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apply loras to refiner like upstream #161

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lllyasviel
2024-02-09 21:40:19 -08:00
parent 371687a1da
commit bd0878754c
1 changed files with 4 additions and 1 deletions
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5
modules/sd_samplers_common.py
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@@ -6,6 +6,7 @@ from PIL import Image
from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
from modules.shared import opts, state from modules.shared import opts, state
from modules_forge.forge_sampler import sampling_prepare, sampling_cleanup from modules_forge.forge_sampler import sampling_prepare, sampling_cleanup
from modules import extra_networks
import k_diffusion.sampling import k_diffusion.sampling
@@ -182,7 +183,9 @@ def apply_refiner(cfg_denoiser, x):
with sd_models.SkipWritingToConfig(): with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=refiner_checkpoint_info) sd_models.reload_model_weights(info=refiner_checkpoint_info)
devices.torch_gc() if not cfg_denoiser.p.disable_extra_networks:
extra_networks.activate(cfg_denoiser.p, cfg_denoiser.p.extra_network_data)
cfg_denoiser.p.setup_conds() cfg_denoiser.p.setup_conds()
cfg_denoiser.update_inner_model() cfg_denoiser.update_inner_model()