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We gave four frontier image editing models the same photo and 30 edits in a row: Ideogram 4.5 keeps most of the room intact, while the others drift, GPT Image 2.5 Sunburst most visibly. We've seen some interesting demos of multi-turn editing consistency from the latest image editing models,... so we ran our own test: GPT Image 2.5 Sunburst, #1 on our Image Editing leaderboard, against Ideogram 4.5, FLUX 3 and Nano Banana 2.1. Our leaderboard scores single edits; this tests what happens when 30 consecutive changes stack up, with each model editing its own previous output through a 30-step real estate staging sequence: light the fire, add a sofa, repaint the walls, swap day for twilight, and more. Why are the final results so different? Ideogram's Ideogram 4.5 and BlackForestLabsAI - Unofficial's FLUX 3 edit locally. On small edits like adding a vase of tulips, we measured that they left 95% or more of the image essentially untouched. GPT Image 2.5 (Sunburst) re-renders most of the entire image on every edit, leaving only about a fifth of the image unchanged, so small shifts in colour and detail compound over turns. Nano Banana 2.1 sits in between: its edits stay local, but the rest of the image shifts slightly and gradually darkens.show more

Artificial Analysis

157,099 subscribers

52,850 Aufrufe • vor 1 Tag •via X (Twitter)

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9 Kommentare

Profilbild von serx · fireply.ai

serx · fireply.aivor 1 Tag

95% untouched on a tulip vase edit is the number i care about

Profilbild von Som Dutt | AI/ML Analyst

Som Dutt | AI/ML Analystvor 1 Tag

Best single edit ≠ best editor. Once edits stack, pixel preservation beats raw quality. Would love to see “% unchanged per edit” tracked on the leaderboard itself.

Profilbild von aqui

aquivor 1 Tag

funny how they marketed Image 2.5 as the most consistent editing model

Profilbild von Tatsuya

Tatsuyavor 1 Tag

ideogram winning this is the surprise, nobody talks about them

Profilbild von Simo | ai-costguard

Simo | ai-costguardvor 1 Tag

Each edit looks fine. The sequence is where it falls apart

Profilbild von Wei佳

Wei佳vor 1 Tag

Ideogram editing locally while GPT re-renders everything each turn. That's your drift, right there.

Profilbild von Mehmed II

Mehmed IIvor 1 Tag

so ideogram ftw GPT image has always been shite for consistency across edits

Profilbild von Lover of Apps

Lover of Appsvor 1 Tag

I already put Ideogram 4.5 in my workflow for edits.

Profilbild von Rakesh Sahni

Rakesh Sahnivor 1 Tag

"add tulips" shouldn't mean "change the whole room." the 95% vs roughly 20% gap is striking. i'd like to know what "unchanged" means here: - identical pixels - OR are tiny colour shifts allowed? that cutoff matters when reading those numbers.

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Santiago

25,886 Aufrufe • vor 11 Monaten

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,392 Aufrufe • vor 1 Jahr

Today, we introduce HunyuanImage 3.0-Instruct, a native multimodal model focusing on image-editing by integrating visual understanding with precise image synthesis! 🚀 It understands input images and reasons before generating images. Built on an 80B-parameter MoE architecture (13B activated), it natively unifies deep multimodal comprehension and high-fidelity generation. 🧠 A "Thinking" Model with Native CoT & MixGRPO: The model doesn’t just execute commands, it processes them through a Native Chain-of-Thought (CoT) schema. Enhanced by our self-developed MixGRPO algorithm, it reasons through complex instructions to achieve flawless intent alignment and human-preference consistency. 🎨 Precise Editing & Multi-Image Fusion: The model enables accurate image editing by adding, removing, or modifying elements while keeping non-target areas perfectly intact. It also excels at seamless multi-image fusion, synthesizing complex scenes by extracting and blending elements from multiple sources into a unified, consistent output. 🏆 SOTA Performance: HunyuanImage 3.0-Instruct sets a new benchmark in visual quality and alignment, delivering performance that matches leading proprietary models. We aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant image generation ecosystem. 🛠️🎨 💻Try it at (PC only):

Today, we introduce HunyuanImage 3.0-Instruct, a native multimodal model focusing on image-editing by integrating visual understanding with precise image synthesis! 🚀 It understands input images and reasons before generating images. Built on an 80B-parameter MoE architecture (13B activated), it natively unifies deep multimodal comprehension and high-fidelity generation. 🧠 A "Thinking" Model with Native CoT & MixGRPO: The model doesn’t just execute commands, it processes them through a Native Chain-of-Thought (CoT) schema. Enhanced by our self-developed MixGRPO algorithm, it reasons through complex instructions to achieve flawless intent alignment and human-preference consistency. 🎨 Precise Editing & Multi-Image Fusion: The model enables accurate image editing by adding, removing, or modifying elements while keeping non-target areas perfectly intact. It also excels at seamless multi-image fusion, synthesizing complex scenes by extracting and blending elements from multiple sources into a unified, consistent output. 🏆 SOTA Performance: HunyuanImage 3.0-Instruct sets a new benchmark in visual quality and alignment, delivering performance that matches leading proprietary models. We aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant image generation ecosystem. 🛠️🎨 💻Try it at (PC only):

Tencent Hy

126,668 Aufrufe • vor 8 Monaten

Google DeepMind Developers: How Nano Banana Was Made In this episode, we sat down with Google DeepMind’s Oliver Wang and Nicole Brichtova to discuss how Nano Banana was created, why it became so viral, and what’s in store for the future of image and video editing. 00:00 Intro 02:00 The origin of Nano Banana 06:20 Seeing yourself in AI 11:00 Control & character consistency 17:10 AI in education and visual learning 24:10 2D vs 3D: Debate over world models 31:10 The Japan phenomenon 35:00 From images to video 41:00 Working with artists 47:30 The next era of image models 53:50 Closing thoughts Oliver Wang Nicole Brichtova Guido Appenzeller Yoko

Google DeepMind Developers: How Nano Banana Was Made In this episode, we sat down with Google DeepMind’s Oliver Wang and Nicole Brichtova to discuss how Nano Banana was created, why it became so viral, and what’s in store for the future of image and video editing. 00:00 Intro 02:00 The origin of Nano Banana 06:20 Seeing yourself in AI 11:00 Control & character consistency 17:10 AI in education and visual learning 24:10 2D vs 3D: Debate over world models 31:10 The Japan phenomenon 35:00 From images to video 41:00 Working with artists 47:30 The next era of image models 53:50 Closing thoughts Oliver Wang Nicole Brichtova Guido Appenzeller Yoko

a16z

97,860 Aufrufe • vor 11 Monaten