Grainy texture, overall blur, and images that get dirtier with every edit are three different problems in GPT Image 2, and they rarely share a cause. OpenAI has not published an explanation for the grain and tiling "grime" users report, so the reliable fix is to find the layer where quality drops. Check in this order: publishing and compression, the quality setting, prompt detail, the edit chain, and finally the model itself (GPT Image 2 versus GPT Image 2.5 Sunburst), changing one variable at a time.
Grain, in this context, is fine noise-like texture spread over areas that should be smooth, such as skies, skin, walls, and flat backgrounds. Sometimes it shows up as a repeating, tile-like pattern. Blur is the opposite failure: detail that should be there is missing. The table below sends each symptom to the check that usually settles it.
Match the symptom to the layer that causes it
Settings and advice in this table follow OpenAI's image prompting guide as of September 30, 2026. The first row mixes in user reports, because OpenAI documents no cause for that symptom.
| Symptom | Most likely layer | What to check | Fix to try first |
|---|---|---|---|
| Grainy or "dirty" texture, repeating tile-like grime on skies, skin, or flat areas | Model output (user-reported, no official cause) or a low quality level | Is the grain in the original download at 100% zoom? Which quality level made it? | Regenerate at high, describe smooth surfaces in the prompt, then compare GPT Image 2.5 Sunburst if it persists |
| Whole image soft, fine detail missing | Quality level or output size | Quality value and the output's pixel dimensions versus the display size | Step up to medium or high and request a size that matches where the image will be shown |
| Blocky edges, banding, or smeared detail that appears after upload or export | Compression or resizing after generation | Compare the original file with the published copy at the same zoom | Keep PNG or lower the compression, export once at the final size, and leave the prompt alone |
| Gets dirtier or drifts with each edit | Edit chain | How many edits deep the image is, compared with the last clean version | Return to the last clean version, make one change per turn, and restate what must stay |
| Small text muddy or misspelled | Quality level and known text limits | Quality value, amount of text, text size | Use high, put the exact wording in quotes, cut the text down, and proofread |
| Looks worse in ChatGPT than through the API | Product surface | Which settings each surface lets you change | In ChatGPT you only control the prompt and aspect ratio; use the API when you need quality or format control |
Rule out compression and resizing first
If the original file is clean and only the published copy looks blocky or soft, the damage happened after generation, and no prompt change will repair it. This is the cheapest check, so do it before spending another generation.
Download the original image rather than taking a screenshot. Open it next to the copy you published and zoom both to 100% on the same region. Flat areas and fine edges show compression damage first.
In the API, the image generation guide lists PNG as the default output. JPEG and WebP accept an output_compression value from 0 to 100%, and that setting has no effect on PNG. A heavily compressed JPEG or WebP gives you a smaller file with more visible blocks and banding. If you need a small file, generate PNG, then compress once at the final display size with a tool that lets you preview the result.
Chat apps, social networks, and CMS image pipelines often re-encode or resize uploads. Each extra save as JPEG adds loss. If the published copy passes through several of these steps, fix the pipeline: upload a larger original, turn off automatic recompression where the platform allows it, or export at the exact display size so the platform has less to rescale.
Check the quality level before rewriting the prompt
A low quality level is the first setting to rule out when detail looks soft. OpenAI's guide puts the order plainly: "Compare quality levels before rewriting the prompt."
As of September 30, 2026, gpt-image-2 accepts quality values of auto (the default), low, medium, and high. The GPT Image 2.5 models add xhigh and max. OpenAI's advice runs in both directions. If the output falls short, test a higher setting, and once it meets your requirement, test lower settings to save time and cost. The guide also warns that "a higher setting doesn't guarantee a better result for every prompt," so compare rather than assume.
Text is the clearest case. For small text, dense information, or several fonts, the guide recommends comparing medium and high. For slides and charts with small labels, legends, or axes, it recommends quality="high". Even then, OpenAI lists text rendering among the model's remaining limitations, so proofread every word.
Size is not a substitute for quality. GPT Image 2 custom sizes must keep each edge at or below 3,840 px, use multiples of 16, stay within a 3:1 ratio, and contain 655,360 to 8,294,400 pixels. OpenAI labels outputs above 3,686,400 pixels (2560x1440) as experimental. A larger canvas therefore does not promise a cleaner image. If the image simply needs more pixels for print or a large display, the size rules and valid 4K requests are covered in GPT Image 2 4K: Supported Sizes, Size Rules, and API Requests.
Describe the surfaces you want
A vague prompt leaves texture and finish to the model. OpenAI's guide recommends describing visible details, meaning materials, lighting, colors, medium, framing, and texture, and asking for "photorealistic" explicitly when you want a photograph.
For grain complaints, that means naming smooth surfaces as smooth and removing words that invite texture. Terms like "film photo," "35mm," "vintage," "gritty," or "high ISO" can reasonably produce grain because grain is part of those looks. If you want them, keep them. If you don't, replace them with a lighting and finish description.
Compare a vague prompt with a specific one:
hljs textVague: a woman standing by a window, cinematic Specific: Photorealistic portrait of a woman standing by a large window, soft even daylight from the left, smooth skin texture, clean matte white wall behind her, sharp focus on the eyes, no film grain, no noise, no text or watermark.
The exclusions at the end follow OpenAI's advice to state what you don't want, which the guide illustrates with unwanted text, logos, and watermarks. A "no grain" instruction is a reasonable test, not a guaranteed fix. Run it through the comparison procedure below before trusting it.
Stop an edit chain from getting dirtier
Each edit turn feeds the previous output back in as the next input, so small changes can accumulate. OpenAI's guide states that "repeated edits can still change details you intended to preserve."
These rules come from the same guide, plus one practical step at the start:
- Go back to the last clean version. Editing an image that already shows grain carries the grain forward. Restart from the most recent version you would have approved.
- Ask for one change per turn. Say "change only X" and list the details to preserve, such as face, product shape, labels, colors, and background.
- Restate the constraints when the result drifts. Repeat what must stay the same instead of assuming the model remembers it.
- Composite when a region must stay identical. If an area has to remain pixel-for-pixel the same, OpenAI's advice is to paste the approved edit into the original image rather than rely on prompting alone.
- Don't look for a fidelity switch. For
gpt-image-2, OpenAI says to omitinput_fidelity, because image inputs are always processed at high fidelity.
If an image has gone through many rounds, write one consolidated prompt describing the final result and generate it fresh. That resets the chain entirely, and you can compare the fresh version against the edited one as a single test.
Test GPT Image 2.5 Sunburst when GPT Image 2 falls short
If the quality is high, the prompt is specific, the edit chain is short, and the original file still looks grainy, the model is the variable left. OpenAI's migration advice for exactly this case is to test GPT Image 2.5 Sunburst first.
The image prompting guide describes the two GPT Image 2.5 models this way, as of September 30, 2026:
| Model | Optimized for | Image quality compared with GPT Image 2 |
|---|---|---|
| GPT Image 2.5 Flare | Speed (small model) | Comparable |
| GPT Image 2.5 Sunburst | Quality (base model) | Higher |
OpenAI adds that both models improve precise editing and subject preservation. Moving to Flare is therefore a speed decision, not a quality upgrade. Try xhigh or max on Sunburst only when they fix a requirement high does not meet within your latency budget.
The guide makes no statement about grain or noise specifically. Some third-party sites claim GPT Image 2.5 "fixed" the noise problem, but OpenAI has not said so. Compare the two models on your own prompts before treating the switch as a cure.
To run that model comparison without writing API calls, YingTu can run the same prompt on GPT Image 2 VIP, GPT Image 2.5 Flare, or GPT Image 2.5 Sunburst for $0.03 per image each, or on Nano Banana 2 ($0.055) and Nano Banana Pro ($0.09), at sizes up to 4K, as of September 30, 2026. You use your own LaoZhang API key. It does not clean up an existing grainy image. It only makes side-by-side generations quicker. Prices and sizes are on the GPT Image 2 Online page.
Run a one-variable comparison
Two runs of the same GPT Image 2 request usually differ, and there is no seed to pin, so one before-and-after pair can mislead you. This procedure separates a real improvement from normal run-to-run variation:
- Freeze the setup. Use the same surface (ChatGPT, API, or another tool), the same prompt, size, input images, and output format.
- Make the baseline twice. Generate two images with the current settings and save the original files.
- Change exactly one variable. Choose one: quality level, output format plus compression, model (GPT Image 2 versus GPT Image 2.5 Sunburst), or edit chain (fresh generation versus the edited version).
- Make the variant twice. Generate two images with that single change and save the originals.
- Compare the same regions. Check sky, skin, flat background, fine edges, and small text at 100% zoom and at the final display size.
- Decide. Keep the change only if both variant images beat both baseline images on the symptom and no new defect appears. If the four images overlap, the variable you changed is not the cause, so move to the next layer in the table.
Keep a short record so the result stays usable later:
| Field | Baseline | Variant |
|---|---|---|
| Surface and model | e.g. API, gpt-image-2 | Same, unless the model is the variable |
| Prompt, size, inputs | Exact values | Same |
| Quality, format, compression | Exact values | Only the variable differs |
| Files | Two originals | Two originals |
| Result on the symptom | Grain / blur / drift noted per region | Same regions |
If the same grain survives every layer, keep the image out of final delivery. Save the originals, settings, and prompt so you can report a reproducible case instead of a general "quality got worse."
What you can change in ChatGPT and in the API
In ChatGPT, you control the prompt and the aspect ratio. As of September 30, 2026, OpenAI's Images in ChatGPT help page covers changing the aspect ratio but documents no quality, resolution, or output format controls.
That shortens the ChatGPT checklist to:
- Download the original instead of screenshotting it.
- Make the prompt specific about materials, lighting, and surfaces.
- Start a new generation from a consolidated prompt instead of stacking more edits on a grainy image. If the grain appears only late in a long conversation, one fresh-chat test tells you whether the conversation is a factor for that prompt.
The API adds quality, size, output format, compression, and model choice, which are the settings the one-variable comparison needs. If ChatGPT output keeps failing on the same prompt, reproducing it through the API or a tool that exposes those settings is the next step.
What users have reported about grain
The following are user reports, not statements from OpenAI. A Reddit r/ChatGPT thread from spring 2026, titled "GPT Image 2 keeps adding weird tiling texture/grime," collects examples of repeating dirt-like texture. An OpenAI Developer Community thread started on April 22, 2026, lists visible noise patterns on structured surfaces and clouds among collected GPT Image 2 issues. An older Developer Community thread about gpt-image-1 reported grainier results after several high-fidelity edits in a row.
These reports confirm the symptom is widespread enough to be recognizable. They do not establish a cause. That is why the layered checks above matter more than any single workaround shared in these threads.
FAQ
Why are ChatGPT images grainy?
OpenAI has not published a cause for the grain users report in GPT Image 2 output. In practice, grain traces back to one of four layers: recompression after download or upload, texture-inviting words in the prompt, a long edit chain, or the model output itself. Check them in that order, one change at a time.
How do I increase ChatGPT image quality?
Download the original file, write a specific prompt that names materials, lighting, and smooth surfaces, and regenerate from a clean prompt instead of editing a degraded image repeatedly. The Images in ChatGPT help page lists no quality setting. For control over quality level, size, and format, use the API, where high and a matching output size are the first settings to test.
Why is my ChatGPT image blurry?
A blurry result has three common explanations: the image was resized or recompressed after generation, it is displayed larger than it was generated, or the prompt left detail vague. Compare the original file at 100% zoom. If the original is sharp, fix the export or upload step. If the original is soft, regenerate with more specific detail, or in the API, at a higher quality level and the right size.
Does GPT Image 2.5 fix the grain?
OpenAI does not say so. It states that GPT Image 2.5 Sunburst has higher image quality than GPT Image 2 and that Flare is comparable to GPT Image 2, with no specific claim about noise. Sunburst is the model OpenAI suggests testing when GPT Image 2 falls short, and a same-prompt comparison of both is the reliable answer for your own images.
