From d8cd0d6746bed12b54bbf2f31e25dbb22d0242f1 Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Wed, 28 Jan 2026 15:37:31 +0100 Subject: [PATCH 01/19] Update README with new image and project details Updated image and modified project description for clarity. --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 1345df6..9b27da5 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,10 @@ # ComfyUI-Wan-TimeToMove A native comfyui port of kijai's WanVideo-Wrapper TimeToMove -WanVideo TTM nodes image +WanTTM nodes (updated) https://github.com/user-attachments/assets/551eac0d-c5fe-49a8-b1a2-3884d0ece746 -**This node is still WIP.** For now only the [lcm sampler](https://github.com/GiusTex/ComfyUI-Wan-TimeToMove/blob/main/k_diffusion/sampling.py#L1020) supports TimeToMove, and the generated frames are a bit dark (this color difference is seen especially when a first frame is passed). - The second sampler can be found here: `https://github.com/GiusTex/ComfyUI-MoreEfficientSamplers` but you can change it, and the scheduler used is this: `https://github.com/BigStationW/flowmatch_scheduler-comfyui`, useful when you use lightx loras. ### Download From 80c0ab7714768795d15a4e18e9ffd4370ce566dd Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Wed, 28 Jan 2026 15:38:47 +0100 Subject: [PATCH 02/19] Delete k_diffusion directory --- k_diffusion/sampling.py | 1811 --------------------------------------- 1 file changed, 1811 deletions(-) delete mode 100644 k_diffusion/sampling.py diff --git a/k_diffusion/sampling.py b/k_diffusion/sampling.py deleted file mode 100644 index 3019949..0000000 --- a/k_diffusion/sampling.py +++ /dev/null @@ -1,1811 +0,0 @@ -import math -from functools import partial - -from scipy import integrate -import torch -from torch import nn -import torchsde -from tqdm.auto import trange, tqdm - -from comfy.k_diffusion import utils -from comfy.k_diffusion import deis -from comfy.k_diffusion import sa_solver -import comfy.model_patcher -import comfy.model_sampling - -def append_zero(x): - return torch.cat([x, x.new_zeros([1])]) - - -def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): - """Constructs the noise schedule of Karras et al. (2022).""" - ramp = torch.linspace(0, 1, n, device=device) - min_inv_rho = sigma_min ** (1 / rho) - max_inv_rho = sigma_max ** (1 / rho) - sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho - return append_zero(sigmas).to(device) - - -def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): - """Constructs an exponential noise schedule.""" - sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() - return append_zero(sigmas) - - -def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): - """Constructs an polynomial in log sigma noise schedule.""" - ramp = torch.linspace(1, 0, n, device=device) ** rho - sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) - return append_zero(sigmas) - - -def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): - """Constructs a continuous VP noise schedule.""" - t = torch.linspace(1, eps_s, n, device=device) - sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t)) - return append_zero(sigmas) - - -def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'): - """Constructs the noise schedule proposed by Tiankai et al. (2024). """ - epsilon = 1e-5 # avoid log(0) - x = torch.linspace(0, 1, n, device=device) - clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max) - lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon) - sigmas = clamp(torch.exp(lmb)) - return sigmas - - - -def to_d(x, sigma, denoised): - """Converts a denoiser output to a Karras ODE derivative.""" - return (x - denoised) / utils.append_dims(sigma, x.ndim) - - -def get_ancestral_step(sigma_from, sigma_to, eta=1.): - """Calculates the noise level (sigma_down) to step down to and the amount - of noise to add (sigma_up) when doing an ancestral sampling step.""" - if not eta: - return sigma_to, 0. - sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) - sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 - return sigma_down, sigma_up - - -def default_noise_sampler(x, seed=None): - if seed is not None: - generator = torch.Generator(device=x.device) - generator.manual_seed(seed) - else: - generator = None - - return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) - - -class BatchedBrownianTree: - """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" - - def __init__(self, x, t0, t1, seed=None, **kwargs): - self.cpu_tree = kwargs.pop("cpu", True) - t0, t1, self.sign = self.sort(t0, t1) - w0 = kwargs.pop('w0', None) - if w0 is None: - w0 = torch.zeros_like(x) - self.batched = False - if seed is None: - seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),) - elif isinstance(seed, (tuple, list)): - if len(seed) != x.shape[0]: - raise ValueError("Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.") - self.batched = True - w0 = w0[0] - else: - seed = (seed,) - if self.cpu_tree: - t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu() - self.trees = tuple(torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed) - - @staticmethod - def sort(a, b): - return (a, b, 1) if a < b else (b, a, -1) - - def __call__(self, t0, t1): - t0, t1, sign = self.sort(t0, t1) - device, dtype = t0.device, t0.dtype - if self.cpu_tree: - t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float() - w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign) - return w if self.batched else w[0] - - -class BrownianTreeNoiseSampler: - """A noise sampler backed by a torchsde.BrownianTree. - - Args: - x (Tensor): The tensor whose shape, device and dtype to use to generate - random samples. - sigma_min (float): The low end of the valid interval. - sigma_max (float): The high end of the valid interval. - seed (int or List[int]): The random seed. If a list of seeds is - supplied instead of a single integer, then the noise sampler will - use one BrownianTree per batch item, each with its own seed. - transform (callable): A function that maps sigma to the sampler's - internal timestep. - """ - - def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): - self.transform = transform - t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) - self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) - - def __call__(self, sigma, sigma_next): - t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) - return self.tree(t0, t1) / (t1 - t0).abs().sqrt() - - -def sigma_to_half_log_snr(sigma, model_sampling): - """Convert sigma to half-logSNR log(alpha_t / sigma_t).""" - if isinstance(model_sampling, comfy.model_sampling.CONST): - # log((1 - t) / t) = log((1 - sigma) / sigma) - return sigma.logit().neg() - return sigma.log().neg() - - -def half_log_snr_to_sigma(half_log_snr, model_sampling): - """Convert half-logSNR log(alpha_t / sigma_t) to sigma.""" - if isinstance(model_sampling, comfy.model_sampling.CONST): - # 1 / (1 + exp(half_log_snr)) - return half_log_snr.neg().sigmoid() - return half_log_snr.neg().exp() - - -def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4): - """Adjust the first sigma to avoid invalid logSNR.""" - if len(sigmas) <= 1: - return sigmas - if isinstance(model_sampling, comfy.model_sampling.CONST): - if sigmas[0] >= 1: - sigmas = sigmas.clone() - sigmas[0] = model_sampling.percent_to_sigma(percent_offset) - return sigmas - - -def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor: - """Compute the result of h*phi_1(h) in exponential integrator methods.""" - return torch.expm1(h) - - -def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor: - """Compute the result of h*phi_2(h) in exponential integrator methods.""" - return (torch.expm1(h) - h) / h - - -@torch.no_grad() -def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - if s_churn > 0: - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - 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 - return x - - -@torch.no_grad() -def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): - return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) - """Ancestral sampling with Euler method steps.""" - extra_args = {} if extra_args is None else extra_args - 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) - 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}) - - if sigma_down == 0: - x = denoised - else: - d = to_d(x, sigmas[i], denoised) - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - -@torch.no_grad() -def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None): - """Ancestral sampling with Euler method steps.""" - extra_args = {} if extra_args is None else extra_args - 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) - # 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}) - - if sigmas[i + 1] == 0: - x = denoised - else: - downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta - sigma_down = sigmas[i + 1] * downstep_ratio - alpha_ip1 = 1 - sigmas[i + 1] - alpha_down = 1 - sigma_down - renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5 - # Euler method - sigma_down_i_ratio = sigma_down / sigmas[i] - x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised - if eta > 0: - x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff - return x - -@torch.no_grad() -def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - if s_churn > 0: - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - 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 - if sigmas[i + 1] == 0: - # Euler method - x = x + d * dt - else: - # Heun's method - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - d_prime = (d + d_2) / 2 - x = x + d_prime * dt - return x - - -@torch.no_grad() -def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - if s_churn > 0: - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Euler method - dt = sigmas[i + 1] - sigma_hat - x = x + d * dt - else: - # DPM-Solver-2 - sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() - dt_1 = sigma_mid - sigma_hat - dt_2 = sigmas[i + 1] - sigma_hat - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - return x - - -@torch.no_grad() -def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): - return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) - - """Ancestral sampling with DPM-Solver second-order steps.""" - extra_args = {} if extra_args is None else extra_args - 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) - 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, sigmas[i], denoised) - if sigma_down == 0: - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver-2 - sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() - dt_1 = sigma_mid - sigmas[i] - dt_2 = sigma_down - sigmas[i] - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - -@torch.no_grad() -def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """Ancestral sampling with DPM-Solver second-order steps.""" - extra_args = {} if extra_args is None else extra_args - 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) - downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta - sigma_down = sigmas[i+1] * downstep_ratio - alpha_ip1 = 1 - sigmas[i+1] - alpha_down = 1 - sigma_down - renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 - - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x, sigmas[i], denoised) - if sigma_down == 0: - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver-2 - sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() - dt_1 = sigma_mid - sigmas[i] - dt_2 = sigma_down - sigmas[i] - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff - return x - -def linear_multistep_coeff(order, t, i, j): - if order - 1 > i: - raise ValueError(f'Order {order} too high for step {i}') - def fn(tau): - prod = 1. - for k in range(order): - if j == k: - continue - prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) - return prod - return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] - - -@torch.no_grad() -def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - sigmas_cpu = sigmas.detach().cpu().numpy() - ds = [] - 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) - ds.append(d) - if len(ds) > order: - ds.pop(0) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - cur_order = min(i + 1, order) - coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] - x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) - return x - - -class PIDStepSizeController: - """A PID controller for ODE adaptive step size control.""" - def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): - self.h = h - self.b1 = (pcoeff + icoeff + dcoeff) / order - self.b2 = -(pcoeff + 2 * dcoeff) / order - self.b3 = dcoeff / order - self.accept_safety = accept_safety - self.eps = eps - self.errs = [] - - def limiter(self, x): - return 1 + math.atan(x - 1) - - def propose_step(self, error): - inv_error = 1 / (float(error) + self.eps) - if not self.errs: - self.errs = [inv_error, inv_error, inv_error] - self.errs[0] = inv_error - factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 - factor = self.limiter(factor) - accept = factor >= self.accept_safety - if accept: - self.errs[2] = self.errs[1] - self.errs[1] = self.errs[0] - self.h *= factor - return accept - - -class DPMSolver(nn.Module): - """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" - - def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): - super().__init__() - self.model = model - self.extra_args = {} if extra_args is None else extra_args - self.eps_callback = eps_callback - self.info_callback = info_callback - - def t(self, sigma): - return -sigma.log() - - def sigma(self, t): - return t.neg().exp() - - def eps(self, eps_cache, key, x, t, *args, **kwargs): - if key in eps_cache: - return eps_cache[key], eps_cache - sigma = self.sigma(t) * x.new_ones([x.shape[0]]) - eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) - if self.eps_callback is not None: - self.eps_callback() - return eps, {key: eps, **eps_cache} - - def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): - eps_cache = {} if eps_cache is None else eps_cache - h = t_next - t - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - x_1 = x - self.sigma(t_next) * h.expm1() * eps - return x_1, eps_cache - - def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): - eps_cache = {} if eps_cache is None else eps_cache - h = t_next - t - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - s1 = t + r1 * h - u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps - eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) - x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) - return x_2, eps_cache - - def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): - eps_cache = {} if eps_cache is None else eps_cache - h = t_next - t - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - s1 = t + r1 * h - s2 = t + r2 * h - u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps - eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) - u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) - eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) - x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) - 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, 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') - - m = math.floor(nfe / 3) + 1 - ts = torch.linspace(t_start, t_end, m + 1, device=x.device) - - if nfe % 3 == 0: - orders = [3] * (m - 2) + [2, 1] - else: - orders = [3] * (m - 1) + [nfe % 3] - - for i in range(len(orders)): - eps_cache = {} - t, t_next = ts[i], ts[i + 1] - if eta: - sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) - t_next_ = torch.minimum(t_end, self.t(sd)) - su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 - else: - t_next_, su = t_next, 0. - - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - denoised = x - self.sigma(t) * eps - if self.info_callback is not None: - self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) - - if orders[i] == 1: - x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) - elif orders[i] == 2: - x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) - else: - x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) - - x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) - - 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, 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 - if not forward and eta: - raise ValueError('eta must be 0 for reverse sampling') - h_init = abs(h_init) * (1 if forward else -1) - atol = torch.tensor(atol) - rtol = torch.tensor(rtol) - s = t_start - x_prev = x - accept = True - pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) - info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} - - while s < t_end - 1e-5 if forward else s > t_end + 1e-5: - eps_cache = {} - t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) - if eta: - sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) - t_ = torch.minimum(t_end, self.t(sd)) - su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 - else: - t_, su = t, 0. - - eps, eps_cache = self.eps(eps_cache, 'eps', x, s) - denoised = x - self.sigma(s) * eps - - if order == 2: - x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) - x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) - else: - x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) - x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) - delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) - error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 - accept = pid.propose_step(error) - if accept: - x_prev = x_low - x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) - s = t - info['n_accept'] += 1 - else: - info['n_reject'] += 1 - info['nfe'] += order - info['steps'] += 1 - - if self.info_callback is not None: - self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) - - return x, info - - -@torch.no_grad() -def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): - """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" - if sigma_min <= 0 or sigma_max <= 0: - raise ValueError('sigma_min and sigma_max must not be 0') - with tqdm(total=n, disable=disable) as pbar: - dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) - if callback is not None: - dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) - return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) - - -@torch.no_grad() -def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, 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, return_info=False): - """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" - if sigma_min <= 0 or sigma_max <= 0: - raise ValueError('sigma_min and sigma_max must not be 0') - with tqdm(disable=disable) as pbar: - dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) - if callback is not None: - dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) - x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) - if return_info: - return x, info - return x - - -@torch.no_grad() -def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): - return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) - - """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" - extra_args = {} if extra_args is None else extra_args - 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]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - 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}) - if sigma_down == 0: - # Euler method - d = to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver++(2S) - t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) - r = 1 / 2 - h = t_next - t - s = t + r * h - x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised - denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 - # Noise addition - 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_RF(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 - 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]]) - sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1 - lambda_fn = lambda sigma: ((1-sigma)/sigma).log() - - # logged_x = x.unsqueeze(0) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta - sigma_down = sigmas[i+1] * downstep_ratio - alpha_ip1 = 1 - sigmas[i+1] - alpha_down = 1 - sigma_down - renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 - # 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}) - if sigmas[i + 1] == 0: - # Euler method - d = to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver++(2S) - if sigmas[i] == 1.0: - sigma_s = 0.9999 - else: - t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down) - r = 1 / 2 - h = t_down - t_i - s = t_i + r * h - sigma_s = sigma_fn(s) - # sigma_s = sigmas[i+1] - sigma_s_i_ratio = sigma_s / sigmas[i] - u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised - D_i = model(u, sigma_s * s_in, **extra_args) - sigma_down_i_ratio = sigma_down / sigmas[i] - x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i - # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff) - # Noise addition - if sigmas[i + 1] > 0 and eta > 0: - x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff - # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0) - return x - - -@torch.no_grad() -def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): - """DPM-Solver++ (stochastic).""" - 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() - seed = extra_args.get("seed", None) - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - # DPM-Solver++ - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - lambda_s_1 = lambda_s + r * h - fac = 1 / (2 * r) - - sigma_s_1 = sigma_fn(lambda_s_1) - - alpha_s = sigmas[i] * lambda_s.exp() - alpha_s_1 = sigma_s_1 * lambda_s_1.exp() - alpha_t = sigmas[i + 1] * lambda_t.exp() - - # Step 1 - sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta) - lambda_s_1_ = sd.log().neg() - h_ = lambda_s_1_ - lambda_s - x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised - if eta > 0 and s_noise > 0: - x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su - denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) - - # Step 2 - sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta) - lambda_t_ = sd.log().neg() - h_ = lambda_t_ - lambda_s - denoised_d = (1 - fac) * denoised + fac * denoised_2 - x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d - if eta > 0 and s_noise > 0: - x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su - return x - - -@torch.no_grad() -def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): - """DPM-Solver++(2M).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - old_denoised = None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - if old_denoised is None or sigmas[i + 1] == 0: - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised - else: - h_last = t - t_fn(sigmas[i - 1]) - r = h_last / h - denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d - old_denoised = denoised - return x - - -@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'): - """DPM-Solver++(2M) SDE.""" - if len(sigmas) <= 1: - return x - - if solver_type not in {'heun', 'midpoint'}: - raise ValueError('solver_type must be \'heun\' or \'midpoint\'') - - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - old_denoised = None - h, h_last = None, None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - # DPM-Solver++(2M) SDE - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - - alpha_t = sigmas[i + 1] * lambda_t.exp() - - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * 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) - elif solver_type == 'midpoint': - x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (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 - h_last = h - return x - - -@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_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """DPM-Solver++(3M) SDE.""" - - if len(sigmas) <= 1: - return x - - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - denoised_1, denoised_2 = None, None - h, h_1, h_2 = None, None, None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - - alpha_t = sigmas[i + 1] * lambda_t.exp() - - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised - - if h_2 is not None: - # DPM-Solver++(3M) SDE - r0 = h_1 / h - r1 = h_2 / h - d1_0 = (denoised - denoised_1) / r0 - d1_1 = (denoised_1 - denoised_2) / r1 - d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) - d2 = (d1_0 - d1_1) / (r0 + r1) - phi_2 = h_eta.neg().expm1() / h_eta + 1 - phi_3 = phi_2 / h_eta - 0.5 - x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2 - elif h_1 is not None: - # DPM-Solver++(2M) SDE - r = h_1 / h - d = (denoised - denoised_1) / r - phi_2 = h_eta.neg().expm1() / h_eta + 1 - x = x + (alpha_t * phi_2) * d - - 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 - - denoised_1, denoised_2 = denoised, denoised_1 - h_1, h_2 = h, h_1 - return x - - -@torch.no_grad() -def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - 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_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_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(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: - 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(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_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): - 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_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) - - -def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): - alpha_cumprod = 1 / ((sigma * sigma) + 1) - alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) - alpha = (alpha_cumprod / alpha_cumprod_prev) - - mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) - if sigma_prev > 0: - mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) - return mu - -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 - 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) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) - if sigmas[i + 1] != 0: - x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) - return x - - -@torch.no_grad() -def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): - return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) - - -from ..utils import add_noise_at_step - -@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 - - ttm_reference_latents = extra_args.get("ttm_reference_latents") - ttm_start_step = extra_args.get("ttm_start_step") - ttm_end_step = extra_args.get("ttm_end_step") - latent_image = extra_args.get("latent_image") - motion_mask = extra_args.get("motion_mask") - - 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, (idx, _)) in zip(trange(len(sigmas) - 1, disable=disable), enumerate(sigmas[ttm_start_step:])): - - if ttm_reference_latents is not None and (idx + ttm_start_step) < ttm_end_step: - if idx + ttm_start_step + 1 < len(sigmas): - noise = x.to(ttm_reference_latents.device) - sigma = sigmas[idx + ttm_start_step + 1].to(ttm_reference_latents.device) - noisy_latents = add_noise_at_step(ttm_reference_latents, - noise, - sigma - ).to(latent_image.device, latent_image.dtype) - noise = latent_image * (1 - motion_mask) + noisy_latents * motion_mask - else: # If you are at last step - noise = latent_image * (1 - motion_mask) + ttm_reference_latents * motion_mask - x = noise.to(s_in.device) - - denoised = model(x, sigmas[i] * s_in, **extra_args) - - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - x = denoised - if sigmas[i + 1] > 0: - x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) - return x - - - -@torch.no_grad() -def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - s_end = sigmas[-1] - for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - eps = torch.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - 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 - if sigmas[i + 1] == s_end: - # Euler method - x = x + d * dt - elif sigmas[i + 2] == s_end: - - # Heun's method - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - - w = 2 * sigmas[0] - w2 = sigmas[i+1]/w - w1 = 1 - w2 - - d_prime = d * w1 + d_2 * w2 - - - x = x + d_prime * dt - - else: - # Heun++ - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - dt_2 = sigmas[i + 2] - sigmas[i + 1] - - x_3 = x_2 + d_2 * dt_2 - denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) - d_3 = to_d(x_3, sigmas[i + 2], denoised_3) - - w = 3 * sigmas[0] - w2 = sigmas[i + 1] / w - w3 = sigmas[i + 2] / w - w1 = 1 - w2 - w3 - - d_prime = w1 * d + w2 * d_2 + w3 * d_3 - x = x + d_prime * dt - return x - - -#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py -#under Apache 2 license -def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - - x_next = x - - 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: # Denoising step - x_next = denoised - elif order == 1: # First Euler step. - x_next = x_cur + (t_next - t_cur) * d_cur - elif order == 2: # Use one history point. - x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 - elif order == 3: # Use two history points. - x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 - elif order == 4: # Use three history points. - x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 - - 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 - else: - buffer_model.append(d_cur) - - return x_next - - -#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py -#under Apache 2 license -def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - - x_next = x - t_steps = sigmas - - 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: # Denoising step - x_next = denoised - elif order == 1: # First Euler step. - x_next = x_cur + (t_next - t_cur) * d_cur - elif order == 2: # Use one history point. - h_n = (t_next - t_cur) - h_n_1 = (t_cur - t_steps[i-1]) - coeff1 = (2 + (h_n / h_n_1)) / 2 - coeff2 = -(h_n / h_n_1) / 2 - x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) - elif order == 3: # Use two history points. - h_n = (t_next - t_cur) - h_n_1 = (t_cur - t_steps[i-1]) - h_n_2 = (t_steps[i-1] - t_steps[i-2]) - temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 - coeff1 = (2 + (h_n / h_n_1)) / 2 + temp - coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp - coeff3 = temp * h_n_1 / h_n_2 - x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) - elif order == 4: # Use three history points. - h_n = (t_next - t_cur) - h_n_1 = (t_cur - t_steps[i-1]) - h_n_2 = (t_steps[i-1] - t_steps[i-2]) - h_n_3 = (t_steps[i-2] - t_steps[i-3]) - temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 - temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ - * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) - coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 - coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 - coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 - coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 - x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * 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 - - -#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_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 (CFG++).""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - - model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - - uncond_denoised = None - - def post_cfg_function(args): - nonlocal uncond_denoised - uncond_denoised = args["uncond_denoised"] - return args["denoised"] - - 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) - - 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) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp() - alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp() - d = to_d(x, sigmas[i], alpha_s * uncond_denoised) # to noise - - # DDIM stochastic sampling - sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta) - sigma_down = alpha_t * sigma_down - - # Euler method - x = alpha_t * denoised + sigma_down * d - if eta > 0 and s_noise > 0: - x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - - -@torch.no_grad() -def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): - """Euler method steps (CFG++).""" - return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None) - - -@torch.no_grad() -def sample_dpmpp_2s_ancestral_cfg_pp(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 - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - - temp = [0] - def post_cfg_function(args): - temp[0] = args["uncond_denoised"] - return args["denoised"] - - 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) - - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - 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}) - if sigma_down == 0: - # Euler method - d = to_d(x, sigmas[i], temp[0]) - x = denoised + d * sigma_down - else: - # DPM-Solver++(2S) - t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) - # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird - r = 1 / 2 - h = t_next - t - s = t + r * h - x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised - denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) - x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2 - # Noise addition - 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_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): - """DPM-Solver++(2M).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - t_fn = lambda sigma: sigma.log().neg() - - old_uncond_denoised = None - uncond_denoised = None - def post_cfg_function(args): - nonlocal uncond_denoised - uncond_denoised = args["uncond_denoised"] - return args["denoised"] - - 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) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - if old_uncond_denoised is None or sigmas[i + 1] == 0: - denoised_mix = -torch.exp(-h) * uncond_denoised - else: - h_last = t - t_fn(sigmas[i - 1]) - r = h_last / h - denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised) - x = denoised + denoised_mix + torch.exp(-h) * x - old_uncond_denoised = uncond_denoised - return x - -@torch.no_grad() -def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False): - extra_args = {} if extra_args is None else extra_args - 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]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - phi1_fn = lambda t: torch.expm1(t) / t - phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t - - old_sigma_down = None - old_denoised = 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) - 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}) - if sigma_down == 0 or old_denoised is None: - # Euler method - if cfg_pp: - d = to_d(x, sigmas[i], uncond_denoised) - x = denoised + d * sigma_down - else: - d = to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # Second order multistep method in https://arxiv.org/pdf/2308.02157 - t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1]) - h = t_next - t - c2 = (t_prev - t_old) / h - - phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) - b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0) - b2 = torch.nan_to_num(phi2_val / c2, nan=0.0) - - if cfg_pp: - x = x + (denoised - uncond_denoised) - x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised) - else: - x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised) - - # Noise addition - if sigmas[i + 1] > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - - if cfg_pp: - old_denoised = uncond_denoised - else: - old_denoised = denoised - old_sigma_down = sigma_down - return x - -@torch.no_grad() -def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): - return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False) - -@torch.no_grad() -def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): - return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True) - -@torch.no_grad() -def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - 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=False) - -@torch.no_grad() -def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - 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., 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) - 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 sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - # Euler method - if cfg_pp: - x = denoised + d * sigmas[i + 1] - else: - x = x + d * dt - - if i >= 1: - # Gradient estimation - d_bar = (ge_gamma - 1) * (d - 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=1.0, noise_sampler=None, noise_scaler=None, max_stage=3): - """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169. - Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py. - """ - extra_args = {} if extra_args is None else extra_args - 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]]) - - def default_er_sde_noise_scaler(x): - return x * ((x ** 0.3).exp() + 10.0) - - noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler - num_integration_points = 200.0 - point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device) - - model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling) - er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t - - old_denoised = None - old_denoised_d = None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - stage_used = min(max_stage, i + 1) - if sigmas[i + 1] == 0: - x = denoised - else: - er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1] - alpha_s = sigmas[i] / er_lambda_s - alpha_t = sigmas[i + 1] / er_lambda_t - r_alpha = alpha_t / alpha_s - r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s) - - # Stage 1 Euler - x = r_alpha * r * x + alpha_t * (1 - r) * denoised - - if stage_used >= 2: - dt = er_lambda_t - er_lambda_s - lambda_step_size = -dt / num_integration_points - lambda_pos = er_lambda_t + point_indice * lambda_step_size - scaled_pos = noise_scaler(lambda_pos) - - # Stage 2 - s = torch.sum(1 / scaled_pos) * lambda_step_size - denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1]) - x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d - - if stage_used >= 3: - # Stage 3 - s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size - denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2) - x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u - old_denoised_d = denoised_d - - if s_noise > 0: - x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0) - old_denoised = denoised - return x - - -@torch.no_grad() -def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5): - """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2. - arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) - """ - extra_args = {} if extra_args is None else extra_args - 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]]) - inject_noise = eta > 0 and s_noise > 0 - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - fac = 1 / (2 * r) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - if sigmas[i + 1] == 0: - x = denoised - continue - - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - lambda_s_1 = torch.lerp(lambda_s, lambda_t, r) - sigma_s_1 = sigma_fn(lambda_s_1) - - alpha_s_1 = sigma_s_1 * lambda_s_1.exp() - alpha_t = sigmas[i + 1] * lambda_t.exp() - - # Step 1 - x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised - if inject_noise: - sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1) - x_2 = x_2 + sde_noise * sigma_s_1 * s_noise - denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) - - # Step 2 - denoised_d = torch.lerp(denoised, denoised_2, fac) - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d - if inject_noise: - segment_factor = (r - 1) * h * eta - sde_noise = sde_noise * segment_factor.exp() - sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1]) - x = x + sde_noise * sigmas[i + 1] * s_noise - return x - - -@torch.no_grad() -def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3): - """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3. - arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) - """ - extra_args = {} if extra_args is None else extra_args - 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]]) - inject_noise = eta > 0 and s_noise > 0 - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - if sigmas[i + 1] == 0: - x = denoised - continue - - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - lambda_s_1 = torch.lerp(lambda_s, lambda_t, r_1) - lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2) - sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2) - - alpha_s_1 = sigma_s_1 * lambda_s_1.exp() - alpha_s_2 = sigma_s_2 * lambda_s_2.exp() - alpha_t = sigmas[i + 1] * lambda_t.exp() - - # Step 1 - x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised - if inject_noise: - sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1) - x_2 = x_2 + sde_noise * sigma_s_1 * s_noise - denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) - - # Step 2 - a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta) - a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2 - x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2) - if inject_noise: - segment_factor = (r_1 - r_2) * h * eta - sde_noise = sde_noise * segment_factor.exp() - sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2) - x_3 = x_3 + sde_noise * sigma_s_2 * s_noise - denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args) - - # Step 3 - b3 = ei_h_phi_2(-h_eta) / r_2 - b1 = ei_h_phi_1(-h_eta) - b3 - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3) - if inject_noise: - segment_factor = (r_2 - 1) * h * eta - sde_noise = sde_noise * segment_factor.exp() - sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1]) - x = x + sde_noise * sigmas[i + 1] * s_noise - return x - - -@torch.no_grad() -def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False): - """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023).""" - if len(sigmas) <= 1: - return x - extra_args = {} if extra_args is None else extra_args - 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]]) - - model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling) - - if tau_func is None: - # Use default interval for stochastic sampling - start_sigma = model_sampling.percent_to_sigma(0.2) - end_sigma = model_sampling.percent_to_sigma(0.8) - tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0) - - max_used_order = max(predictor_order, corrector_order) - x_pred = x # x: current state, x_pred: predicted next state - - h = 0.0 - tau_t = 0.0 - noise = 0.0 - pred_list = [] - - # Lower order near the end to improve stability - lower_order_to_end = sigmas[-1].item() == 0 - - for i in trange(len(sigmas) - 1, disable=disable): - # Evaluation - denoised = model(x_pred, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) - pred_list.append(denoised) - pred_list = pred_list[-max_used_order:] - - predictor_order_used = min(predictor_order, len(pred_list)) - if i == 0 or (sigmas[i + 1] == 0 and not use_pece): - corrector_order_used = 0 - else: - corrector_order_used = min(corrector_order, len(pred_list)) - - if lower_order_to_end: - predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i) - corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i) - - # Corrector - if corrector_order_used == 0: - # Update by the predicted state - x = x_pred - else: - curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1] - b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( - sigmas[i], - curr_lambdas, - lambdas[i - 1], - lambdas[i], - tau_t, - simple_order_2, - is_corrector_step=True, - ) - pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...) - corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) - x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res - - if tau_t > 0 and s_noise > 0: - # The noise from the previous predictor step - x = x + noise - - if use_pece: - # Evaluate the corrected state - denoised = model(x, sigmas[i] * s_in, **extra_args) - pred_list[-1] = denoised - - # Predictor - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - tau_t = tau_func(sigmas[i + 1]) - curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1] - b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( - sigmas[i + 1], - curr_lambdas, - lambdas[i], - lambdas[i + 1], - tau_t, - simple_order_2, - is_corrector_step=False, - ) - pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...) - pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) - h = lambdas[i + 1] - lambdas[i] - x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res - - if tau_t > 0 and s_noise > 0: - noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise - x_pred = x_pred + noise - return x - - -@torch.no_grad() -def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False): - """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023).""" - return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2) From 572da7e6718535e38fef45a79b71f9143736980f Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Wed, 28 Jan 2026 16:01:12 +0100 Subject: [PATCH 03/19] Update README.md --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9b27da5..d6cf9ef 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,16 @@ # ComfyUI-Wan-TimeToMove -A native comfyui port of kijai's WanVideo-Wrapper TimeToMove +A native comfyui port of Kijai's WanVideo-Wrapper TimeToMove WanTTM nodes (updated) -https://github.com/user-attachments/assets/551eac0d-c5fe-49a8-b1a2-3884d0ece746 +VIDEO COMPARISON! (with first frame, video+mask guide, and output) + +### Nodes +The custom node contains 4 new nodes: +- `Encode WanVideo`: taken from wanvideo-wrapper, it encodes the reference video. +- `TTM Latent Add`: taken from wanvideo-wrapper, it embeds in the latent the reference to the driving video. +- `Timove To Move Guider`: this node adds the ttm latent to the latent noise before passing it to the sampling function. +- `CFG Float List Scheduler`: taken from wanvideo-wrapper, it creates a list of cfg values, and submits them step by step, making possible using different cfg values at different steps. The second sampler can be found here: `https://github.com/GiusTex/ComfyUI-MoreEfficientSamplers` but you can change it, and the scheduler used is this: `https://github.com/BigStationW/flowmatch_scheduler-comfyui`, useful when you use lightx loras. From ff27defc45950b164c1b82aa2e74b6c6b9f0130c Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:27:57 +0100 Subject: [PATCH 04/19] Add v2 and wip infos --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d6cf9ef..a0b2f09 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,13 @@ A native comfyui port of Kijai's WanVideo-Wrapper TimeToMove WanTTM nodes (updated) -VIDEO COMPARISON! (with first frame, video+mask guide, and output) +### v2: +- Solved color issue. + +### WIP +- Working to fix bug on longer sequences (around 49 frames seems to work, around 81 frames duplicates the subject) + +[VIDEO COMPARISON] ### Nodes The custom node contains 4 new nodes: From a4dccd9c6c461717a641a1aed411063ce23ab56f Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:29:54 +0100 Subject: [PATCH 05/19] Add TTMGuider class Refactor KSamplerX0Inpaint and KSAMPLER classes to integrate TTMGuider for improved sampling functionality. --- samplers.py | 301 +++++++++++++++++++++++++++++++++++----------------- 1 file changed, 206 insertions(+), 95 deletions(-) diff --git a/samplers.py b/samplers.py index 8db06df..7a3fe78 100644 --- a/samplers.py +++ b/samplers.py @@ -1,109 +1,220 @@ import torch -from comfy.samplers import Sampler -from comfy.extra_samplers import uni_pc - -from .k_diffusion import sampling as k_diffusion_sampling +import comfy +from comfy.model_patcher import ModelPatcher +from comfy.samplers import (sampling_function, process_conds, cast_to_load_options, + preprocess_conds_hooks, get_total_hook_groups_in_conds, + filter_registered_hooks_on_conds) +from .utils import add_noise_at_step -class KSamplerX0Inpaint: - def __init__(self, model, sigmas): - self.inner_model = model - self.sigmas = sigmas - # Add ttm_options to extra_args - def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None, - ttm_reference_latents=None, ttm_start_step=None, - ttm_end_step=None, latent_image=None, motion_mask=None): - - if denoise_mask is not None: - if "denoise_mask_function" in model_options: - denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) - latent_mask = 1. - denoise_mask - x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask - model_options["ttm_reference_latents"] = ttm_reference_latents - model_options["ttm_start_step"] = ttm_start_step - model_options["ttm_end_step"] = ttm_end_step - model_options["latent_image"] = latent_image - model_options["motion_mask"] = motion_mask - out = self.inner_model(x, sigma, model_options=model_options, seed=seed) - if denoise_mask is not None: - out = out * denoise_mask + self.latent_image * latent_mask - return out +class TTMGuider: + def __init__(self, model_patcher: ModelPatcher): + self.model_patcher = model_patcher + self.model_options = model_patcher.model_options + self.original_conds = {} + self.cfg = 1.0 + + def set_conds(self, positive, negative): + self.inner_set_conds({"positive": positive, "negative": negative}) + + def set_cfg(self, cfg): + self.cfg = cfg + def set_ttm_options(self, ttm_options): + self.ttm_reference_latents = ttm_options["ttm_reference_latents"] + self.ttm_start_step = ttm_options["ttm_start_step"] + self.ttm_end_step = ttm_options["ttm_end_step"] + self.latent_image = ttm_options["latent_image"] + self.motion_mask = ttm_options["motion_mask"] + self.start_sampler_step = ttm_options["start_sampler_step"] -class KSAMPLER(Sampler): - def __init__(self, sampler_function, extra_options={}, inpaint_options={}): - self.sampler_function = sampler_function - self.extra_options = extra_options - self.inpaint_options = inpaint_options + def inner_set_conds(self, conds): + for k in conds: + self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) - def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): - extra_args["denoise_mask"] = denoise_mask - model_k = KSamplerX0Inpaint(model_wrap, sigmas) - model_k.latent_image = latent_image - if self.inpaint_options.get("random", False): #TODO: Should this be the default? - generator = torch.manual_seed(extra_args.get("seed", 41) + 1) - model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) + def __call__(self, *args, **kwargs): + return self.outer_predict_noise(*args, **kwargs) + + def outer_predict_noise(self, x, timestep, model_options={}, seed=None): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self.predict_noise, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, self.model_options, is_model_options=True) + ).execute(x, timestep, model_options, seed) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + + sigmas = model_options["sigmas"] + start_sampler_step = model_options["start_sampler_step"] + skipped_sigmas = sigmas[start_sampler_step:] + # 4 < 5 + if len(skipped_sigmas) < len(sigmas): # sampler doesn't have start_step option + sigmas = skipped_sigmas + # 4 == 4 + elif len(skipped_sigmas) == len(sigmas): # sampler already has option + pass # we don't want another sigma less + steps = len(sigmas)-1 + + i = torch.argmin(torch.abs(sigmas - timestep)).item() + + # Cfg part taken from Kijai WanVideo-Wrapper + if isinstance(self.cfg, list): + if steps < len(self.cfg): + print(f"Received {len(self.cfg)} cfg values, but only {steps} steps. Slicing cfg list to match steps.") + self.cfg = self.cfg[:steps] + elif steps > len(self.cfg): + print(f"Received only {len(self.cfg)} cfg values, but {steps} steps. Extending cfg list to match steps.") + self.cfg.extend([self.cfg[-1]] * (steps - len(self.cfg))) + if i == 0: # Print cfg only at first step + print(f"Using per-step cfg list: {self.cfg}") else: - model_k.noise = noise - - noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) - - k_callback = None - total_steps = len(sigmas) - 1 - if callback is not None: - k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) + self.cfg = [self.cfg] * (steps + 1) - samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) - samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) - return samples - + #--------------------------------------------------------- + ttm_ref_latent = model_options["ttm_reference_latents"].to(x.device) + ttm_start_step = max(model_options["ttm_start_step"] - start_sampler_step, 0) + ttm_end_step = model_options["ttm_end_step"] - start_sampler_step + ttm_mask = model_options["motion_mask"].to(x.device) + # Time-to-move (TTM) + if i == 0: # First Step + if ttm_ref_latent is not None: + if ttm_start_step > steps: + raise ValueError("TTM start step is beyond the total number of steps") -def ksampler(sampler_name, ttm_options, extra_options={}, inpaint_options={}): - if sampler_name == "dpm_fast": - def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): - if len(sigmas) <= 1: - return noise + if ttm_end_step > ttm_start_step: + print("Using Time-to-move (TTM)") + print(f"TTM reference latents shape: {ttm_ref_latent.shape}") + print(f"TTM motion mask shape: {ttm_mask.shape}") + print(f"Applying TTM from step {ttm_start_step} to {ttm_end_step}") - sigma_min = sigmas[-1] - if sigma_min == 0: - sigma_min = sigmas[-2] - total_steps = len(sigmas) - 1 - return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) - sampler_function = dpm_fast_function - elif sampler_name == "dpm_adaptive": - def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): - if len(sigmas) <= 1: - return noise + sigma_next = sigmas[ttm_start_step] + x = add_noise_at_step(ttm_ref_latent, + x, + sigma_next.to(x.device) + ).to(x) + elif ttm_ref_latent is not None and (i + ttm_start_step) < ttm_end_step: # Following Steps if i > 0 + if i + ttm_start_step < len(sigmas): + sigma_next = sigmas[i + ttm_start_step] + noisy_latents = add_noise_at_step(ttm_ref_latent, + x, + sigma_next.to(x.device) + ).to(x) + x = x * (1 - ttm_mask) + noisy_latents * ttm_mask + else: + x = x * (1 - ttm_mask) + ttm_ref_latent * ttm_mask + #--------------------------------------------------------- + return sampling_function(self.inner_model, x, timestep, + self.conds.get("negative", None), + self.conds.get("positive", None), + self.cfg[i], + model_options=model_options, seed=seed) - sigma_min = sigmas[-1] - if sigma_min == 0: - sigma_min = sigmas[-2] - return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) - sampler_function = dpm_adaptive_function - elif sampler_name == "lcm": - def lcm_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): - extra_args["ttm_reference_latents"] = ttm_options["ttm_reference_latents"] - extra_args["ttm_start_step"] = ttm_options["ttm_start_step"] - extra_args["ttm_end_step"] = ttm_options["ttm_end_step"] - extra_args["latent_image"] = ttm_options["latent_image"] - extra_args["motion_mask"] = ttm_options["motion_mask"] - return k_diffusion_sampling.sample_lcm(model, noise, sigmas, extra_args=extra_args, callback=callback, disable=disable, **extra_options) - sampler_function = lcm_function - else: - sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) + def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None): + if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = self.inner_model.process_latent_in(latent_image) - return KSAMPLER(sampler_function, extra_options, inpaint_options) + self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed, latent_shapes=latent_shapes) + extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options) + extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas + extra_args = {"model_options": extra_model_options, "seed": seed} + + # Pass ttm options to KSAMPLER.sample + extra_args["model_options"]["ttm_reference_latents"] = self.ttm_reference_latents + extra_args["model_options"]["ttm_start_step"] = self.ttm_start_step + extra_args["model_options"]["ttm_end_step"] = self.ttm_end_step + extra_args["model_options"]["latent_image"] = self.latent_image + extra_args["model_options"]["motion_mask"] = self.motion_mask + extra_args["model_options"]["start_sampler_step"] = self.start_sampler_step + extra_args["model_options"]["sigmas"] = sigmas -def sampler_object(name, ttm_options): - if name == "uni_pc": - sampler = KSAMPLER(uni_pc.sample_unipc) - elif name == "uni_pc_bh2": - sampler = KSAMPLER(uni_pc.sample_unipc_bh2) - elif name == "ddim": - sampler = ksampler("euler", inpaint_options={"random": True}) - elif name == "lcm": - sampler = ksampler(name, ttm_options) - else: - sampler = ksampler(name) - return sampler + executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( + sampler.sample, + sampler, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, extra_args["model_options"], is_model_options=True) + ) + + # run steps and get final samples + samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) + + return self.inner_model.process_latent_out(samples.to(torch.float32)) + + def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None): + self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) + device = self.model_patcher.load_device + + noise = noise.to(device) + latent_image = latent_image.to(device) + sigmas = sigmas.to(device) + cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) + + try: + self.model_patcher.pre_run() + output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) + finally: + self.model_patcher.cleanup() + + comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) + del self.inner_model + del self.loaded_models + return output + + def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + if sigmas.shape[-1] == 0: + return latent_image + + if latent_image.is_nested: + latent_image, latent_shapes = comfy.utils.pack_latents(latent_image.unbind()) + noise, _ = comfy.utils.pack_latents(noise.unbind()) + else: + latent_shapes = [latent_image.shape] + + if denoise_mask is not None: + if denoise_mask.is_nested: + denoise_masks = denoise_mask.unbind() + denoise_masks = denoise_masks[:len(latent_shapes)] + else: + denoise_masks = [denoise_mask] + + for i in range(len(denoise_masks), len(latent_shapes)): + denoise_masks.append(torch.ones(latent_shapes[i])) + + for i in range(len(denoise_masks)): + denoise_masks[i] = comfy.sampler_helpers.prepare_mask(denoise_masks[i], latent_shapes[i], self.model_patcher.load_device) + + if len(denoise_masks) > 1: + denoise_mask, _ = comfy.utils.pack_latents(denoise_masks) + else: + denoise_mask = denoise_masks[0] + + self.conds = {} + for k in self.original_conds: + self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) + preprocess_conds_hooks(self.conds) + + try: + orig_model_options = self.model_options + self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options) + # if one hook type (or just None), then don't bother caching weights for hooks (will never change after first step) + orig_hook_mode = self.model_patcher.hook_mode + if get_total_hook_groups_in_conds(self.conds) <= 1: + self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram + comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options) + filter_registered_hooks_on_conds(self.conds, self.model_options) + executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( + self.outer_sample, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True) + ) + output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) + finally: + cast_to_load_options(self.model_options, device=self.model_patcher.offload_device) + self.model_options = orig_model_options + self.model_patcher.hook_mode = orig_hook_mode + self.model_patcher.restore_hook_patches() + + del self.conds + + if len(latent_shapes) > 1: + output = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(output, latent_shapes)) + return output From 0627443d8b67fded19cd4864a3a46c178f23a6ac Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:31:22 +0100 Subject: [PATCH 06/19] Update nodes with new guider Removed dedicated sampler as it is not necessary. --- nodes.py | 302 +++++++++++++++---------------------------------------- 1 file changed, 81 insertions(+), 221 deletions(-) diff --git a/nodes.py b/nodes.py index ab9df6a..53c47ac 100644 --- a/nodes.py +++ b/nodes.py @@ -1,26 +1,10 @@ -import torch -import logging -from comfy_api.latest import io -from comfy.utils import PROGRESS_BAR_ENABLED import torch.nn.functional as F -import latent_preview -import comfy -from nodes import VAEDecodeTiled, PreviewImage, VAEDecode -from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise -from comfy.samplers import SAMPLER_NAMES -from PIL import Image -from .utils import (pil2tensor, warning, set_preview_method, sample_custom_ultra, - global_preview_method, store_ksampler_results, globals_cleanup, - add_noise_at_step, add_noise_to_reference_video) -from .samplers import sampler_object - - -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -log = logging.getLogger(__name__) +from .samplers import TTMGuider +from .utils import add_noise_to_reference_video # Copied from ComfyUI Wanvideo Wrapper -class WanVideoEncode: +class EncodeWanVideo: @classmethod def INPUT_TYPES(s): return {"required": { @@ -60,21 +44,21 @@ class WanVideoEncode: if latent_strength != 1.0: latents *= latent_strength - - log.info(f"WanVideo Encode: Encoded latents shape {latents.shape}") + + print(f"WanVideo Encode: Encoded latents shape {latents.shape}") return ({"samples": latents, "noise_mask": mask},) - + # Copied from ComfyUI Wanvideo Wrapper -class AddTTMLatent: +class TTMLatentAdd: @classmethod def INPUT_TYPES(s): return {"required": { "latent": ("LATENT", {"tooltip": "wanvideo latent"}), "reference_latents": ("LATENT", {"tooltip": "Reference image to encode"}), - "start_step": ("INT", {"default": 0, "min": -1, "max": 1000, "step": 1, "tooltip": "Start step for whole denoising process"}), - "end_step": ("INT", {"default": 2, "min": 1, "max": 1000, "step": 1, "tooltip": "The step to stop applying TTM"}), + "ttm_start_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Start step to apply TTM latent guide"}), + "ttm_end_step": ("INT", {"default": 3, "min": 1, "max": 1000, "step": 1, "tooltip": "The step to stop applying TTM"}), "ref_masks": ("MASK", {"tooltip": "Reference mask to encode"}), } } @@ -84,9 +68,10 @@ class AddTTMLatent: FUNCTION = "add" CATEGORY = "Wan22 TimeToMove" - def add(self, latent, reference_latents, start_step, end_step, ref_masks): - if end_step < max(0, start_step): - raise ValueError(f"`end_step` ({end_step}) must be >= `start_step` ({start_step}).") + def add(self, latent, reference_latents, ttm_start_step, ttm_end_step, ref_masks): + + if ttm_end_step < max(0, ttm_start_step): + raise ValueError(f"`ttm_end_step` ({ttm_end_step}) must be >= `ttm_start_step` ({ttm_start_step}).") mask_sampled = ref_masks[::4] mask_sampled = mask_sampled.unsqueeze(1).unsqueeze(0) # [1, T, 1, H, W] @@ -106,222 +91,97 @@ class AddTTMLatent: latent["ttm_reference_latents"] = reference_latents["samples"].squeeze(0) # [16, T, H, W] latent["ttm_mask"] = mask_latent.squeeze(0).movedim(1, 0) # [1, T, H, W] - latent["ttm_start_step"] = start_step - latent["ttm_end_step"] = end_step - + latent["ttm_start_step"] = ttm_start_step + latent["ttm_end_step"] = ttm_end_step + return (latent,) -class TTMKSamplerSelect(io.ComfyNode): +class TimeToMoveGuider: @classmethod - def define_schema(cls): - return io.Schema( - node_id="TTMKSamplerSelect", - category="Wan Animate End Reference", - inputs=[ - io.Combo.Input("sampler_name", options=SAMPLER_NAMES, default="lcm"), - io.Latent.Input("latent"), - ], - outputs=[ - io.Sampler.Output(), - ] - ) + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL", ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "Works with a list of floats too (one cfg float per step)"}), + "latent": ("LATENT", {"tooltip": "You can connect here the latent from TTM Latent Add, to pass reference video and ttm options"}), + "start_sampler_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Start step of the whole sampling process. It will automatically skip the selected number of sigmas (starting from the first ones); if the sampler has a start_step option, set the same value here"}), + }, + } + + RETURN_TYPES = ("GUIDER",) + RETURN_NAMES = ("guider",) + FUNCTION = "guide" + CATEGORY = "Wan22 TimeToMove" + + def guide(cls, model, positive, negative, cfg, latent, start_sampler_step): + guider = TTMGuider(model) + guider.set_conds(positive, negative) + guider.set_cfg(cfg) - @classmethod - def execute(cls, sampler_name, latent) -> io.NodeOutput: ttm_options = {} ttm_options["ttm_reference_latents"] = latent.get("ttm_reference_latents", None) ttm_options["ttm_start_step"] = latent["ttm_start_step"] ttm_options["ttm_end_step"] = latent["ttm_end_step"] ttm_options["latent_image"] = latent["samples"] ttm_options["motion_mask"] = latent["ttm_mask"] + ttm_options["start_sampler_step"] = start_sampler_step + guider.set_ttm_options(ttm_options) - sampler = sampler_object(sampler_name, ttm_options) - return io.NodeOutput(sampler) - - get_sampler = execute + return (guider,) -class WanVideoSamplerCustomUltraAdvancedEfficient: - # Image Preview code taken from jags111's efficiency-nodes (TSC_KSampler) - empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0))) - +# Taken from kijai WanVideo-Wrapper +class CFGFloatListScheduler: @classmethod def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "add_noise": ("BOOLEAN", {"default": True}), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "sampler": ("SAMPLER", ), - "sigmas": ("SIGMAS", ), - "latent": ("LATENT", ), - "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), - "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), - "return_with_leftover_noise": ("BOOLEAN", {"default": False}), - "preview_method": (["auto", "latent2rgb", "taesd", "vae_decoded_only", "none"],), - "vae_decode": (["true", "true (tiled)", "false"],), - }, - "optional": { - "optional_vae": ("VAE",), - }, - "hidden": { - "prompt": "PROMPT", - "extra_pnginfo": "EXTRA_PNGINFO", - "my_unique_id": "UNIQUE_ID", - }, - } + return {"required": { + "steps": ("INT", {"default": 30, "min": 2, "max": 1000, "step": 1, "tooltip": "Number of steps to schedule cfg for"} ), + "cfg_scale_start": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.01, "round": 0.01, "tooltip": "CFG scale to use for the steps"}), + "cfg_scale_end": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.01, "round": 0.01, "tooltip": "CFG scale to use for the steps"}), + "interpolation": (["linear", "ease_in", "ease_out"], {"default": "linear", "tooltip": "Interpolation method to use for the cfg scale"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.01,"tooltip": "Start percent of the steps to apply cfg"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.01,"tooltip": "End percent of the steps to apply cfg"}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + }, + } - RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "SAMPLER", "SIGMAS", "LATENT","LATENT", "IMAGE", "VAE",) - RETURN_NAMES = ("model", "positive", "negative", "sampler", "sigmas", "output", "denoised_output", "image", "vae", ) - FUNCTION = "sample" + RETURN_TYPES = ("FLOAT", ) + RETURN_NAMES = ("float_list",) + FUNCTION = "process" CATEGORY = "Wan22 TimeToMove" + DESCRIPTION = "Helper node to generate a list of floats that can be used to schedule cfg scale for the steps, outside the set range cfg is set to 1.0. Taken from Kijai WanVideo-Wrapper" - def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent, start_at_step, end_at_step, return_with_leftover_noise, preview_method, vae_decode, optional_vae=(None,), prompt=None, extra_pnginfo=None, my_unique_id=None): - latent_image = latent["samples"] - latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) - latent["samples"] = latent_image - - # Rename the vae variable - vae = optional_vae - # If vae is not connected, disable vae decoding - if vae == (None,) and vae_decode != "false": - print(f"{warning('Sampler Custom Ultra Advanced Warning:')} No vae input detected, proceeding as if vae_decode was false.\n") - vae_decode = "false" - - # ------------------------------------------------------------------------------------------------------ - def vae_decode_latent(vae, out, vae_decode): - return VAEDecodeTiled().decode(vae,out,320)[0] if "tiled" in vae_decode else VAEDecode().decode(vae,out)[0] - # --------------------------------------------------------------------------------------------------------------- + def process(self, steps, cfg_scale_start, cfg_scale_end, interpolation, start_percent, end_percent, unique_id): - noise_mask = None - if "noise_mask" in latent: - noise_mask = latent["noise_mask"] - - def process_latents(): - x0_output = {} - # Initialize output variables - out = out_denoised = images = preview = previous_preview_method = None + # Create a list of floats for the cfg schedule + cfg_list = [1.0] * steps + start_idx = min(int(steps * start_percent), steps - 1) + end_idx = min(int(steps * end_percent), steps - 1) - if not add_noise: - noise = Noise_EmptyNoise().generate_noise(latent) + for i in range(start_idx, end_idx + 1): + if i >= steps: + break + + if end_idx == start_idx: + t = 0 else: - noise = Noise_RandomNoise(noise_seed).generate_noise(latent) - - #Time-to-move (TTM) - ttm_start_step = 0 - ttm_reference_latents = latent.get("ttm_reference_latents", None) - if ttm_reference_latents is not None: - motion_mask = latent["ttm_mask"].to(latent_image.device, latent_image.dtype) - ttm_start_step = max(latent["ttm_start_step"] - start_at_step, 0) - ttm_end_step = latent["ttm_end_step"] - start_at_step - - if ttm_start_step > end_at_step: - raise ValueError("TTM start step is beyond the total number of steps") - - sigma = sigmas[ttm_start_step] + t = (i - start_idx) / (end_idx - start_idx) - if ttm_end_step > ttm_start_step: - log.info("Using Time-to-move (TTM)") - log.info(f"TTM reference latents shape: {ttm_reference_latents.shape}") - log.info(f"TTM motion mask shape: {motion_mask.shape}") - log.info(f"Applying TTM from step {ttm_start_step} to {ttm_end_step}") + if interpolation == "linear": + factor = t + elif interpolation == "ease_in": + factor = t * t + elif interpolation == "ease_out": + factor = t * (2 - t) - noise = add_noise_at_step(ttm_reference_latents, - noise, - sigma - ).to(latent_image.device, latent_image.dtype) - #-------------------------------------------------------------- - - try: - # Change the global preview method (temporarily) - set_preview_method(preview_method) - - x0_output = {} - callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) + cfg_list[i] = round(cfg_scale_start + factor * (cfg_scale_end - cfg_scale_start), 2) - disable_pbar = not PROGRESS_BAR_ENABLED - - disable_noise = False - if not add_noise: - disable_noise = True - - # Prepare noise for img specified by batch_inds - if disable_noise: - noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") - else: - batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = comfy.sample.prepare_noise(latent_image, noise_seed, batch_inds) - - force_full_denoise = True - if return_with_leftover_noise: - force_full_denoise = False - - device = comfy.model_management.intermediate_device() - model_options = model.model_options - start_step = start_at_step - last_step = end_at_step - denoise_mask = noise_mask + # If start_percent > 0, always include the first step + if start_percent > 0: + cfg_list[0] = 1.0 - samples = sample_custom_ultra(model, device, - noise, - sampler, - positive, negative, - cfg, model_options, - latent_image, - start_step, last_step, - force_full_denoise, denoise_mask, - sigmas, - callback, disable_pbar, noise_seed) - - samples = samples.to(comfy.model_management.intermediate_device()) - - out = latent.copy() - out["samples"] = samples - if "x0" in x0_output: - out_denoised = latent.copy() - out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) - else: - out_denoised = out - - previous_preview_method = global_preview_method() - - # --------------------------------------------------------------------------------------------------------------- - # Decode image if not yet decoded - if "true" in vae_decode: - if images is None: - images = vae_decode_latent(vae, out, vae_decode) - # Store decoded image as base image of no script is detected - store_ksampler_results("image", my_unique_id, images) - - # Define preview images - if preview_method == "none" or (preview_method == "vae_decoded_only" and vae_decode == "false"): - preview = {"images": list()} - elif images is not None: - preview = PreviewImage().save_images(images, prompt=prompt, extra_pnginfo=extra_pnginfo)["ui"] - - # Define a dummy output image - if images is None and vae_decode == "false": - images = WanVideoSamplerCustomUltraAdvancedEfficient.empty_image - - finally: - # Restore global changes - set_preview_method(previous_preview_method) - - return out, out_denoised, preview, images - - # --------------------------------------------------------------------------------------------------------------- - # Clean globally stored objects of non-existant nodes - globals_cleanup(prompt) - # --------------------------------------------------------------------------------------------------------------- - out, out_denoised, preview, images = process_latents() - - result = (model, positive, negative, sampler, sigmas, - out, out_denoised, images, vae,) - - if preview is None: - return {"result": result} - else: - return {"ui": preview, "result": result} + return (cfg_list,) From 66d37cc6c0dfe09977bee2217b10c1aedfc38c79 Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:32:35 +0100 Subject: [PATCH 07/19] removed dedicated sampler dependencies --- utils.py | 117 ------------------------------------------------------- 1 file changed, 117 deletions(-) diff --git a/utils.py b/utils.py index bd50bcd..954549d 100644 --- a/utils.py +++ b/utils.py @@ -1,121 +1,4 @@ import torch -from PIL import Image -import numpy as np -import latent_preview -from comfy.cli_args import args -from comfy.samplers import sample - - -# Convert PIL to Tensor (grabbed from WAS Suite) -def pil2tensor(image: Image.Image) -> torch.Tensor: - return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) - -def format_message(text, color_code): - RESET_COLOR = "\033[0m" - return f"{color_code}{text}{RESET_COLOR}" - -WARNING_COLOR = "\033[93m" # Yellow - -def warning(text): - return format_message(text, WARNING_COLOR) - - -# Set global preview_method -def set_preview_method(method): - if method == 'auto' or method == 'LatentPreviewMethod.Auto': - args.preview_method = latent_preview.LatentPreviewMethod.Auto - elif method == 'latent2rgb' or method == 'LatentPreviewMethod.Latent2RGB': - args.preview_method = latent_preview.LatentPreviewMethod.Latent2RGB - elif method == 'taesd' or method == 'LatentPreviewMethod.TAESD': - args.preview_method = latent_preview.LatentPreviewMethod.TAESD - else: - args.preview_method = latent_preview.LatentPreviewMethod.NoPreviews - - -def sample_custom_ultra(model, device, noise, sampler, positive, negative, cfg, model_options={}, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): - if last_step is not None and last_step < (len(sigmas) - 1): - sigmas = sigmas[:last_step + 1] - if force_full_denoise: - sigmas[-1] = 0 - - if start_step is not None: - if start_step < (len(sigmas) - 1): - sigmas = sigmas[start_step:] - else: - if latent_image is not None: - return latent_image - else: - return torch.zeros_like(noise) - - return sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) - - -# Extract global preview_method -def global_preview_method(): - return args.preview_method - - -# Cache for Efficiency Node models -loaded_objects = { - "ckpt": [], # (ckpt_name, ckpt_model, clip, bvae, [id]) - "refn": [], # (ckpt_name, ckpt_model, clip, bvae, [id]) - "vae": [], # (vae_name, vae, [id]) - "lora": [] # ([(lora_name, strength_model, strength_clip)], ckpt_name, lora_model, clip_lora, [id]) -} - -# Cache for Efficient Ksamplers -last_helds = { - "latent": [], # (latent, [parameters], id) # Base sampling latent results - "image": [], # (image, id) # Base sampling image results - "cnet_img": [] # (cnet_img, [parameters], id) # HiRes-Fix control net preprocessor image results -} - -def store_ksampler_results(key: str, my_unique_id, value, parameters_list=None): - global last_helds - - for i, data in enumerate(last_helds[key]): - id_ = data[-1] # ID will always be the last in the tuple - if id_ == my_unique_id: - # Check if parameters_list is provided or not - updated_data = (value, parameters_list, id_) if parameters_list is not None else (value, id_) - last_helds[key][i] = updated_data - return True - - # If parameters_list is given - if parameters_list is not None: - last_helds[key].append((value, parameters_list, my_unique_id)) - else: - last_helds[key].append((value, my_unique_id)) - return True - - -# This function cleans global variables associated with nodes that are no longer detected on UI -def globals_cleanup(prompt): - global loaded_objects - global last_helds - - # Step 1: Clean up last_helds - for key in list(last_helds.keys()): - original_length = len(last_helds[key]) - last_helds[key] = [ - (*values, id_) - for *values, id_ in last_helds[key] - if str(id_) in prompt.keys() - ] - - # Step 2: Clean up loaded_objects - for key in list(loaded_objects.keys()): - for i, tup in enumerate(list(loaded_objects[key])): - # Remove ids from id array in each tuple that don't exist in prompt - id_array = [id for id in tup[-1] if str(id) in prompt.keys()] - if len(id_array) != len(tup[-1]): - if id_array: - loaded_objects[key][i] = tup[:-1] + (id_array,) - #print(f'Updated tuple at index {i} in {key} in loaded_objects: {loaded_objects[key][i]}') - else: - # If id array becomes empty, delete the corresponding tuple - loaded_objects[key].remove(tup) - #print(f'Deleted tuple at index {i} in {key} in loaded_objects because its id array became empty.') # Copied from ComfyUI Wanvideo Wrapper From 430b9fc7027801a4db2a8f3af1bc40e3c0615035 Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:33:26 +0100 Subject: [PATCH 08/19] removed dedciated sampler and added ttmguider --- __init__.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/__init__.py b/__init__.py index 5008448..e970d6c 100644 --- a/__init__.py +++ b/__init__.py @@ -1,20 +1,20 @@ -from .nodes import (WanVideoEncode, - TTMKSamplerSelect, - AddTTMLatent, - WanVideoSamplerCustomUltraAdvancedEfficient) +from .nodes import (EncodeWanVideo, + TTMLatentAdd, + TimeToMoveGuider, + CFGFloatListScheduler) NODE_CLASS_MAPPINGS = { - "WanVideoEncode": WanVideoEncode, - "TTMKSamplerSelect": TTMKSamplerSelect, - "AddTTMLatent": AddTTMLatent, - "WanVideoSamplerCustomUltraAdvancedEfficient": WanVideoSamplerCustomUltraAdvancedEfficient, + "EncodeWanVideo": EncodeWanVideo, + "TTMLatentAdd": TTMLatentAdd, + "TimeToMoveGuider": TimeToMoveGuider, + "CFGFloatListScheduler": CFGFloatListScheduler, } NODE_DISPLAY_NAME_MAPPINGS = { - "WanVideoEncode": "WanVideo Encode", - "TTMKSamplerSelect": "TimeToMove KSampler Select", - "AddTTMLatent": "Add TTM Latent", - "WanVideoSamplerCustomUltraAdvancedEfficient": "WanVideoSampler Custom Ultra Advanced Efficient", + "EncodeWanVideo": "Encode WanVideo", + "TTMLatentAdd": "TTM Latent Add", + "TimeToMoveGuider": "TimeToMove Guider", + "CFGFloatListScheduler": "CFGFloatListScheduler", } -__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] \ No newline at end of file +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] From 59fc542b8fd2e497904b6b32542c847b75a40c7e Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:34:22 +0100 Subject: [PATCH 09/19] Delete Comfyui_WanTimeToMove_workflow.json --- Comfyui_WanTimeToMove_workflow.json | 3314 --------------------------- 1 file changed, 3314 deletions(-) delete mode 100644 Comfyui_WanTimeToMove_workflow.json diff --git a/Comfyui_WanTimeToMove_workflow.json b/Comfyui_WanTimeToMove_workflow.json deleted file mode 100644 index 005f3b2..0000000 --- a/Comfyui_WanTimeToMove_workflow.json +++ /dev/null @@ -1,3314 +0,0 @@ -{ - 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"ver": "0.3.34", - "Node name for S&R": "ModelSamplingSD3", - "ue_properties": { - "version": "7.0.1", - "widget_ue_connectable": {} - } - }, - "widgets_values": [ - 8.000000000000002 - ] - }, - { - "id": 11, - "type": "PatchModelPatcherOrder", - "pos": [ - 3740, - 950 - ], - "size": [ - 310, - 90 - ], - "flags": {}, - "order": 32, - "mode": 0, - "inputs": [ - { - "name": "model", - "type": "MODEL", - "link": 9 - } - ], - "outputs": [ - { - "name": "MODEL", - "type": "MODEL", - "links": [ - 5 - ] - } - ], - "properties": { - "cnr_id": "comfyui-kjnodes", - "ver": "f7eb33abc80a2aded1b46dff0dd14d07856a7d50", - "Node name for S&R": "PatchModelPatcherOrder", - "ue_properties": { - "version": "7.0.1", - "widget_ue_connectable": {} - } - }, - "widgets_values": [ - "weight_patch_first", - "disabled" - ] - }, - { - "id": 6, - "type": "TorchCompileModelWanVideoV2", - "pos": [ - 3710, - 1080 - ], - "size": [ - 380, - 206 - ], - "flags": {}, - "order": 38, - "mode": 0, - "inputs": [ - { - "name": "model", - "type": "MODEL", - "link": 5 - } - ], - "outputs": [ - { - "name": "MODEL", - "type": "MODEL", - "links": [ - 3, - 482 - ] - } - ], - "properties": { - "cnr_id": "comfyui-kjnodes", - "ver": "f7eb33abc80a2aded1b46dff0dd14d07856a7d50", - "Node name for S&R": "TorchCompileModelWanVideoV2", - "ue_properties": { - "version": "7.0.1", - "widget_ue_connectable": {} - } - }, - "widgets_values": [ - "inductor", - false, - "default", - false, - true, - 64, - true - ] - }, - { - "id": 186, - "type": "MarkdownNote", - "pos": [ - 2550, - 1590 - ], - "size": [ - 310, - 110 - ], - "flags": {}, - "order": 6, - "mode": 0, - "inputs": [], - "outputs": [], - "title": "Prompts", - "properties": { - "ue_properties": { - "version": "7.0.1", - "widget_ue_connectable": {} - } - }, - "widgets_values": [ - "### The ttm code is based on kijai wanvideo wrapper" - ], - "color": "#432", - "bgcolor": "#653" - }, - { - "id": 161, - "type": "MarkdownNote", - "pos": [ - 2880, - 930 - ], - "size": [ - 300, - 110 - ], - 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"outputs": [ - { - "name": "INT", - "type": "INT", - "links": [ - 486, - 487 - ] - } - ], - "title": "Start Refiner Step", - "properties": { - "cnr_id": "comfy-core", - "ver": "0.5.1", - "Node name for S&R": "PrimitiveInt", - "ue_properties": { - "widget_ue_connectable": {}, - "input_ue_unconnectable": {}, - "version": "7.5.2" - } - }, - "widgets_values": [ - 4, - "fixed" - ], - "color": "#232", - "bgcolor": "#353" - }, - { - "id": 88, - "type": "VHS_LoadVideo", - "pos": [ - 1590, - 1730 - ], - "size": [ - 288.1431640625, - 519.0956207730877 - ], - "flags": {}, - "order": 22, - "mode": 0, - "inputs": [ - { - "name": "meta_batch", - "shape": 7, - "type": "VHS_BatchManager", - "link": null - }, - { - "name": "vae", - "shape": 7, - "type": "VAE", - "link": null - }, - { - "name": "frame_load_cap", - "type": "INT", - "widget": { - "name": "frame_load_cap" - }, - "link": 490 - } - ], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "links": [ - 156 - ] - }, - { - "name": "frame_count", - "type": "INT", - "links": null - }, - { - "name": "audio", - "type": "AUDIO", - "links": null - }, - { - "name": "video_info", - "type": "VHS_VIDEOINFO", - "links": null - } - ], - "properties": { - "cnr_id": "comfyui-videohelpersuite", - "ver": "537f3a02269e14ab4b0350d4189a52034e9a3b12", - "Node name for S&R": "VHS_LoadVideo", - "ue_properties": { - "widget_ue_connectable": {}, - "input_ue_unconnectable": {}, - "version": "7.5.2" - } - }, - "widgets_values": { - "video": "TimeToMove_Monkey-Mask (1).mp4", - "force_rate": 0, - "custom_width": 0, - "custom_height": 0, - "frame_load_cap": 81, - "skip_first_frames": 0, - "select_every_nth": 1, - "format": "AnimateDiff", - "videopreview": { - "hidden": false, - "paused": false, - "params": { - "filename": "TimeToMove_Monkey-Mask (1).mp4", - "type": "input", - "format": "video/mp4", - "force_rate": 0, - "custom_width": 0, - "custom_height": 0, - "frame_load_cap": 81, - "skip_first_frames": 0, - "select_every_nth": 1 - } - } - } - }, - { - "id": 90, - "type": "VHS_LoadVideo", - "pos": [ - 900, - 1540 - ], - "size": [ - 288.1431640625, - 519.0956207730877 - ], - "flags": {}, - "order": 21, - "mode": 0, - "inputs": [ - { - "name": "meta_batch", - "shape": 7, - "type": "VHS_BatchManager", - "link": null - }, - { - "name": "vae", - "shape": 7, - "type": "VAE", - "link": null - }, - { - "name": "frame_load_cap", - "type": "INT", - "widget": { - "name": "frame_load_cap" - }, - "link": 489 - } - ], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "links": [ - 404 - ] - }, - { - "name": "frame_count", - "type": "INT", - "links": null - }, - { - "name": "audio", - "type": "AUDIO", - "links": null - }, - { - "name": "video_info", - "type": "VHS_VIDEOINFO", - "links": null - } - ], - "properties": { - "cnr_id": "comfyui-videohelpersuite", - "ver": "537f3a02269e14ab4b0350d4189a52034e9a3b12", - "Node name for S&R": "VHS_LoadVideo", - "ue_properties": { - "widget_ue_connectable": {}, - "input_ue_unconnectable": {}, - "version": "7.5.2" - } - }, - "widgets_values": { - "video": "TimeToMove_Monkey-Motion_signal (1).mp4", - "force_rate": 0, - "custom_width": 0, - "custom_height": 0, - "frame_load_cap": 81, - "skip_first_frames": 0, - "select_every_nth": 1, - "format": "AnimateDiff", - "videopreview": { - "hidden": false, - "paused": false, - "params": { - "filename": "TimeToMove_Monkey-Motion_signal (1).mp4", - "type": "input", - "format": "video/mp4", - "force_rate": 0, - "custom_width": 0, - "custom_height": 0, - "frame_load_cap": 81, - "skip_first_frames": 0, - "select_every_nth": 1 - } - } - } - }, - { - "id": 187, - "type": "PrimitiveInt", - "pos": [ - 2600, - 1150 - ], - "size": [ - 210, - 86 - ], - "flags": {}, - "order": 10, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "INT", - "type": "INT", - "links": [ - 484, - 485 - ] - } - ], - "title": "Steps", - "properties": { - "cnr_id": "comfy-core", - "ver": "0.5.1", - "Node name for S&R": "PrimitiveInt", - "ue_properties": { - "widget_ue_connectable": {}, - 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] - } - ], - "properties": { - "cnr_id": "rgthree-comfy", - "ver": "944d5353a1b0a668f40844018c3dc956b95a67d7", - "randomMax": 1125899906842624, - "randomMin": 0, - "ue_properties": { - "version": "7.0.1", - "widget_ue_connectable": {} - } - }, - "widgets_values": [ - -1, - "", - "", - "" - ], - "color": "#232", - "bgcolor": "#353" - }, - { - "id": 123, - "type": "WanVideoSamplerCustomUltraAdvancedEfficient", - "pos": [ - 3210, - 1000 - ], - "size": [ - 470.1861328125, - 984.1240885416667 - ], - "flags": {}, - "order": 43, - "mode": 0, - "inputs": [ - { - "name": "model", - "type": "MODEL", - "link": 483 - }, - { - "name": "positive", - "type": "CONDITIONING", - "link": 303 - }, - { - "name": "negative", - "type": "CONDITIONING", - "link": 304 - }, - { - "name": "sampler", - "type": "SAMPLER", - "link": 463 - }, - { - "name": "sigmas", - "type": "SIGMAS", - "link": 306 - }, - { - "name": "latent", - "type": "LATENT", - "link": 462 - }, - { - "name": "optional_vae", - "shape": 7, - "type": "VAE", - "link": 308 - }, - { - "name": "noise_seed", - "type": "INT", - "widget": { - "name": "noise_seed" - }, - "link": 309 - }, - { - "name": "end_at_step", - "type": "INT", - "widget": { - "name": "end_at_step" - }, - "link": 486 - } - ], - "outputs": [ - { - "name": "model", - "type": "MODEL", - "links": null - }, - { - "name": "positive", - "type": "CONDITIONING", - "links": [ - 412 - ] - }, - { - "name": "negative", - "type": "CONDITIONING", - "links": [ - 413 - ] - }, - { - "name": "sampler", - "type": "SAMPLER", - "links": [] - }, - { - "name": "sigmas", - "type": "SIGMAS", - "links": null - }, - { - "name": "output", - "type": "LATENT", - "links": [ - 410 - ] - }, - { - "name": "denoised_output", - "type": "LATENT", - "links": null - }, - { - "name": "image", - "type": "IMAGE", - "links": [] - }, - { - "name": "vae", - "type": "VAE", - "links": [ - 414 - ] - } - ], - "properties": { - "Node name for S&R": "WanVideoSamplerCustomUltraAdvancedEfficient", - "ue_properties": { - "widget_ue_connectable": {}, - "input_ue_unconnectable": {}, - "version": "7.5.2" - } - }, - "widgets_values": [ - true, - 772565087426431, - "randomize", - 1, - 0, - 10000, - true, - "auto", - "false" - ], - "color": "#232", - "bgcolor": "#353" - }, - { - "id": 180, - "type": "MarkdownNote", - "pos": [ - 4210, - 790 - ], - "size": [ - 580, - 170 - ], - "flags": {}, - "order": 13, - "mode": 0, - "inputs": [], - "outputs": [], - "title": "Prompts", - "properties": { - "ue_properties": { - "version": "7.0.1", - "widget_ue_connectable": {} - } - }, - "widgets_values": [ - "### Those samplers are based on Efficient Samplers' node, and accept a custom sampler, a custom scheduler (more info below), and a start-stop step (https://github.com/GiusTex/ComfyUI-MoreEfficientSamplers)" - ], - "color": "#432", - "bgcolor": "#653" - }, - { - "id": 149, - "type": "SamplerCustomUltraAdvancedEfficient", - "pos": [ - 4440, - 1000 - ], - "size": [ - 398.405859375, - 936.2705729166667 - ], - "flags": {}, - "order": 44, - "mode": 0, - "inputs": [ - 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advanced samplers from other custom node --- ...deo_2_2_I2V_A14B_TimeToMove_workflow1.json | 2779 +++++++++++++++++ ...deo_2_2_I2V_A14B_TimeToMove_workflow2.json | 2564 +++++++++++++++ 2 files changed, 5343 insertions(+) create mode 100644 wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json create mode 100644 wanvideo_2_2_I2V_A14B_TimeToMove_workflow2.json diff --git a/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json b/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json new file mode 100644 index 0000000..b6fa26e --- /dev/null +++ b/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json @@ -0,0 +1,2779 @@ +{ + "id": "50c7e0a6-6c6a-44ee-a68c-6b9ccab41177", + "revision": 0, + "last_node_id": 80, + "last_link_id": 144, + "nodes": [ + { + "id": 35, + "type": "CFGFloatListScheduler", + "pos": [ + 5940, + 1240 + ], + "size": [ + 232.167578125, + 182 + ], + "flags": {}, + "order": 16, + "mode": 0, + "inputs": [ + { + "name": "steps", + "type": "INT", + "widget": { + "name": "steps" + }, + "link": 48 + } + ], 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[ + { + "id": 1, + "title": "Load high noise model", + "bounding": [ + 50, + -270, + 1760, + 320 + ], + "color": "#3f789e", + "font_size": 24, + "flags": {} + }, + { + "id": 2, + "title": "Load image and videos", + "bounding": [ + 550, + 450, + 840, + 1090 + ], + "color": "#88A", + "font_size": 24, + "flags": {} + }, + { + "id": 3, + "title": "Prepare latent & add TTM guide", + "bounding": [ + 1430, + 80, + 920, + 880 + ], + "color": "#a1309b", + "font_size": 24, + "flags": {} + }, + { + "id": 4, + "title": "Load low noise model (as refiner)", + "bounding": [ + 2910, + -340, + 840, + 660 + ], + "color": "#3f789e", + "font_size": 24, + "flags": {} + } + ], + "config": {}, + "extra": { + "ds": { + "scale": 0.5989500000000002, + "offset": [ + -839.976835959564, + 302.31480090157686 + ] + }, + "frontendVersion": "1.37.11", + "node_versions": { + "ComfyUI-WanVideoWrapper": "5a2383621a05825d0d0437781afcb8552d9590fd", + "comfy-core": "0.3.26", + "ComfyUI-VideoHelperSuite": "0a75c7958fe320efcb052f1d9f8451fd20c730a8" + }, + "VHS_latentpreview": true, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true, + "workflowRendererVersion": "LG", + "ue_links": [], + "links_added_by_ue": [] + }, + "version": 0.4 +} \ No newline at end of file From 5c34214566f866556f81642cd39b4db950e68fee Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:39:10 +0100 Subject: [PATCH 11/19] updated readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a0b2f09..a0daaf1 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ A native comfyui port of Kijai's WanVideo-Wrapper TimeToMove The custom node contains 4 new nodes: - `Encode WanVideo`: taken from wanvideo-wrapper, it encodes the reference video. - `TTM Latent Add`: taken from wanvideo-wrapper, it embeds in the latent the reference to the driving video. -- `Timove To Move Guider`: this node adds the ttm latent to the latent noise before passing it to the sampling function. +- `Timove To Move Guider`: this node adds the ttm latent to the latent noise before passing it to the sampling function. This node removes the necessity of a dedicated sampler. - `CFG Float List Scheduler`: taken from wanvideo-wrapper, it creates a list of cfg values, and submits them step by step, making possible using different cfg values at different steps. The second sampler can be found here: `https://github.com/GiusTex/ComfyUI-MoreEfficientSamplers` but you can change it, and the scheduler used is this: `https://github.com/BigStationW/flowmatch_scheduler-comfyui`, useful when you use lightx loras. From c10f4aac5531d90983f2b151bc23e9e0317a9c51 Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 11:58:59 +0100 Subject: [PATCH 12/19] update readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a0daaf1..62a21fe 100644 --- a/README.md +++ b/README.md @@ -3,13 +3,13 @@ A native comfyui port of Kijai's WanVideo-Wrapper TimeToMove WanTTM nodes (updated) -### v2: +### Updates: - Solved color issue. ### WIP - Working to fix bug on longer sequences (around 49 frames seems to work, around 81 frames duplicates the subject) -[VIDEO COMPARISON] +[VIDEO...] ### Nodes The custom node contains 4 new nodes: From 16d24c23e04e7672d16ab1a333babe3d41db2a0d Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 12:16:01 +0100 Subject: [PATCH 13/19] update readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 62a21fe..c14faee 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,9 @@ The custom node contains 4 new nodes: - `Timove To Move Guider`: this node adds the ttm latent to the latent noise before passing it to the sampling function. This node removes the necessity of a dedicated sampler. - `CFG Float List Scheduler`: taken from wanvideo-wrapper, it creates a list of cfg values, and submits them step by step, making possible using different cfg values at different steps. -The second sampler can be found here: `https://github.com/GiusTex/ComfyUI-MoreEfficientSamplers` but you can change it, and the scheduler used is this: `https://github.com/BigStationW/flowmatch_scheduler-comfyui`, useful when you use lightx loras. +### Other custom nodes used: +- The advanced sampler used in the [second workflow](https://github.com/GiusTex/ComfyUI-Wan-TimeToMove/blob/TTM-v2/wanvideo_2_2_I2V_A14B_TimeToMove_workflow2.json) can be found [here](https://github.com/GiusTex/ComfyUI-MoreEfficientSamplers). You can still use the native comfyui `sampler custom advanced` using [this](https://github.com/GiusTex/ComfyUI-Wan-TimeToMove/blob/TTM-v2/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json) workflow. +- The scheduler used is [this](https://github.com/BigStationW/flowmatch_scheduler-comfyui), useful for models using lightx loras. You can still use other samplers/schedulers. ### Download To install ComfyUI-Wan-TimeToMove, follow these steps: From b5a495230256691368ff8f2a3e482b112b21340f Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 16:23:03 +0100 Subject: [PATCH 14/19] updated image and uploaded example video --- README.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index c14faee..7dc4c8f 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,13 @@ # ComfyUI-Wan-TimeToMove A native comfyui port of Kijai's WanVideo-Wrapper TimeToMove -WanTTM nodes (updated) +ComfyUI-TTM-nodes + +https://github.com/user-attachments/assets/0b201e7a-d3c6-417f-8293-e20e8d2872fb ### Updates: - Solved color issue. - -### WIP -- Working to fix bug on longer sequences (around 49 frames seems to work, around 81 frames duplicates the subject) - -[VIDEO...] +- Fixed other bugs. ### Nodes The custom node contains 4 new nodes: From 216958780123b81008c2c9590bd3de255d675bdb Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 16:31:14 +0100 Subject: [PATCH 15/19] Delete wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json --- ...deo_2_2_I2V_A14B_TimeToMove_workflow1.json | 2779 ----------------- 1 file changed, 2779 deletions(-) delete mode 100644 wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json diff --git a/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json b/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json deleted file mode 100644 index b6fa26e..0000000 --- a/wanvideo_2_2_I2V_A14B_TimeToMove_workflow1.json +++ /dev/null @@ -1,2779 +0,0 @@ -{ - 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"workflowRendererVersion": "LG", + "ue_links": [], + "links_added_by_ue": [] + }, + "version": 0.4 +} \ No newline at end of file From 81dde54963bc3e2f1dab4e31532a74f51c9de02f Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 16:34:52 +0100 Subject: [PATCH 18/19] fixed some bugs --- samplers.py | 106 +++++++++++++++++++++++++--------------------------- 1 file changed, 51 insertions(+), 55 deletions(-) diff --git a/samplers.py b/samplers.py index 7a3fe78..c806b2a 100644 --- a/samplers.py +++ b/samplers.py @@ -43,60 +43,21 @@ class TTMGuider: ).execute(x, timestep, model_options, seed) def predict_noise(self, x, timestep, model_options={}, seed=None): - - sigmas = model_options["sigmas"] - start_sampler_step = model_options["start_sampler_step"] - skipped_sigmas = sigmas[start_sampler_step:] - # 4 < 5 - if len(skipped_sigmas) < len(sigmas): # sampler doesn't have start_step option - sigmas = skipped_sigmas - # 4 == 4 - elif len(skipped_sigmas) == len(sigmas): # sampler already has option - pass # we don't want another sigma less - steps = len(sigmas)-1 - - i = torch.argmin(torch.abs(sigmas - timestep)).item() - - # Cfg part taken from Kijai WanVideo-Wrapper - if isinstance(self.cfg, list): - if steps < len(self.cfg): - print(f"Received {len(self.cfg)} cfg values, but only {steps} steps. Slicing cfg list to match steps.") - self.cfg = self.cfg[:steps] - elif steps > len(self.cfg): - print(f"Received only {len(self.cfg)} cfg values, but {steps} steps. Extending cfg list to match steps.") - self.cfg.extend([self.cfg[-1]] * (steps - len(self.cfg))) - if i == 0: # Print cfg only at first step - print(f"Using per-step cfg list: {self.cfg}") - else: - self.cfg = [self.cfg] * (steps + 1) - #--------------------------------------------------------- - ttm_ref_latent = model_options["ttm_reference_latents"].to(x.device) - ttm_start_step = max(model_options["ttm_start_step"] - start_sampler_step, 0) - ttm_end_step = model_options["ttm_end_step"] - start_sampler_step - ttm_mask = model_options["motion_mask"].to(x.device) + sigmas = model_options["sigmas"] + noise = model_options["noise"] + i = torch.argmin(torch.abs(sigmas - timestep)).item() + + ttm_ref_latent = model_options["ttm_reference_latents"] + ttm_start_step = model_options["ttm_start_step"] + ttm_end_step = model_options["ttm_end_step"] + ttm_mask = model_options["motion_mask"] # Time-to-move (TTM) - if i == 0: # First Step - if ttm_ref_latent is not None: - if ttm_start_step > steps: - raise ValueError("TTM start step is beyond the total number of steps") - - if ttm_end_step > ttm_start_step: - print("Using Time-to-move (TTM)") - print(f"TTM reference latents shape: {ttm_ref_latent.shape}") - print(f"TTM motion mask shape: {ttm_mask.shape}") - print(f"Applying TTM from step {ttm_start_step} to {ttm_end_step}") - - sigma_next = sigmas[ttm_start_step] - x = add_noise_at_step(ttm_ref_latent, - x, - sigma_next.to(x.device) - ).to(x) - elif ttm_ref_latent is not None and (i + ttm_start_step) < ttm_end_step: # Following Steps if i > 0 + if (i + ttm_start_step) < ttm_end_step: if i + ttm_start_step < len(sigmas): sigma_next = sigmas[i + ttm_start_step] noisy_latents = add_noise_at_step(ttm_ref_latent, - x, + noise, sigma_next.to(x.device) ).to(x) x = x * (1 - ttm_mask) + noisy_latents * ttm_mask @@ -119,14 +80,49 @@ class TTMGuider: extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas extra_args = {"model_options": extra_model_options, "seed": seed} + #--------------------------------------------------------- + skipped_sigmas = sigmas[self.start_sampler_step:] + # 4 < 5 + if len(skipped_sigmas) < len(sigmas): # sampler doesn't have start_step option + sigmas = skipped_sigmas + # 4 == 4 + elif len(skipped_sigmas) == len(sigmas): # sampler already has option + pass # we don't want another sigma less + steps = len(sigmas)-1 + extra_args["model_options"]["steps"] = steps + #--------------------------------------------------------- # Pass ttm options to KSAMPLER.sample - extra_args["model_options"]["ttm_reference_latents"] = self.ttm_reference_latents - extra_args["model_options"]["ttm_start_step"] = self.ttm_start_step - extra_args["model_options"]["ttm_end_step"] = self.ttm_end_step - extra_args["model_options"]["latent_image"] = self.latent_image - extra_args["model_options"]["motion_mask"] = self.motion_mask - extra_args["model_options"]["start_sampler_step"] = self.start_sampler_step + ttm_start_step = max(self.ttm_start_step - self.start_sampler_step, 0) + ttm_end_step = self.ttm_end_step - self.start_sampler_step + + extra_args["model_options"]["ttm_reference_latents"] = self.ttm_reference_latents.to(noise.device) + extra_args["model_options"]["ttm_start_step"] = ttm_start_step + extra_args["model_options"]["ttm_end_step"] = ttm_end_step + extra_args["model_options"]["motion_mask"] = self.motion_mask.to(noise.device) extra_args["model_options"]["sigmas"] = sigmas + extra_args["model_options"]["noise"] = noise + + if ttm_start_step > steps: + raise ValueError("TTM start step is beyond the total number of steps") + + if ttm_end_step > ttm_start_step: + print("Using Time-to-move (TTM)") + print(f"TTM reference latents shape: {self.ttm_reference_latents.shape}") + print(f"TTM motion mask shape: {self.motion_mask.shape}") + print(f"Applying TTM from step {ttm_start_step} to {ttm_end_step}") + #--------------------------------------------------------- + # Cfg schedule taken from Kijai WanVideo-Wrapper + if isinstance(self.cfg, list): + if steps < len(self.cfg): + print(f"Received {len(self.cfg)} cfg values, but only {steps} steps. Slicing cfg list to match steps.") + self.cfg = self.cfg[:steps] + elif steps > len(self.cfg): + print(f"Received only {len(self.cfg)} cfg values, but {steps} steps. Extending cfg list to match steps.") + self.cfg.extend([self.cfg[-1]] * (steps - len(self.cfg))) + print(f"Using per-step cfg list: {self.cfg}") + else: + self.cfg = [self.cfg] * (steps + 1) + #--------------------------------------------------------- executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( sampler.sample, From e41d50416da71108d89813b7a2e71e35367edd26 Mon Sep 17 00:00:00 2001 From: Gius <112352961+GiusTex@users.noreply.github.com> Date: Thu, 29 Jan 2026 16:35:29 +0100 Subject: [PATCH 19/19] added back comfyui batch dim --- nodes.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/nodes.py b/nodes.py index 53c47ac..8fa476e 100644 --- a/nodes.py +++ b/nodes.py @@ -89,8 +89,8 @@ class TTMLatentAdd: mode="nearest" ) - latent["ttm_reference_latents"] = reference_latents["samples"].squeeze(0) # [16, T, H, W] - latent["ttm_mask"] = mask_latent.squeeze(0).movedim(1, 0) # [1, T, H, W] + latent["ttm_reference_latents"] = reference_latents["samples"] + latent["ttm_mask"] = mask_latent.movedim(2, 1) latent["ttm_start_step"] = ttm_start_step latent["ttm_end_step"] = ttm_end_step @@ -106,7 +106,7 @@ class TimeToMoveGuider: "negative": ("CONDITIONING", ), "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "Works with a list of floats too (one cfg float per step)"}), "latent": ("LATENT", {"tooltip": "You can connect here the latent from TTM Latent Add, to pass reference video and ttm options"}), - "start_sampler_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Start step of the whole sampling process. It will automatically skip the selected number of sigmas (starting from the first ones); if the sampler has a start_step option, set the same value here"}), + "start_sampler_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Start step of the whole sampling process. It will automatically skip the selected number of sigmas (starting from the first ones); if the sampler has a start_step option and you changed its value, set the same here"}), }, }