It is a latent diffusion model with two multilingual text encoders:
- mCLIP-XLMR 560M parameters
- mT5-encoder-small 146M parameters
These encoders and multilingual training datasets unveil the real multilingual text2image generation experience!
UNet 1.2B parameters
Check our jupyter notebooks with examples in ./notebooks folder
from kandinsky2 import get_kandinsky2
model = get_kandinsky2('cuda', task_type='text2img')
images = model.generate_text2img('кошка', batch_size=4, h=512, w=512, num_steps=75, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
from kandinsky2 import get_kandinsky2
from PIL import Image
import numpy as np
model = get_kandinsky2('cuda', task_type='inpainting')
init_image = Image.open('image.jpg')
mask = np.ones((512, 512), dtype=np.float32)
mask[100:] = 0
images = model.generate_inpainting('красная кошка', init_image, mask, num_steps=50, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
from kandinsky2 import get_kandinsky2
from PIL import Image
model = get_kandinsky2('cuda', task_type='img2img')
init_image = Image.open('image.jpg')
images = model.generate_img2img('кошка', init_image, strength=0.8, num_steps=50, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)