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#This settings file can be loaded back to Latent Majesty Diffusion. If you like your setting consider sharing it to the settings library at https://huggingface.co/datasets/multimodalart/latent-majesty-diffusion-settings
[basic_settings]
#Perceptor things
latent_diffusion_guidance_scale = 1.75
clip_guidance_scale = 5000
aesthetic_loss_scale = 400
augment_cuts=True
#Init image settings
starting_timestep = 0.9
init_scale = 1000
init_brightness = 0.0
init_noise = 0.57
[advanced_settings]
#Add CLIP Guidance and all the flavors or just run normal Latent Diffusion
use_cond_fn = True
#Custom schedules for cuts. Check out the schedules documentation here
custom_schedule_setting = [[50, 1000, 8], 'gfpgan:1.5', [5, 200, 5]]
#Cut settings
clamp_index = [2, 1.6]
cut_overview = [8]*500 + [4]*500
cut_innercut = [0]*500 + [4]*500
cut_blur_n = [0]*1300
cut_blur_kernel = 3
cut_ic_pow = 0.5
cut_icgray_p = [0.1]*300 + [0]*1000
cutn_batches = 1
range_index = [0]*200 + [3]*400 + [0]*1000
active_function = "softsign"
ths_method= "softsign"
tv_scales = [50]*1 + [20]*1 + [0]*2
#If you uncomment this line you can schedule the CLIP guidance across the steps. Otherwise the clip_guidance_scale will be used
clip_guidance_schedule = [5000]*1000
#Apply symmetric loss (force simmetry to your results)
symmetric_loss_scale = 0
#Latent Diffusion Advanced Settings
#Use when latent upscale to correct satuation problem
scale_div = 1
#Magnify grad before clamping by how many times
opt_mag_mul = 15
opt_ddim_eta = 1.3
opt_eta_end = 1
opt_temperature = 0.95
#Grad advanced settings
grad_center = False
#Lower value result in more coherent and detailed result, higher value makes it focus on more dominent concept
grad_scale=0.25
score_modifier = True
threshold_percentile = 0.9
threshold = 1.2
var_index = [1]*1000
var_range = 0.5
mean_index = [10]*1000
mean_range = 0.2
#Init image advanced settings
init_rotate=False
mask_rotate=False
init_magnitude = 0.15
#More settings
RGB_min = -0.95
RGB_max = 0.95
#How to pad the image with cut_overview
padargs = {'mode': 'constant', 'value': -1}
flip_aug=False
#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14
experimental_aesthetic_embeddings = True
#How much you want this to influence your result
experimental_aesthetic_embeddings_weight = 0.3
#9 are good aesthetic embeddings, 0 are bad ones
experimental_aesthetic_embeddings_score = 8
# For fun dont change except if you really know what your are doing
grad_blur = False
compress_steps = 0
compress_factor = 0.1
punish_steps = 0
punish_factor = 0.8
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