import folder_paths import fcbh.sd import fcbh.model_sampling import torch class LCM(fcbh.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) x0 = model_input - model_output * sigma sigma_data = 0.5 scaled_timestep = timestep * 10.0 #timestep_scaling c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 return c_out * x0 + c_skip * model_input class ModelSamplingDiscreteLCM(torch.nn.Module): def __init__(self): super().__init__() self.sigma_data = 1.0 timesteps = 1000 beta_start = 0.00085 beta_end = 0.012 betas = torch.linspace(beta_start**0.5, beta_end**0.5, timesteps, dtype=torch.float32) ** 2 alphas = 1.0 - betas alphas_cumprod = torch.cumprod(alphas, dim=0) original_timesteps = 50 self.skip_steps = timesteps // original_timesteps alphas_cumprod_valid = torch.zeros((original_timesteps), dtype=torch.float32) for x in range(original_timesteps): alphas_cumprod_valid[original_timesteps - 1 - x] = alphas_cumprod[timesteps - 1 - x * self.skip_steps] sigmas = ((1 - alphas_cumprod_valid) / alphas_cumprod_valid) ** 0.5 self.set_sigmas(sigmas) def set_sigmas(self, sigmas): self.register_buffer('sigmas', sigmas) self.register_buffer('log_sigmas', sigmas.log()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1) def sigma(self, timestep): t = torch.clamp(((timestep - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp() def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item() def rescale_zero_terminal_snr_sigmas(sigmas): alphas_cumprod = 1 / ((sigmas * sigmas) + 1) alphas_bar_sqrt = alphas_cumprod.sqrt() # Store old values. alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() # Shift so the last timestep is zero. alphas_bar_sqrt -= (alphas_bar_sqrt_T) # Scale so the first timestep is back to the old value. alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) # Convert alphas_bar_sqrt to betas alphas_bar = alphas_bar_sqrt**2 # Revert sqrt alphas_bar[-1] = 4.8973451890853435e-08 return ((1 - alphas_bar) / alphas_bar) ** 0.5 class ModelSamplingDiscrete: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["eps", "v_prediction", "lcm"],), "zsnr": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, zsnr): m = model.clone() sampling_base = fcbh.model_sampling.ModelSamplingDiscrete if sampling == "eps": sampling_type = fcbh.model_sampling.EPS elif sampling == "v_prediction": sampling_type = fcbh.model_sampling.V_PREDICTION elif sampling == "lcm": sampling_type = LCM sampling_base = ModelSamplingDiscreteLCM class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced() if zsnr: model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas)) m.add_object_patch("model_sampling", model_sampling) return (m, ) class RescaleCFG: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, multiplier): def rescale_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] sigma = args["sigma"] sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) x_orig = args["input"] #rescale cfg has to be done on v-pred model output x = x_orig / (sigma * sigma + 1.0) cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) #rescalecfg x_cfg = uncond + cond_scale * (cond - uncond) ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) x_rescaled = x_cfg * (ro_pos / ro_cfg) x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) m = model.clone() m.set_model_sampler_cfg_function(rescale_cfg) return (m, ) NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "RescaleCFG": RescaleCFG, }