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CFG++ for gradient estimation sampler (#7809)

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chaObserv
2025-04-29 01:51:35 +08:00
committed by GitHub
parent 772b4c5945
commit c15909bb62
2 changed files with 30 additions and 6 deletions
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34
comfy/k_diffusion/sampling.py
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@@ -1345,28 +1345,52 @@ def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, cal
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
@torch.no_grad()
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):
"""Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
old_d = None
uncond_denoised = None
def post_cfg_function(args):
nonlocal uncond_denoised
uncond_denoised = args["uncond_denoised"]
return args["denoised"]
if cfg_pp:
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
d = to_d(x, sigmas[i], denoised)
if cfg_pp:
d = to_d(x, sigmas[i], uncond_denoised)
else:
d = to_d(x, sigmas[i], denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
dt = sigmas[i + 1] - sigmas[i]
if i == 0:
# Euler method
x = x + d * dt
if cfg_pp:
x = denoised + d * sigmas[i + 1]
else:
x = x + d * dt
else:
# Gradient estimation
d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
x = x + d_bar * dt
if cfg_pp:
d_bar = (ge_gamma - 1) * (d - old_d)
x = denoised + d * sigmas[i + 1] + d_bar * dt
else:
d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
x = x + d_bar * dt
old_d = d
return x
@torch.no_grad()
def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True)
@torch.no_grad()
def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, noise_scaler=None, max_stage=3):
"""