46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
import os
|
|
import torch
|
|
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)
|
|
xl_refiner = core.load_model(xl_refiner_filename)
|
|
del xl_base.vae
|
|
|
|
|
|
@torch.no_grad()
|
|
def process(positive_prompt, negative_prompt, width=1024, height=1024, batch_size=1):
|
|
positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt)
|
|
negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt)
|
|
|
|
positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt)
|
|
negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt)
|
|
|
|
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=batch_size)
|
|
|
|
sampled_latent = core.ksample(
|
|
unet=xl_base.unet,
|
|
positive_condition=positive_conditions,
|
|
negative_condition=negative_conditions,
|
|
latent_image=empty_latent,
|
|
steps=30, start_at_step=0, end_at_step=20, return_with_leftover_noise=True, add_noise=True
|
|
)
|
|
|
|
sampled_latent = core.ksample(
|
|
unet=xl_refiner.unet,
|
|
positive_condition=positive_conditions_refiner,
|
|
negative_condition=negative_conditions_refiner,
|
|
latent_image=sampled_latent,
|
|
steps=30, start_at_step=20, end_at_step=30, return_with_leftover_noise=False, add_noise=False
|
|
)
|
|
|
|
decoded_latent = core.decode_vae(vae=xl_refiner.vae, latent_image=sampled_latent)
|
|
|
|
images = core.image_to_numpy(decoded_latent)
|
|
return images
|