(cfg_scale,
enable_cfg_interval,
timesteps: torch.Tensor,
t_scale: int = 1000)
| 141 | |
| 142 | # https://arxiv.org/pdf/2404.07724 Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models |
| 143 | def get_cfg_scale_list(cfg_scale, |
| 144 | enable_cfg_interval, |
| 145 | timesteps: torch.Tensor, |
| 146 | t_scale: int = 1000): |
| 147 | use_cfg = (timesteps / t_scale) < enable_cfg_interval |
| 148 | cfg_list = torch.ones_like(timesteps) |
| 149 | cfg_list[use_cfg] = cfg_scale |
| 150 | return cfg_list |
| 151 | |
| 152 | |
| 153 | class FlowMatchScheduler(): |
nothing calls this directly
no outgoing calls
no test coverage detected