kw: generated art, photo essays, art generation, AI art, experiments
A few days ago I posted about the first image I generated using DALL•E 2, four years ago, and the way the most recent art engines responded to the same prompt. I noticed that the four images from November 2022 were quite diverse, while the various sets of four images from the newer software tended to be more similar to one another. I did a test of the internal diversity of one of those engines, which I'll discuss below, but first, my favorite among them:
This image and its three siblings were produced by the Seedream 5.5 model (what I call an engine). (See Coda at the end)
Here is a montage of the 16 images produced four days ago:
From top to bottom, the engines (also identified in brief in the file names) were
- Nano Banana 2.1
- Seedream 5.5
- GPT Image 2.6 Flare
- GPT Image 2.6 Sunburst
When I was making the images I was immediately struck by the similarity of all those produced by GPT Image 2.6: Deciduous trees at the left, a river from near-center to lower right, a range of mountains dominated by one peak, great similarity of cloud styles, and the setting sun either in the image or just outside the frame. Secondly, the four Nano Banana 2.1 images resemble one another nearly as much: masses of trees both left and right, a river from near center more directly toward the viewer, similarity of cloud styles, and upon close examination, one or two people in each image in the middle ground toward one side; however, the mountains are more diverse from image to image, and one image has a nice set of sunbeams.
The Seedream 5.5 images are not so obviously similar: the trees vary more in size and location, the water features are more diverse, the clouds vary more, but the mountains are also single-peak dominated in three of the four images.
Some of the art engines I used in the past had the option to set the Seed, or to leave it up to the software. The Seed is used in a random-number generator to produce the initial spate of noise, which is then run many times (20 to 50) through a "denoising" algorithm until it settles on the final image. What is not stated in the "help" text is that the Seed also affects the placement of elements of the image, and perhaps the identity of some of them. Thus, when I would set the Seed to, for example 142857 (which is 999,999/7), a specific prompt would always yield exactly the same image. However, when the prompt is changed, even a little, but the Seed is the same, a different image is produced. By experimentation I found that if I set the seed to 142857 and asked for four results (four "generations"), the actual Seeds used would go in sequence: 142857, 142858, 142859, 142860. One of the older engines, no longer available, would helpfully include the Seed in the file name.
From the notes above I conclude that the engines I used to produce these images vary in the amount of influence the Seed has on the contents and arrangement of elements in the final images. Noting that Seedream 5.5 produces images with the greatest diversity, at least using such a short prompt, I experimented further. This time I set the number of images to produce at 2, and ran it twice. Here are the preview images; the first pair is on the right, and the second pair is on the left:
I have to say, without really digging into the weeds, that running Seedream 5.5 twice produced about the same level of diversity as running it to produce four images all in one go. My favorite is the fourth image, which is on the left of this montage, and shown at the top of this page. It is the only one with people clearly visible; one other has a few distant dwellings and perhaps very small people in a meadow in the distance. This fourth image also has overall coloration I like better than the others.
I hope to find out more about how an art engine actually composes an image, so that denoising has a target, a conceptual image to denoise towards.
To conclude, I'll supply the other three images full size, and you're free to download them for your own use if you like.
CODA, 8 hours later. It occurred to me to try this with Nano Banana 2.1: Produce two images, wait a bit, and produce two more. The images thus produced were a little different from those produced four days ago, but the overall diversity was still significantly less than the images produced by Seedream 5.5. My advice? Pick the engine to use depending on the amount of variation you prefer.











































