Stable Diffusion v1 vs v2
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Stable Diffusion v2 uses for text embedding. Stable Diffusion v1 uses Open AI's for text embedding. The reasons for this change are:
A larger text encoder model improves image quality
Open AI's CLIP models are opensource, but the models were trained with proprietary data.
Stable Diffusion v1.4 is with
237k steps at resolution 256×256 on dataset.
194k steps at resolution 512×512 on .
225k steps at 512×512 on ““ with 10% dropping of text conditioning.
Stable Diffusion v2 is with
550k steps at the resolution 256x256 on a subset of filtered for explicit pornographic material, using the with punsafe=0.1 and an >= 4.5.
850k steps at the resolution 512x512 on the same dataset on images with resolution >= 512x512.
150k steps using a on the same dataset.
Resumed for another 140k steps on 768x768 images.
additional 55k steps on the same dataset (with punsafe=0.1)
another 155k extra steps with punsafe=0.98
So basically, they turned off the NSFW filter in the last training steps.
Users generally find it harder to use Stable Diffusion v2 to control styles and generate celebrities. Although Stability AI did not explicitly filter out artist and celebrity names, their effects are much weaker in v2. This is likely due to the difference in training data. Open AI’s proprietary data may have more artwork and celebrity photos. Their data is probably highly filtered so that everything and everyone looks fine and pretty.
is fine-tuned on v2.0