# Ideogram 4.0 发布：开放权重模型，原生2K分辨率与改进文本渲染

- 来源：The Decoder：AI News（RSS）
- 作者：Matthias Bastian
- 发布时间：2026-06-04 02:34
- AIHOT 分数：66
- AIHOT 链接：https://aihot.virxact.com/items/cmpyf55j704iuslaxp1xrelax
- 原文链接：https://the-decoder.com/ideogram-4-0-drops-as-an-open-weight-model-with-native-2k-resolution-and-improved-text-rendering

## AI 摘要

Ideogram 发布 4.0 版本文本到图像模型，采用开放权重，支持原生2K分辨率、边界框控制和改进的文本渲染。在 DesignArena 排行榜上，该模型位列所有开放模型之首；仅 OpenAI 和 Google 的闭源系统得分更高。商业使用需购买付费许可证。

## 正文

Ideogram 4.0 drops as an open-weight model with native 2K resolution and improved text rendering

Ideogram has released version 4.0 of its text-to-image model as an open-weight model.

According to Ideogram, the new features include native 2K resolution, transparent backgrounds, precise layout control via bounding boxes, and improved text rendering in images, useful for logos and posters. Editable text and layers are coming soon, the company says.

The model can run on your own hardware and be fine-tuned with your own data. Weights and code are available for download on GitHub, but commercial use requires a paid license.

According to the DesignArena leaderboard, Ideogram 4.0 ranks first among all open-weight models. Only closed models from OpenAI and Google score higher. In the text-to-image arena, it also takes first place in quality mode and ninth overall. The model is available in three quality tiers via Ideogram's own hosted API, according to the Ideogram website:

Quality level Price per image Turbo 0.03 dollar Default 0.06 dollar Quality 0.10 dollar

Ideogram 4.0 is also available on the web and across partner platforms, including Hugging Face, ComfyUI, fal, Runware, Magnific, Krea AI, Leonardo AI, Picsart, Cloudflare, Replicate, Gamma, Flora AI, and Kittl. In our benchmark prompt, the model easily outperforms Midjourney v8, lands roughly on par with Flux, but falls short of GPT-Image-2, Nano Banana Pro, or Luma Uni-1.1. That's just one prompt, though, and it mainly tests prompt following and the model's ability to render abstract concepts unlikely to appear in the training data, like a horse-riding astronaut. As always, your own testing is a must.

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