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Runtime error
Runtime error
| import torch | |
| import warnings | |
| def get_fast_schedule(origial_timesteps, fast_after_steps, fast_rate): | |
| if fast_after_steps >= len(origial_timesteps) - 1: | |
| return origial_timesteps | |
| new_timesteps = torch.cat((origial_timesteps[:fast_after_steps], origial_timesteps[fast_after_steps+1::fast_rate]), dim=0) | |
| return new_timesteps | |
| def dynamically_adjust_inference_steps(scheduler, index, t): | |
| prev_t = scheduler.timesteps[index+1] if index+1 < len(scheduler.timesteps) else -1 | |
| scheduler.num_inference_steps = scheduler.config.num_train_timesteps // (t - prev_t) | |
| if index+1 < len(scheduler.timesteps): | |
| if scheduler.config.num_train_timesteps // scheduler.num_inference_steps != t - prev_t: | |
| warnings.warn(f"({scheduler.config.num_train_timesteps} // {scheduler.num_inference_steps}) != ({t} - {prev_t}), so the step sizes may not be accurate") | |
| else: | |
| # as long as we hit final cumprob, it should be fine. | |
| if scheduler.config.num_train_timesteps // scheduler.num_inference_steps > t - prev_t: | |
| warnings.warn(f"({scheduler.config.num_train_timesteps} // {scheduler.num_inference_steps}) > ({t} - {prev_t}), so the step sizes may not be accurate") | |