Upload The_Vram_Goes_Brrrrrr.cfg

Updated version, I have only had pleasant results with this one,
The more specific you are, the more coherent the result can be.
but also, not good for pixel art, and cartoon, the GPU Ram error will be more frequently here.

The_Vram_Goes_Brrrrrr.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
+    [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
+