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add karras scheduling variants
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@ -26,6 +26,17 @@ samplers_k_diffusion = [
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
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]
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if opts.show_karras_scheduler_variants:
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k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
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k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
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k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
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samplers_k_diffusion_ka = [
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('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
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('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
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('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
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]
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samplers_k_diffusion.extend(samplers_k_diffusion_ka)
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samplers_data_k_diffusion = [
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SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
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for label, funcname, aliases in samplers_k_diffusion
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@ -345,6 +356,8 @@ class KDiffusionSampler:
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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elif self.funcname.endswith('ka'):
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
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else:
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sigmas = self.model_wrap.get_sigmas(steps)
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x = x * sigmas[0]
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