Model releases and comparisons
Launches, benchmarks, capabilities, and comparative evaluations of image-generation models.
32%
Best tweets about AI Image Generation
50 Best Tweets About AI Image Generation (2026)
Discover the best tweets about AI image generation, covering models, prompting, workflows, visual consistency, design experiments, and commercial use.
AI image models, prompt techniques, editing workflows, visual results, consistency, licensing, and professional use.
- Creators
- 43
- Updated
Original Xholic analysis
What 50 top AI Image Generation posts reveal
The 50-post dataset is predominantly supportive (72%) and media-led (92% include media). Recurring discussion areas include model comparisons, visual quality, editing workflows, commercial uses, prompting, and structured visuals. The strongest supplied theme median belongs to open-source tools and licensing (55.72), while posts also document artifacts, perceived artificiality in ads, and consent or likeness concerns.
- Dominant tone
- Positive
- Median score
- 18.3
- Leading format
- Case Study
- Recent posts
- 36%
74% of posts
All-time engagement
28% of posts
Published in 90 days
Conversation map
The themes creators return to
Visual quality and AI artifacts
Visual fidelity, photorealism, AI artifacts, perceived artificiality, and quality-control concerns.
28%
Editing, control, and workflows
Image editing, compositional control, layer extraction, inpainting, consistency, LoRA training, and production workflows.
26%
Commercial design and advertising
Commercial image use for ads, product photography, branding, decks, UI mockups, and design ideation.
24%
Prompting and creative direction
Prompt structure, art direction, templates, and iterative techniques for improving generated visuals.
24%
Text, layouts, and structured visuals
Typography, infographics, diagrams, UI screens, multilingual text, and structured-layout generation.
22%
Open-source tools and licensing
Open-source models, local/self-hosted tools, ComfyUI integrations, repositories, and licensing.
16%
Image-generation technical foundations
Technical explainers on diffusion, guidance, model architectures, denoising, and image-generation pipelines.
12%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 70
- Median reposts
- 13
- Median replies
- 7
- Median views
- 5.6K
Posts with media make up 92% of this collection. Their median all-time score is 18.3, compared with 11.9 for text-only posts.
Format mix
- Case Study 28% · score 3.48
- Tutorial 28% · score 33.7
- Announcement 26% · score 26.2
- Opinion 10% · score 17.5
Consensus and debate
Where creators agree, and where they do not
Shared view
Structured visuals recur as a capability focus
Posts repeatedly highlight text rendering, structured layouts, multilingual output, and UI or infographic generation as image-model capabilities. The cited posts make model-specific capability claims, including layout controls and structured outputs.
Shared view
Prompting is presented as creative direction
Practical guidance commonly recommends specifying context, composition, lighting, palette, copy, and aspect ratio, then reviewing and iterating. One post also proposes using an LLM to turn rough ideas into more technical creative direction.
Shared view
Editability is a recurring workflow need
Posts describe generation alongside ad creation, slide redesign, format conversion, and layer-level changes. They frame conversion into editable formats and post-generation editing as remaining workflow needs.
Shared view
Open tooling appears in workflow discussions
Self-hosting, ComfyUI support, open-weight models, and licensing appear in the cited posts as workflow and infrastructure options. The ERNIE-Image post specifically identifies an Apache-2.0 license.
Open debate
High-quality claims coexist with artifact examples
One post argues that well-prompted outputs can be difficult to distinguish from high-quality human work. Other posts cite a game image with poor horse anatomy and an ad featuring a bicycle with two sets of handlebars as examples of visible failures.
Open debate
Commercial-use enthusiasm is tempered by fidelity concerns
Posts range from enthusiasm about AI-generated product imagery to cautions about production mistakes. A cited research summary reports that AI-generated ads were statistically indistinguishable from human-made sibling ads after campaign-level controls, while ads perceived as AI-like had lower click-through rates.
Open debate
Creative guidance is accompanied by consent and likeness concerns
One instructional post calls for respecting copyright and avoiding deceptive content. Other posts raise concerns about visual remixing, likeness, notification, opt-out, and possible misuse; these are reported as platform and creator concerns in the cited posts.
What performs
Patterns behind standout posts
Outliers span critique, tools, workflows, and releases
The deterministic analytics identifies these five posts as all-time-score outliers: 2415.67, 1788.32, 1025.87, 940.05, and 150.63, respectively. Their subjects include a visual-quality critique, a repository list, an ad-generation workflow, a model announcement, and a practical model guide.
Tutorials perform strongly, but are not the top median format
Tutorials have a supplied median all-time score of 33.71. This exceeds the medians for announcements (26.168), opinions (17.487), and case studies (3.48), but not Stories (69.44) or the single List post (1788.32). Three of the five score outliers are classified as tutorials.
Open-source tools and licensing has the highest theme median
Open-source tools and licensing has the highest supplied theme median all-time score, 55.72. The cited posts cover a self-hosted image studio, ComfyUI model support, and Apache-2.0-licensed ERNIE-Image.
Statistical standouts
- View standout post 1 Score 2415.7 · 132× median
- View standout post 2 Score 1788.3 · 97.72× median
- View standout post 3 Score 1025.9 · 56.06× median
- View standout post 4 Score 940.0 · 51.37× median
- View standout post 5 Score 150.6 · 8.23× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Simon Smith
@_simonsmith
2 posts
-
2. BURKOV
@burkov
2 posts
-
3. ComfyUI
@ComfyUI
2 posts
-
4. Eric Seufert
@eric_seufert
2 posts
-
5. ModelScope
@ModelScope2022
2 posts
-
6. Shushant Lakhyani
@shushant_l
2 posts
ComfyUI posts focus on model integrations and structured output
ComfyUI’s two cited posts announce support for Ideogram 4.0 and ERNIE-Image. Their stated highlights include layout control, multilingual text, structured outputs, and, for ERNIE-Image, Apache-2.0 licensing.
Simon Smith connects generation to ideation and conversion
Simon Smith’s cited posts discuss slide-by-slide deck redesign and AI-assisted industrial-design exploration. One explicitly identifies format conversion and easy editing as gaps to address.
Shushant Lakhyani publishes instructional explainers
Shushant Lakhyani’s cited posts are educational explainers on prompt components, generation modes, workflow stages, diffusion-style pipelines, and common generation mistakes.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best AI Image Generation tweets
Top AI Image Generation tweets from 43 creators
Ranked 01–50
- 01
- 02
@heygurisingh ·
10 GitHub repos so good they shouldn't be free. 1. AutoHedge An autonomous hedge fund built in Python with four AI agents: a director generates investment theses, a quant validates them, a risk manager decides position size, and an execution agent places orders. Operates live on Solana. With 'pip install -U autohedge', you can start trading immediately. repo → https://t.co/YTc8jHhRLo 2. Vibe-Trading A trading system using a Directed Acyclic Graph (DAG) model, featuring 64 finance skills and 29 preset specialist agent swarms. Includes analysis methods like Ichimoku, Elliott Wave, SMC, Black-Scholes, full Greeks, and risk parity. Its crypto desk provides liquidation heatmaps and token unlock tracking. You can observe agents debating strategies in real time. repo → https://t.co/LcBLbWMcAk 3. Fincept Terminal A Bloomberg Terminal replacement that runs on your laptop. CFA levels 1, 2, and 3 analytics. 20+ investor AI agents (Buffett, Dalio, Soros). 100+ data connectors, including Polygon, World Bank, and IMF. Bloomberg charges $24,000 a year. This is free. repo → https://t.co/5D3m8P5Sfu 4. LibreChat Every model ChatGPT runs, plus Claude, Gemini, DeepSeek, and 20 more. Self-hosted. Native MCP support. You own the data, the history, the infrastructure. OpenAI charges $20/month to use their wrapper. This costs nothing to use your own. repo → https://t.co/h5Wn1OIJzR 5. Open Higgsfield AI A self-hosted cinema studio with 200+ AI models. Flux, Midjourney, Sora, Kling, Veo, GPT-4o, SDXL all in one interface. Text to image. Image to video. Cinema mode with pro camera controls. No subscription. Your data stays local. repo → https://t.co/iQwE8LmB9N 6. Open-LLM-VTuber A Live2D AI companion that runs offline, sees your screen, hears your voice, and never forgets. Inner thoughts are shown as a separate text layer, so you watch the reasoning happen before words come out. Pet mode floats it on your desktop. Swap the LLM in one config line. repo → https://t.co/JcFPhFt69G 7. Claude Ads A free Claude Code skill that runs 190 audit checks across Google, Meta, YouTube, LinkedIn, TikTok, and Microsoft Ads. 6 parallel subagents firing at once. Consolidates into a single Ads Health Score ranked by revenue impact. Agencies charge $4,000 a month for this. repo → https://t.co/b0jENyIACp 8. Agentic Inbox Cloudflare just open-sourced an email client where an AI agent reads your inbox and drafts your replies. Runs entirely on Cloudflare Workers. Each mailbox lives in its own Durable Object. Your email never leaves your Cloudflare account. One click deploys it. repo → https://t.co/RNTsIsZu9m 9. Camofox Browser An open source headless browser that makes AI agents invisible to bot detection. Spoofs navigator properties, WebGL, AudioContext, and WebRTC at the C++ level. The browser does not look modified because it genuinely is not. Accessibility tree output drops token cost by 90%. repo → https://t.co/J59ke0F3Kg 10. Hyperframes HeyGen open-sourced a video framework that does everything Remotion does without React, without JSX, without teaching your AI agent a new format. The agent writes HTML. The framework renders MP4. GSAP, Lottie, and Three.js all work. Same HTML always produces the same file. repo → https://t.co/t5iMhxzSMk These are not toys. Each one replaces a paid product you're still being charged for. Pick one. Install it. Plug it into your workflow. 100% free. 100% open source.
- 03
@mikefutia ·
Nano Banana 2 + Claude Code can generate 40 static ads in one shot. You just paste in a brand URL and let it rip. 00:00 Intro 00:34 Setting up the project 02:17 Building the skill file 05:01 Adding 40 ad templates 06:32 FAL AI image generation setup 10:47 Running the full workflow 15:23 Results & final outputs 17:17 Where to get the full Playbook 👇
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- 04
@Alibaba_Qwen ·
🎨 Meet Qwen-Image-3.0 — the third generation of our foundational image generation model. If 1.0 was about "Precision," and 2.0 added "Variety, Completeness, Beauty & Authenticity," then 3.0 comes down to a single word: Real (实). Three dimensions of "Real": 📰 Rich Content — prompts up to 4.5k tokens. One-pass generation of complex layouts: newspapers, storyboards, exam papers — even a 3×3 infographic grid or picture-in-picture-in-picture UIs. 🔬 Authentic Details — text legible down to 10px, full LaTeX paper pages, pores, hair strands & near-photographic skin texture. 🌏 Deep Knowledge — native rendering in 12 languages, 100+ art styles, realistic UIs (web / games / livestreams), plus world knowledge & live web retrieval. Not just "good-looking" — genuinely useful. Image generation as a real productivity tool for design, content, education & e-commerce. Go create 🏃🎨 💬Qwen Chat: https://t.co/941HmITJ2W 📝Blog: https://t.co/5mnS4uI9Ar
- 05
@gregisenberg ·
chatgpt images 2.0 has been live for 24h so let's dig in how to use ChatGPT Images 2.0 to create product photos, brand books, UI mockups, and ad creative that actually looks real: 1. GPT Images 2.0 now does 2K resolution, 3:1 aspect ratios, and spits out 8 images per prompt. text rendering is way better across multiple languages. it also has thinking mode where it searches the web before generating. 2. the biggest lesson with images 2.0: you have to be extremely specific. if you give it a lazy prompt you get stock photos. give it camera type, lighting conditions, color palette, and subject details and it cooks. 3. product photography is where it shines. I created a full brand shoot for a skincare line. golden hour lighting, Mediterranean aesthetic, slight imperfections in the subjects. every image looked like a real photo shoot. 4. use it to create visual directions before you make video ads. I prompted 8 directions for the same Shopify ad story. Wes Anderson, Nike, cinematic, Apple shot on iPhone. the cinematic and Nike styles were the strongest. 5. UI mockups work now. give it your app, a feature description, the resolution, and say you want realistic data in every cell. it gave me four clean variations of a leaderboard screen. 6. apparel and merch: generate photorealistic product shots before you print anything. test if people would buy it before you spend money on production. 7. illustrations got a massive upgrade. editorial style, flat vector, limited color palettes. use these to make proposals, one-pagers, and decks look professional. 8. every business has four creative bottlenecks: marketing content, internal docs and decks, explaining things visually, and testing before building. Images 2.0 helps with all four. 9. five things you need in every prompt: context (what is this for), style references (name specific brands or aesthetics), palette (use hex codes), real copy (no lorem ipsum), and aspect ratios so it drops into production without rework. 10. use ChatGPT itself to help you write better prompts. you might not know camera types or lighting terms. ask it to help you build the prompt before you generate. also in this episode: I share a startup idea someone should steal: a learn to draw app with AI feedback on every sketch. $5/month. I put it into Claude Design and got three incredible wireframe directions. I share a framework for finding vertical AI agent businesses. find a boring pain point, map the workflow, do the job as a service first, document edge cases, then add agents to replace the steps. and I share an AI tool called No Scroll that blew me away in 5 minutes. it monitors the internet for you and texts you only what matters. the onboarding felt like talking to a real person. episode is live on @startupideaspod (walkthrough, tips, prompts) im rooting for you, so share this with your friends and enjoy watch
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- 06
@HeyAbhishek ·
creating anime with AI is crazy now I used ChatGPT Image 2.0 to design a full anime short film storyboard. Then Seedance 2.0 turned it into a cinematic animated scene in minutes. step by step tutorial with prompts: 👇
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- 07
@ComfyUI ·
Ideogram 4.0 is now natively supported on ComfyUI @ideogram_ai v4.0 is an open-weight 9.3B text-to-image foundation model. It is exclusively trained on structured JSON caption datasets for precise scene description. It features flawless text rendering, professional layout control, customizable color palettes, and bounding box adjustment. Both open-sourced and API node versions are supported in ComfyUI
- 08
@ComfyUI ·
ERNIE-Image is now in ComfyUI An open-source 8B DiT text-to-image model from @ErnieforDevs, licensed under Apache-2.0. Key highlights: - Open-source under Apache-2.0 license - Precise multilingual text rendering (EN, ZH, and more) - Complex instruction following — multi-object, knowledge-intensive prompts - Structured outputs (comics, multi-panel scenes) - Realistic to cinematic aesthetics
- 09
@goyalshaliniuk ·
Generative AI is not magic, it is math that creates. From generating art and code to simulating human creativity, every GenAI model works differently, but with one shared goal: creation from data. Here is a simple breakdown of the five major types of Generative AI models shaping today’s AI revolution 👇 1. Diffusion Models Learn by adding and removing noise from data to create realistic outputs. Used in image and art generation tools like DALL·E 3, Stable Diffusion, and Midjourney. 2. GANs (Generative Adversarial Networks) Use two neural networks - a generator and a discriminator, that compete to produce lifelike data. Power deepfake videos, face synthesis, and AI art. 3. Variational Autoencoders (VAEs) Compress data into a compact representation and decode it to generate new versions. Used in image reconstruction, anomaly detection, and creative design. 4. Autoregressive Models Predict the next word, note, or pixel based on previous ones in a sequence. Used in text generation, music composition, and time-series forecasting. 5. Transformers Use self-attention to understand relationships across sequences for highly contextual generation. Power modern AI systems like GPT, Claude, and Gemini for text, code, and image generation. Generative AI Is Redefining Creativity Each model type brings a new layer of intelligence, from reasoning to imagination. Learn how these models work, and you’ll understand the core of AI’s creative power.
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- 10
- 11
@ihteshamali ·
🚨BREAKING: A Chinese top university and UC Berkeley just trained an AI that searches the internet before generating images, and the results are insane. Every AI image model today is working from frozen memory. Ask it to generate a portrait of the 2024 Pritzker Prize architect with his name on a desk placard, and it either hallucinates or fails entirely. The knowledge cutoff makes visual accuracy impossible for anything current. Their fix is called Gen-Searcher, and the core idea is wild: instead of prompting a model to generate directly, they trained an agent that goes and searches the web first. Multi-hop deep search. Text retrieval. Image retrieval. Webpage browsing. All before a single pixel gets generated. Here's how it actually works: → You give it a prompt requiring real-world knowledge — a celebrity, a landmark, a game character with specific stats, a chemical compound with exact values → Gen-Searcher reasons through what it needs, fires off multi-step web searches, pulls reference images for visual grounding, and browses pages when snippets aren't enough → It composes a fully grounded prompt plus reference images and hands both to the image generator → The image generator finally has what it needs to get things right The training setup is equally clever. They built 16K samples from scratch because this data doesn't exist anywhere. Gemini 3 Pro generated search-intensive prompts across 20 categories, then acted as the search agent to produce agentic trajectories, then Nano Banana Pro synthesized the ground-truth images. The reward design is what makes the RL stage work. Pure image reward is too noisy because even correct information can fail with a weak generator. Pure text reward ignores whether the gathered evidence actually produces good images. They split the difference with a dual reward combining both signals at 50/50, and the ablation proves neither works alone. The numbers are brutal for every baseline: → Qwen-Image baseline: 14.98 K-Score on KnowGen → Qwen-Image + Gen-Searcher: 31.52. That's a 16-point gain. → Nano Banana Pro baseline: 50.38 → Nano Banana Pro + Gen-Searcher: 53.30 The wildest part? Gen-Searcher was trained with Qwen-Image as the rollout generator, but transfers directly to Seedream 4.5 and Nano Banana Pro at test time with zero additional training. The search-and-grounding policy generalizes across backends. Every image model right now is generating from memory. Gen-Searcher is the first trained agent that goes and looks things up first.
- 12
@shushant_l ·
I'm genuinely surprised most people still struggle to generate great AI images. Here's the complete AI image generation guide you'll wish you had from day one. --- 1. Learn what AI image generation is and how it creates images from prompts, photos, sketches, or references. --- 2. Understand the five main workflows: Text to Image, Image to Image, Editing, Inpainting, and Outpainting. --- 3. Use style transfer to transform images into anime, watercolor, oil painting, 3D, and more. --- 4. Focus on the universal prompt formula instead of writing random prompts. --- 5. Always define a clear subject before adding extra details. --- 6. Describe the action, environment, composition, and lighting for better results. --- 7. Choose the right camera angle, mood, colors, and visual style. --- 8. Specify image quality and aspect ratio for your intended platform. --- 9. Use AI to create photorealistic images, illustrations, logos, posters, comics, and UI designs. --- 10. Generate product mockups, marketing creatives, and social media graphics in minutes. --- 11. Pick the correct aspect ratio for posts, reels, YouTube, presentations, or photography. --- 12. Experiment with styles like cinematic, anime, pixel art, cyberpunk, minimalist, and sketch. --- 13. Keep prompts simple, specific, and easy for the AI to understand. --- 14. Improve results by describing lighting, camera settings, composition, and mood. --- 15. Avoid vague prompts, conflicting styles, overloaded instructions, and missing details. --- 16. Edit existing images by replacing objects, removing backgrounds, changing outfits, or improving lighting. --- 17. Create consistent characters, branded visuals, and professional product photography. --- 18. Follow a structured workflow from idea to prompt, generation, review, editing, upscaling, export, and publishing. --- 19. Save your best prompts, iterate often, and experiment with different AI models. --- 20. Follow ethical guidelines by respecting copyright, avoiding deceptive content, and using AI responsibly. --- To learn more, check the infographic.
- 13
@ModelScope2022 ·
Anima Preview3 is here 👏👏👏 2B anime/illustration text-to-image model by CircleStone Labs! 🎨 Trained on millions of anime images + ~800k non-anime art images. No synthetic data. ✅ Danbooru tags, natural language, or mixed — all work ⚡ Preview3: extended 1024 high-res training, expanded rare artist dataset vs Preview2 🛠️ DiffSynth-Studio supported for inference and training. Drop-in ComfyUI workflow included. Note: It's still training. This is a mid-checkpoint. 🤖 https://t.co/wj4XC2DFXh
- 14
@_simonsmith ·
Just had Codex redesign a presentation slide-by-slide using GPT Image 2, generating an entire image for each slide, then packaging the images into a new deck. The result is gorgeous and incorporates text, logos, and visuals from the original deck perfectly. I think OpenAI’s bet that creating visual assets will start with image generation is the right one. Visual ideation shouldn’t be constrained by implementation details. Those should come later. That’s how it’s long worked in the creative professions with which I’m most familiar. The trick now is flawless recreation of generated images in any desired format, be it HTML/CSS, SVG, InDesign, whatever. And also easy editing of generated images. Those two things could drive more use of Codex and GPT models. If I could generate any design and then convert that to any arbitrary format, it would address a lot of design use cases, perhaps most.
- 15
@internetfreedom ·
Your Instagram photos may now be training someone else’s AI. Meta has enabled its new Muse AI image generator for users by default. It allows people to generate AI images of you by using your public Instagram handle, with no notification when your photos are used and no way to remove images created before you opt out. To opt out: Profile → Menu → Sharing and reuse → Turn off the reuse options under “Allow people to reuse your content on Instagram and with AI features at Meta.”
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- 16
- 17
@TheAIColony ·
Breaking: Someone just turned GPT-Image-2 into a copy-paste tool. A GitHub repo dropped this month with 1.7k stars in days. Inside: ready-made prompts for portraits, posters, character design, UI mockups, convenience store neon photography, even Song Dynasty social media interfaces. Most people staring at GPT-Image-2 cannot write a prompt that produces what they see in their head. This solves that. Every prompt in the repo is structured. Lighting. Composition. Style references. Camera angles. Color palette. The exact things you need to specify to get cinematic output instead of generic AI slop. The convenience store neon photography prompts alone are worth the bookmark. Same for the UI mockup templates. People are paying $200 for prompt courses that contain less than what is sitting in this repo for free. If you have been generating mediocre images and wondering why everyone else's look like ads, this is the gap.
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- 21
@cyrilXBT ·
MICROSOFT JUST DROPPED AN IMAGE MODEL THAT BEAT CHATGPT IMAGE EDITING ON LAUNCH DAY AND MOST PEOPLE COMPLETELY MISSED IT. MAI Image 2.5. Launched June 2. Hit number 2 in the world for AI image editing on day one. Above Nano Banana 2. Above Grok Imagine. Number 3 in text-to-image generation. On launch day. Here is what makes it different from everything else in the image editing space: - Precise local edits without destroying the rest of the image. - Face consistency across multiple edits and viewpoints. - Readable text inside images. Labels, signs, and mockups that actually look right. - Scene-aware object placement with realistic shadows and perspective. - Two deployment options: Full version for maximum quality. Flash version for faster and cheaper. - Already built into PowerPoint. Rolling into OneDrive photo editing. The biggest opportunity is not generating images. It is building an entire content production workflow around image editing that runs automatically. Bookmark this before your next content project. Follow @cyrilXBT for every Microsoft AI release worth paying attention to the moment it drops.
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- 22
@ModelScope2022 ·
🎨 ERNIE-Image is now open source! 8B DiT text-to-image from PaddlePaddle, two variants. 🚀 ✅ ERNIE-Image (50-step): GENEval SOTA (0.8856). Strong on dense text, complex layouts, structured generation. ⚡️ ERNIE-Image-Turbo (8-step via DMD + RL): strongest open-source model at 8-step inference. GENEval 0.8667, LongTextBench 0.9655. Prompt Enhancer included. Runs on 24GB VRAM. 🤖 https://t.co/Ryw3AXlNDg 🌍 https://t.co/kVmvtKIPJX
- 23
@vasuman ·
The majority of the anti-AI crowd insists that AI output is “slop” to try and win the argument. Yes there's a lot of slop, because people prompt poorly. But well-prompted AI now produces images, video, and code that's almost impossible to distinguish from great quality human work. Pretending it's low quality doesn't strengthen your case, it just makes you sound like you don’t know how to use AI well. You can be staunchly anti-AI and still admit that AI has gotten incredibly powerful. In fact that is the only honest version of the argument. The reality is most people against AI parroted these talking points from people who only ever used GPT-3.5-Turbo and first gen image models. Give the latest models a spin and update your worldview.
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@Ubermenscchh ·
Uni-1 by @LumaLabsAI just raised the bar quietly. Other models guess what you want. This one thinks before it creates. You can see it in how it handles reflections, textures, and depth. Nothing feels faked. This is reasoning to image, not text to image. Honestly surprised more people aren't talking about this yet :
- 26
@BenjaminDEKR ·
Am I the only one not impressed by this? It makes perfect sense that AI image generation should be able to do "fake screenshots" well. There are millions (potentially unlimited) examples to train on, desktop apps are standard and all follow known patterns, icons always look the same. It would be weird if AI *couldn't* do this.
- 27
@thetripathi58 ·
I tested Uni-1 by @LumaLabsAI and I’m impressed. It’s not just text-to-image, it feels like reasoning-to-image. The Uni-1 actually thinks through the composition instead of just generating pixels. You can see it in details like glass refraction. It’s more about clear thinking than clever prompts, and it’s already ahead of NB Pro and GPT Image 1.5.
- 28
@ai_for_success ·
Microsoft has released MAI Image 2 Efficient, a fast and scalable image generation model built for high volume developer workflows. The quality is seriously impressive. - 22% faster and 4x more efficient than MAI-Image-2 - Outpaces leading text-to-image models by 40% on average - Built on the same architecture as the top-ranking MAI-Image-2 - Optimized for high-volume production and real-time conversational experiences - Features a sharp visual style ideal for illustration and animation - Available now in Microsoft Foundry and MAI Playground - Pricing starts at $5 per 1M input tokens and $19.50 per 1M output tokens
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- 29
@aliscodes ·
If you're using AI image generators and you're not using Magic Layers by Canva yet, let me put you on. You can take the images that AI creates, plug them into Canva, and actually separate all the items inside of the image. You can even change the text color, move elements, delete objects, full control. Here's how: - Take the AI-generated image - Paste it into Canva - Click Edit → Magic Layers - Wait a few seconds - Voila, all items are detached Every element becomes editable. Text, objects, backgrounds, all separate layers. It's like having Photoshop layers but automated in 10 seconds. Game changer for AI-generated content that needs tweaks.
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@shushant_l ·
I'm shocked most people use AI image generators without knowing what actually happens behind the scenes. Here's how AI turns a simple text prompt into a stunning image step by step. --- 📂 AI Image Generation ┃ ┣ 📂 Big Idea ┃ ┣ 📂 Learn From Billions Of Image Text Pairs ┃ ┣ 📂 Understand Visual Patterns ┃ ┣ 📂 Encode Relationships ┃ ┣ 📂 Start From Random Noise ┃ ┗ 📂 Generate Brand New Images ┃ ┣ 📂 Complete Pipeline ┃ ┣ 📂 Text Prompt ┃ ┣ 📂 Text Encoding ┃ ┣ 📂 Latent Representation ┃ ┣ 📂 Random Noise ┃ ┣ 📂 Iterative Denoising ┃ ┣ 📂 Image Refinement ┃ ┣ 📂 Decoder ┃ ┗ 📂 Final Image ┃ ┣ 📂 Prompt ┃ ┣ 📂 Subject ┃ ┣ 📂 Action ┃ ┣ 📂 Environment ┃ ┣ 📂 Lighting ┃ ┣ 📂 Camera ┃ ┣ 📂 Style ┃ ┣ 📂 Mood ┃ ┗ 📂 Colors ┃ ┣ 📂 Text Understanding ┃ ┣ 📂 CLIP ┃ ┣ 📂 T5 ┃ ┣ 📂 Multimodal Encoders ┃ ┣ 📂 Semantic Embeddings ┃ ┗ 📂 Prompt Interpretation ┃ ┣ 📂 Latent Space ┃ ┣ 📂 Compressed Representation ┃ ┣ 📂 Faster Processing ┃ ┣ 📂 Lower Memory Usage ┃ ┣ 📂 Better Scalability ┃ ┗ 📂 Higher Image Quality ┃ ┣ 📂 Image Creation ┃ ┣ 📂 Start With Random Noise ┃ ┣ 📂 Predict Noise ┃ ┣ 📂 Remove Noise ┃ ┣ 📂 Reveal Shapes ┃ ┣ 📂 Form Objects ┃ ┣ 📂 Add Textures ┃ ┣ 📂 Apply Lighting ┃ ┗ 📂 Generate Fine Details ┃ ┣ 📂 Denoising Model ┃ ┣ 📂 U-Net ┃ ┣ 📂 Diffusion Transformer (DiT) ┃ ┣ 📂 Noise Prediction ┃ ┣ 📂 Progressive Refinement ┃ ┗ 📂 Image Reconstruction ┃ ┣ 📂 Cross Attention ┃ ┣ 📂 Match Words To Regions ┃ ┣ 📂 Align Prompt With Image ┃ ┣ 📂 Focus On Important Details ┃ ┣ 📂 Preserve Context ┃ ┗ 📂 Improve Accuracy ┃ ┣ 📂 Guidance ┃ ┣ 📂 Classifier Free Guidance ┃ ┣ 📂 Prompt Following ┃ ┣ 📂 Creativity Control ┃ ┣ 📂 Stronger Prompt Adherence ┃ ┗ 📂 Output Variation ┃ ┣ 📂 Decoding ┃ ┣ 📂 Convert Latents To Pixels ┃ ┣ 📂 Restore Colors ┃ ┣ 📂 Recover Textures ┃ ┣ 📂 Rebuild Shadows ┃ ┗ 📂 Produce Final Image ┃ ┣ 📂 Training ┃ ┣ 📂 Billions Of Image Text Pairs ┃ ┣ 📂 Add Noise ┃ ┣ 📂 Predict Removed Noise ┃ ┣ 📂 Compare With Ground Truth ┃ ┣ 📂 Update Parameters ┃ ┗ 📂 Repeat Billions Of Times ┃ ┣ 📂 Generation Modes ┃ ┣ 📂 Text To Image ┃ ┣ 📂 Image To Image ┃ ┣ 📂 Inpainting ┃ ┣ 📂 Outpainting ┃ ┣ 📂 Style Transfer ┃ ┗ 📂 Reference Guided Generation ┃ ┣ 📂 Modern Improvements ┃ ┣ 📂 Better Prompt Understanding ┃ ┣ 📂 More Realistic Anatomy ┃ ┣ 📂 Improved Typography ┃ ┣ 📂 Better Lighting ┃ ┣ 📂 Higher Image Quality ┃ ┣ 📂 Stronger Composition ┃ ┣ 📂 More Consistent Characters ┃ ┣ 📂 Faster Generation ┃ ┗ 📂 Better Image Editing ┃ ┣ 📂 Common Mistakes ┃ ┣ 📂 Ambiguous Prompts ┃ ┣ 📂 Poor Spatial Reasoning ┃ ┣ 📂 Incorrect Object Counting ┃ ┣ 📂 Weak Text Rendering ┃ ┣ 📂 Unrealistic Physics ┃ ┗ 📂 Limited World Knowledge ┃ ┣ 📂 Applications ┃ ┣ 📂 Graphic Design ┃ ┣ 📂 Marketing ┃ ┣ 📂 Advertising ┃ ┣ 📂 Social Media ┃ ┣ 📂 Product Visualization ┃ ┣ 📂 Concept Art ┃ ┣ 📂 Game Development ┃ ┣ 📂 Film Production ┃ ┣ 📂 Architecture ┃ ┣ 📂 Interior Design ┃ ┣ 📂 Fashion ┃ ┣ 📂 Education ┃ ┗ 📂 E-commerce ┃ ┗ 📂 Simple Analogy ┣ 📂 Start With Random Noise ┣ 📂 Reveal Basic Shapes ┣ 📂 Form Objects ┣ 📂 Add Lighting ┣ 📂 Apply Textures ┣ 📂 Refine Fine Details ┗ 📂 Finish The Complete Image
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@eric_seufert ·
AI-generated ads perform roughly as well as human-created ads, unless consumers suspect they were generated by AI. Researchers from the Technical University of Munich, Columbia Business School, Harvard Business School, and elsewhere analyzed more than 16 billion ad impressions and 116 million clicks from Tabola. In a novel quasi-experiment, they compared the performance of AI-generated and human-generated "sibling ads": ads from the same campaigns, for the same products, and with the same launch dates. They find that CTR performance for AI-generated ads is statistically "indistinguishable" from human-created ads when controlling for campaign-level factors. But they also reached an equally important conclusion: ads that look AI-generated perform worse. The researchers identified a set of visual traits that consumers identify as "AI-like," and found that perceived artificiality reduced click-through rates even when the ads were not explicitly flagged as AI-generated. Ironically, the authors find that a not-insignificant number of human-generated ads were perceived as AI-generated for possessing those visual traits. This is an important result. The cost of creative production through generative AI is decreasing rapidly, especially for static images. But not all AI-generated creative performs equivalently. Teams need to consider an optimization for their AI-enabled workflow beyond mere volume, which is the perceived visual fidelity and provenance of the ads being generated. Link to paper below. Thanks to @garjoh_canuck for flagging this for me.
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@alexgoughcooper ·
This is the first time I've seen Meta's AI image generator make static variations that look good enough to launch. They're not all good ads, but they're significantly better than what we saw two months ago. This reinforces the idea of persona-based advertising for me. AI will soon handle your visual swings - Meta wants us to focus on message testing, as that's what can unlock true scale.
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@burkov ·
Generating an image from a sentence used to require a separate classifier trained on noisy images just to steer the output toward the prompt. This paper from OpenAI shows you can drop that. The core idea is classifier-free guidance: during training the text caption is sometimes replaced with an empty one, so a single model learns to predict image noise both with and without text; at generation time you take the difference between the text-conditioned and unconditioned predictions and exaggerate it, which sharpens how closely the image follows the caption with no extra classifier. The authors built a 3.5 billion parameter diffusion model around this, and human evaluators preferred its output to DALL-E (OpenAI's then-main system at twelve billion parameters) about 87% of the time for photorealism, even when DALL-E was allowed to generate many candidates and keep the best; they also finetuned the same model to fill erased image regions from a prompt, with matching shadows and reflections, making text-driven editing practical. Classifier-free guidance went on to become standard and is still used in the systems leading the field in 2026 (FLUX.2, later Stable Diffusion, the models behind DALL-E 3 and Imagen). Read with an AI tutor; https://t.co/c6zqRqaPCD PDF: https://t.co/sp7MZljI1X
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@arena ·
MAI-Image-2 debuts at #5 in the Image Arena! Highlights: - #5 in Text-to-Image overall - #5 for 3D Imaging & Modeling, Cartoon, Anime & Fantasy, Photorealistic & Cinematic Imagery, Art and Portraits - #6 for Product, Branding & Commercial Design Congrats to the @MicrosoftAI team on this milestone!
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@VaibhavSisinty ·
Grok just turned AI image generation into a film studio. xAI rolled out Imagine Agent Mode on Grok web. 🤯 Still in beta mode. It's not a new model. It's a new way of working. You drop a brief on an infinite canvas. The agent plans the steps. Generates the assets. Edits them. Iterates. All in the same workspace. → Tell it "generate a 1-minute cinematic film." It builds the film. → Tell it "create a complete manga set." It lays out the panels. → Tell it "build UGC product stories." It ships the whole set. You give the brief. It does the work. Until today, every AI image tool made you the operator. Grok just made you the director. vc: @XFreeze
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@VaibhavSisinty ·
ChatGPT just launched Images 2.0 and it's wild. 🤯 the generator era of AI images is over. here's what ChatGPT Images 2.0 can actually do: → it thinks first, then paints. plans the image, checks its own work, fixes mistakes on its own before showing you anything. → it can browse the web while making images. ask for a poster of the OpenAI merch store and it pulls the real products from the real website. → it finally writes readable text in images. small text, posters, presentation slides, UI screens. the stuff every other image AI used to mess up. → it works in any language. the demo showed a full Japanese manga page where the text actually made sense. → it can make real working QR codes inside the image. you can scan it and it opens. nobody has done this before. → you can ask for wide banners or tall posters. ready for Instagram stories, LinkedIn banners, or pitch deck slides without any cropping. → it handles every style well. real photos, cinematic shots, pixel art, anime, sketches. all with the same consistency. → every image comes out at 2K quality. → it is live right now on ChatGPT for free users. thinking mode is on for Plus, Pro, and Business. the old image AI was a brush. this one is a designer that happens to use pixels.
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@eric_seufert ·
Advantage+ Creative generated an ad for REI featuring a bike with two sets of handlebars. Creative Enhancements are turned on by default in Advantage+, so incidents like these are to be expected (we've seen similar cases with True Classic, Kirruna, and Lectric). The inevitability of generative image hiccups like this -- which, to be fair, are amusing anecdotes and not particularly significant -- was one of the motivations for building DeCANT for creative variant pre-testing. For a variety of reasons, advertisers will want to own their creative production process.
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@edmundtian ·
Stop using “Plain English” to prompt AI. If you want studio-quality visuals, you don't need a better prompt. You need an LLM to turn your vision into technical direction. You need an AI Art Director. First photo: "make me a vogue model" Second photo: "Studio Vogue fashion portrait of the person in the reference image. Neutral gray backdrop. Soft diffused key light. High-end editorial styling. Photography inspired by Annie Leibovitz and Peter Lindbergh. Clear skin detail, elegant expression, subtle shadows. Minimal color tones, refined and tasteful." Most people don't have the technical knowledge to direct "soft diffused key light." Even if you do, why would you waste your time typing all that out yourself? Use your free AI Art Director. Here's the 3 step workflow to campaign-ready visuals: 1. Creative exploration > Stop talking to the image generator directly. Give your raw thoughts to an LLM (your Art Director). Ask for 3 distinct creative directions. 2. The 3x Sprint > Run all 3 prompts concurrently in Nano Banana Pro. Since modern platforms run concurrent generations, it takes the same time to generate 3 images as it does 1. You just cut your feedback loop by 67%. 3. The Feedback Loop > Review the results. Don't touch the prompt. Go back to your AI Art Director and give it "Director Language" feedback: "I like the lighting in #1 but the composition of #3. Refine the prompt to combine them." Repeat this process until you're happy with the result. You have the access to an entire creative agency at your fingertips. It's time to use it.
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@maxwellfinn ·
GPT Image Gen 2 is really impressive. First thing I made with the most basic prompt possible: “Create an infographic for Instagram post about how advertisers can use cognitive biases” A year ago we were excited if these models spelled the few words of text we told it to use correctly…
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@FellMentKE ·
Baidu has open-sourced ERNIE-Image, a new text-to-image model. I tested it by generating a simple four-panel poster to see how it handles layout, specific text, and instruction following. Prompt used: Create a 4:5 infographic with a clean 2x2 grid, pastel backgrounds, rounded borders, and clear sans-serif text. Top header: ERNIE-Image Open Source. Top left: Instruction Following, two characters interacting, label “multiple subjects”. Top right: Text Rendering, show the word “hello” translated into three languages (English, Japanese, Chinese), each on its own line. Bottom left: Structured Layouts, small comic-style poster with title “How To”. Bottom right: My Quick Take, include: Prompt control: strong, Text accuracy: good, Layout: clean. Keep all text large, clear, and readable. No extra or random text. Quick reflection: It was easy to generate a structured layout. I mainly looked at whether the text came out clean and accurate, since that’s often where image models struggle. The model responded well to layout constraints and handled key text elements more reliably than others I’ve used.
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@pankajkumar_dev ·
Microsoft launches MAI-Image-2 – a new text-to-image AI built with photographers and designers - Developed with professional photographers, designers, and visual storytellers - Highly photorealistic output with natural lighting, accurate skin tones, and realistic environments - Strong in-image text generation (infographics, presentations, posters, diagrams) - Capable of rich, cinematic, highly detailed scenes - Ranked #3 on the Arena(dot)ai text-to-image leaderboard - Live in MAI Playground (US users) playground(dot)microsoft(dot)ai/chat - Coming soon to Copilot, Bing Image Creator, and public API - Commercial use requires application approval
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@rahulnanda86 ·
Just tried ImagineArt 2.0 - Text to image. Blown away by the how real it has gotten!! Prompt - Ultra-realistic editorial portrait of a South Asian woman in her mid-20s, seated casually on a low wall in front of a vibrant graffiti mural in an urban alley. Outfit: flowy boho-chic blouse with delicate embroidery, loose linen trousers, layered beaded bracelets, and leather sandals. Pose: relaxed yet elegant, one leg bent, elbow resting on knee, head slightly tilted, direct calm gaze into camera. Hair: naturally wavy, soft flyaways catching light, mid-brown with subtle warm highlights. Skin realism: healthy, luminous, slightly warm undertone, freckles preserved, subtle oil sheen on nose and cheekbones, no smoothing or retouching, soft blush from natural light. Environment & background: urban alley with bright graffiti murals and wall textures, slight street grit, scattered leaves, muted reflections on wet pavement, soft depth blur separating subject from background. Lighting: natural daylight filtered through overhead urban structures, soft shadows, warm highlight on face, realistic ambient bounce from nearby walls, volumetric dust/light haze subtly catching sunlight. Camera & optics: medium telephoto portrait, shallow depth of field (eyes sharp, background creamy), slight perspective compression for flattering proportions, handheld natural feel. Color & tone: vibrant but realistic colors, skin tones true-to-life, fabrics and graffiti saturated without looking artificial, gentle contrast with lifted shadows, subtle film-like grain for tactile quality. @ImagineArt_X @imagineart_creo
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@_simonsmith ·
I'm really loving this trend of having GPT Image 2 create nature-inspired objects. For this one I had Codex (5.5) spin up subagents to brainstorm ideas, pick its favorite, then visualize it. I feel like with some minor tweaks you could make this chair surprisingly comfortable. I also feel like this is a more generalizable approach with AI that lets us explore new design ideas that we'd never try before, in part because most of us don't have the requisite skill even if we have the desire. It's like vibe industrial design.
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@Chinwe_Owanta ·
AI image generation is not a walk in the park. You’d have to observe and learn the patterns of each model to know which fits in best for your desired outcome. What works on one model can completely flop on another. Same prompt, totally different output, totally different energy. And that’s before you even get to style consistency, aspect ratios, failed attempts and multiple variations before settling on the perfect one. It’s an entire learning curve that doesn’t get talked about enough.
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@BlackLabelAdvsr ·
AI is out of control. If you need more proof that AI is getting insanely good, may this post serve as a notice. Seeing these images, you would think that I used a crazy, complicated prompt. You would be wrong! This is all I typed: "Create some lifestyle pictures promoting a creatine product. The environment should be the interior of a modern gym. Use a male bodybuilder with brown hair. Ultra HD." Tweaking them further would be a breeze. Let's say you want different lighting, or maybe you want another person in the background. Piece of cake in a matter of seconds. Nano Banana can do this. Grok can do this. Midjourney can do this. Flux can do this. Bootstrapped Amazon sellers are ditching their designers in droves over this. Honestly, I can't blame them. The quality is literally insane. And it's only 2026! I'm curious to see some of your AI images. Please post them below in the comments.
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@FrancescoCiull4 ·
Generating tech diagrams and thumbnails with AI has always been a struggle, especially with text rendering and logical layouts. I have been testing Uni-1 by @lumalabsai, their new reasoning-to-image model. Instead of just generating pixels based on keywords, it first processes the prompt's structural logic. I asked it to map out a Multi-Agent AI architecture. As you can see, the text is clean, the layout makes sense, and the node connections are accurate. It is a very interesting step forward for structural generation compared to standard models like GPT Image 1.5 or NB Pro. You should give it a try here: https://t.co/3MlSRVSqR0
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@glenngabe ·
How are people feeling about this?? I now get why YouTube expanded its likeness detection to all users... Creators can opt-out of visual remixing -> YouTube adds new AI remix feature to Shorts "Using Google’s new Omni AI image generation tools, YouTube users will now be able to easily generate alternate versions of Shorts videos (other people's Shorts)." "Though it may also lead to misrepresentation, which is very likely why YouTube has now expanded its likeness detection tools to all users. The capacity to change the context of Shorts may also lead to misuse, and YouTube is hoping to limit this by ensuring that creators are aware of any remix of their content, while also alerting users to any depictions of their image." https://t.co/7Bz5hM7zDW
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