Create Stunning Firefly Text Effects with Stable Diffusion

Table of Contents

1. Introduction
2. Trying out Adobe's generative AI Firefly
3. Understanding stable diffusion
4. Experimenting with different approaches
5. Using ControlNet for better results
6. Generating text effects with single letters
7. Challenges with generating full words
8. Combining individual letters for word generation
9. Adding image-to-image guidance for improved results
10. Final steps for achieving perfect text effects
11. Conclusion

Trying out Adobe's Generative AI Firefly

Adobe recently announced an exciting development - they have removed the waitlist for their generative AI tool, Firefly. As someone who has never had access to Firefly before, I was thrilled to give it a try. If you haven't already checked it out, you can visit firefly.adobe.com. One feature that particularly fascinated me was the text effect. I was curious to understand how Adobe was able to perfect the look of text, considering generative art models often struggle with drawing texts. In order to explore this further, I decided to experiment with stable diffusion, an open-source text-to-image model.

Understanding Stable Diffusion

Stable diffusion is a text-to-image model that allows users to generate images based on text prompts. I discovered this model and its web version on stabledefusionweb.com. For the purpose of my experiment, I chose to use the stable diffusion's web UI for my M1 MacBook. It's important to note that diffusion models generally face challenges when it comes to drawing text accurately. With this knowledge, I proceeded to try different approaches to achieve the desired text effect.

Experimenting with Different Approaches

My initial instinct was to attempt a simple text font and see if I could produce something similar to Firefly's effects. I decided to start with the prompt of the letter "A" made with intricate gold ornaments. Unfortunately, this approach turned out to be a complete fail. While the overall effect looked decent, stable diffusion completely distorted the shape of the letter "A," making it look more like the letter "B." Although not entirely unexpected, this outcome motivated me to search for a solution that would allow me to have more control over the structure of the text.

Using ControlNet for Better Results

I learned about ControlNet, a neural network structure that enables users to manipulate the outputs of diffusion models by adding specific conditions on top of them. ControlNet provides the ability to control poses, expressions, the layout of the image, and even convert scribbles into real-life images. To get started with ControlNet, I installed the extension for Automatic 11. With my control image - a black text on a white background generated using Canva - uploaded to the ControlNet section, I used the same prompt as before and selected the "invote" preprocessor. I also chose the "liner" model. To my delight, this approach produced perfect text generations, giving me the precise look I desired.

Generating Text Effects with Single Letters

Buoyed by my success with a single letter, I wondered if the same approach would yield favorable results with complete words. I followed the same process, creating a control image by overlaying thick black text on a white background and uploading it to the ControlNet section. However, this time, I encountered unreliable outcomes. While the ice effect looked quite good, the bread effect was not impressive, and the sushi effect produced a distorted letter "H." Additionally, the cake effect created a cake behind the word instead of the word itself. It was apparent that I needed to rethink my approach to achieve consistent and reliable text effects.

Challenges with Generating Full Words

Realizing that individual letters yielded more reliable results, I decided to generate the letters separately and then combine them to form complete words. To maintain consistency, I used the same seed for all the letters. However, even with individual letters, the generations were not entirely reliable. It often took several attempts to obtain the desired shape for each letter. For example, the letter "A" in the donut effect and the letter "B" in the bread effect needed multiple tries to achieve the desired outcome. Additionally, there was excessive noise in the background, making the task of removing the background and combining the letters together challenging.

Adding Image-to-Image Guidance for Improved Results

To address the challenges faced in extracting letters and combining them, I considered utilizing the image-to-image technique. This approach involved creating a text mask from the first output image and using it as input in the image-to-image process. Additionally, I used the same prompt and control net image as before. These iterations produced significantly better outputs than the previous attempts, displaying cleaner and more precise text effects. With this technique, I successfully generated various text effects, including an exquisite sushi design and an ornate golden ornament text.

Final Steps for Achieving Perfect Text Effects

After several rounds of experimentation and refinement, I settled on a four-step process that consistently produced perfect text effects. Firstly, I generated a letter using text-to-image and a control image. While this initial output served as a good starting point, it still contained noise and lacked a clean background. To address these issues, I created a text mask from the first output and used it as input for the image-to-image generation. The second round of generation using image-to-image greatly improved the overall quality, resulting in cleaner and more precise text effects. Removing the background from these second-generation outputs allowed me to combine the letters together seamlessly. This approach yielded remarkable results, demonstrating the potential of stable diffusion and ControlNet in generating captivating and intricate text effects.

Conclusion

In conclusion, Adobe's generative AI tool, Firefly, and stable diffusion bring a new dimension to text effects. While diffusion models generally struggle with drawing text accurately, combining stable diffusion with ControlNet and image-to-image techniques allows users to achieve remarkable results. Although some challenges are involved, experimenting with different approaches and refining the process can lead to perfect and visually captivating text effects. Whether it's creating impressive letter-standalone designs or combining individual letters to form complete words, stable diffusion unlocks endless possibilities for expressive and aesthetically pleasing text effects. With further exploration and experimentation, users can leverage these techniques to add a touch of creativity and innovation to their design projects.