always more controllable than a short precise one.
A practical troubleshooting framework
When an output is wrong, identify which type of failure occurred.
Subject failure
The wrong person, product, object, wardrobe, or identity appeared.
Fix: strengthen reference roles and preservation constraints.
Composition failure
The correct content appeared in the wrong place.
Fix: specify framing, placement, scale, camera position, and negative space.
Style failure
The content is correct but the aesthetic is wrong.
Fix: describe visible properties such as palette, texture, lighting, material treatment, exposure, and photographic characteristics.
Text failure
Copy is misspelled, duplicated, or poorly placed.
Fix: quote exact copy, limit the amount of text, specify count and placement, and prohibit extra text.
Editing failure
The requested change worked but unrelated parts also changed.
Fix: use CHANGE / PRESERVE / INTEGRATE / EXCLUDE and repeat important invariants.
Consistency failure
A character, product, or scene drifts across iterations.
Fix: repeat defining characteristics, narrow each edit, return to the last approved version when necessary, and use Sunburst when greater preservation is needed.
Do not solve every failure by making instructions longer
More words do not automatically produce more control.
When something goes wrong:
- Identify the specific failure.
- Add a constraint for that failure.
- Keep the parts that already worked.
- Regenerate or make a narrow edit.
For example, if the generated photograph is correct except that the lighting looks too polished, you do not need to rewrite the subject, wardrobe, environment, and composition.
Add:
Replace the polished commercial lighting with ordinary available daylight. Preserve everything else.
A helpful reminder
The strongest GPT Image 2.5 instructions usually behave more like creative briefs than collections of magic keywords.
For complex images, define:
What are we making?
What information must appear?
Where should it appear?
What should remain consistent?
What should never appear?
For structured outputs like diagrams or storyboards, think in terms of hierarchy and relationships.
For transparency, think about edges.
For text, treat every word as exact copy.
For troubleshooting, fix the specific failure instead of rebuilding the entire instruction.
AI can make mistakes, so review important details before using generated assets in production.
Key takeaway
GPT Image 2.5 is most controllable when the structure of the desired image is made explicit.
Text should be treated as exact copy.
Diagrams should be treated as layouts.
Storyboards should be treated as sequences.
Sketches should be treated as spatial constraints.
Transparent assets should be treated as extraction tasks.
The more clearly you describe the job of every element, the less the model has to guess.