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Make a few more samplers deterministic.

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comfyanonymous 2025-01-07 02:12:12 -05:00
parent d055325783
commit 4209edf48d
1 changed files with 6 additions and 4 deletions
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10
comfy/k_diffusion/sampling.py
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@ -475,7 +475,7 @@ class DPMSolver(nn.Module):
return x_3, eps_cache
def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
if not t_end > t_start and eta:
raise ValueError('eta must be 0 for reverse sampling')
@ -514,7 +514,7 @@ class DPMSolver(nn.Module):
return x
def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
if order not in {2, 3}:
raise ValueError('order should be 2 or 3')
forward = t_end > t_start
@ -894,7 +894,8 @@ def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
@ -914,7 +915,8 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None,
@torch.no_grad()
def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)