wehub-resource-sync
76 lines
3.2 KiB
Python
Executable File
76 lines
3.2 KiB
Python
Executable File
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
|
|
# Some pipelines concatenate the unconditional and text guide in forward.
|
|
self.previous_residual = None
|
|
# Some pipelines perform forward propagation separately on the unconditional and text guide.
|
|
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
|