Amazing Logos
Text prompts serve as a versatile and universally accessible method to guide the generation of images. As large language models evolve, text-to-image models are increasingly capable of interpreting both literal and semantic meanings of text prompts. Nevertheless, these models often face challenges in accurately rendering complex or abstract concepts based solely on textual descriptions. This misalignment between the generated content and its intended semantics presents several issues that need addressing.
An investigation into the limits of text encoding within generative models initiated a study focusing on the design potential of text-to-image models. This aimed to discern the AI’s capacity to generate novel design elements from abstract prompts. A
key inquiry was whether AI could derive minimalist designs akin to Nike’s emblem from prompts like ”a logo for a sports brand emphasizing athletic excellence, innovation, and pushing performance boundaries,” or if it would default to creating literal
representations similar to existing sports logos.
Misalignment of Text Encoding
Wu et al. (2023) explore the disentanglement capabilities of stable diffusion models, demonstrating that these models can effectively differentiate between various image attributes. This disentanglement is facilitated by adjusting input text embeddings from neutral to style-specific descriptions during the later stages of the denoising process.
The disentanglement property of stable diffusion models. The top image is generated conditioned on “a photo of a person”. The bottom image is generated with all descriptions replaced with “a photo of a person with a smile”, and changes the person’s identity. The middle image is generated by partially replacing descriptions at later steps and maintaining the person’s identity. (Wu et al., 2022)
For instance, the prompt “A photo of a woman” might yield significantly different results from “A photo of a woman with a smile.” Although modifying text embeddings can help segregate different attributes, this approach struggles with fine, localized edits and may be overwhelmed by overly detailed neutral descriptions.
Moreover, dependency on prompt engineering often encounters inherent limitations as the model's semantic interpretation can significantly diverge from human understanding. Such dependence is typically restricted by the labels in the training dataset. For instance, using specialized terminology like “monogram” to describe a straightforward “black and white” graphic may lead to unforeseen results, largely due to the annotators' limited domain-specific knowledge.
Despite potential improvements in semantic understanding and better alignment of text embeddings with image and art concepts as large image generation models become more complex and larger, users may still face difficulties in articulating abstract concepts through text. The main challenge arises from the discrepancy between how datasets are annotated and how users describe their desired outcomes using the same vocabulary.
Case Study: Logo Generation
An investigation into the limits of text encoding within generative models initiated a study focusing on the design potential of text-to-image models. This aimed to discern the AI's capacity to generate novel design elements from abstract prompts. A key inquiry was whether AI could derive minimalist designs akin to Nike's emblem from prompts like "a logo for a sports brand emphasizing athletic excellence, innovation, and pushing performance boundaries," or if it would default to creating literal representations similar to existing sports logos.
Prompt used: “Design a minimalistic logo for a sports brand emphasizing athletic excellence, innovation, and performance boundaries.
The above comparision illustrates a critical challenge in assessing AI-generated images for brand logo design. While the models succeeded in generating graphics with a sports theme, the nuanced symbolism and brand narratives, which are central to effective logo design, were not as clearly interpreted or presented. This underlines a significant gap in AI's ability to embody abstract and culturally resonant design elements in its visual creations. In conventional design methodology, creatives often engage with clients to distill and agree upon symbolic elements that represent the brand. These elements serve as a foundation for the final logo that embodies the company’s intended message. This iterative and collaborative aspect of the design is absent in AI's text-to-image process, leaving evaluators without a clear metric to determine the significance or quality of the generated logos.
Fine-tuning the model
To further investigate design tasks, I fine-tuned an existing model based on Stable Diffusion v1.5. by RunwayML. The experiment aimed to determine the extent to which a text-to-image model could excel in generating logos without advancements in text encoding or refinement of semantic embedding alignments with the input prompts.
To learn for about the technical details, see this Github repo
The dataset, “amazing logos v4,” was personally curated by me and consists of nearly 400,000 images of logos. These images were gathered from renowned design websites such as logo-archive.org and logolounge.com and are designated for non-commercial use. The researcher utilized these images to fine-tune the Stable Diffusion v1.5 model over 21 epochs, consuming approximately 500 GPU-hours on an Nvidia RTX 3090.
For data labeling, I designed a structured prompt that integrates the company’s name, descriptive elements of the logo design, and the company’s country and industry. This approach ensures that each text prompt effectively combines key design keywords with specific details pertinent to the company. The structure of the prompt is as follows:
Simple elegant logo for {company name}, {concept} {country}, {industry}, successful vibe, minimalist, thought-provoking, abstract, recognizable
An example prompt
Positive prompt:
Simple elegant logo for Dartmouth College, D Pine tree circle United States, education, successful vibe, minimalist, thought-provoking, abstract, recognizable
Negative prompt:
out of frame, low res, wooden background, collage
The study explored the model’s proficiency in assimilating designated shapes into logo designs, a process that involved modifying input prompts to include shape descriptors like ‘circle,’ ‘square,’ ‘wave,’ or ‘triangle.’ Observations from revealed a trend towards more minimalist logos as the model progressed through training epochs. Notably, the visual effectiveness varied with each shape and at different stages of training—‘square’ and ‘dot’ at epoch 15, and ‘wave’ and ‘triangle’ at epoch 18 showcased distinct design strengths.
Challenges mounted when the model was tasked with more abstract prompts. As depicted in, judging the adequacy of responses to a prompt that required designing a digital art logo featuring ‘D’, ‘A’, ‘computer’, ‘art’, and ‘circle’ proved difficult. The indistinct context made it hard to confirm if the results resonated with the prompt’s requirements. This uncertainty affirms that abstract symbolism demands a richer context for its interpretation.
Fine-tuned model with ControlNet
To address the challenge of using text prompts to describe abstract concepts and shapes, ControlNet is applied to specify the desired shapes. By adjusting the strength of control weights, we can constrain the overall look and feel while allowing flexibility for text prompts to guide design details.
Positive prompt:
Simple elegant logo for Dartmouth College, D Pine tree circle United States, education, successful vibe, minimalist, thought-provoking, abstract, recognizable
Image input
input image
Positive prompt:
Simple elegant logo for Dartmouth College, D Pine tree United States, education, successful vibe, minimalist, thought-provoking, abstract, recognizable
input image
Positive prompt:
Simple elegant logo for Dartmouth College, D Pine tree United States, education, successful vibe, minimalist, thought-provoking, abstract, recognizable
input image
Positive prompt:
Simple elegant logo for Dartmouth College, D Pine tree United States, education, successful vibe, minimalist, thought-provoking, abstract, recognizable
Current AI models demonstrate variable success in integrating specified shapes into logos. In some cases, these shapes become central themes, while in others, they appear as subtle details. This variability underscores a key limitation of AI in logo generation: the difficulty in combining new graphic elements with their social or cultural meanings based solely on textual prompts.
Graphically, techniques like ControlNet can help guide AI to produce more desired results by imposing constraints on the output. However, this approach assumes that users have a clear design concept in mind from the outset.
When relying exclusively on natural language to describe design requirements, even state-of-the-art models like ChatGPT-4, Midjourney, or Google Gemini, alongside advanced methodologies like chain of thought reasoning, struggle with interpreting both the literal and cultural contexts of prompts. This highlights the ongoing challenge for AI systems in accurately understanding and integrating nuanced cultural contexts into their generated outputs.
Learn more at: https://github.com/iamkaikai/Amazing_logo