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From 36078b25801787f0a0f145143637f46d33d8c389 Mon Sep 17 00:00:00 2001 |
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From: Ashen <[email protected]> |
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Date: Fri, 7 Apr 2023 22:04:35 -0700 |
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Subject: [PATCH] karras v2 experimental |
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k_diffusion/sampling.py | 36 ++++++++++++++++++++++++++++++++++++ |
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1 file changed, 36 insertions(+) |
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@@ -605,3 +605,39 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No |
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x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d |
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old_denoised = denoised |
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return x |
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+ |
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+ |
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[email protected]_grad() |
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+def sample_dpmpp_2m_test(model, x, sigmas, extra_args=None, callback=None, disable=None): |
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+ """DPM-Solver++(2M).""" |
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+ extra_args = {} if extra_args is None else extra_args |
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+ s_in = x.new_ones([x.shape[0]]) |
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+ sigma_fn = lambda t: t.neg().exp() |
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+ t_fn = lambda sigma: sigma.log().neg() |
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+ old_denoised = None |
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+ |
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+ for i in trange(len(sigmas) - 1, disable=disable): |
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+ denoised = model(x, sigmas[i] * s_in, **extra_args) |
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+ if callback is not None: |
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+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
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+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) |
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+ h = t_next - t |
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+ |
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+ t_min = min(sigma_fn(t_next), sigma_fn(t)) |
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+ t_max = max(sigma_fn(t_next), sigma_fn(t)) |
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+ |
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+ if old_denoised is None or sigmas[i + 1] == 0: |
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+ x = (t_min / t_max) * x - (-h).expm1() * denoised |
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+ else: |
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+ h_last = t - t_fn(sigmas[i - 1]) |
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+ |
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+ h_min = min(h_last, h) |
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+ h_max = max(h_last, h) |
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+ r = h_max / h_min |
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+ |
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+ h_d = (h_max + h_min) / 2 |
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+ denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised |
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+ x = (t_min / t_max) * x - (-h_d).expm1() * denoised_d |
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+ |
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+ old_denoised = denoised |
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+ return x |
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\ No newline at end of file |
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-- |
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2.40.0 |
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