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| def preparse_loras_multipliers(loras_multipliers): | |
| if isinstance(loras_multipliers, list): | |
| return [multi.strip(" \r\n") if isinstance(multi, str) else multi for multi in loras_multipliers] | |
| loras_multipliers = loras_multipliers.strip(" \r\n") | |
| loras_mult_choices_list = loras_multipliers.replace("\r", "").split("\n") | |
| loras_mult_choices_list = [multi.strip() for multi in loras_mult_choices_list if len(multi)>0 and not multi.startswith("#")] | |
| loras_multipliers = " ".join(loras_mult_choices_list) | |
| return loras_multipliers.split(" ") | |
| def expand_slist(slists_dict, mult_no, num_inference_steps, model_switch_step ): | |
| def expand_one(slist, num_inference_steps): | |
| if not isinstance(slist, list): slist = [slist] | |
| new_slist= [] | |
| if num_inference_steps <=0: | |
| return new_slist | |
| inc = len(slist) / num_inference_steps | |
| pos = 0 | |
| for i in range(num_inference_steps): | |
| new_slist.append(slist[ int(pos)]) | |
| pos += inc | |
| return new_slist | |
| phase1 = slists_dict["phase1"][mult_no] | |
| phase2 = slists_dict["phase2"][mult_no] | |
| if isinstance(phase1, float) and isinstance(phase2, float) and phase1 == phase2: | |
| return phase1 | |
| return expand_one(phase1, model_switch_step) + expand_one(phase2, num_inference_steps - model_switch_step) | |
| def parse_loras_multipliers(loras_multipliers, nb_loras, num_inference_steps, merge_slist = None, max_phases = 2, model_switch_step = None): | |
| if model_switch_step is None: | |
| model_switch_step = num_inference_steps | |
| def is_float(element: any) -> bool: | |
| if element is None: | |
| return False | |
| try: | |
| float(element) | |
| return True | |
| except ValueError: | |
| return False | |
| loras_list_mult_choices_nums = [] | |
| slists_dict = { "model_switch_step": model_switch_step} | |
| slists_dict["phase1"] = phase1 = [1.] * nb_loras | |
| slists_dict["phase2"] = phase2 = [1.] * nb_loras | |
| if isinstance(loras_multipliers, list) or len(loras_multipliers) > 0: | |
| list_mult_choices_list = preparse_loras_multipliers(loras_multipliers) | |
| for i, mult in enumerate(list_mult_choices_list): | |
| current_phase = phase1 | |
| if isinstance(mult, str): | |
| mult = mult.strip() | |
| phase_mult = mult.split(";") | |
| shared_phases = len(phase_mult) <=1 | |
| if len(phase_mult) > max_phases: | |
| return "", "", f"Loras can not be defined for more than {max_phases} Denoising phase{'s' if max_phases>1 else ''} for this model" | |
| for phase_no, mult in enumerate(phase_mult): | |
| if phase_no > 0: current_phase = phase2 | |
| if "," in mult: | |
| multlist = mult.split(",") | |
| slist = [] | |
| for smult in multlist: | |
| if not is_float(smult): | |
| return "", "", f"Lora sub value no {i+1} ({smult}) in Multiplier definition '{multlist}' is invalid" | |
| slist.append(float(smult)) | |
| else: | |
| if not is_float(mult): | |
| return "", "", f"Lora Multiplier no {i+1} ({mult}) is invalid" | |
| slist = float(mult) | |
| if shared_phases: | |
| phase1[i] = phase2[i] = slist | |
| else: | |
| current_phase[i] = slist | |
| else: | |
| phase1[i] = phase2[i] = float(mult) | |
| if merge_slist is not None: | |
| slists_dict["phase1"] = phase1 = merge_slist["phase1"] + phase1 | |
| slists_dict["phase2"] = phase2 = merge_slist["phase2"] + phase2 | |
| loras_list_mult_choices_nums = [ expand_slist(slists_dict, i, num_inference_steps, model_switch_step ) for i in range(len(phase1)) ] | |
| loras_list_mult_choices_nums = [ slist[0] if isinstance(slist, list) else slist for slist in loras_list_mult_choices_nums ] | |
| return loras_list_mult_choices_nums, slists_dict, "" | |
| def update_loras_slists(trans, slists_dict, num_inference_steps, model_switch_step = None ): | |
| from mmgp import offload | |
| sz = len(slists_dict["phase1"]) | |
| slists = [ expand_slist(slists_dict, i, num_inference_steps, model_switch_step ) for i in range(sz) ] | |
| nos = [str(l) for l in range(sz)] | |
| offload.activate_loras(trans, nos, slists ) | |