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Add inverse noise scaling function.
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@ -20,6 +20,9 @@ class EPS:
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noise += latent_image
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noise += latent_image
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return noise
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return noise
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def inverse_noise_scaling(self, sigma, latent):
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return latent
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class V_PREDICTION(EPS):
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class V_PREDICTION(EPS):
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def calculate_denoised(self, sigma, model_output, model_input):
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def calculate_denoised(self, sigma, model_output, model_input):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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@ -546,6 +546,7 @@ class KSAMPLER(Sampler):
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k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
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k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
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samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
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samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
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samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
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return samples
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return samples
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