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nightmareai /majesty-diffusion:ff97d797

Input schema

The fields you can use to run this model with an API. If you don’t give a value for a field its default value will be used.

Field Type Default value Description
clip_prompt
string
The portrait of a Majestic Princess, trending on artstation
Prompt for CLIP guidance
latent_prompt
string
The portrait of a Majestic Princess, trending on artstation
Prompt for latent diffusion
height
integer (enum)
256

Options:

128, 192, 256, 320, 384

Output height (output will be scaled up 1.5x with default settings)
width
integer (enum)
256

Options:

128, 192, 256, 320, 384

Output width (output will be scaled up 1.5x with default settings)
init_image
string
Initial image
init_mask
string
A mask same width and height as the original image with the color black indicating where to inpaint
init_scale
integer
1000
Controls how much the init image should influence the final result. Experiment with values around 1000
init_brightness
number
0
Init image brightness
latent_scale
integer
12
The `latent_diffusion_guidance_scale` will determine how much the `latent_prompts` affect the image. Lower help with text interpretation, higher help with composition. Try values between 0-15. If you see too much text, lower it
clip_perceptors
string
### Preloaded with image [clip - mlfoundations - ViT-B-16--openai] [clip - mlfoundations - ViT-L-14--openai] [clip - mlfoundations - ViT-B-32--laion2b_e16] ### Download on demand #[clip - mlfoundations - ViT-B-32--openai], #[clip - mlfoundations - RN50x4--openai] #[clip - mlfoundations - RN50x64--openai] #[clip - mlfoundations - RN50x16--openai] #[clip - mlfoundations - ViT-L-14--laion400m_e32] #[clip - mlfoundations - ViT-L-14-336--openai] #[clip - mlfoundations - ViT-B-16--laion400m_e32] #[clip - mlfoundations - ViT-B-16-plus-240--laion400m_e32] #[clip - sajjjadayobi - clipfa] #[clip - navervision - kelip_ViT-B/32] #[cloob - crowsonkb - cloob_laion_400m_vit_b_16_32_epochs]
List of CLIP perceptor models to load, one per line. More models will consume more memory. Uses https://github.com/dmarx/Multi-Modal-Comparators.
clip_scale
integer
16000
CLIP guidance scale
aesthetic_loss_scale
integer
400
Aesthetic loss scale
starting_timestep
number
0.9
Starting timestep
model
string (enum)
finetuned

Options:

original, finetuned, ongo, erlich

Latent diffusion model (ongo and erlich may need to download, taking extra time)
custom_schedule
string
[ [50, 1000, 8], "gfpgan:1.5", "scale:.9", "noise:.55", [5,300,4] ]
Custom schedules, one per line. See the Majestic Guide for documentation.
latent_negatives
string
Negative prompts for Latent Diffusion
output_steps
integer (enum)
10

Options:

0, 5, 10, 20

Steps between outputs, 0 to disable progressive output. Minor speed impact.
advanced_settings
string
[advanced_settings] #Add CLIP Guidance and all the flavors or just run normal Latent Diffusion use_cond_fn = True #Cut settings clamp_index = [2.4, 2.1] cut_overview = [8]*500 + [4]*500 cut_innercut = [0]*500 + [4]*500 cut_ic_pow = 0.2 cut_icgray_p = [0.1]*300 + [0]*1000 cutn_batches = 1 cut_blur_n = [0] * 300 + [0] * 1000 cut_blur_kernel = 3 range_index = [0]*200+[5e4]*400+[0]*1000 active_function = 'softsign' ths_method = 'softsign' tv_scales = [600] * 1 + [50] * 1 + [0] * 2 #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_plms = False 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]*400+[0]*600 mean_range = 0.75 #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 = 200 compress_factor = 0.1 punish_steps = 200 punish_factor = 0.5
Advanced settings (can override values above)

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{'items': {'format': 'uri', 'type': 'string'},
 'title': 'Output',
 'type': 'array',
 'x-cog-array-type': 'iterator'}