hallatore氏改良版DPM++ 2M Karras

彩度を少し犠牲にする代わりに滑らかな画像を出力できる

比較画像

$ ~~\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py

の一番下に次のコードをコピペする

code:DPM++ 2M Karras v2.py

@torch.no_grad()

def sample_dpmpp_2mV2(model, x, sigmas, extra_args=None, callback=None, disable=None):

"""DPM-Solver++(2M) V2."""

extra_args = {} if extra_args is None else extra_args

s_in = x.new_ones([x.shape0])

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, sigmasi * s_in, **extra_args)

if callback is not None:

callback({'x': x, 'i': i, 'sigma': sigmasi, 'sigma_hat': sigmasi, 'denoised': denoised})

t, t_next = t_fn(sigmasi), t_fn(sigmasi + 1)

h = t_next - t

if old_denoised is None or sigmasi + 1 == 0:

x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised

else:

h_last = t - t_fn(sigmasi - 1)

r = h_last / h

denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised

x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d

#test new sampler

sigma_progress = i / len(sigmas)

adjustment_factor = 1 + (0.15 * (sigma_progress * sigma_progress))

old_denoised = denoised * adjustment_factor

return x

hallatore氏改良版DPM++ 2M Karras#640a9da9e2dacc000080443bの0.15を0.20や0.25に編集して強さを変えられる(らしい)

$ ~~\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py

29行目の下に以下を追加する

code:DPM++ 2M Karras v2

('DPM++ 2M Karras v2', 'sample_dpmpp_2mV2', 'k_dpmpp_2m_ka', {'scheduler': 'karras'}),

https://gyazo.com/dd4171781db1d2d22419bc370c75c216