Since Stable Diffusion v1 is fine-tuned on 512×512 images, generating images larger than 512×512 could result in duplicate objects, e.g., theinfamous two heads.由于稳定扩散 v1 在 512×512 图像上进行了微调,因此生成大于 512×512 的图像可能会导致重复的对象,例如臭名昭著的两个头。 Image up...
07. Stable Diffusion Interpolation V2.1,这是由 @ygantigravity 和 @pharmapsychotic 制作的 Google Colab 版本,无生成次数限制,需要注册 Hugging Face 账号,需要谷歌账号,需要科学上网,这个版本有多文本多种子混合模式,似乎可以生成视频,感兴趣的朋友可以研究一下,同样需要下载并上传「sd-v1-4.ckpt」文件至谷歌云盘...
Therefore, the best image size for Stable Diffusion depends on your preference and goal. If you want to generate high-quality images for printing or displaying on large screens, you might want to use larger sizes. If you want to generate quick sketches or thumbnails for brainstorming or protot...
A method to fine tune weights for CLIP and Unet, the language model and the actual image de-noiser used by Stable Diffusion, generously donated to the world by our friends at Novel AI in autumn 2022. Works in the same way as Lora except for sharing weights for some layers. Multiplier ca...
Using Stable Diffusion for Image Generation Subscribe To Our Youtube Channel There are primarily two ways that you can use Stable Diffusion to create AI images, either through an API on your local machine or through an online software program likeDreamStudio,WriteSonic, or others. ...
1. How to useStableDiffusionPipeline Before diving into the theoretical aspects of how Stable Diffusion functions, let's try it out a bit 🤗. In this section, we show how you can run text to image inference in just a few lines of code!
Stable Diffusion is a text-to-image model that empowers you to create high-quality images within seconds. When real-time interaction with this type of model is the goal, ensuring a smooth user experience depends on the use of accelerated hardware for...
Stable Diffusion despite the fact that it is 19x larger than the model studied in the previous article. Optimizing Core ML for Stable Diffusion and simplifying model conversion makes it easier for developers to incorporate this technology in their apps in a privacy-preserving and economically ...
the researchers at Stability AI found that they could improve local, high-frequency details in generated images by improving the AutoEncoder. To do this, they trained the same AutoEncoder as the original Stable Diffusion at a significantly larger batch size of 256 compared to the original 9. Th...
We can test our fine-tuned model by running the cells below the “Inference” section of the notebook. The first cell loads the model we just trained and creates a new Stable Diffusion pipeline from which to sample images. We can set a seed to control random effects in the second c...