Upload coherent_pizzapigeon.cfg

That can give you the coherent, realistic and detailed results on T4, also, you will give better results with texts, btw will fail non-realistic things like cartoon, pixel art, etc.

coherent_pizzapigeon.cfg

@@ -0,0 +1,96 @@
+    #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
+    #THIS SETTING CAN RUN IN T4!!!
+    [clip_list]
+    perceptors = ['[clip - mlfoundations - ViT-B-16--openai]', '[clip - mlfoundations - ViT-L-14--openai]', '[clip - mlfoundations - RN50x16--openai]', '[clip - mlfoundations - ViT-B-32--laion2b_e16]']
+    
+    [basic_settings]
+    #Perceptor things
+    #Everthing Disabled Here.
+    #width = 256
+    #height = 256
+    #latent_diffusion_guidance_scale = 10
+    #clip_guidance_scale = 16000
+    #aesthetic_loss_scale = 200
+    #augment_cuts=True
+
+    #Init image settings
+    starting_timestep = 0.9
+    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', [20, 200, 8], 'gfpgan:1.0', [50, 220, 4]]
+
+    #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 = 0.6
+    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
+