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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, model_switch_step2 ):
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]
phase3 = slists_dict["phase3"][mult_no]
shared = slists_dict["shared"][mult_no]
if shared:
if isinstance(phase1, float): return phase1
return expand_one(phase1, num_inference_steps)
else:
if isinstance(phase1, float) and isinstance(phase2, float) and isinstance(phase3, float) and phase1 == phase2 and phase2 == phase3: return phase1
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)
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):
if model_switch_step is None:
model_switch_step = num_inference_steps
if model_switch_step2 is None:
model_switch_step2 = 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 = { "model_switch_step2": model_switch_step2}
slists_dict["phase1"] = phase1 = [1.] * nb_loras
slists_dict["phase2"] = phase2 = [1.] * nb_loras
slists_dict["phase3"] = phase3 = [1.] * nb_loras
slists_dict["shared"] = shared = [False] * nb_loras
if isinstance(loras_multipliers, list) or len(loras_multipliers) > 0:
list_mult_choices_list = preparse_loras_multipliers(loras_multipliers)[:nb_loras]
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 not shared_phases and len(phase_mult) != nb_phases :
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"
for phase_no, mult in enumerate(phase_mult):
if phase_no == 1:
current_phase = phase2
elif phase_no == 2:
current_phase = phase3
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 in Phase {phase_no+1}"
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] = phase3[i] = slist
shared[i] = True
else:
current_phase[i] = slist
else:
phase1[i] = phase2[i] = phase3[i] = float(mult)
shared[i] = True
if merge_slist is not None:
slists_dict["phase1"] = phase1 = merge_slist["phase1"] + phase1
slists_dict["phase2"] = phase2 = merge_slist["phase2"] + phase2
slists_dict["phase3"] = phase3 = merge_slist["phase3"] + phase3
slists_dict["shared"] = shared = merge_slist["shared"] + shared
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)) ]
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, phase_switch_step = None, phase_switch_step2 = None):
from mmgp import offload
sz = len(slists_dict["phase1"])
slists = [ expand_slist(slists_dict, i, num_inference_steps, phase_switch_step, phase_switch_step2 ) for i in range(sz) ]
nos = [str(l) for l in range(sz)]
offload.activate_loras(trans, nos, slists )
def get_model_switch_steps(timesteps, guide_phases, model_switch_phase, switch_threshold, switch2_threshold ):
total_num_steps = len(timesteps)
model_switch_step = model_switch_step2 = None
for i, t in enumerate(timesteps):
if guide_phases >=2 and model_switch_step is None and t <= switch_threshold: model_switch_step = i
if guide_phases >=3 and model_switch_step2 is None and t <= switch2_threshold: model_switch_step2 = i
if model_switch_step is None: model_switch_step = total_num_steps
if model_switch_step2 is None: model_switch_step2 = total_num_steps
phases_description = ""
if guide_phases > 1:
phases_description = "Denoising Steps: "
phases_description += f" Phase 1 = None" if model_switch_step == 0 else f" Phase 1 = 1:{ min(model_switch_step,total_num_steps) }"
if model_switch_step < total_num_steps:
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) }"
if guide_phases > 2 and model_switch_step2 < total_num_steps:
phases_description += f", Phase 3 = {model_switch_step2 +1}:{ total_num_steps}"
return model_switch_step, model_switch_step2, phases_description
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