mlsanityData to 14 Oct 2026

Style Transfer to Calvin and Hobbes comics using Stable Diffusion

arXiv 2312.03993 ↗First posted 7 Dec 2023cs.CV cs.AIcited by 2

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

This project report summarizes our journey to perform stable diffusion fine-tuning on a dataset containing Calvin and Hobbes comics. The purpose is to convert any given input image into the comic style of Calvin and Hobbes, essentially performing style transfer. We train stable-diffusion-v1.5 using Low Rank Adaptation (LoRA) to efficiently speed up the fine-tuning process. The diffusion itself is handled by a Variational Autoencoder (VAE), which is a U-net. Our results were visually appealing for the amount of training time and the quality of input data that went into training.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 12 of its 15 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

12 of 15 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Adding Conditional Control to Text-to-Image Diffusion Models10 Feb
  2. 2022InstructPix2Pix: Learning to Follow Image Editing Instructions17 Nov
  3. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation25 Aug

Other references · 9

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2023GPT-4 Technical Reportcited by 18,396
  5. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  6. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  7. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  8. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  9. 2022FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videoscited by 46

Built on by

Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024A Survey on LoRA of Large Language Modelscited by 67
  2. 2024Low-Rank Adaptation for Foundation Models: A Comprehensive Reviewcited by 37

Similar, not cited

Close in wording, no citation either way

  1. 2024DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer
  2. 2023Stable Diffusion Reference Only: Image Prompt and Blueprint Jointly Guided Multi-Condition Diffusion Model for Secondary Painting
  3. 2025Style Composition within Distinct LoRA modules for Traditional Art
  4. 2023Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
  5. 2024Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt
  6. 2023Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
  7. 2025DyArtbank: Diverse Artistic Style Transfer via Pre-trained Stable Diffusion and Dynamic Style Prompt Artbank
  8. 2026Abstract-LoRA: Unlocking Single-Image Style Transfer through Targeted U-Net Block Training