Stable Diffusion

Notes on the different versions of Stable Diffusion from what I can find online.

Stable Diffusion 1

Stable diffusion consists of three main components

• CLIP text encoder
• VAE
• UNet latent diffusion model

The main difference between stable diffusion and other diffusion models is that the diffusion operations happens in a low-resolution latent space. For a 512x512 image, the latent may only be 64x64, a factor of 8 times smaller. This significantly reduces the compute resources necessary.

U-Net

See U-Net for Stable Diffusion and Transformer for Stable Diffusion U-Net

At a high-level Stable diffusion uses a U-Net with 4 down blocks, one mid block, and 4 up blocks. Note that the last down block and first mid block do not change the resolution.

Stable Diffusion 2

Release Blog Post

Stable Diffusion 2 replaces the CLIP model with OpenCLIP, a retraining of CLIP using the publicly available LAION-5B dataset with NSFW images removed. By default they generate both 512x512 and 768x768 images.

In additional, SD2 also includes the release of the following:

• Super-resolution model
• Depth to image model
• Inpainting model

Stable Diffusion XL

Stable Diffusion XL is a larger model trained on 1024x1024 images.

Stable Diffusion (XL) Turbo

Blog post ADD Paper

Released Nov 2023, SD-Turbo and SDXL-Turbo are fine-tuned versions of SD2 and SDXL trained using adversarial diffusion distillation (ADD).

ADD applies fine-tuning using an adversarial loss (from GANs) and a score distillation loss (from DreamFusion) such that each iteration the model produces a complete image. This allows SD-Turbo to produce realistic images in a single iteration while preserving the ability to contine refining the images with additional diffusion iterations.

Stable Cascade

Release blog post Stable Cascade introduces a latent generator

Stable Diffusion 3

Stable Diffusion 3 replaces the diffusion UNet with a diffusion transformer (DiT).

See Also

• Wikipedia: Stable_Diffusion