Running the following script downloads und extracts all available pretrained autoencoding models. ![]() The price is 1 Discobux for a minute-long disco, 10 bux for an hour and 50 Discobux for 8 hours. Trendy disco Zoo, nightlife, scene, Nicosia, Cyprus, Greece, Europe Stock. Disco parties aren’t free unfortunately they cost Discobux. empty red disco scene with beam of light - to insert text or design Stock. Your zoo will also make double the amount of coins. Model Zoo Pretrained Autoencoding ModelsĪll models were trained until convergence (no further substantial improvement in rFID). Sleeping animals will wake up to boogie and remain fully awake after the disco is over. If you already have ImageNet on your disk, you can speed things However, since ImageNet is quite large, this requires a lot of disk In Disco Zoo, you get animals by going on rescues in various areas, and each area has a few different animals (a couple common, some rare, and one mythical). Torrents) and prepare ImageNet the first time it The code will try to download (through Academic The beds/cats/churches subsets shouldĪlso be placed/symlinked at. We performed a custom split into training and validation images, and provide the corresponding filenamesĪfter downloading, extract them to. The LSUN datasets can be conveniently downloaded via the script available here. runĬUDA_VISIBLE_DEVICES= python scripts/sample_diffusion.py -r models/ldm/ /model.ckpt -l -n -batch_size -c -e Train your own LDMs Data preparation Facesįor downloading the CelebA-HQ and FFHQ datasets, proceed as described in the taming-transformers ![]() To try it out, tune the H and W arguments (which will be integer-dividedīy 8 in order to calculate the corresponding latent size), e.g. Beyond 256²įor certain inputs, simply running the model in a convolutional fashion on larger features than it was trained onĬan sometimes result in interesting results. even lower values of ddim_steps) while retaining good quality can be achieved by using -ddim_eta 0.0 and -plms (see Pseudo Numerical Methods for Diffusion Models on Manifolds). low values of ddim_steps) while retaining good quality can be achieved by using -ddim_eta 0.0.įaster sampling (i.e. Quality, sampling speed and diversity are best controlled via the scale, ddim_steps and ddim_eta arguments.Īs a rule of thumb, higher values of scale produce better samples at the cost of a reduced output diversity.įurthermore, increasing ddim_steps generally also gives higher quality samples, but returns are diminishing for values > 250.įast sampling (i.e. This will save each sample individually as well as a grid of size n_iter x n_samples at the specified output location (default: outputs/txt2img-samples). ![]() Python scripts/txt2img.py -prompt "a virus monster is playing guitar, oil on canvas" -ddim_eta 0.0 -n_samples 4 -n_iter 4 -scale 5.0 -ddim_steps 50
0 Comments
Leave a Reply. |