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Enable CFG++ to DPM++ 2M SDE samplers #9820

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Enable CFG++ to DPM++ 2M SDE samplers #9820

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f9ca750
add cfg++ to dpmpp 2m sde samplers
Balladie Sep 12, 2025
3de7d3b
remove redundant op when cfg_pp=False
Balladie Sep 14, 2025
cae6be5
apply ruff
Balladie Sep 14, 2025
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53 changes: 49 additions & 4 deletions comfy/k_diffusion/sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -810,7 +810,7 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No


@torch.no_grad()
def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint', cfg_pp=False):
"""DPM-Solver++(2M) SDE."""
if len(sigmas) <= 1:
return x
Expand All @@ -830,6 +830,16 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl

old_denoised = None
h, h_last = None, 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)
Expand All @@ -846,28 +856,43 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl

alpha_t = sigmas[i + 1] * lambda_t.exp()

current_denoised = uncond_denoised if cfg_pp else denoised

x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised

if cfg_pp:
x = x + alpha_t * (-h_eta).exp().neg() * (current_denoised - denoised)

if old_denoised is not None:
r = h_last / h
if solver_type == 'heun':
x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)
x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (current_denoised - old_denoised)
elif solver_type == 'midpoint':
x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)
x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (current_denoised - old_denoised)

if eta > 0 and s_noise > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise

old_denoised = denoised
old_denoised = current_denoised
h_last = h
return x


@torch.no_grad()
def sample_dpmpp_2m_sde_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type, cfg_pp=True)


@torch.no_grad()
def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)


@torch.no_grad()
def sample_dpmpp_2m_sde_heun_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type, cfg_pp=True)


@torch.no_grad()
def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""DPM-Solver++(3M) SDE."""
Expand Down Expand Up @@ -940,6 +965,16 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)


@torch.no_grad()
def sample_dpmpp_2m_sde_heun_cfg_pp_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde_heun_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)


@torch.no_grad()
def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
if len(sigmas) <= 1:
Expand All @@ -950,6 +985,16 @@ def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=Non
return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)


@torch.no_grad()
def sample_dpmpp_2m_sde_cfg_pp_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)


@torch.no_grad()
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
if len(sigmas) <= 1:
Expand Down
5 changes: 3 additions & 2 deletions comfy/samplers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -729,8 +729,9 @@ def max_denoise(self, model_wrap, sigmas):

KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_cfg_pp", "dpmpp_2m_sde_cfg_pp_gpu",
"dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_2m_sde_heun_cfg_pp", "dpmpp_2m_sde_heun_cfg_pp_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu",
"ddpm", "lcm", "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
"gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"]

class KSAMPLER(Sampler):
Expand Down
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