mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
128 lines
6.8 KiB
Python
128 lines
6.8 KiB
Python
def preparse_loras_multipliers(loras_multipliers):
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if isinstance(loras_multipliers, list):
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return [multi.strip(" \r\n") if isinstance(multi, str) else multi for multi in loras_multipliers]
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loras_multipliers = loras_multipliers.strip(" \r\n")
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loras_mult_choices_list = loras_multipliers.replace("\r", "").split("\n")
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loras_mult_choices_list = [multi.strip() for multi in loras_mult_choices_list if len(multi)>0 and not multi.startswith("#")]
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loras_multipliers = " ".join(loras_mult_choices_list)
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return loras_multipliers.split(" ")
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def expand_slist(slists_dict, mult_no, num_inference_steps, model_switch_step, model_switch_step2 ):
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def expand_one(slist, num_inference_steps):
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if not isinstance(slist, list): slist = [slist]
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new_slist= []
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if num_inference_steps <=0:
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return new_slist
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inc = len(slist) / num_inference_steps
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pos = 0
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for i in range(num_inference_steps):
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new_slist.append(slist[ int(pos)])
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pos += inc
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return new_slist
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phase1 = slists_dict["phase1"][mult_no]
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phase2 = slists_dict["phase2"][mult_no]
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phase3 = slists_dict["phase3"][mult_no]
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shared = slists_dict["shared"][mult_no]
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if shared:
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if isinstance(phase1, float): return phase1
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return expand_one(phase1, num_inference_steps)
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else:
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if isinstance(phase1, float) and isinstance(phase2, float) and isinstance(phase3, float) and phase1 == phase2 and phase2 == phase3: return phase1
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return expand_one(phase1, model_switch_step) + expand_one(phase2, model_switch_step2 - model_switch_step) + expand_one(phase3, num_inference_steps - model_switch_step2)
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def parse_loras_multipliers(loras_multipliers, nb_loras, num_inference_steps, merge_slist = None, nb_phases = 2, model_switch_step = None, model_switch_step2 = None):
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if model_switch_step is None:
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model_switch_step = num_inference_steps
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if model_switch_step2 is None:
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model_switch_step2 = num_inference_steps
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def is_float(element: any) -> bool:
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if element is None:
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return False
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try:
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float(element)
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return True
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except ValueError:
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return False
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loras_list_mult_choices_nums = []
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slists_dict = { "model_switch_step": model_switch_step}
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slists_dict = { "model_switch_step2": model_switch_step2}
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slists_dict["phase1"] = phase1 = [1.] * nb_loras
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slists_dict["phase2"] = phase2 = [1.] * nb_loras
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slists_dict["phase3"] = phase3 = [1.] * nb_loras
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slists_dict["shared"] = shared = [False] * nb_loras
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if isinstance(loras_multipliers, list) or len(loras_multipliers) > 0:
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list_mult_choices_list = preparse_loras_multipliers(loras_multipliers)[:nb_loras]
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for i, mult in enumerate(list_mult_choices_list):
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current_phase = phase1
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if isinstance(mult, str):
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mult = mult.strip()
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phase_mult = mult.split(";")
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shared_phases = len(phase_mult) <=1
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if not shared_phases and len(phase_mult) != nb_phases :
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return "", "", f"if the ';' syntax is used for one Lora multiplier, the multipliers for its {nb_phases} denoising phases should be specified for this multiplier"
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for phase_no, mult in enumerate(phase_mult):
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if phase_no == 1:
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current_phase = phase2
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elif phase_no == 2:
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current_phase = phase3
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if "," in mult:
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multlist = mult.split(",")
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slist = []
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for smult in multlist:
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if not is_float(smult):
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return "", "", f"Lora sub value no {i+1} ({smult}) in Multiplier definition '{multlist}' is invalid in Phase {phase_no+1}"
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slist.append(float(smult))
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else:
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if not is_float(mult):
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return "", "", f"Lora Multiplier no {i+1} ({mult}) is invalid"
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slist = float(mult)
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if shared_phases:
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phase1[i] = phase2[i] = phase3[i] = slist
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shared[i] = True
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else:
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current_phase[i] = slist
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else:
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phase1[i] = phase2[i] = phase3[i] = float(mult)
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shared[i] = True
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if merge_slist is not None:
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slists_dict["phase1"] = phase1 = merge_slist["phase1"] + phase1
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slists_dict["phase2"] = phase2 = merge_slist["phase2"] + phase2
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slists_dict["phase3"] = phase3 = merge_slist["phase3"] + phase3
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slists_dict["shared"] = shared = merge_slist["shared"] + shared
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loras_list_mult_choices_nums = [ expand_slist(slists_dict, i, num_inference_steps, model_switch_step, model_switch_step2 ) for i in range(len(phase1)) ]
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loras_list_mult_choices_nums = [ slist[0] if isinstance(slist, list) else slist for slist in loras_list_mult_choices_nums ]
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return loras_list_mult_choices_nums, slists_dict, ""
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def update_loras_slists(trans, slists_dict, num_inference_steps, phase_switch_step = None, phase_switch_step2 = None):
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from mmgp import offload
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sz = len(slists_dict["phase1"])
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slists = [ expand_slist(slists_dict, i, num_inference_steps, phase_switch_step, phase_switch_step2 ) for i in range(sz) ]
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nos = [str(l) for l in range(sz)]
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offload.activate_loras(trans, nos, slists )
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def get_model_switch_steps(timesteps, guide_phases, model_switch_phase, switch_threshold, switch2_threshold ):
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total_num_steps = len(timesteps)
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model_switch_step = model_switch_step2 = None
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for i, t in enumerate(timesteps):
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if guide_phases >=2 and model_switch_step is None and t <= switch_threshold: model_switch_step = i
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if guide_phases >=3 and model_switch_step2 is None and t <= switch2_threshold: model_switch_step2 = i
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if model_switch_step is None: model_switch_step = total_num_steps
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if model_switch_step2 is None: model_switch_step2 = total_num_steps
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phases_description = ""
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if guide_phases > 1:
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phases_description = "Denoising Steps: "
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phases_description += f" Phase 1 = None" if model_switch_step == 0 else f" Phase 1 = 1:{ min(model_switch_step,total_num_steps) }"
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if model_switch_step < total_num_steps:
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phases_description += f", Phase 2 = None" if model_switch_step == model_switch_step2 else f", Phase 2 = {model_switch_step +1}:{ min(model_switch_step2,total_num_steps) }"
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if guide_phases > 2 and model_switch_step2 < total_num_steps:
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phases_description += f", Phase 3 = {model_switch_step2 +1}:{ total_num_steps}"
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return model_switch_step, model_switch_step2, phases_description
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