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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: |
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[model] |
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latent_diffusion_model = finetuned |
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#THIS SETTING CAN RUN IN T4!!! |
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[clip_list] |
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perceptors = ['[clip - mlfoundations - ViT-B-16--openai]', '[clip - mlfoundations - ViT-L-14--openai]', '[clip - mlfoundations - RN50x16--openai]', '[clip - mlfoundations - ViT-B-32--laion2b_e16]'] |
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[basic_settings] |
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#Perceptor things |
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#Everthing Disabled Here. |
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#width = 256 |
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#height = 256 |
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#latent_diffusion_guidance_scale = 10 |
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#clip_guidance_scale = 16000 |
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#aesthetic_loss_scale = 200 |
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#augment_cuts=True |
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#Init image settings |
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starting_timestep = 0.9 |
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init_scale = 1000 |
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init_brightness = 0.0 |
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[advanced_settings] |
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#Add CLIP Guidance and all the flavors or just run normal Latent Diffusion |
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use_cond_fn = True |
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#Custom schedules for cuts. Check out the schedules documentation here |
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custom_schedule_setting = [[30, 1000, 8], 'gfpgan:1.5', [20, 200, 8], 'gfpgan:1.0', [50, 220, 4]] |
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#Cut settings |
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clamp_index = [2.4, 2.1] |
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cut_overview = [8]*500 + [4]*500 |
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cut_innercut = [0]*500 + [4]*500 |
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cut_blur_n = [0]*1300 |
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cut_blur_kernel = 3 |
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cut_ic_pow = 0.6 |
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cut_icgray_p = [0.1]*300 + [0]*1000 |
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cutn_batches = 1 |
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range_index = [0]*200 + [50000.0]*400 + [0]*1000 |
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active_function = "softsign" |
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ths_method= "clamp" |
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tv_scales = [150]*1 + [0]*3 |
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#If you uncomment this line you can schedule the CLIP guidance across the steps. Otherwise the clip_guidance_scale will be used |
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clip_guidance_schedule = [16000]*1000 |
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#Apply symmetric loss (force simmetry to your results) |
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symmetric_loss_scale = 0 |
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#Latent Diffusion Advanced Settings |
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#Use when latent upscale to correct satuation problem |
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scale_div = 1 |
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#Magnify grad before clamping by how many times |
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opt_mag_mul = 20 |
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opt_ddim_eta = 1.3 |
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opt_eta_end = 1.1 |
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opt_temperature = 0.98 |
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#Grad advanced settings |
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grad_center = False |
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#Lower value result in more coherent and detailed result, higher value makes it focus on more dominent concept |
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grad_scale=0.25 |
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score_modifier = True |
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threshold_percentile = 0.85 |
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threshold = 1 |
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var_index = [2]*300 + [0]*700 |
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var_range = 0.5 |
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mean_index = [0]*1000 |
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mean_range = 0.75 |
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#Init image advanced settings |
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init_rotate=False |
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mask_rotate=False |
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init_magnitude = 0.18215 |
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#More settings |
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RGB_min = -0.95 |
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RGB_max = 0.95 |
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#How to pad the image with cut_overview |
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padargs = {'mode': 'constant', 'value': -1} |
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flip_aug=False |
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#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14 |
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experimental_aesthetic_embeddings = True |
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#How much you want this to influence your result |
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experimental_aesthetic_embeddings_weight = 0.3 |
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#9 are good aesthetic embeddings, 0 are bad ones |
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experimental_aesthetic_embeddings_score = 8 |
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# For fun dont change except if you really know what your are doing |
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grad_blur = False |
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compress_steps = 200 |
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compress_factor = 0.1 |
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punish_steps = 200 |
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punish_factor = 0.5 |
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