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Running
on
Zero
Running
on
Zero
| from dataclasses import dataclass | |
| from pathlib import Path | |
| from typing import NamedTuple | |
| from src.eunms import Model_Type, Scheduler_Type, Gradient_Averaging_Type, Epsilon_Update_Type | |
| class RunConfig: | |
| model_type : Model_Type = Model_Type.SDXL_Turbo | |
| scheduler_type : Scheduler_Type = Scheduler_Type.EULER | |
| prompt: str = "" | |
| num_inference_steps: int = 4 | |
| num_inversion_steps: int = 100 | |
| opt_lr: float = 0.1 | |
| opt_iters: int = 0 | |
| opt_none_inference_steps: bool = False | |
| guidance_scale: float = 0.0 | |
| # pipe_inversion: DiffusionPipeline = None | |
| # pipe_inference: DiffusionPipeline = None | |
| save_gpu_mem: bool = False | |
| do_reconstruction: bool = True | |
| loss_kl_lambda: float = 10.0 | |
| max_num_aprox_steps_first_step: int = 1 | |
| num_aprox_steps: int = 10 | |
| inversion_max_step: float = 1.0 | |
| gradient_averaging_type: Gradient_Averaging_Type = Gradient_Averaging_Type.NONE | |
| gradient_averaging_first_step_range: tuple = (0, 10) | |
| gradient_averaging_step_range: tuple = (0, 10) | |
| noise_friendly_inversion: bool = False | |
| update_epsilon_type: Epsilon_Update_Type = Gradient_Averaging_Type.NONE | |
| #pip2pip zero | |
| lambda_ac: float = 20.0 | |
| lambda_kl: float = 20.0 | |
| num_reg_steps: int = 5 | |
| num_ac_rolls: int = 5 | |
| def __post_init__(self): | |
| pass |