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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()
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