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#Optimized to run on Colab Free
#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://github.com/multimodalart/MajestyDiffusion
[clip_list]
perceptors = ['[clip - mlfoundations - ViT-B-16--openai]', '[clip - mlfoundations - ViT-B-32--laion2b_e16]', '[clip - mlfoundations - ViT-B-16--laion400m_e32]']
[basic_settings]
#Perceptor things
latent_diffusion_guidance_scale = 2
clip_guidance_scale = 5000
aesthetic_loss_scale = 500
augment_cuts=True
#Init image settings
starting_timestep = 0.9
init_scale = 1000
init_brightness = 0.0
init_noise = 0.6
[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 = [[200, 1000, 8], [50, 200, 5]]
#Cut settings
clamp_index = [0.8]*1000
cut_overview = [8]*500 + [4]*500
cut_innercut = [0]*500 + [4]*500
cut_ic_pow = 0.1
cut_icgray_p = [0.1]*300 + [0]*1000
cutn_batches = 1
range_index = [0]*1300
active_function = 'softsign'
tv_scales = [1200]*1 + [600]*3
latent_tv_loss = True
#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 = 0.5
#Magnify grad before clamping by how many times
opt_mag_mul = 15
opt_ddim_eta = 1.4
opt_eta_end = 1.0
opt_temperature = 0.975
#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=0.5
#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
cc = 60
#Deactivating new stuff from 1.5
score_modifier = False
compress_steps = 0
punish_steps = 0 |