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| """ | |
| LCM + LTX scheduler combining Latent Consistency Model with RectifiedFlow (LTX). | |
| Optimized for Lightning LoRA compatibility and ultra-fast inference. | |
| """ | |
| import torch | |
| import math | |
| from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput | |
| class LCMScheduler(SchedulerMixin): | |
| """ | |
| LCM + LTX scheduler combining Latent Consistency Model with RectifiedFlow. | |
| - LCM: Enables 2-8 step inference with consistency models | |
| - LTX: Uses RectifiedFlow for better flow matching dynamics | |
| Optimized for Lightning LoRAs and ultra-fast, high-quality generation. | |
| """ | |
| def __init__(self, num_train_timesteps: int = 1000, num_inference_steps: int = 4, shift: float = 1.0): | |
| self.num_train_timesteps = num_train_timesteps | |
| self.num_inference_steps = num_inference_steps | |
| self.shift = shift | |
| self._step_index = None | |
| def set_timesteps(self, num_inference_steps: int, device=None, shift: float = None, **kwargs): | |
| """Set timesteps for LCM+LTX inference using RectifiedFlow approach""" | |
| self.num_inference_steps = min(num_inference_steps, 8) # LCM works best with 2-8 steps | |
| if shift is None: | |
| shift = self.shift | |
| # RectifiedFlow (LTX) approach: Use rectified flow dynamics for better sampling | |
| # This creates a more optimal path through the probability flow ODE | |
| t = torch.linspace(0, 1, self.num_inference_steps + 1, dtype=torch.float32) | |
| # Apply rectified flow transformation for better dynamics | |
| # This is the key LTX component - rectified flow scheduling | |
| sigma_max = 1.0 | |
| sigma_min = 0.003 / 1.002 | |
| # Rectified flow uses a more sophisticated sigma schedule | |
| # that accounts for the flow matching dynamics | |
| sigmas = sigma_min + (sigma_max - sigma_min) * (1 - t) | |
| # Apply shift for flow matching (similar to other flow-based schedulers) | |
| sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) | |
| self.sigmas = sigmas | |
| self.timesteps = self.sigmas[:-1] * self.num_train_timesteps | |
| if device is not None: | |
| self.timesteps = self.timesteps.to(device) | |
| self.sigmas = self.sigmas.to(device) | |
| self._step_index = None | |
| def step(self, model_output: torch.Tensor, timestep: torch.Tensor, sample: torch.Tensor, **kwargs) -> SchedulerOutput: | |
| """ | |
| Perform LCM + LTX step combining consistency model with rectified flow. | |
| - LCM: Direct consistency model prediction for fast inference | |
| - LTX: RectifiedFlow dynamics for optimal probability flow path | |
| """ | |
| if self._step_index is None: | |
| self._init_step_index(timestep) | |
| # Get current and next sigma values from RectifiedFlow schedule | |
| sigma = self.sigmas[self._step_index] | |
| if self._step_index + 1 < len(self.sigmas): | |
| sigma_next = self.sigmas[self._step_index + 1] | |
| else: | |
| sigma_next = torch.zeros_like(sigma) | |
| # LCM + LTX: Combine consistency model approach with rectified flow dynamics | |
| # The model_output represents the velocity field in the rectified flow ODE | |
| # LCM allows us to take larger steps while maintaining consistency | |
| # RectifiedFlow step: x_{t+1} = x_t + v_θ(x_t, t) * (σ_next - σ) | |
| # This is the core flow matching equation with LTX rectified dynamics | |
| sigma_diff = (sigma_next - sigma) | |
| while len(sigma_diff.shape) < len(sample.shape): | |
| sigma_diff = sigma_diff.unsqueeze(-1) | |
| # LCM consistency: The model is trained to be consistent across timesteps | |
| # allowing for fewer steps while maintaining quality | |
| prev_sample = sample + model_output * sigma_diff | |
| self._step_index += 1 | |
| return SchedulerOutput(prev_sample=prev_sample) | |
| def _init_step_index(self, timestep): | |
| """Initialize step index based on current timestep""" | |
| if isinstance(timestep, torch.Tensor): | |
| timestep = timestep.to(self.timesteps.device) | |
| indices = (self.timesteps == timestep).nonzero() | |
| if len(indices) > 0: | |
| self._step_index = indices[0].item() | |
| else: | |
| # Find closest timestep if exact match not found | |
| diffs = torch.abs(self.timesteps - timestep) | |
| self._step_index = torch.argmin(diffs).item() | |