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Running
on
Zero
| import numpy as np | |
| import torch | |
| def get_teacache_coefficients(model_name): | |
| if "wan2.1-t2v-1.3b" or "wan2.1-fun-1.3b" or "Wan2.1-Fun-V1.1-1.3B" 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" or "wan2.1-fun-14b" in model_name.lower(): | |
| return [8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -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 | |
| 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 |