This is a status report demonstrating the capabilities and shortcomings of Hatsu Preview1. The code and a more comprehensive report will be provided upon proper release.

Hatsu Preview1 is a 117M + 63M parameter tags-to-image generation model. It is created with the main objective of setting a simple baseline for anime models. The model consists of a 117M parameter image generator and a 63M parameter condition generator.

The image generator operates in a 56x80 RGB space, while the condition generator creates 1024D condition vectors given a set of tags. For ease of use, the condition generator accepts arbitrary subsets of the tags instead of requiring the user to specify every tag. As in a previous exploration, the 1024D vectors are encodings created by a pretrained tagger. The condition supports a total of ~9k rating/general/character tags, although not all of them are well-understood.

The training dataset is a 1.1M-image 1girl solo subset of the Danbooru2023 dataset, downscaled to 56x80. The image generator is trained for 57M images, and the condition generator is trained for 268M images. The combined training can be completed in ~1.5 days on a 4090 GPU.

Other than removing explicit images, the generated images are not cherry-picked. Each row of 4 images shares the same condition.

bare shoulders, between breasts, blunt bangs, breasts, brown hair, bug, butterfly, cleavage, closed mouth, dot nose, dress, field, flower, green eyes, hair flower, hair ornament, hairpin, jewelry, large breasts, long hair, looking at viewer, lying, necklace, sleeveless, sleeveless dress, sundress, thighs, very long hair, white butterfly, white dress, white flower, sensitive `

These images directly use the embeddings created by the tagger from an reference image instead of using the condition generator.