| import numpy as np |
| import torch |
|
|
|
|
| def get_teacache_coefficients(model_name): |
| if "wan2.1-t2v-1.3b" or "wan2.1-fun-1.3b" in model_name.lower(): |
| return [-5.21862437e04, 9.23041404e03, -5.28275948e02, 1.36987616e01, -4.99875664e-02] |
| elif "wan2.1-t2v-14b" in model_name.lower(): |
| return [-3.03318725e05, 4.90537029e04, -2.65530556e03, 5.87365115e01, -3.15583525e-01] |
| elif "wan2.1-i2v-14b-480p" in model_name.lower(): |
| return [2.57151496e05, -3.54229917e04, 1.40286849e03, -1.35890334e01, 1.32517977e-01] |
| elif "wan2.1-i2v-14b-720p" or "wan2.1-fun-14b" in model_name.lower(): |
| return [8.10705460e03, 2.13393892e03, -3.72934672e02, 1.66203073e01, -4.17769401e-02] |
| else: |
| print(f"The model {model_name} is not supported by TeaCache.") |
| return None |
|
|
|
|
| class TeaCache: |
| """ |
| Timestep Embedding Aware Cache, a training-free caching approach that estimates and leverages |
| the fluctuating differences among model outputs across timesteps, thereby accelerating the inference. |
| Please refer to: |
| 1. https://github.com/ali-vilab/TeaCache. |
| 2. Liu, Feng, et al. "Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model." arXiv preprint arXiv:2411.19108 (2024). |
| """ |
|
|
| def __init__( |
| self, |
| coefficients: list[float], |
| num_steps: int, |
| rel_l1_thresh: float = 0.0, |
| num_skip_start_steps: int = 0, |
| offload: bool = True, |
| ): |
| if num_steps < 1: |
| raise ValueError(f"`num_steps` must be greater than 0 but is {num_steps}.") |
| if rel_l1_thresh < 0: |
| raise ValueError(f"`rel_l1_thresh` must be greater than or equal to 0 but is {rel_l1_thresh}.") |
| if num_skip_start_steps < 0 or num_skip_start_steps > num_steps: |
| raise ValueError( |
| "`num_skip_start_steps` must be great than or equal to 0 and " |
| f"less than or equal to `num_steps={num_steps}` but is {num_skip_start_steps}." |
| ) |
| self.coefficients = coefficients |
| self.num_steps = num_steps |
| self.rel_l1_thresh = rel_l1_thresh |
| self.num_skip_start_steps = num_skip_start_steps |
| self.offload = offload |
| self.rescale_func = np.poly1d(self.coefficients) |
|
|
| self.cnt = 0 |
| self.should_calc = True |
| self.accumulated_rel_l1_distance = 0 |
| self.previous_modulated_input = None |
| |
| self.previous_residual = None |
| |
| self.previous_residual_cond = None |
| self.previous_residual_uncond = None |
|
|
| @staticmethod |
| def compute_rel_l1_distance(prev: torch.Tensor, cur: torch.Tensor) -> torch.Tensor: |
| rel_l1_distance = (torch.abs(cur - prev).mean()) / torch.abs(prev).mean() |
|
|
| return rel_l1_distance.cpu().item() |
|
|
| def reset(self): |
| self.cnt = 0 |
| self.should_calc = True |
| self.accumulated_rel_l1_distance = 0 |
| self.previous_modulated_input = None |
| self.previous_residual = None |
| self.previous_residual_cond = None |
| self.previous_residual_uncond = None |
|
|
