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fix: turbo scheduler loading issue (#3065) 

* fix: correctly load ModelPatcher

* feat: do not load model at all, not needed
This commit is contained in:
Manuel Schmid 2024-05-31 22:24:19 +02:00 committed by GitHub
parent 3ef663c5b7
commit 7899261755
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2 changed files with 2 additions and 3 deletions
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3
ldm_patched/contrib/external_custom_sampler.py
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@ -107,8 +107,7 @@ class SDTurboScheduler:
def get_sigmas(self, model, steps, denoise):
start_step = 10 - int(10 * denoise)
timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
ldm_patched.modules.model_management.load_models_gpu([model])
sigmas = model.model.model_sampling.sigma(timesteps)
sigmas = model.model_sampling.sigma(timesteps)
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
return (sigmas, )

2
modules/sample_hijack.py
View File

@ -175,7 +175,7 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
elif scheduler_name == "sgm_uniform":
sigmas = normal_scheduler(model, steps, sgm=True)
elif scheduler_name == "turbo":
sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0]
elif scheduler_name == "align_your_steps":
model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1'
sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0]