| import os | |
| import modules.core as core | |
| from modules.path import modelfile_path | |
| xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0.safetensors') | |
| xl_refiner_filename = os.path.join(modelfile_path, 'sd_xl_refiner_1.0.safetensors') | |
| xl_base = core.load_model(xl_base_filename) | |
| positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt='a handsome man in forest') | |
| negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt='bad, ugly') | |
| empty_latent = core.generate_empty_latent(width=1024, height=1024, batch_size=1) | |
| sampled_latent = core.ksample( | |
| unet=xl_base.unet, | |
| positive_condition=positive_conditions, | |
| negative_condition=negative_conditions, | |
| latent_image=empty_latent | |
| ) | |
| decoded_latent = core.decode_vae(vae=xl_base.vae, latent_image=sampled_latent) | |
| images = core.image_to_numpy(decoded_latent) | |
| for image in images: | |
| import cv2 | |
| cv2.imwrite('a.png', image[:, :, ::-1]) | |