add new samplers
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@ -23,6 +23,8 @@ samplers_k_diffusion = [
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('Heun', 'sample_heun', ['k_heun']),
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('DPM2', 'sample_dpm_2', ['k_dpm_2']),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
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]
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samplers_data_k_diffusion = [
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@ -37,6 +39,8 @@ samplers = [
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SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
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]
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samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
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samplers_for_img2img.remove(samplers_for_img2img[6])
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samplers_for_img2img.remove(samplers_for_img2img[6])
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sampler_extra_params = {
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'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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@ -309,7 +313,12 @@ class KDiffusionSampler:
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x = x * sigmas[0]
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extra_params_kwargs = self.initialize(p)
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if 'sigma_min' in inspect.signature(self.func).parameters:
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if 'n' in inspect.signature(self.func).parameters:
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samples = self.func(self.model_wrap_cfg, x, sigma_min=self.model_wrap.sigmas[0].item(), sigma_max=self.model_wrap.sigmas[-1].item(), n=steps, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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return samples
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samples = self.func(self.model_wrap_cfg, x, sigma_min=self.model_wrap.sigmas[0].item(), sigma_max=self.model_wrap.sigmas[-1].item(), extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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return samples
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samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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return samples
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