Color Theory (Study)

This is a small study on tagging color concepts in the context of artworks that make use of color theory.

I'm not an expert on any of these topics, just enjoying learning & sharing.

I've also used these as an comparative study on DoRA/LoRA training (I prefer the DoRA results).

Usage Guide:

These tags are already common Danbooru tags,

so applying some of the techniques here to other models might work for more aesthetic color compositions.

Color Harmony

Extreme unity leads to under-stimulation,

extreme complexity leads to over-stimulation.

Harmony is a dynamic equilibrium.

Analogous Colors

Analogous colors are any three colors which are side by side on a 12-part color wheel,

such as yellow-green, yellow, and yellow-orange.

Usually one of the three colors predominates.

Complementary Colors

Complementary colors are any two colors which are directly opposite each other,

such as red-green, red-purple and yellow-green.

Tagging:

For color triggers, theme/background should be interchangeable - but I gave more focus to theme tagging.

Color wheel (clockwise order):

red theme

orange theme

yellow theme

green theme

blue theme

purple theme

Saturation:

colorful

high contrast

black theme

white theme

brown theme

grey theme

cyan theme

pink theme

Lighting:

light

light beams

dappled light

dark

shadow

Complementary Colors:

yellow, purple

orange, cyan

red, green

blue, orange

Composition:

spot color

scenery

simple background

gradient background

Three Color Palette:

For a 3 color palette, can aim to apply colors to the generation by the rule of thirds for a nice balance.

For a character focus, hair, eyes, accessories can work well for a 10% element.

A {color} theme works well for the 30% element.

And a {color} background works well for the remaing 60%.

Example combination:

green background, cyan theme, red eyes

Stepping through the color gamut of available tags with an X/Y plot on Prompt/SR:

green,yellow,orange



cyan,green,yellow



red,purple,blue

Selective Coloring:

Selective coloring is a photography technique where one color is prominent in the final shot whereas all the other colors have either been changed to monochrome or had their color saturation levels lowered.

White:

spot color, white theme, white backgroun, light, light beam, dappled light

Black:

spot color, black theme, dark, shadow

License

Fair AI Public License 1.0-SD

描述:

This is a DoRA, not your typically made LoRA. A1111 v1.9.0 and up is required to use this. Forge users must download two files, which can be found here. Go to stable-diffusion-webui\ldm_patched\modules and replace the files there.

When used at strength 1, some artifacting occurs for certain prompts in Forge

Training settings:

DoRA training required updating the lycoris submodule:

https://github.com/bmaltais/kohya_ss/issues/2589#issuecomment-2171903484

Kohya training toml

bucket_no_upscale = true

bucket_reso_steps = 256

cache_latents = true

cache_latents_to_disk = true

caption_extension = ".txt"

clip_skip = 2

dynamo_backend = "no"

enable_bucket = true

epoch = 30

full_bf16 = true

gradient_accumulation_steps = 1

gradient_checkpointing = true

huber_c = 0.1

huber_schedule = "snr"

learning_rate = 1.0

logging_dir = "E:/work/LoRa_work/logging"

loss_type = "l2"

lr_scheduler = "cosine"

lr_scheduler_args = []

lr_scheduler_num_cycles = 1

lr_scheduler_power = 1

max_bucket_reso = 2048

max_data_loader_n_workers = 0

max_grad_norm = 1

max_timestep = 1000

max_token_length = 225

max_train_epochs = 30

max_train_steps = 3395

metadata_author = "motimalu"

min_bucket_reso = 512

min_snr_gamma = 5

mixed_precision = "bf16"

multires_noise_discount = 0.3

multires_noise_iterations = 6

network_alpha = 16

network_args = [ "preset=full", "conv_dim=1", "conv_alpha=1", "rank_dropout=0",

"bypass_mode=False", "dora_wd=True", "module_dropout=0", "use_tucker=False",

"use_scalar=False", "rank_dropout_scale=False", "algo=locon", "train_norm=False",]

network_dim = 32

network_module = "lycoris.kohya"

no_half_vae = true

noise_offset_type = "Multires"

optimizer_args = []

optimizer_type = "Prodigy"

output_dir = "D:/model_output"

output_name = "pony_color_theory_dora_v1"

pretrained_model_name_or_path =

"C:/workspace/webui_forge_cu121_torch21/webui/models/Stable-diffusion/ponyDiffusionV6XL_v6Start

WithThisOne.safetensors"

prior_loss_weight = 1

resolution = "1024,1024"

sample_every_n_epochs = 10

sample_prompts = "D:/model_output\\prompt.txt"

sample_sampler = "euler_a"

save_every_n_epochs = 30

save_model_as = "safetensors"

save_precision = "bf16"

seed = 339491249

text_encoder_lr = 1

train_batch_size = 6

train_data_dir = "C:/data/color_theory"

unet_lr = 1

xformers = true