Stable Diffusion Reference Only: Image Prompt and Blueprint Jointly
  Guided Multi-Condition Diffusion Model for Secondary Painting
Hao Ai Lu Sheng
Stable Diffusion and ControlNet have achieved excellent results in the field
of image generation and synthesis. However, due to the granularity and method
of its control, the efficiency improvement is limited for professional artistic
creations such as comics and animation production whose main work is secondary
painting. In the current workflow, fixing characters and image styles often
need lengthy text prompts, and even requires further training through
TextualInversion, DreamBooth or other methods, which is very complicated and
expensive for painters. Therefore, we present a new method in this paper,
Stable Diffusion Reference Only, a images-to-image self-supervised model that
uses only two types of conditional images for precise control generation to
accelerate secondary painting. The first type of conditional image serves as an
image prompt, supplying the necessary conceptual and color information for
generation. The second type is blueprint image, which controls the visual
structure of the generated image. It is natively embedded into the original
UNet, eliminating the need for ControlNet. We released all the code for the
module and pipeline, and trained a controllable character line art coloring
model at https://github.com/aihao2000/stable-diffusion-reference-only, that
achieved state-of-the-art results in this field. This verifies the
effectiveness of the structure and greatly improves the production efficiency
of animations, comics, and fanworks.