Digit Lovers vs Artists

There’s a pattern in photography.

Two kinds of photographers. Those who take photos with their camera. And those who play for hours on their Mac, with apps, with digits, HDR, and I correct this and that.

I already wrote articles about those.

Take a car.

You can be the tech type. You want to open it and fix this and that. Or you want to drive it to the lake and breath fresh air.

Today I’m very busy making images with Artificial intelligence. I have an Artstation (here: https://quick_eyed_sky.artstation.com/ ) and a YouTube channel here: https://www.youtube.com/c/JPRobocat

Very soon, I noticed the same pattern.

To make images, you need a Google Colab, which is a Python program with a bunch of settings, like a machine, and you enter a “prompt”, which is a “phrase describing what you want to see”.

There are 2 camps.

  • Most humans are obsessed and focused on SETTINGS, they study these for weeks, very deeply, line by line. They’re obsessed with numbers, and digits.
  • The other camp is obsessed with the PROMPT, which is like poetry. Choice of words. The way you present them. Their weight. Etc.

It’s always the same pattern.

  • Camp 1 is very serious. They are logical. They are focused on their numbers. They want DETAILS, crunchy pictures, they want lines. Techs.
  • Camp 2 is very casual. They try things. They read poetry. They enter lyrics and try to find new artists. They want mood, light, and composition. Poets.

You know what? Both camps are OK. But I’m in the 2nd one. You knew that already, right?

Do you watch the beach and the mountain, or do you watch your car’s engine?

Thanks for reading!

Do you focus on the prompt or on the digits everywhere? What about “clamp_max”: 0.05? Should I double it? Or should I add “ominous sky” in the prompt? Where do you like to work?

“text_prompts”: {
“0”: [
“Greg Rutkowski, long butterfly airship in the summer sky, Artstation”
],
“100”: [
“This set of prompts starts at frame 100”,
“This prompt has weight five:5”
]
},
“image_prompts”: {},
“clip_guidance_scale”: 50000,
“tv_scale”: 0,
“range_scale”: 150,
“sat_scale”: 0,
“cutn_batches”: 4,
“max_frames”: 10000,
“interp_spline”: “Linear”,
“init_image”: null,
“init_scale”: 1000,
“skip_steps”: 0,
“frames_scale”: 1500,
“frames_skip_steps”: “60%”,
“perlin_init”: false,
“perlin_mode”: “mixed”,
“skip_augs”: false,
“randomize_class”: true,
“clip_denoised”: false,
“clamp_grad”: true,
“clamp_max”: 0.05,
“seed”: 2397292033,
“fuzzy_prompt”: false,
“rand_mag”: 0.05,
“eta”: 0.8,
“width”: 1600,
“height”: 832,
“diffusion_model”: “512x512_diffusion_uncond_finetune_008100”,
“use_secondary_model”: true,
“steps”: 350,
“diffusion_steps”: 700,
“ViTB32”: true,
“ViTB16”: true,
“ViTL14”: false,
“RN101”: false,
“RN50”: true,
“RN50x4”: false,
“RN50x16”: false,
“RN50x64”: false,
“cut_overview”: “[12]400+[4]600″,
“cut_innercut”: “[4]400+[12]600″,
“cut_ic_pow”: 1,
“cut_icgray_p”: “[0.2]400+[0]600″,
“key_frames”: true,
“angle”: “0:(0)”,
“zoom”: “0: (1), 10: (1.05)”,
“translation_x”: “0: (0)”,
“translation_y”: “0: (0)”,
“video_init_path”: “/content/training.mp4”,
“extract_nth_frame”: 2
}