Question 1

True or false: Stable Diffusion can be run entirely offline on a consumer GPU with as little as 6GB of VRAM.

Accepted Answer

True. Optimized versions run on 4-6GB VRAM GPUs, making it accessible for local, offline use.

Question 2

True or false: Stable Diffusion can generate images up to 4K resolution natively without any upscaling.

Accepted Answer

False. It generates at 512x512 or 768x768 pixels natively; higher resolutions require upscalers or external tools.

Question 3

True or false: Stable Diffusion's latent space is 48 times smaller than the pixel space, enabling faster generation.

Accepted Answer

True. The VAE compresses images from pixel space to a 48x smaller latent space, drastically reducing computation.

Question 4

True or false: Stable Diffusion was released under a permissive license that allows commercial use without restrictions.

Accepted Answer

False. The CreativeML Open RAIL-M license includes use-based restrictions, such as not generating harmful content.

Question 5

True or false: Stable Diffusion was originally trained exclusively on images from Flickr.

Accepted Answer

True. The LAION-5B dataset used for training included a massive amount of images from Flickr, though not exclusively.

Question 6

True or false: The 'Stable' in Stable Diffusion refers to its ability to avoid mode collapse during training.

Accepted Answer

False. It's named after the 'stability' of the latent diffusion process, not training stability or mode collapse.

Question 7

True or false: Stable Diffusion 3 uses a new architecture called a diffusion transformer instead of a U-Net.

Accepted Answer

True. Stable Diffusion 3 replaced the traditional U-Net backbone with a transformer-based diffusion model.

Question 8

True or false: The original Stable Diffusion 1.x model was trained on 2.3 billion text-image pairs.

Accepted Answer

False. It was trained on a subset of LAION-5B, which had 5.85 billion pairs, but the model used a curated 2.3 billion subset.

Stable Diffusion Trivia Questions