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"fix" the sampler

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yoinked 2024-06-29 13:54:07 -07:00 committed by GitHub
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commit ebecee7f68
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1 changed files with 59 additions and 9 deletions
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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):
@ -946,9 +947,58 @@ 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):
def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
temp = [0]
@ -963,16 +1013,16 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No
for i in trange(len(sigmas) - 1, disable=disable):
sigma_hat = sigmas[i]
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x - denoised + temp[0], sigma_hat, denoised)
d = to_d(x, sigma_hat, temp[0])
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
x = denoised + d * sigmas[i + 1]
return x
@torch.no_grad()
def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@ -991,15 +1041,15 @@ def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None,
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x - denoised + temp[0], sigmas[i], denoised)
d = to_d(x, sigmas[i], temp[0])
# Euler method
dt = sigma_down - sigmas[i]
x = x + d * dt
x = denoised + d * sigma_down
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
return x
@torch.no_grad()
def sample_dpmpp_2s_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
def sample_dpmpp_2s_ancestral_pp_cfg(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""Ancestral sampling with DPM-Solver++(2S) second-order steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@ -1023,9 +1073,9 @@ def sample_dpmpp_2s_ancestral_pp(model, x, sigmas, extra_args=None, callback=Non
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
if sigma_down == 0:
# Euler method
d = to_d(x - denoised + temp[0], sigmas[i], denoised)
d = to_d(x, sigmas[i], temp[0])
dt = sigma_down - sigmas[i]
x = x + d * dt
x = denoised + d * sigma_down
else:
# DPM-Solver++(2S)
t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)