Methods for Ensuring Consistent Generation in Diffusion Models
DOI:
https://doi.org/10.31649/1997-9266-2024-175-4-75-85Keywords:
deep learning, image generation, generative diffusion models, generation consistency, conceptual consistencyAbstract
The article investigates the problem of consistent generation in diffusion models. Modern generative diffusion models are capable of creating high-precision images, but maintaining the consistency between the related generation results remains a challenging task. The key methods for ensuring generation consistency are analyzed. Additionally, a new type of consistency is introduced — conceptual consistency, which allows for assessing the models’ ability not only to reproduce existing styles and objects but also to generate entirely new visual ideas that the model has never encountered during training. The existing methods for ensuring consistency are analyzed, and their advantages and disadvantages are identified. The image-to-image generation method based on an input reference image has the advantage of simplicity in implementation. Fine-tuning methods like DreamBooth and LoRA DreamBooth provide broader control over object consistency. ControlNet models ensure shape consistency using a special input image that serves as a guide shape in the reverse diffusion process. Noise inversion methods allow for more precise control and iterative refinement of the resulting images through manipulations with the noise space, enabling the generation of more stylistically and conceptually consistent images. The StyleAligned method, using a shared attention mechanism, can ensure the stylistic consistency of generated images. Understanding the capabilities and limitations of methods for ensuring diffusion generation consistency allows for selecting the most effective set of tools according to the task at hand. Diffusion models continue to evolve and expand into new areas, so achieving reliable and universal consistency in diffusion models could pave the way for even more creative and effective solutions.
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