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From 36078b25801787f0a0f145143637f46d33d8c389 Mon Sep 17 00:00:00 2001
From: Ashen <[email protected]>
Date: Fri, 7 Apr 2023 22:04:35 -0700
Subject: [PATCH] karras v2 experimental
---
k_diffusion/sampling.py | 36 ++++++++++++++++++++++++++++++++++++
1 file changed, 36 insertions(+)
diff --git a/k_diffusion/sampling.py b/k_diffusion/sampling.py
index f050f88..4d5df2a 100644
--- a/k_diffusion/sampling.py
+++ b/k_diffusion/sampling.py
@@ -605,3 +605,39 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
old_denoised = denoised
return x
+
+
[email protected]_grad()
+def sample_dpmpp_2m_test(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ """DPM-Solver++(2M)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+ old_denoised = None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+ h = t_next - t
+
+ t_min = min(sigma_fn(t_next), sigma_fn(t))
+ t_max = max(sigma_fn(t_next), sigma_fn(t))
+
+ if old_denoised is None or sigmas[i + 1] == 0:
+ x = (t_min / t_max) * x - (-h).expm1() * denoised
+ else:
+ h_last = t - t_fn(sigmas[i - 1])
+
+ h_min = min(h_last, h)
+ h_max = max(h_last, h)
+ r = h_max / h_min
+
+ h_d = (h_max + h_min) / 2
+ denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
+ x = (t_min / t_max) * x - (-h_d).expm1() * denoised_d
+
+ old_denoised = denoised
+ return x
\ No newline at end of file
--
2.40.0
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