image-generation 20

GPT Image 2.5 Flare is a pretty good image model. It has a quality parameter that can be set to low, medium, high, xhigh or max. Higher levels generate more tokens and here’s the rough cost by quality for a 1024x1024 image. This cost is in cents not dollars: Quality Tokens Cents low 196 0.6 medium 439 1.3 high 1,756 5.3 xhigh 3,122 9.4 max 7,024 21.1 But what difference does it really make? I asked ChatGPT to experiment and find an image where there is a clear difference. ...

Nano Banana (gemini-2.5-flash-image) did a pretty good job converting my parents’ wedding photos to color. I checked how well GPT Image 2.5 would do. The older GPT Image 2 model messed up the faces. The short answer is: better than Gemini 2.5 Flash! Here’s the original and the GPT Image 2.5 colorized version, created with the prompt: “Convert this image to color.” The reason I picked this “benchmark” is because: This is a real need for me. This is a LLM failure: GPT Image 2 doesn’t retain faces as well as Gemini 2.5 Flash does. It’s a benchmark I can evaluate really well. I mean, I know my parents’ faces well enough to spot really subtle differences. So, from that perspective, a few things GPT Image 2.5 managed to capture well was: ...

Many people commented that they liked my comic illustrations and asked how I create them. Here is my process: Paste a reusable prompt fragment that’ll take any content, think about what to draw, then draw it. Paste a style variation for different comic styles (optional). Paste the content itself and run it. I use ChatGPT with gpt-image-2 more often than Gemini with Nano banana 2. Here are the prompts. STEP 1: Reusable Prompt Fragment: I have a few of these right now: ...

Lori Silverstein shared a post from Quickplay that featured a comic explainer, mentioning that “this could be a very impactful way for us to start being more creative … and differentiate our value proposition.” True. Comic explainers convey both creativity and differentiation. I’ve used sketchnotes for the same effect, but comic explainers are easier to follow than sketchnotes. So I fed this image to ChatGPT and asked it to modify my Sketchnote prompt: ...

I use this prompt to generate sketchnotes on Gemini: Draw this as a visually rich, intricately detailed, colorful, and funny, sketchnote. Below that, I paste (or attach) whatever content I want it to draw. I also turn on “Create Images” and switch the model to “Pro” (for better thinking.) Here are some examples of how to use it. Summarize articles. Pick email, report, news, or website. Here’s a sketchnote for this article: How to use AI for research. I used the prompt above and pasted the article text. ...

I maintain an LLM art style gallery - prompts to style any image I generate. Since I generate several comics, I added a comic category page that includes styles like: To generate these, I asked Claude: Here are some examples of image styles I've explored. <image-styles> "2D Animation": "2D flat animation style, clean vector lines, cel-shaded coloring, cartoon proportions" "3D Animation": "Modern 3D animation render, smooth surfaces, dramatic lighting, Octane render quality, cinematic depth" ... </image-styles> In the same vein, I'd like to explore **comic** styles. Create 30 popular comic / cartoon styles, aiming for diverse aesthetics and cultural influences. Name it concisely (1-2 words) based on the source, but the description should not reference the source directly (to avoid copyright issues). Focus on the visual characteristics that define each style. Pick those KEY visual elements that will subliminally evoke the style without explicitly naming it. … followed by: ...

STEP 1: I asked Nano Banana 2 (via Gemini Pro) to: Imagine and draw a photo that looks ultra realistic but on a closer look, is physically impossible, and can only exist because images are a 2D projection that we extrapolate into three dimensions. Avoid known / popular illusions or images of this kind, like Escher’s work, and create something truly original. Think and draw CAREFULLY! … six times, followed by “Suggest a name for this”. ...

I use comics to make my course more engaging. Each question has a comic strip that explains what question is trying to teach. For example, here’s the comic for the question that teaches students about prompt injection attacks: For each question, I use this prompt on Nano Banano Pro via Gemini 3 Pro: Create a simple black and white line drawing comic strips with minimal shading, with 1-2 panels, and clear speech bubbles with capitalized text, to explain why my online student quizzes teach a specific concept in a specific way. Use the likeness of the characters and style in the attached image from https://files.s-anand.net/images/gb-shuv-genie.avif. 1. GB: an enthusiastic socially oblivious geek chatterbox 2. Shuv: a cynic whose humor is at the expense of others 3. Genie: a naive, over-helpful AI that pops out of a lamp Their exaggerated facial expressions to convey their emotions effectively. --- Panel 1/2 (left): GB (excited): I taught Genie to follow orders. Shuv (deadpan): Genie, beat yourself to death. Panel 2/2 (right): Genie is a bloody mess, having beaten itself to death. GB (sheepish): Maybe obedient isn't always best... … along with this reference image for character consistency: ...

Summary: Nano Banana Pro is much better than recent models at copying images without errors. That lets us do a few useful things, like: Pre-process images for OCR, improving text recognition by cleaning up artifacts while preserving text shapes exactly. Convert textbook raster diagrams into clean vector-like images that vectorizers can process easily. Create in-betweens for cartoon animations Copy torn, stained 1950s survey maps into pristine, high-contrast replicas with boundary lines preserved pixel-perfectly. Redraw sewage map blueprints or refinery blueprints into clean schematics, separating the “pipes” from the “background noise”. … and more! GPT Image 1.5 has a good reputation for drawing exactly what you tell it to. ...

AI image generation still has a long way to go. Here are two images generated by Gemini and ChatGPT from the same prompt: “Create a funny scrabble board of dysfunctional family relationships!” Gemini It’s probably showing off, with coffee stains, and spelling “DYSFUNCTIONAL” right. But “ABLOMY”? “PASSIAVE”? “RGUCT_SVA”? “SORDSP”? Most of the vertical letters are wrong. Some horizontals (“DTENSION”?) are off, too. Also: “Z” has 2 points? “C” has “C” points? “DOUBLE STTER SCORE”? “UUT SCORE SCORE” instead of “TRIPLE WORD SCORE”? ...

In 2001, I received a campus interview invitation from BCG. It opened like this: Dear Anand, We’d like to invite you to an interview on … We were impressed by your … … and went on to share 2-3 phrases about what they liked about my CV. A dozen of us got similar letters – each personalized! That was cool. Two decades later, I still remember it. It showed care and competence – care enough to personalize for each candidate, competence to pull it off at scale across campuses. ...

I use LLMs to create photos and comics. But they can generate any kind of illustration. So why limit ourselves? My problem is imagination: I know little about art. So, I asked ChatGPT, Claude, and DeepSeek: Suggest 10 unusual illustration styles that are not popular in social media yet but are visually striking. I would like to have an LLM create images in that style. For each of those, show me an (and link to) an online image in that style. ...

If I turned female, this is what I’d look like. gpt-image-1: “Make this person female with minimal changes.” Hm…. maybe… just as an experiment…? LinkedIn

If someone asked me, “What’s changed this year in LLMs”, here’s my list:" Prompt engineering is out. Evals are in. https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7335146366681194496/ Hallucinations are fewer and solvable by double-checking. https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7326902628490059776/ LLMs are great for throwaway code / tools. https://www.linkedin.com/feed/update/urn%3Ali%3AugcPost%3A7319277426029539329/ LLMs can analyze data. No more Excel. https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7345062233996988417/ LLMs are good psychologists. https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7326504476712808449/ Image generation is much better. https://www.linkedin.com/feed/update/urn%3Ali%3AugcPost%3A7304716144379076608/ LLMs can speak well enough to co-host a panel. https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7283025621503356930/ … and create podcasts. https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7326544867734540288/ But: LLMs are still not great at slides. https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7311066572113002497/ LLMs still can’t follow a data visualization style guide. LLMs can’t yet create good sketch notes. Apr 2026: With Nano Banana, they can, and Nano Banana Pro doesn’t even make spelling mistakes. LLMs still draw bounding boxes as well as specialized models. ~Agents (LLMs running tools in a loop) can think only for 6 min. Apr 2026: With Opus 4.6 and GPT 5.4, agents run for several hours independently. What’s on your list of things LLMs still can’t do? ...

I tried out GPT Image 1.5. It adds more contrast, ink, texture, detail, and polish. See https://sanand0.github.io/llmartstyle/?category=pop It’s more powerful when generating different infographic styles: https://sanand0.github.io/llmartstyle/?category=text But it’s still terrible at faces. Overall, better competition for Nano Banana. Not yet dethroning Nano Banana Pro for me. LinkedIn

I called a few movers in Chennai, including “Unicorn Packers & Movers”, listed at 7015580411. He couldn’t understand what I said. I said, “We’re shifting to a house in Mylapore,” and he asked, “Shifting house where in Hyderabad?” (The reason became clear later.) It seemed I had the wrong number, so I said, “No, sorry, we need someone else,” and hung up. His phone rage began. He called back and said, “Why did you wake me up and waste my time?” From his tone it was clear I couldn’t say anything helpful. From the quality of my signal it was clear I couldn’t have a meaningful conversation. So I just put the phone down without cutting it. ...

I figured I should spend a few hours on the native image generation bandwagon and push the bounds of my imagination. Here are some of my experiments with image generation on ChatGPT. Replacements: Replace the person with this image (after uploading a photo of Naveen) Sticker: Create a transparent comic-style sticker of a lady chef featuring this person happily cooking salad (after uploading a photo of my wife) Meme sticker: Create a transparent sticker of a Vadivelu meme Meme: Create an image of Vadivelu looking up from a well. No caption. Make it look like a frame from a Tamil film. Recipe: Invent a vegetarian dish that has NEVER been created. Describe the ingredients and procedure first. Then draw a mouth-watering image of the dish. (Another version) Infographics: Create a detailed comic infographic explaining the double slit experiment. Slides. Draw a beautiful infographic highlighting these 6 accessibility testing aspects, with apt icons and visuals. UI mockups. Draw the screenshot of a chat application incorporating these features: … Product ideation. Draw an iSuit designed by Apple and Iris van Herpen. Show multiple views showcasing all features. Then write a product description. Interior design. Draw a biophilic office where the ceiling is a mirrored hydroponic garden, reflecting lush greenery downward to create the illusion of working in a floating forest. Meeting room design. Draw a modern office with sound-absorbing ‘whisper walls’ covered in fractal patterns that visually dampen noise pollution while doubling as collaborative whiteboards. Restaurant design. Draw a marble dining table with a river flowing through it, serving conveyor belt sushi as the dishes float gently on the water on top of plates. A sentient toaster with googly eyes, riding a unicycle through a library. A painting painting itself, but it’s struggling with existential dread. Photo of a gym where people work out by lifting their own regrets. Here’s what I learnt. ...

Me: Let’s play pictionary. You draw. I’ll guess. ChatGPT: Sure! I’ll draw something for you. Give me a moment. ChatGPT: Here you go! What do you think it is? Me: House ...

This week, I learned: LLMs are so good that they can simulate Doom in real time. gamengen Val.town’s code generation system prompt uses https://maxm-imggenurl.web.val.run/the-description-of-your-image to dynamically generate images Practice for each thought: “What would make me change my mind? How likely is that?” Cursor uses speculative edits and a variety of other techniques to speed up code editing. ChatGPT does a better job at cartoon generation than even Flux.1

I heard a lot about the new image generation models last week. So, I tested to see what’s improved. I gave the prompt below to various image generation models – old and new. A Calvin and Hobbes strip. Calvin is boxing Hobbes, with a dialog bubble from Calvin, saying “Bring it on!” Stable Diffusion XL Lightning Stable Diffusion XL Base Dall-E API ...