42 lines
2.1 KiB
Python
42 lines
2.1 KiB
Python
import torch
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from modules import sd_samplers_kdiffusion, sd_samplers_common
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from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
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from ldm_patched.modules.samplers import calculate_sigmas_scheduler
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class AlterSampler(sd_samplers_kdiffusion.KDiffusionSampler):
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def __init__(self, sd_model, sampler_name, scheduler_name):
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self.sampler_name = sampler_name
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self.scheduler_name = scheduler_name
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self.unet = sd_model.forge_objects.unet
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sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
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super().__init__(sampler_function, sd_model, None)
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def get_sigmas(self, p, steps):
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if self.scheduler_name == 'turbo':
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timesteps = torch.flip(torch.arange(1, steps + 1) * float(1000.0 / steps) - 1, (0,)).round().long().clip(0, 999)
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sigmas = self.unet.model.model_sampling.sigma(timesteps)
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sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
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else:
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sigmas = calculate_sigmas_scheduler(self.unet.model, self.scheduler_name, steps)
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return sigmas.to(self.unet.load_device)
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def build_constructor(sampler_name, scheduler_name):
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def constructor(m):
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return AlterSampler(m, sampler_name, scheduler_name)
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return constructor
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samplers_data_alter = [
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sd_samplers_common.SamplerData('DDPM', build_constructor(sampler_name='ddpm', scheduler_name='normal'), ['ddpm'], {}),
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sd_samplers_common.SamplerData('DDPM Karras', build_constructor(sampler_name='ddpm', scheduler_name='karras'), ['ddpm_karras'], {}),
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sd_samplers_common.SamplerData('Euler A Turbo', build_constructor(sampler_name='euler_ancestral', scheduler_name='turbo'), ['euler_ancestral_turbo'], {}),
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sd_samplers_common.SamplerData('DPM++ 2M Turbo', build_constructor(sampler_name='dpmpp_2m', scheduler_name='turbo'), ['dpmpp_2m_turbo'], {}),
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sd_samplers_common.SamplerData('DPM++ 2M SDE Turbo', build_constructor(sampler_name='dpmpp_2m_sde', scheduler_name='turbo'), ['dpmpp_2m_sde_turbo'], {}),
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sd_samplers_common.SamplerData('LCM Karras', build_constructor(sampler_name='lcm', scheduler_name='karras'), ['lcm_karras'], {}),
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]
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