Stability AI正式发布了DeepFloyd IF图像生成模型 这个模型可以准确绘制文字

A text prompt is passed through the frozen T5-XXL language model to convert it into a qualitative text representation. 
将文本提示通过冻结的T5-XXL语言模型传递，将其转换为定性文本表示。

Stage 1: A base diffusion model transforms the qualitative text into a 64×64 image. This process is as magical as witnessing a vinyl record’s grooves turn into music. The DeepFloyd team has trained three versions of the base model, each with different parameters: IF-I 400M, IF-I 900M and IF-I 4.3B. 
阶段1：基础扩散模型将定性文本转换为64×64图像。这个过程就像看到黑胶唱片的凹槽变成音乐一样神奇。DeepFloyd团队已经训练了三个版本的基础模型，每个模型都有不同的参数：IF-I 400M、IF-I 900M和IF-I 4.3B。

Stage 2: To ‘amplify’ the image, two text-conditional super-resolution models (Efficient U-Net) are applied to the output of the base model. The first of these upscales the 64×64 image to a 256×256 image. Again, several versions of this model are available: IF-II 400M and IF-II 1.2B. 
第二阶段：为了“放大”图像，将两个文本条件的超分辨率模型（Efficient U-Net）应用于基础模型的输出。其中第一个将64×64的图像放大到256×256的图像。同样，有几个版本的模型可用：IF-II 400M和IF-II 1.2B。

Stage 3: The second super-resolution diffusion model is applied to produce a vivid 1024×1024 image. The final third stage model IF-III has 700M parameters. Note: We have not released this third-stage model yet; however, the modular character of the IF model allows us to use other upscale models – like the Stable Diffusion x4 Upscaler – in the third stage. 
第三阶段：应用第二个超分辨扩散模型生成逼真的1024×1024图像。最终的第三阶段模型IF-III具有700M参数。注意：我们尚未发布此第三阶段模型；但是，IF模型的模块化特性允许我们在第三阶段使用其他升级模型，例如稳定扩散x4升频器。