From 5489d5af044d9160b50df9a928ef57ca7497ce8b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 11 Feb 2023 03:18:27 -0500 Subject: [PATCH] Add uni_pc sampler to KSampler* nodes. --- comfy/extra_samplers/uni_pc.py | 851 +++++++++++++++++++++++++++++++++ comfy/samplers.py | 56 +-- 2 files changed, 879 insertions(+), 28 deletions(-) create mode 100644 comfy/extra_samplers/uni_pc.py diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py new file mode 100644 index 00000000..3fe4ef3b --- /dev/null +++ b/comfy/extra_samplers/uni_pc.py @@ -0,0 +1,851 @@ +#code taken from: https://github.com/wl-zhao/UniPC and modified + +import torch +import torch.nn.functional as F +import math + +from tqdm.auto import trange, tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + sampling_function, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + output = sampling_function(model, x, t_input, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1' + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + # print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dimss) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, + ): + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + steps = len(timesteps) - 1 + if method == 'multistep': + assert steps >= order + # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + print(timesteps) + assert timesteps.shape[0] - 1 == steps + # with torch.no_grad(): + for step_index in trange(steps + 1): + if step_index == 0: + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + elif step_index < order: + init_order = step_index + # Init the first `order` values by lower order multistep DPM-Solver. + # for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + else: + step = step_index + # for step in range(order, steps + 1): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + # print('this step order:', step_order) + if step == steps: + # print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + else: + raise NotImplementedError() + if denoise_to_zero: + x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] + + + +def sample_unipc(model, noise, image, sigmas, sampling_function, extra_args=None, callback=None, disable=None): + timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0] + for s in range(timesteps.shape[0]): + timesteps[s] = (model.sigma_to_t(timesteps[s]) / 1000) + (1 / len(model.sigmas)) + + ns = NoiseScheduleVP('discrete', alphas_cumprod=model.inner_model.alphas_cumprod) + + if image is not None: + img = image * ns.marginal_alpha(timesteps[0]) + noise * ns.marginal_std(timesteps[0]) + else: + img = noise + + if sigmas[-1] == 0: + timesteps[-1] = (1 / len(model.sigmas)) + + device = noise.device + + + model_fn = model_wrapper( + model.inner_model.apply_model, + sampling_function, + ns, + model_type="noise", + guidance_type="uncond", + model_kwargs=extra_args, + ) + + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False) + x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=3, lower_order_final=True) + return x diff --git a/comfy/samplers.py b/comfy/samplers.py index d5a34efd..7f6dc972 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -1,5 +1,6 @@ from .k_diffusion import sampling as k_diffusion_sampling from .k_diffusion import external as k_diffusion_external +from .extra_samplers import uni_pc import torch import contextlib import model_management @@ -20,12 +21,8 @@ class CFGDenoiser(torch.nn.Module): uncond = self.inner_model(x, sigma, cond=uncond) return uncond + (cond - uncond) * cond_scale -class CFGDenoiserComplex(torch.nn.Module): - def __init__(self, model): - super().__init__() - self.inner_model = model - def forward(self, x, sigma, uncond, cond, cond_scale): - def get_area_and_mult(cond, x_in, sigma): +def sampling_function(model_function, x, sigma, uncond, cond, cond_scale): + def get_area_and_mult(cond, x_in): area = (x_in.shape[2], x_in.shape[3], 0, 0) strength = 1.0 min_sigma = 0.0 @@ -34,12 +31,7 @@ class CFGDenoiserComplex(torch.nn.Module): area = cond[1]['area'] if 'strength' in cond[1]: strength = cond[1]['strength'] - if 'min_sigma' in cond[1]: - min_sigma = cond[1]['min_sigma'] - if 'max_sigma' in cond[1]: - max_sigma = cond[1]['max_sigma'] - if sigma < min_sigma or sigma > max_sigma: - return None + input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] mult = torch.ones_like(input_x) * strength @@ -58,26 +50,25 @@ class CFGDenoiserComplex(torch.nn.Module): mult[:,:,:,area[1] + area[3] - 1 - t:area[1] + area[3] - t] *= ((1.0/rr) * (t + 1)) return (input_x, mult, cond[0], area) - def calc_cond_uncond_batch(cond, uncond, x_in, sigma, max_total_area): + def calc_cond_uncond_batch(model_function, cond, uncond, x_in, sigma, max_total_area): out_cond = torch.zeros_like(x_in) out_count = torch.ones_like(x_in)/100000.0 out_uncond = torch.zeros_like(x_in) out_uncond_count = torch.ones_like(x_in)/100000.0 - sigma_cmp = sigma[0] COND = 0 UNCOND = 1 to_run = [] for x in cond: - p = get_area_and_mult(x, x_in, sigma_cmp) + p = get_area_and_mult(x, x_in) if p is None: continue to_run += [(p, COND)] for x in uncond: - p = get_area_and_mult(x, x_in, sigma_cmp) + p = get_area_and_mult(x, x_in) if p is None: continue @@ -120,7 +111,7 @@ class CFGDenoiserComplex(torch.nn.Module): c = torch.cat(c) sigma_ = torch.cat([sigma] * batch_chunks) - output = self.inner_model(input_x, sigma_, cond=c).chunk(batch_chunks) + output = model_function(input_x, sigma_, cond=c).chunk(batch_chunks) del input_x for o in range(batch_chunks): @@ -141,9 +132,16 @@ class CFGDenoiserComplex(torch.nn.Module): max_total_area = model_management.maximum_batch_area() - cond, uncond = calc_cond_uncond_batch(cond, uncond, x, sigma, max_total_area) + cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, sigma, max_total_area) return uncond + (cond - uncond) * cond_scale +class CFGDenoiserComplex(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + def forward(self, x, sigma, uncond, cond, cond_scale): + return sampling_function(self.inner_model, x, sigma, uncond, cond, cond_scale) + def simple_scheduler(model, steps): sigs = [] ss = len(model.sigmas) / steps @@ -186,7 +184,7 @@ class KSampler: SCHEDULERS = ["karras", "normal", "simple"] SAMPLERS = ["sample_euler", "sample_euler_ancestral", "sample_heun", "sample_dpm_2", "sample_dpm_2_ancestral", "sample_lms", "sample_dpm_fast", "sample_dpm_adaptive", "sample_dpmpp_2s_ancestral", "sample_dpmpp_sde", - "sample_dpmpp_2m"] + "sample_dpmpp_2m", "uni_pc"] def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None): self.model = model @@ -256,10 +254,6 @@ class KSampler: else: return torch.zeros_like(noise) - noise *= sigmas[0] - if latent_image is not None: - noise += latent_image - positive = positive[:] negative = negative[:] #make sure each cond area has an opposite one with the same area @@ -274,10 +268,16 @@ class KSampler: precision_scope = contextlib.nullcontext with precision_scope(self.device): - if self.sampler == "sample_dpm_fast": - samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) - elif self.sampler == "sample_dpm_adaptive": - samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + if self.sampler == "uni_pc": + samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) else: - samples = getattr(k_diffusion_sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + noise *= sigmas[0] + if latent_image is not None: + noise += latent_image + if self.sampler == "sample_dpm_fast": + samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + elif self.sampler == "sample_dpm_adaptive": + samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + else: + samples = getattr(k_diffusion_sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) return samples.to(torch.float32)