#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
    [model]
    latent_diffusion_model = finetuned
    
    [clip_list]
    perceptors = ['[clip - mlfoundations - ViT-B-16--openai]', '[clip - mlfoundations - RN50x16--openai]', '[clip - mlfoundations - ViT-L-14--laion400m_e32]', '[clip - mlfoundations - ViT-B-16-plus-240--laion400m_e32]', '[clip - mlfoundations - ViT-B-32--laion2b_e16]']
    
    [basic_settings]
    #Perceptor things
    
    width = 256
    height = 256
    
    latent_diffusion_guidance_scale = 10
    clip_guidance_scale = 135000
    aesthetic_loss_scale = 400
    augment_cuts=True

    #Init image settings
    starting_timestep = 0.02
    init_scale = 1000 
    init_brightness = 0.0
    
    [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 = [[30, 1000, 8], 'gfpgan:1.5', 'scale:.9', [20, 200, 8], 'gfpgan:1', 'scale:.9', [50, 220, 2], 'gfpgan:1']

    #Cut settings
    clamp_index = [2.4, 2.1]
    cut_overview = [8]*500 + [4]*500
    cut_innercut = [0]*500 + [4]*500
    cut_blur_n = [0]*1300
    cut_blur_kernel = 3
    cut_ic_pow = 5.6
    cut_icgray_p = [0.1]*300 + [0]*1000
    cutn_batches = 1
    range_index = [0]*200 + [50000.0]*400 + [0]*1000
    active_function = "softsign"
    ths_method= "clamp"
    tv_scales = [150]*1 + [0]*3

    #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 = [16000]*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 = 20
    opt_ddim_eta = 1.3
    opt_eta_end = 1.1
    opt_temperature = 0.98

    #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 = 0.85
    threshold = 1
    var_index = [2]*300 + [0]*700
    var_range = 0.5
    mean_index = [0]*1000
    mean_range = 0.75

    #Init image advanced settings
    init_rotate=False
    mask_rotate=False
    init_magnitude = 0.18215

    #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 = 200
    compress_factor = 0.1
    punish_steps = 200
    punish_factor = 0.5