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Add DEIS order 3 sampler.

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Order 4 seems to give bad results.
This commit is contained in:
comfyanonymous
2024-06-26 22:40:05 -04:00
parent 175fe02522
commit 44947e7ad4
3 changed files with 173 additions and 1 deletions
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50
comfy/k_diffusion/sampling.py
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@@ -7,6 +7,7 @@ import torchsde
from tqdm.auto import trange, tqdm
from . import utils
from . import deis
import comfy.model_patcher
def append_zero(x):
@@ -946,6 +947,55 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
return x_next
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
#under Apache 2 license
@torch.no_grad()
def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
x_next = x
t_steps = sigmas
coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)
buffer_model = []
for i in trange(len(sigmas) - 1, disable=disable):
t_cur = sigmas[i]
t_next = sigmas[i + 1]
x_cur = x_next
denoised = model(x_cur, t_cur * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d_cur = (x_cur - denoised) / t_cur
order = min(max_order, i+1)
if t_next <= 0:
order = 1
if order == 1: # First Euler step.
x_next = x_cur + (t_next - t_cur) * d_cur
elif order == 2: # Use one history point.
coeff_cur, coeff_prev1 = coeff_list[i]
x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
elif order == 3: # Use two history points.
coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
elif order == 4: # Use three history points.
coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
if len(buffer_model) == max_order - 1:
for k in range(max_order - 2):
buffer_model[k] = buffer_model[k+1]
buffer_model[-1] = d_cur.detach()
else:
buffer_model.append(d_cur.detach())
return x_next
@torch.no_grad()
def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):