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import os
import re
import torch
import numpy as np
import gradio as gr
import cv2
from PIL import Image
from shared.utils.hf import build_hf_url
def test_vace(base_model_type):
return base_model_type in ["vace_14B", "vace_14B_2_2", "vace_1.3B", "vace_multitalk_14B", "vace_standin_14B", "vace_lynx_14B", "vace_ditto_14B"]
def test_class_i2v(base_model_type):
return base_model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "flf2v_720p", "fantasy", "multitalk", "infinitetalk", "i2v_2_2_multitalk", "animate", "chrono_edit", "steadydancer", "wanmove", "scail", "i2v_2_2_svi2pro" ]
def test_class_t2v(base_model_type):
return base_model_type in ["t2v", "t2v_2_2", "alpha", "alpha2", "lynx"]
def test_oneframe_overlap(base_model_type):
return test_class_i2v(base_model_type) and not (test_multitalk(base_model_type) or base_model_type in ["animate", "scail"] or test_svi2pro(base_model_type)) or test_wan_5B(base_model_type)
def test_class_1_3B(base_model_type):
return base_model_type in [ "vace_1.3B", "t2v_1.3B", "recam_1.3B","phantom_1.3B","fun_inp_1.3B"]
def test_multitalk(base_model_type):
return base_model_type in ["multitalk", "vace_multitalk_14B", "i2v_2_2_multitalk", "infinitetalk"]
def test_standin(base_model_type):
return base_model_type in ["standin", "vace_standin_14B"]
def test_lynx(base_model_type):
return base_model_type in ["lynx_lite", "vace_lynx_lite_14B", "lynx", "vace_lynx_14B", "alpha_lynx"]
def test_alpha(base_model_type):
return base_model_type in ["alpha", "alpha2", "alpha_lynx"]
def test_wan_5B(base_model_type):
return base_model_type in ["ti2v_2_2", "lucy_edit"]
def test_i2v_2_2(base_model_type):
return base_model_type in ["i2v_2_2", "i2v_2_2_multitalk", "i2v_2_2_svi2pro"]
def test_svi2pro(base_model_type):
return base_model_type in ["i2v_2_2_svi2pro"]
class family_handler():
@staticmethod
def query_supported_types():
return ["multitalk", "infinitetalk", "fantasy", "vace_14B", "vace_14B_2_2", "vace_multitalk_14B", "vace_standin_14B", "vace_lynx_14B",
"t2v_1.3B", "standin", "lynx_lite", "lynx", "t2v", "t2v_2_2", "vace_1.3B", "vace_ditto_14B", "phantom_1.3B", "phantom_14B",
"recam_1.3B", "animate", "alpha", "alpha2", "alpha_lynx", "chrono_edit",
"i2v", "i2v_2_2", "i2v_2_2_multitalk", "ti2v_2_2", "lucy_edit", "flf2v_720p", "fun_inp_1.3B", "fun_inp", "mocha", "steadydancer", "wanmove", "scail", "i2v_2_2_svi2pro"]
@staticmethod
def query_family_maps():
models_eqv_map = {
"flf2v_720p" : "i2v",
"i2v_2_2_svi2pro": "i2v_2_2",
"t2v_1.3B" : "t2v",
"t2v_2_2" : "t2v",
"alpha" : "t2v",
"alpha2" : "t2v",
"lynx" : "t2v",
"standin" : "t2v",
"vace_standin_14B" : "vace_14B",
"vace_lynx_14B" : "vace_14B",
"vace_14B_2_2": "vace_14B",
}
models_comp_map = {
"vace_14B" : [ "vace_multitalk_14B", "vace_standin_14B", "vace_lynx_lite_14B", "vace_lynx_14B", "vace_14B_2_2"],
"t2v" : [ "vace_14B", "vace_1.3B" "vace_multitalk_14B", "vace_standin_14B", "vace_lynx_lite_14B", "vace_lynx_14B", "vace_14B_2_2", "t2v_1.3B", "phantom_1.3B","phantom_14B", "standin", "lynx_lite", "lynx", "alpha", "alpha2"],
"i2v" : [ "fantasy", "multitalk", "flf2v_720p" ],
"i2v_2_2" : ["i2v_2_2_multitalk", "i2v_2_2_svi2pro"],
"fantasy": ["multitalk"],
}
return models_eqv_map, models_comp_map
@staticmethod
def query_model_family():
return "wan"
@staticmethod
def query_family_infos():
return {"wan":(0, "Wan2.1"), "wan2_2":(1, "Wan2.2") }
@staticmethod
def register_lora_cli_args(parser):
parser.add_argument(
"--lora-dir-i2v",
type=str,
default=os.path.join("loras", "wan_i2v"),
help="Path to a directory that contains Wan i2v Loras "
)
parser.add_argument(
"--lora-dir",
type=str,
default=os.path.join("loras", "wan"),
help="Path to a directory that contains Wan t2v Loras"
)
parser.add_argument(
"--lora-dir-wan-1-3b",
type=str,
default=os.path.join("loras", "wan_1.3B"),
help="Path to a directory that contains Wan 1.3B Loras"
)
parser.add_argument(
"--lora-dir-wan-5b",
type=str,
default=os.path.join("loras", "wan_5B"),
help="Path to a directory that contains Wan 5B Loras"
)
parser.add_argument(
"--lora-dir-wan-i2v",
type=str,
default=os.path.join("loras", "wan_i2v"),
help="Path to a directory that contains Wan i2v Loras"
)
@staticmethod
def get_lora_dir(base_model_type, args):
i2v = test_class_i2v(base_model_type) and not test_i2v_2_2(base_model_type)
wan_dir = getattr(args, "lora_dir_wan", None) or getattr(args, "lora_dir", None) or os.path.join("loras", "wan")
wan_i2v_dir = getattr(args, "lora_dir_wan_i2v", None) or getattr(args, "lora_dir_i2v", None) or os.path.join("loras", "wan_i2v")
wan_1_3b_dir = getattr(args, "lora_dir_wan_1_3b", None) or os.path.join("loras", "wan_1.3B")
wan_5b_dir = getattr(args, "lora_dir_wan_5b", None) or os.path.join("loras", "wan_5B")
if i2v:
return wan_i2v_dir
if "1.3B" in base_model_type:
return wan_1_3b_dir
if base_model_type in ["ti2v_2_2", "ovi"]:
return wan_5b_dir
return wan_dir
@staticmethod
def set_cache_parameters(cache_type, base_model_type, model_def, inputs, skip_steps_cache):
i2v = test_class_i2v(base_model_type)
resolution = inputs["resolution"]
width, height = resolution.split("x")
pixels = int(width) * int(height)
if cache_type == "mag":
skip_steps_cache.update({
"magcache_thresh" : 0,
"magcache_K" : 2,
})
if base_model_type in ["t2v", "mocha"] and "URLs2" in model_def:
def_mag_ratios = [1.00124, 1.00155, 0.99822, 0.99851, 0.99696, 0.99687, 0.99703, 0.99732, 0.9966, 0.99679, 0.99602, 0.99658, 0.99578, 0.99664, 0.99484, 0.9949, 0.99633, 0.996, 0.99659, 0.99683, 0.99534, 0.99549, 0.99584, 0.99577, 0.99681, 0.99694, 0.99563, 0.99554, 0.9944, 0.99473, 0.99594, 0.9964, 0.99466, 0.99461, 0.99453, 0.99481, 0.99389, 0.99365, 0.99391, 0.99406, 0.99354, 0.99361, 0.99283, 0.99278, 0.99268, 0.99263, 0.99057, 0.99091, 0.99125, 0.99126, 0.65523, 0.65252, 0.98808, 0.98852, 0.98765, 0.98736, 0.9851, 0.98535, 0.98311, 0.98339, 0.9805, 0.9806, 0.97776, 0.97771, 0.97278, 0.97286, 0.96731, 0.96728, 0.95857, 0.95855, 0.94385, 0.94385, 0.92118, 0.921, 0.88108, 0.88076, 0.80263, 0.80181]
elif base_model_type in ["i2v_2_2"]:
def_mag_ratios = [0.99191, 0.99144, 0.99356, 0.99337, 0.99326, 0.99285, 0.99251, 0.99264, 0.99393, 0.99366, 0.9943, 0.9943, 0.99276, 0.99288, 0.99389, 0.99393, 0.99274, 0.99289, 0.99316, 0.9931, 0.99379, 0.99377, 0.99268, 0.99271, 0.99222, 0.99227, 0.99175, 0.9916, 0.91076, 0.91046, 0.98931, 0.98933, 0.99087, 0.99088, 0.98852, 0.98855, 0.98895, 0.98896, 0.98806, 0.98808, 0.9871, 0.98711, 0.98613, 0.98618, 0.98434, 0.98435, 0.983, 0.98307, 0.98185, 0.98187, 0.98131, 0.98131, 0.9783, 0.97835, 0.97619, 0.9762, 0.97264, 0.9727, 0.97088, 0.97098, 0.96568, 0.9658, 0.96045, 0.96055, 0.95322, 0.95335, 0.94579, 0.94594, 0.93297, 0.93311, 0.91699, 0.9172, 0.89174, 0.89202, 0.8541, 0.85446, 0.79823, 0.79902]
elif test_wan_5B(base_model_type):
if inputs.get("image_start", None) is not None and inputs.get("video_source", None) is not None : # t2v
def_mag_ratios = [0.99505, 0.99389, 0.99441, 0.9957, 0.99558, 0.99551, 0.99499, 0.9945, 0.99534, 0.99548, 0.99468, 0.9946, 0.99463, 0.99458, 0.9946, 0.99453, 0.99408, 0.99404, 0.9945, 0.99441, 0.99409, 0.99398, 0.99403, 0.99397, 0.99382, 0.99377, 0.99349, 0.99343, 0.99377, 0.99378, 0.9933, 0.99328, 0.99303, 0.99301, 0.99217, 0.99216, 0.992, 0.99201, 0.99201, 0.99202, 0.99133, 0.99132, 0.99112, 0.9911, 0.99155, 0.99155, 0.98958, 0.98957, 0.98959, 0.98958, 0.98838, 0.98835, 0.98826, 0.98825, 0.9883, 0.98828, 0.98711, 0.98709, 0.98562, 0.98561, 0.98511, 0.9851, 0.98414, 0.98412, 0.98284, 0.98282, 0.98104, 0.98101, 0.97981, 0.97979, 0.97849, 0.97849, 0.97557, 0.97554, 0.97398, 0.97395, 0.97171, 0.97166, 0.96917, 0.96913, 0.96511, 0.96507, 0.96263, 0.96257, 0.95839, 0.95835, 0.95483, 0.95475, 0.94942, 0.94936, 0.9468, 0.94678, 0.94583, 0.94594, 0.94843, 0.94872, 0.96949, 0.97015]
else: # i2v
def_mag_ratios = [0.99512, 0.99559, 0.99559, 0.99561, 0.99595, 0.99577, 0.99512, 0.99512, 0.99546, 0.99534, 0.99543, 0.99531, 0.99496, 0.99491, 0.99504, 0.99499, 0.99444, 0.99449, 0.99481, 0.99481, 0.99435, 0.99435, 0.9943, 0.99431, 0.99411, 0.99406, 0.99373, 0.99376, 0.99413, 0.99405, 0.99363, 0.99359, 0.99335, 0.99331, 0.99244, 0.99243, 0.99229, 0.99229, 0.99239, 0.99236, 0.99163, 0.9916, 0.99149, 0.99151, 0.99191, 0.99192, 0.9898, 0.98981, 0.9899, 0.98987, 0.98849, 0.98849, 0.98846, 0.98846, 0.98861, 0.98861, 0.9874, 0.98738, 0.98588, 0.98589, 0.98539, 0.98534, 0.98444, 0.98439, 0.9831, 0.98309, 0.98119, 0.98118, 0.98001, 0.98, 0.97862, 0.97859, 0.97555, 0.97558, 0.97392, 0.97388, 0.97152, 0.97145, 0.96871, 0.9687, 0.96435, 0.96434, 0.96129, 0.96127, 0.95639, 0.95638, 0.95176, 0.95175, 0.94446, 0.94452, 0.93972, 0.93974, 0.93575, 0.9359, 0.93537, 0.93552, 0.96655, 0.96616]
elif test_class_1_3B(base_model_type): #text 1.3B
def_mag_ratios = [1.0124, 1.02213, 1.00166, 1.0041, 0.99791, 1.00061, 0.99682, 0.99762, 0.99634, 0.99685, 0.99567, 0.99586, 0.99416, 0.99422, 0.99578, 0.99575, 0.9957, 0.99563, 0.99511, 0.99506, 0.99535, 0.99531, 0.99552, 0.99549, 0.99541, 0.99539, 0.9954, 0.99536, 0.99489, 0.99485, 0.99518, 0.99514, 0.99484, 0.99478, 0.99481, 0.99479, 0.99415, 0.99413, 0.99419, 0.99416, 0.99396, 0.99393, 0.99388, 0.99386, 0.99349, 0.99349, 0.99309, 0.99304, 0.9927, 0.9927, 0.99228, 0.99226, 0.99171, 0.9917, 0.99137, 0.99135, 0.99068, 0.99063, 0.99005, 0.99003, 0.98944, 0.98942, 0.98849, 0.98849, 0.98758, 0.98757, 0.98644, 0.98643, 0.98504, 0.98503, 0.9836, 0.98359, 0.98202, 0.98201, 0.97977, 0.97978, 0.97717, 0.97718, 0.9741, 0.97411, 0.97003, 0.97002, 0.96538, 0.96541, 0.9593, 0.95933, 0.95086, 0.95089, 0.94013, 0.94019, 0.92402, 0.92414, 0.90241, 0.9026, 0.86821, 0.86868, 0.81838, 0.81939]#**(0.5)# In our papaer, we utilize the sqrt to smooth the ratio, which has little impact on the performance and can be deleted.
elif i2v:
if pixels >= 1280*720:
def_mag_ratios = [0.99428, 0.99498, 0.98588, 0.98621, 0.98273, 0.98281, 0.99018, 0.99023, 0.98911, 0.98917, 0.98646, 0.98652, 0.99454, 0.99456, 0.9891, 0.98909, 0.99124, 0.99127, 0.99102, 0.99103, 0.99215, 0.99212, 0.99515, 0.99515, 0.99576, 0.99572, 0.99068, 0.99072, 0.99097, 0.99097, 0.99166, 0.99169, 0.99041, 0.99042, 0.99201, 0.99198, 0.99101, 0.99101, 0.98599, 0.98603, 0.98845, 0.98844, 0.98848, 0.98851, 0.98862, 0.98857, 0.98718, 0.98719, 0.98497, 0.98497, 0.98264, 0.98263, 0.98389, 0.98393, 0.97938, 0.9794, 0.97535, 0.97536, 0.97498, 0.97499, 0.973, 0.97301, 0.96827, 0.96828, 0.96261, 0.96263, 0.95335, 0.9534, 0.94649, 0.94655, 0.93397, 0.93414, 0.91636, 0.9165, 0.89088, 0.89109, 0.8679, 0.86768]
else:
def_mag_ratios = [0.98783, 0.98993, 0.97559, 0.97593, 0.98311, 0.98319, 0.98202, 0.98225, 0.9888, 0.98878, 0.98762, 0.98759, 0.98957, 0.98971, 0.99052, 0.99043, 0.99383, 0.99384, 0.98857, 0.9886, 0.99065, 0.99068, 0.98845, 0.98847, 0.99057, 0.99057, 0.98957, 0.98961, 0.98601, 0.9861, 0.98823, 0.98823, 0.98756, 0.98759, 0.98808, 0.98814, 0.98721, 0.98724, 0.98571, 0.98572, 0.98543, 0.98544, 0.98157, 0.98165, 0.98411, 0.98413, 0.97952, 0.97953, 0.98149, 0.9815, 0.9774, 0.97742, 0.97825, 0.97826, 0.97355, 0.97361, 0.97085, 0.97087, 0.97056, 0.97055, 0.96588, 0.96587, 0.96113, 0.96124, 0.9567, 0.95681, 0.94961, 0.94969, 0.93973, 0.93988, 0.93217, 0.93224, 0.91878, 0.91896, 0.90955, 0.90954, 0.92617, 0.92616]
else: # text 14B
def_mag_ratios = [1.02504, 1.03017, 1.00025, 1.00251, 0.9985, 0.99962, 0.99779, 0.99771, 0.9966, 0.99658, 0.99482, 0.99476, 0.99467, 0.99451, 0.99664, 0.99656, 0.99434, 0.99431, 0.99533, 0.99545, 0.99468, 0.99465, 0.99438, 0.99434, 0.99516, 0.99517, 0.99384, 0.9938, 0.99404, 0.99401, 0.99517, 0.99516, 0.99409, 0.99408, 0.99428, 0.99426, 0.99347, 0.99343, 0.99418, 0.99416, 0.99271, 0.99269, 0.99313, 0.99311, 0.99215, 0.99215, 0.99218, 0.99215, 0.99216, 0.99217, 0.99163, 0.99161, 0.99138, 0.99135, 0.98982, 0.9898, 0.98996, 0.98995, 0.9887, 0.98866, 0.98772, 0.9877, 0.98767, 0.98765, 0.98573, 0.9857, 0.98501, 0.98498, 0.9838, 0.98376, 0.98177, 0.98173, 0.98037, 0.98035, 0.97678, 0.97677, 0.97546, 0.97543, 0.97184, 0.97183, 0.96711, 0.96708, 0.96349, 0.96345, 0.95629, 0.95625, 0.94926, 0.94929, 0.93964, 0.93961, 0.92511, 0.92504, 0.90693, 0.90678, 0.8796, 0.87945, 0.86111, 0.86189]
skip_steps_cache.def_mag_ratios = def_mag_ratios
else:
if i2v:
if pixels >= 1280*720:
coefficients= [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683]
else:
coefficients= [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01]
else:
if test_class_1_3B(base_model_type):
coefficients= [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01]
else:
coefficients= [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404]
skip_steps_cache.coefficients = coefficients
@staticmethod
def query_model_def(base_model_type, model_def):
extra_model_def = {}
if "URLs2" in model_def:
extra_model_def["no_steps_skipping"] = True
extra_model_def["compile"] = ["transformer","transformer2"]
text_encoder_folder = "umt5-xxl"
extra_model_def["text_encoder_URLs"] = [
build_hf_url("DeepBeepMeep/Wan2.1", text_encoder_folder, "models_t5_umt5-xxl-enc-bf16.safetensors"),
build_hf_url("DeepBeepMeep/Wan2.1", text_encoder_folder, "models_t5_umt5-xxl-enc-quanto_int8.safetensors"),
]
extra_model_def["text_encoder_folder"] = text_encoder_folder
extra_model_def["i2v_class"] = i2v = test_class_i2v(base_model_type)
extra_model_def["t2v_class"] = t2v = test_class_t2v(base_model_type)
extra_model_def["multitalk_class"] = multitalk = test_multitalk(base_model_type)
extra_model_def["standin_class"] = standin = test_standin(base_model_type)
extra_model_def["lynx_class"] = lynx = test_lynx(base_model_type)
extra_model_def["alpha_class"] = alpha = test_alpha(base_model_type)
extra_model_def["wan_5B_class"] = wan_5B = test_wan_5B(base_model_type)
extra_model_def["vace_class"] = vace_class = test_vace(base_model_type)
extra_model_def["color_correction"] = True
extra_model_def["svi2pro"] = svi2pro = test_svi2pro(base_model_type)
extra_model_def["i2v_2_2"] = i2v_2_2 = test_i2v_2_2(base_model_type)
if multitalk or base_model_type in ["fantasy"]:
if multitalk:
extra_model_def["audio_prompt_choices"] = True
extra_model_def["any_audio_prompt"] = True
if base_model_type in ["vace_multitalk_14B", "vace_standin_14B", "vace_lynx_14B"]:
extra_model_def["parent_model_type"] = "vace_14B"
group = "wan"
if base_model_type in ["t2v_2_2", "vace_14B_2_2"] or test_i2v_2_2(base_model_type):
profiles_dir = "wan_2_2"
group = "wan2_2"
elif i2v:
profiles_dir = "wan_i2v"
if base_model_type in ["chrono_edit"]:
profiles_dir = "wan_chrono_edit"
elif test_wan_5B(base_model_type):
profiles_dir = "wan_2_2_5B"
group = "wan2_2"
elif test_class_1_3B(base_model_type):
profiles_dir = "wan_1.3B"
elif test_alpha(base_model_type):
profiles_dir = "wan_alpha"
else:
profiles_dir = "wan"
if (test_class_t2v(base_model_type) or vace_class or base_model_type in ["chrono_edit"]) and not test_alpha(base_model_type):
extra_model_def["vae_upsampler"] = [1,2]
extra_model_def["profiles_dir"] = [profiles_dir]
extra_model_def["group"] = group
if base_model_type in ["animate"]:
fps = 30
elif multitalk:
fps = 25
elif base_model_type in ["fantasy"]:
fps = 23
elif wan_5B:
fps = 24
else:
fps = 16
extra_model_def["fps"] =fps
multiple_submodels = "URLs2" in model_def
if vace_class:
frames_minimum, frames_steps = 17, 4
else:
frames_minimum, frames_steps = 5, 4
extra_model_def.update({
"frames_minimum" : frames_minimum,
"frames_steps" : frames_steps,
"sliding_window" : base_model_type in ["multitalk", "infinitetalk", "t2v", "t2v_2_2", "fantasy", "animate", "lynx"] or test_class_i2v(base_model_type) or test_wan_5B(base_model_type) or vace_class, #"ti2v_2_2",
"multiple_submodels" : multiple_submodels,
"guidance_max_phases" : 3,
"skip_layer_guidance" : True,
"flow_shift": True,
"cfg_zero" : True,
"cfg_star" : True,
"adaptive_projected_guidance" : True,
"tea_cache" : not (base_model_type in ["i2v_2_2"] or test_wan_5B(base_model_type) or multiple_submodels),
"mag_cache" : True,
"keep_frames_video_guide_not_supported": base_model_type in ["infinitetalk"],
"sample_solvers":[
("unipc", "unipc"),
("euler", "euler"),
("dpm++", "dpm++"),
("flowmatch causvid", "causvid"),
("lcm + ltx", "lcm"), ]
})
if i2v:
extra_model_def["motion_amplitude"] = True
if base_model_type in ["i2v_2_2"]:
extra_model_def["i2v_v2v"] = True
extra_model_def["extract_guide_from_window_start"] = True
extra_model_def["guide_custom_choices"] = {
"choices":[("Use Text & Image Prompt Only", ""),
("Video to Video guided by Text Prompt & Image", "GUV"),
("Video to Video guided by Text/Image Prompt and Restricted to the Area of the Video Mask", "GVA")],
"default": "",
"show_label" : False,
"letters_filter": "GUVA",
"label": "Video to Video"
}
extra_model_def["mask_preprocessing"] = {
"selection":[ "", "A"],
"visible": False
}
if svi2pro:
extra_model_def["image_ref_choices"] = {
"choices": [("No Anchor Image", ""),
("Anchor Images For Each Window", "KI"),
],
"letters_filter": "KI",
"show_label" : False,
}
extra_model_def["all_image_refs_are_background_ref"] = True
extra_model_def["parent_model_type"] = "i2v_2_2"
if base_model_type in ["i2v", "flf2v_720p"] or test_i2v_2_2(base_model_type):
extra_model_def["black_frame"] = True
if t2v:
if not alpha:
extra_model_def["guide_custom_choices"] = {
"choices":[("Use Text Prompt Only", ""),
("Video to Video guided by Text Prompt", "GUV"),
("Video to Video guided by Text Prompt and Restricted to the Area of the Video Mask", "GVA")],
"default": "",
"show_label" : False,
"letters_filter": "GUVA",
"label": "Video to Video"
}
extra_model_def["mask_preprocessing"] = {
"selection":[ "", "A"],
"visible": False
}
extra_model_def["v2i_switch_supported"] = True
if base_model_type in ["wanmove"]:
extra_model_def["custom_guide"] = { "label": "Trajectory File", "required": True, "file_types": [".npy"]}
extra_model_def["i2v_trajectory"] = True
if base_model_type in ["steadydancer"]:
extra_model_def["guide_custom_choices"] = {
"choices":[
("Use Control Video Poses to Animate Person in Start Image", "V"),
("Use Control Video Poses filterd with Mask Video to Animate Person in Start Image", "VA"),
],
"default": "PVB",
"letters_filter": "PVBA",
"label": "Type of Process",
"scale": 3,
"show_label" : False,
}
extra_model_def["custom_preprocessor"] = "Extracting Pose Information"
extra_model_def["alt_guidance"] = "Condition Guidance"
extra_model_def["no_guide2_refresh"] = True
extra_model_def["no_mask_refresh"] = True
extra_model_def["control_video_trim"] = True
if base_model_type in ["scail"]:
extra_model_def["guide_custom_choices"] = {
"choices": [
("Animate One Person", "V#1#"),
("Animate Two Persons", "V#2#"),
("Animate Three Persons", "V#3#"),
("Animate Four Persons", "V#4#"),
("Animate Five Persons", "V#5#"),
],
"default": "V#1#",
"letters_filter": "V#12345",
"label": "Type of Process",
"scale": 3,
"show_label": True,
}
extra_model_def["preprocess_all"] = True
extra_model_def["custom_preprocessor"] = "Extracting 3D Pose (NLFPose)"
extra_model_def["forced_guide_mask_inputs"] = True
extra_model_def["keep_frames_video_guide_not_supported"] = True
extra_model_def["mask_preprocessing"] = {
"selection": ["", "A", "NA"],
"visible": True,
"label": "Persons Locations"
}
extra_model_def["control_video_trim"] = True
extra_model_def["extract_guide_from_window_start"] = True
extra_model_def["return_image_refs_tensor"] = True
# extra_model_def["image_ref_choices"] = {
# "choices": [
# ("No Reference Image", ""),
# ("Reference Image of People", "I"),
# ],
# "visible": True,
# "letters_filter":"I",
# }
if base_model_type in ["infinitetalk"]:
extra_model_def["no_background_removal"] = True
extra_model_def["all_image_refs_are_background_ref"] = True
extra_model_def["guide_custom_choices"] = {
"choices":[
("Images to Video, each Reference Image will start a new shot with a new Sliding Window", "KI"),
("Sparse Video to Video, one Image will by extracted from Video for each new Sliding Window", "RUV"),
("Video to Video, amount of motion transferred depends on Denoising Strength", "GUV"),
],
"default": "KI",
"letters_filter": "RGUVKI",
"label": "Video to Video",
"scale": 3,
"show_label" : False,
}
extra_model_def["custom_video_selection"] = {
"choices":[
("Smooth Transitions", ""),
("Sharp Transitions", "0"),
],
"trigger": "",
"label": "Custom Process",
"letters_filter": "0",
"show_label" : False,
"scale": 1,
}
# extra_model_def["at_least_one_image_ref_needed"] = True
if base_model_type in ["lucy_edit"]:
extra_model_def["keep_frames_video_guide_not_supported"] = True
extra_model_def["guide_preprocessing"] = {
"selection": ["UV"],
"labels" : { "UV": "Control Video"},
"visible": False,
}
if base_model_type in ["animate"]:
extra_model_def["guide_custom_choices"] = {
"choices":[
("Animate Person in Reference Image using Motion of Whole Control Video", "PVBKI"),
("Animate Person in Reference Image using Motion of Targeted Person in Control Video", "PVBXAKI"),
("Replace Person in Control Video by Person in Ref Image", "PVBAIH#"),
("Replace Person in Control Video by Person in Ref Image. See Through Mask", "PVBAI#"),
],
"default": "PVBKI",
"letters_filter": "PVBXAKIH#",
"label": "Type of Process",
"scale": 3,
"show_label" : False,
}
extra_model_def["custom_video_selection"] = {
"choices":[
("None", ""),
("Apply Relighting", "1"),
],
"trigger": "#",
"label": "Custom Process",
"type": "checkbox",
"letters_filter": "1",
"show_label" : False,
"scale": 1,
}
extra_model_def["mask_preprocessing"] = {
"selection":[ "", "A", "XA"],
"visible": False
}
extra_model_def["video_guide_outpainting"] = [0,1]
extra_model_def["keep_frames_video_guide_not_supported"] = True
extra_model_def["extract_guide_from_window_start"] = True
extra_model_def["forced_guide_mask_inputs"] = True
extra_model_def["no_background_removal"] = True
extra_model_def["background_removal_label"]= "Remove Backgrounds behind People (Animate Mode Only)"
extra_model_def["background_ref_outpainted"] = False
extra_model_def["return_image_refs_tensor"] = True
extra_model_def["guide_inpaint_color"] = 0
if vace_class:
extra_model_def["control_net_weight_name"] = "Vace"
extra_model_def["control_net_weight_size"] = 2
extra_model_def["guide_preprocessing"] = {
"selection": ["", "UV", "PV", "DV", "SV", "LV", "CV", "MV", "V", "PDV", "PSV", "PLV" , "DSV", "DLV", "SLV"],
"labels" : { "V": "Use Vace raw format"}
}
extra_model_def["mask_preprocessing"] = {
"selection": ["", "A", "NA", "XA", "XNA", "YA", "YNA", "WA", "WNA", "ZA", "ZNA"],
}
extra_model_def["image_ref_choices"] = {
"choices": [("None", ""),
("People / Objects", "I"),
("Landscape followed by People / Objects (if any)", "KI"),
("Positioned Frames followed by People / Objects (if any)", "FI"),
],
"letters_filter": "KFI",
}
extra_model_def["background_removal_label"]= "Remove Backgrounds behind People / Objects, keep it for Landscape or Positioned Frames"
extra_model_def["video_guide_outpainting"] = [0,1]
extra_model_def["pad_guide_video"] = True
extra_model_def["guide_inpaint_color"] = 127.5
extra_model_def["forced_guide_mask_inputs"] = True
extra_model_def["return_image_refs_tensor"] = True
extra_model_def["v2i_switch_supported"] = True
if lynx:
extra_model_def["set_video_prompt_type"]="Q"
extra_model_def["control_net_weight_alt_name"] = "Lynx"
extra_model_def["image_ref_choices"]["choices"] = [("None", ""),
("People / Objects (if any) then a Face", "I"),
("Landscape followed by People / Objects (if any) then a Face", "KI"),
("Positioned Frames followed by People / Objects (if any) then a Face", "FI")]
extra_model_def["background_removal_label"]= "Remove Backgrounds behind People / Objects, keep it for Landscape, Lynx Face or Positioned Frames"
extra_model_def["no_processing_on_last_images_refs"] = 1
if base_model_type in ["vace_ditto_14B"]:
del extra_model_def["guide_preprocessing"], extra_model_def["image_ref_choices"], extra_model_def["video_guide_outpainting"]
extra_model_def["mask_preprocessing"] = { "selection": ["", "A"], }
extra_model_def["model_modes"] = {
"choices": [
("Global", 0),
("Global Style", 1),
("Sim 2 Real", 2)],
"default": 0,
"label" : "Ditto Process"
}
if base_model_type in ["chrono_edit"]:
extra_model_def["model_modes"] = {
"choices": [
("Fast Image Transformation", 0),
("Long Image Transformation", 1),
("Temporal Reasoning Video", 2),],
"default": 0,
"label" : "Chrono Edit Process"
}
extra_model_def["custom_video_length"] = True
if (not vace_class) and standin:
extra_model_def["v2i_switch_supported"] = True
extra_model_def["image_ref_choices"] = {
"choices": [
("No Reference Image", ""),
("Reference Image is a Person Face", "I"),
],
"visible": False,
"letters_filter":"I",
}
extra_model_def["one_image_ref_needed"] = True
if (not vace_class) and lynx:
extra_model_def["fit_into_canvas_image_refs"] = 0
extra_model_def["guide_custom_choices"] = {
"choices":[("Use Reference Image which is a Person Face", ""),
("Video to Video guided by Text Prompt & Reference Image", "GUV"),
("Video to Video on the Area of the Video Mask", "GVA")],
"default": "",
"letters_filter": "GUVA",
"label": "Video to Video",
"show_label" : False,
}
extra_model_def["mask_preprocessing"] = {
"selection":[ "", "A"],
"visible": False
}
extra_model_def["image_ref_choices"] = {
"choices": [
("No Reference Image", ""),
("Reference Image is a Person Face", "I"),
],
"visible": False,
"letters_filter":"I",
}
extra_model_def["one_image_ref_needed"] = True
extra_model_def["set_video_prompt_type"]= "Q"
extra_model_def["no_background_removal"] = True
extra_model_def["v2i_switch_supported"] = True
extra_model_def["control_net_weight_alt_name"] = "Lynx"
if base_model_type in ["phantom_1.3B", "phantom_14B"]:
extra_model_def["image_ref_choices"] = {
"choices": [("Reference Image", "I")],
"letters_filter":"I",
"visible": False,
}
if base_model_type in ["recam_1.3B"]:
extra_model_def["keep_frames_video_guide_not_supported"] = True
extra_model_def["model_modes"] = {
"choices": [
("Pan Right", 1),
("Pan Left", 2),
("Tilt Up", 3),
("Tilt Down", 4),
("Zoom In", 5),
("Zoom Out", 6),
("Translate Up (with rotation)", 7),
("Translate Down (with rotation)", 8),
("Arc Left (with rotation)", 9),
("Arc Right (with rotation)", 10),
],
"default": 1,
"label" : "Camera Movement Type"
}
extra_model_def["guide_preprocessing"] = {
"selection": ["UV"],
"labels" : { "UV": "Control Video"},
"visible" : False,
}
extra_model_def["video_length_locked"] = 81
if base_model_type in ["chrono_edit"]:
from .chono_edit_prompt import image_prompt_enhancer_instructions
extra_model_def["image_prompt_enhancer_instructions"] = image_prompt_enhancer_instructions
extra_model_def["video_prompt_enhancer_instructions"] = image_prompt_enhancer_instructions
extra_model_def["image_outputs"] = True
extra_model_def["prompt_enhancer_choices_allowed"] = ["TI"]
if vace_class or base_model_type in ["animate", "t2v", "t2v_2_2", "lynx"] :
image_prompt_types_allowed = "TVL"
elif base_model_type in ["infinitetalk"]:
image_prompt_types_allowed = "TSVL"
elif base_model_type in ["ti2v_2_2"]:
image_prompt_types_allowed = "TSVL"
elif base_model_type in ["lucy_edit"]:
image_prompt_types_allowed = "TVL"
elif multitalk or base_model_type in ["fantasy", "steadydancer", "scail"] or svi2pro:
image_prompt_types_allowed = "SVL"
elif i2v:
image_prompt_types_allowed = "SEVL"
else:
image_prompt_types_allowed = ""
extra_model_def["image_prompt_types_allowed"] = image_prompt_types_allowed
if base_model_type in ["mocha"]:
extra_model_def["guide_custom_choices"] = {
"choices":[
("Transfer Person In Reference Images (Second Image must be a Close Up) in Control Video", "VAI"),
],
"default": "VAI",
"letters_filter": "VAI",
"label": "Type of Process",
"scale": 3,
"show_label" : False,
"visible": True,
}
extra_model_def["background_removal_color"] = [128, 128, 128]
if base_model_type in ["fantasy"] or multitalk:
extra_model_def["audio_guidance"] = True
extra_model_def["NAG"] = vace_class or t2v or i2v
if test_oneframe_overlap(base_model_type):
extra_model_def["sliding_window_defaults"] = { "overlap_min" : 1, "overlap_max" : 1, "overlap_step": 0, "overlap_default": 1}
elif svi2pro:
extra_model_def["sliding_window_defaults"] = { "overlap_min" : 4, "overlap_max" : 4, "overlap_step": 0, "overlap_default": 4}
# if base_model_type in ["phantom_1.3B", "phantom_14B"]:
# extra_model_def["one_image_ref_needed"] = True
return extra_model_def
@staticmethod
def get_vae_block_size(base_model_type):
return 32 if test_wan_5B(base_model_type) or base_model_type in ["scail"] else 16
@staticmethod
def get_rgb_factors(base_model_type ):
from shared.RGB_factors import get_rgb_factors
if test_wan_5B(base_model_type): base_model_type = "ti2v_2_2"
latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("wan", base_model_type)
return latent_rgb_factors, latent_rgb_factors_bias
@staticmethod
def query_model_files(computeList, base_model_type, model_def=None):
if test_wan_5B(base_model_type):
wan_files = []
else:
wan_files = ["Wan2.1_VAE.safetensors", "Wan2.1_VAE_upscale2x_imageonly_real_v1.safetensors"]
if base_model_type in ["fantasy"]:
wan_files.append("fantasy_proj_model.safetensors")
download_def = [{
"repoId" : "DeepBeepMeep/Wan2.1",
"sourceFolderList" : ["xlm-roberta-large", "umt5-xxl", "" ],
"fileList" : [ [ "models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors", "sentencepiece.bpe.model", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json"], ["special_tokens_map.json", "spiece.model", "tokenizer.json", "tokenizer_config.json"], wan_files ]
}]
if base_model_type == "scail":
# SCAIL pose extraction (NLFPose torchscript). Kept separate so it isn't downloaded for every model.
download_def += [
{
"repoId": "DeepBeepMeep/Wan2.1",
"sourceFolderList": ["pose"],
"fileList": [["nlf_l_multi_0.3.2.eager.safetensors", "nlf_l_multi_0.3.2.eager.meta.json"]],
}
]
if test_wan_5B(base_model_type):
download_def += [ {
"repoId" : "DeepBeepMeep/Wan2.2",
"sourceFolderList" : [""],
"fileList" : [ [ "Wan2.2_VAE.safetensors"] ]
}]
return download_def
@staticmethod
def custom_preprocess(base_model_type, video_guide, video_mask, pre_video_guide=None, max_workers = 1, expand_scale = 0, video_prompt_type = None, **kwargs):
from shared.utils.utils import convert_tensor_to_image
ref_image = convert_tensor_to_image(pre_video_guide[:, 0])
frames = video_guide
mask_frames = None if video_mask is None else video_mask
if base_model_type == "scail":
extract_max_people = lambda s: int(m.group(1)) if (m := re.search(r'#(\d+)#', s)) else 1
# ref_image = ref_image.resize( (ref_image.width // 2, ref_image.height // 2), resample=Image.LANCZOS )
from .scail import ScailPoseProcessor
scail_max_people = extract_max_people(video_prompt_type)
scail_multi_person = scail_max_people > 1
processor = ScailPoseProcessor(multi_person=scail_multi_person, max_people=scail_max_people)
video_guide_processed = processor.extract_and_render(
frames,
ref_image=ref_image,
mask_frames=mask_frames,
align_pose=True
)
if video_guide_processed.numel() == 0:
gr.Info("Unable to detect a Person")
return None, None, None, None
return video_guide_processed, None, video_mask, None
else:
# Steadydancer
from .steadydancer.pose_align import PoseAligner
aligner = PoseAligner()
outputs = aligner.align(frames, ref_image, ref_video_mask=None, align_frame=0, max_frames=None, augment=True, include_composite=False, cpu_resize_workers=max_workers, expand_scale=expand_scale)
video_guide_processed, video_guide_processed2 = outputs["pose_only"], outputs["pose_aug"]
if video_guide_processed.numel() == 0:
return None, None, None, None
return video_guide_processed, video_guide_processed2, None, None
@staticmethod
def load_model(model_filename, model_type, base_model_type, model_def, quantizeTransformer = False, text_encoder_quantization = None, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized= False, submodel_no_list = None, text_encoder_filename = None, VAE_upsampling = None, **kwargs):
from .configs import WAN_CONFIGS
if test_class_i2v(base_model_type):
cfg = WAN_CONFIGS['i2v-14B']
else:
cfg = WAN_CONFIGS['t2v-14B']
# cfg = WAN_CONFIGS['t2v-1.3B']
from . import WanAny2V
wan_model = WanAny2V(
config=cfg,
checkpoint_dir="ckpts",
model_filename=model_filename,
submodel_no_list = submodel_no_list,
model_type = model_type,
model_def = model_def,
base_model_type=base_model_type,
text_encoder_filename= text_encoder_filename,
quantizeTransformer = quantizeTransformer,
dtype = dtype,
VAE_dtype = VAE_dtype,
mixed_precision_transformer = mixed_precision_transformer,
save_quantized = save_quantized,
VAE_upsampling = VAE_upsampling,
)
pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model }
if wan_model.vae2 is not None:
pipe["vae2"] = wan_model.vae2.model
if hasattr(wan_model,"model2") and wan_model.model2 is not None:
pipe["transformer2"] = wan_model.model2
if hasattr(wan_model, "clip"):
pipe["text_encoder_2"] = wan_model.clip.model
return wan_model, pipe
@staticmethod
def fix_settings(base_model_type, settings_version, model_def, ui_defaults):
if ui_defaults.get("sample_solver", "") == "":
ui_defaults["sample_solver"] = "unipc"
if settings_version < 2.24:
if (model_def.get("multiple_submodels", False) or ui_defaults.get("switch_threshold", 0) > 0) and ui_defaults.get("guidance_phases",0)<2:
ui_defaults["guidance_phases"] = 2
if settings_version == 2.24 and ui_defaults.get("guidance_phases",0) ==2:
mult = model_def.get("loras_multipliers","")
if len(mult)> 1 and len(mult[0].split(";"))==3: ui_defaults["guidance_phases"] = 3
if settings_version < 2.27:
if base_model_type in "infinitetalk":
guidance_scale = ui_defaults.get("guidance_scale", None)
if guidance_scale == 1:
ui_defaults["audio_guidance_scale"]= 1
video_prompt_type = ui_defaults.get("video_prompt_type", "")
if "I" in video_prompt_type:
video_prompt_type = video_prompt_type.replace("KI", "0KI")
ui_defaults["video_prompt_type"] = video_prompt_type
if settings_version < 2.28:
if base_model_type in "infinitetalk":
video_prompt_type = ui_defaults.get("video_prompt_type", "")
if "U" in video_prompt_type:
video_prompt_type = video_prompt_type.replace("U", "RU")
ui_defaults["video_prompt_type"] = video_prompt_type
if settings_version < 2.31:
if base_model_type in ["recam_1.3B"]:
video_prompt_type = ui_defaults.get("video_prompt_type", "")
if not "V" in video_prompt_type:
video_prompt_type += "UV"
ui_defaults["video_prompt_type"] = video_prompt_type
ui_defaults["image_prompt_type"] = ""
if test_oneframe_overlap(base_model_type):
ui_defaults["sliding_window_overlap"] = 1
if settings_version < 2.32:
image_prompt_type = ui_defaults.get("image_prompt_type", "")
if test_class_i2v(base_model_type) and len(image_prompt_type) == 0 and "S" in model_def.get("image_prompt_types_allowed",""):
ui_defaults["image_prompt_type"] = "S"
if settings_version < 2.37:
if base_model_type in ["animate"]:
video_prompt_type = ui_defaults.get("video_prompt_type", "")
if "1" in video_prompt_type:
video_prompt_type = video_prompt_type.replace("1", "#1")
ui_defaults["video_prompt_type"] = video_prompt_type
if settings_version < 2.38:
if base_model_type in ["infinitetalk"]:
video_prompt_type = ui_defaults.get("video_prompt_type", "")
if "Q" in video_prompt_type:
video_prompt_type = video_prompt_type.replace("Q", "0")
ui_defaults["video_prompt_type"] = video_prompt_type
if settings_version < 2.39:
if base_model_type in ["fantasy"]:
audio_prompt_type = ui_defaults.get("audio_prompt_type", "")
if not "A" in audio_prompt_type:
audio_prompt_type += "A"
ui_defaults["audio_prompt_type"] = audio_prompt_type
if settings_version < 2.40:
if base_model_type in ["animate"]:
remove_background_images_ref = ui_defaults.get("remove_background_images_ref", None)
if remove_background_images_ref !=0:
ui_defaults["remove_background_images_ref"] = 0
if settings_version < 2.42 and test_svi2pro(base_model_type):
ui_defaults.update({
"sliding_window_size": 81,
"sliding_window_overlap" : 4,
})
@staticmethod
def update_default_settings(base_model_type, model_def, ui_defaults):
ui_defaults.update({
"sample_solver": "unipc",
})
if test_class_i2v(base_model_type) and "S" in model_def["image_prompt_types_allowed"]:
ui_defaults["image_prompt_type"] = "S"
if base_model_type in ["fantasy"]:
ui_defaults.update({
"audio_guidance_scale": 5.0,
"sliding_window_overlap" : 1,
"audio_prompt_type": "A",
})
elif base_model_type in ["multitalk"]:
ui_defaults.update({
"guidance_scale": 5.0,
"flow_shift": 7, # 11 for 720p
"sliding_window_discard_last_frames" : 4,
"sample_solver" : "euler",
"audio_prompt_type": "A",
"adaptive_switch" : 1,
})
elif base_model_type in ["infinitetalk"]:
ui_defaults.update({
"guidance_scale": 5.0,
"flow_shift": 7, # 11 for 720p
"sliding_window_overlap" : 9,
"sliding_window_size": 81,
"sample_solver" : "euler",
"video_prompt_type": "0KI",
"remove_background_images_ref" : 0,
"adaptive_switch" : 1,
})
elif base_model_type in ["standin"]:
ui_defaults.update({
"guidance_scale": 5.0,
"flow_shift": 7, # 11 for 720p
"sliding_window_overlap" : 9,
"video_prompt_type": "I",
"remove_background_images_ref" : 1 ,
})
elif (base_model_type in ["lynx_lite", "lynx", "alpha_lynx"]):
ui_defaults.update({
"guidance_scale": 5.0,
"flow_shift": 7, # 11 for 720p
"sliding_window_overlap" : 9,
"video_prompt_type": "I",
"denoising_strength": 0.8,
"remove_background_images_ref" : 0,
})
elif base_model_type in ["phantom_1.3B", "phantom_14B"]:
ui_defaults.update({
"guidance_scale": 7.5,
"flow_shift": 5,
"remove_background_images_ref": 1,
"video_prompt_type": "I",
# "resolution": "1280x720"
})
elif base_model_type in ["vace_14B", "vace_multitalk_14B"]:
ui_defaults.update({
"sliding_window_discard_last_frames": 0,
})
elif base_model_type in ["ti2v_2_2"]:
ui_defaults.update({
"image_prompt_type": "T",
})
if base_model_type in ["recam_1.3B", "lucy_edit"]:
ui_defaults.update({
"video_prompt_type": "UV",
})
elif base_model_type in ["animate"]:
ui_defaults.update({
"video_prompt_type": "PVBKI",
"mask_expand": 20,
"audio_prompt_type": "R",
"remove_background_images_ref" : 0,
"force_fps": "control",
})
elif base_model_type in ["vace_ditto_14B"]:
ui_defaults.update({
"video_prompt_type": "V",
})
elif base_model_type in ["mocha"]:
ui_defaults.update({
"video_prompt_type": "VAI",
"audio_prompt_type": "R",
"force_fps": "control",
})
elif base_model_type in ["steadydancer"]:
ui_defaults.update({
"video_prompt_type": "VA",
"image_prompt_type": "S",
"audio_prompt_type": "R",
"force_fps": "control",
"alt_guidance_scale" : 2.0,
})
elif base_model_type in ["scail"]:
ui_defaults.update({
"video_prompt_type": "V#1#",
"image_prompt_type": "S",
"audio_prompt_type": "R",
"force_fps": "control",
"sliding_window_overlap" : 1,
"sliding_window_size": 81,
})
if test_svi2pro(base_model_type):
ui_defaults.update({
"sliding_window_size": 81,
"sliding_window_overlap" : 4,
})
if test_wan_5B(base_model_type):
ui_defaults.update({
"sliding_window_size": 121,
})
if base_model_type in ["i2v_2_2"]:
ui_defaults.update({"masking_strength": 0.1, "denoising_strength": 0.9})
if base_model_type in ["chrono_edit"]:
ui_defaults.update({"image_mode": 1, "prompt_enhancer":"TI"})
if test_oneframe_overlap(base_model_type):
ui_defaults["sliding_window_overlap"] = 1
ui_defaults["sliding_window_color_correction_strength"]= 0
if test_multitalk(base_model_type):
ui_defaults["audio_guidance_scale"] = 4
if model_def.get("multiple_submodels", False):
ui_defaults["guidance_phases"] = 2
@staticmethod
def validate_generative_settings(base_model_type, model_def, inputs):
if base_model_type in ["infinitetalk"]:
video_source = inputs["video_source"]
image_refs = inputs["image_refs"]
video_prompt_type = inputs["video_prompt_type"]
image_prompt_type = inputs["image_prompt_type"]
if ("V" in image_prompt_type or "L" in image_prompt_type) and image_refs is None:
video_prompt_type = video_prompt_type.replace("I", "").replace("K","")
inputs["video_prompt_type"] = video_prompt_type
elif base_model_type in ["vace_standin_14B", "vace_lynx_14B"]:
image_refs = inputs["image_refs"]
video_prompt_type = inputs["video_prompt_type"]
if image_refs is not None and len(image_refs) == 1 and "K" in video_prompt_type:
gr.Info("Warning, Ref Image that contains the Face to transfer is Missing: if 'Landscape and then People or Objects' is selected beside the Landscape Image Ref there should be another Image Ref that contains a Face.")
elif base_model_type in ["chrono_edit"]:
model_mode = inputs["model_mode"]
inputs["video_length"] = 5 if model_mode==0 else 29
inputs["image_mode"] = 0 if model_mode==2 else 1
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