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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master thesis : Generative AI methods to create comic strips

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Charles, Romain ULiège
Promotor(s) : Geurts, Pierre ULiège ; Roekens, Joachim
Date of defense : 26-Jan-2024 • Permalink : http://hdl.handle.net/2268.2/19591
Details
Title : Master thesis : Generative AI methods to create comic strips
Translated title : [fr] Méthodes d'IA générative pour créer des bandes dessinées
Author : Charles, Romain ULiège
Date of defense  : 26-Jan-2024
Advisor(s) : Geurts, Pierre ULiège
Roekens, Joachim 
Committee's member(s) : Van Droogenbroeck, Marc ULiège
Huynh-Thu, Vân Anh ULiège
Language : English
Number of pages : 144
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] The recent surge in interest surrounding generative AI, particularly stable diffusion, highlights their transformative potential in creating realistic content across various domains, from images to text and music. These advancements promise to revolutionize content generation, opening up new creative possibilities. However, challenges persist, notably in ensuring high image quality and consistency.

The challenges addressed include generating minimally pixelated, high-resolution images swiftly, and maintaining consistency across characters, scenes, and style within comic panels. Achieving 100% consistency in stable diffusion remains elusive due to the inherent randomness in AI models trained on diverse datasets.

The research aims to create a tool enabling individuals with limited drawing skills to produce comics using generative AI. Key findings are presented, starting with an exploration of generative AI and stable diffusion, comparing older models with newer SDXL1.0 models, and selecting ComfyUI as the ideal user interface. The study delves into workflows, testing image generation for text-to-image, text-to-image with ControlNet, inpainting, and maintaining consistency through effective prompts.

The research explores alternative solutions, focusing on LoRAs for fine-tuning models and achieving both consistency and flexibility. Tests reveal LoRAs' potential in altering character appearances, generating cartoon-style images, and providing conclusive results for prompt-driven modifications. The integration of LoRAs into ComfyUI is discussed.

In conclusion, the research successfully achieves its primary objectives, showcasing the tool's capability to generate consistent, high-quality comic panels. Despite challenges, the findings contribute to advancing generative AI applications. The implications extend to potential uses in the creative industry, emphasizing the tool's adaptability and user-friendly nature.


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Author

  • Charles, Romain ULiège Université de Liège > Mast. ing. civ. sc. don. fin. spéc.

Promotor(s)

Committee's member(s)

  • Van Droogenbroeck, Marc ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
    ORBi View his publications on ORBi
  • Huynh-Thu, Vân Anh ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Total number of views 63
  • Total number of downloads 26










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