Add initial defaults

dango233_princesses.cfg

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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://github.com/multimodalart/MajestyDiffusion
+    [clip_list]
+    perceptors = ['[clip - mlfoundations - ViT-B-16--openai]', '[clip - mlfoundations - ViT-B-16--laion400m_e32]', '[clip - mlfoundations - ViT-B-32--laion2b_e16]']
+    
+    [basic_settings]
+    #Perceptor things
+    
+    #clip_prompts = ['portrait of a princess in sanctuary, hyperrealistic painting trending on artstation']
+    #atent_prompts = ['portrait of a princess in sanctuary, hyperrealistic painting trending on artstation']
+    #latent_negatives = ['']
+    #image_prompts = []
+    
+    
+    latent_diffusion_guidance_scale = 2
+    clip_guidance_scale = 5000
+    aesthetic_loss_scale = 200
+    augment_cuts=True
+
+    #Init image settings
+    starting_timestep = 0.9
+    init_scale = 0 
+    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 = [[30, 900.0, 8], 'gfpgan:1', [20, 200, 6]]
+
+    #Cut settings
+    clamp_index = [1]*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 = 10
+    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
+    #Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14
+    experimental_aesthetic_embeddings = False
+    #How much you want this to influence your result
+    experimental_aesthetic_embeddings_weight = 1
+    #9 are good aesthetic embeddings, 0 are bad ones
+    experimental_aesthetic_embeddings_score = 8
+    
+    #Deactivating new stuff from 1.5
+    score_modifier = False
+    compress_steps = 0
+    punish_steps = 0

default.cfg

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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
+    

defaults_v1_3.cfg

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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

makeitrad_defaults.cfg

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+[clip_list]
+perceptors = ["[clip - mlfoundations - ViT-B-16-plus-240--laion400m_e32]", "[clip - mlfoundations - ViT-B-16--openai]", "[clip - mlfoundations - ViT-B-32--openai]"]
+
+[basic_settings]
+latent_diffusion_guidance_scale = 8.85
+clip_guidance_scale = 5000
+aesthetic_loss_scale = 500
+augment_cuts=True
+
+[advanced_settings]
+symmetric_loss_scale = 0.8
+scale_div = 0.5
+opt_ddim_eta = 1.2
+opt_eta_end = 1.05
+opt_temperature = .95
+anti_jpg = 0.3
+clamp_index = [.45]*1000 
+cut_overview = [8]*1000
+cut_innercut = [2]*500 + [2]*500
+cut_ic_pow = .2
+cut_icgray_p = [.1]*300+[0]*1000
+cutn_batches = 1
+range_index = [0]*300 + [0]*1000 
+active_function = "softsign" # function to manipulate the gradient - help things to stablize
+tv_scales = [45]*1+[20]*3
+
+#Deactivating new stuff from 1.4
+score_modifier = False
+compress_steps = 0
+punish_steps = 0

the_other_zippy_defaults.cfg

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+#by Zippy (https://github.com/aredden or Zippy#1111 on colab - not to be mistaken with Disco Diffusion Zippy)
+[clip_list]
+perceptors = ['[clip - mlfoundations - ViT-B-16--openai]', '[clip - mlfoundations - ViT-L-14--openai]', '[clip - mlfoundations - ViT-B-32--openai]', '[clip - mlfoundations - ViT-B-16-plus-240--laion400m_e32]']
+
+[basic_settings]
+latent_diffusion_guidance_scale = 8.85
+clip_guidance_scale = 5000
+aesthetic_loss_scale = 500
+
+[advanced_settings]
+clamp_index = [.45]*1000
+cut_overview = [8]*1000
+cut_innercut = [2]*500 + [2]*500
+cut_ic_pow = .2
+cut_icgray_p = [.1]*300+[0]*1000
+cutn_batches = 1
+range_index = [0]*1300
+active_function = "softsign"
+tv_scales = [45]*1 + [20]*3
+latent_tv_loss = True
+symmetric_loss_scale = 0.8
+
+#Deactivating new stuff from 1.5
+score_modifier = False
+compress_steps = 0
+punish_steps = 0