mlsanityData to 7 Oct 2026

CLIP-CLOP: CLIP-Guided Collage and Photomontage

arXiv 2205.03146 ↗First posted 6 May 2022cs.CV cs.AIcited by 11

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Abstract (arXiv)

The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art. Increasingly more sophisticated generators enhanced the artworks' realism and visual appearance, and creative prompt engineering enabled stylistic expression. Guided by an artist-in-the-loop ideal, we design a gradient-based generator to produce collages. It requires the human artist to curate libraries of image patches and to describe (with prompts) the whole image composition, with the option to manually adjust the patches' positions during generation, thereby allowing humans to reclaim some control of the process and achieve greater creative freedom. We explore the aesthetic potentials of high-resolution collages, and provide an open-source Google Colab as an artistic tool.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 6 May 2022.

Builds on

14 of 30 references.

References · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  6. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  8. 2022VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidancecited by 285
  9. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 216
  10. 2021CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoderscited by 186
  11. 2021FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizationcited by 71
  12. 2021Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Conceptscited by 40
2 more
  1. 2021Generative Art Using Neural Visual Grammars and Dual Encoderscited by 6
  2. 2021Human in the Loop for Machine Creativitycited by 4

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 3; 2025: 1.

11 papers.

  1. 2022VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Modelscited by 112
  2. 2023SVGDreamer: Text Guided SVG Generation with Diffusion Modelcited by 75
  3. 2024Empowering LLMs to Understand and Generate Complex Vector Graphicscited by 63
  4. 2023DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Modelscited by 54
  5. 2025NeuralSVG: An Implicit Representation for Text-to-Vector Generationcited by 16
  6. 2023Controlled and Conditional Text to Image Generation with Diffusion Priorcited by 6
  7. 2022Stroke-based Rendering: From Heuristics to Deep Learningcited by 4
  8. 2024Do Generalised Classifiers really work on Human Drawn Sketches?cited by 2
  9. 2023Neural Collage Transfer: Artistic Reconstruction via Material Manipulationcited by 0
  10. 2023CLIPDraw++: Text-to-Sketch Synthesis with Simple Primitivescited by 0
  11. 2024SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generationcited by 0