| import numpy as np
|
| import torch
|
|
|
| def get_teacache_coefficients(model_name):
|
| if "wan2.1-t2v-1.3b" in model_name.lower() or "wan2.1-fun-1.3b" in model_name.lower() \
|
| or "wan2.1-fun-v1.1-1.3b" in model_name.lower() or "wan2.1-vace-1.3b" in model_name.lower():
|
| return [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02]
|
| elif "wan2.1-t2v-14b" in model_name.lower():
|
| return [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01]
|
| elif "wan2.1-i2v-14b-480p" in model_name.lower():
|
| return [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01]
|
| elif "wan2.1-i2v-14b-720p" in model_name.lower() or "wan2.1-fun-14b" in model_name.lower() or "wan2.2-fun" in model_name.lower() \
|
| or "wan2.2-i2v-a14b" in model_name.lower() or "wan2.2-t2v-a14b" in model_name.lower() or "wan2.2-ti2v-5b" in model_name.lower() \
|
| or "wan2.2-s2v" in model_name.lower() or "wan2.1-vace-14b" in model_name.lower() or "wan2.2-vace-fun" in model_name.lower() \
|
| or "wan2.2-animate" in model_name.lower():
|
| return [8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02]
|
| elif "qwen-image" in model_name.lower():
|
|
|
| return [-4.50000000e+02, 2.80000000e+02, -4.50000000e+01, 3.20000000e+00, -2.00000000e-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
|
|
|
