erlich

Channel your inner designer and make any type of logo based on your text prompt. Only your imagination is the limit!

Hold: 0
Required: 15,000 $wRAI
21.3k runs
Demo
Examples
Input
prompt
Your text prompt.
negative
(optional) Negate the model's prediction for this text from the model's prediction for the target text.
init_image
(optional) Initial image to use for the model's prediction. If provided alongside a mask, the image will be inpainted instead.
mask
a mask image for inpainting an init_image. white pixels = keep, black pixels = discard. resized to width = image width/8, height = image height/8
guidance_scale
Classifier-free guidance scale. Higher values will result in more guidance toward caption, with diminishing returns. Try values between 1.0 and 40.0. In general, going above 5.0 will introduce some artifacting. (minimum: -20; maximum: 100)
steps
Number of diffusion steps to run. Due to PLMS sampling, using more than 100 steps is unnecessary and may simply produce the exact same output. (minimum: 15; maximum: 250)
batch_size
Batch size. (higher = slower) (minimum: 1; maximum: 16)
width
Target width
height
Target height
init_skip_fraction
Fraction of sampling steps to skip when using an init image. Defaults to 0.0 if init_image is not specified and 0.5 if init_image is specified. (maximum: 1)
aesthetic_rating
Aesthetic rating (1-9) - embed to use.
aesthetic_weight
Aesthetic weight (0-1). How much to guide towards the aesthetic embed vs the prompt embed.
seed
Seed for random number generator. If empty, a random seed will be chosen.
Whether to return intermediate outputs. Enable to visualize the diffusion process and/or debug the model. May slow down inference.
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Output
Input
seed
-1
steps
100
width
256
height
256
prompt
paper plane logo with shadow of plane flying around the world, logo, digital art
batch_size
6
guidance_scale
5
aesthetic_rating
9
aesthetic_weight
0.1
init_skip_fraction
0
Output

Readme

Erlich is the text2image latent diffusion model from CompVis (with additions from glid-3-xl) finetuned on a dataset collected from LAION-5B named Large Logo Dataset. It consists of roughly 100K images of logos with captions generated via BLIP using aggressive re-ranking.