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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])
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