mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-05 22:42:29 +00:00
186 lines
7.6 KiB
Python
186 lines
7.6 KiB
Python
import torch
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def test_class_i2v(base_model_type):
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return base_model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "flf2v_720p", "fantasy", "multitalk", ] #"hunyuan_i2v",
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class family_handler():
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@staticmethod
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def get_wan_text_encoder_filename(text_encoder_quantization):
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text_encoder_filename = "ckpts/umt5-xxl/models_t5_umt5-xxl-enc-bf16.safetensors"
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if text_encoder_quantization =="int8":
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text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_int8")
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return text_encoder_filename
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@staticmethod
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def query_modules_files():
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return {
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"vace_14B" : ["ckpts/wan2.1_Vace_14B_module_mbf16.safetensors", "ckpts/wan2.1_Vace_14B_module_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_Vace_14B_module_quanto_mfp16_int8.safetensors"],
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"vace_1.3B" : ["ckpts/wan2.1_Vace_1_3B_module.safetensors"],
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"fantasy": ["ckpts/wan2.1_fantasy_speaking_14B_bf16.safetensors"],
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"multitalk": ["ckpts/wan2.1_multitalk_14B_mbf16.safetensors", "ckpts/wan2.1_multitalk_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_multitalk_14B_quanto_mfp16_int8.safetensors"]
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}
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@staticmethod
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def query_model_def(base_model_type, model_def):
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extra_model_def = {}
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if "URLs2" in model_def:
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extra_model_def["no_steps_skipping"] = True
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extra_model_def["i2v_class"] = test_class_i2v(base_model_type)
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vace_class = base_model_type in ["vace_14B", "vace_1.3B", "vace_multitalk_14B"]
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extra_model_def["vace_class"] = vace_class
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if base_model_type in ["multitalk", "vace_multitalk_14B"]:
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fps = 25
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elif base_model_type in ["fantasy"]:
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fps = 23
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elif base_model_type in ["ti2v_2_2"]:
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fps = 24
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else:
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fps = 16
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extra_model_def["fps"] =fps
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if vace_class:
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frames_minimum, frames_steps = 17, 4
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else:
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frames_minimum, frames_steps = 5, 4
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extra_model_def.update({
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"frames_minimum" : frames_minimum,
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"frames_steps" : frames_steps,
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"sliding_window" : base_model_type in ["multitalk", "t2v", "fantasy"] or test_class_i2v(base_model_type) or vace_class, #"ti2v_2_2",
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"guidance_max_phases" : 2,
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"skip_layer_guidance" : True,
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"cfg_zero" : True,
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"cfg_star" : True,
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"adaptive_projected_guidance" : True,
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"skip_steps_cache" : True,
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})
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return extra_model_def
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@staticmethod
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def query_supported_types():
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return ["multitalk", "fantasy", "vace_14B", "vace_multitalk_14B",
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"t2v_1.3B", "t2v", "vace_1.3B", "phantom_1.3B", "phantom_14B",
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"recam_1.3B",
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"i2v", "i2v_2_2", "ti2v_2_2", "flf2v_720p", "fun_inp_1.3B", "fun_inp"]
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@staticmethod
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def query_family_maps():
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models_eqv_map = {
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"flf2v_720p" : "i2v",
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"t2v_1.3B" : "t2v",
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}
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models_comp_map = {
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"vace_14B" : [ "vace_multitalk_14B"],
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"t2v" : [ "vace_14B", "vace_1.3B" "vace_multitalk_14B", "t2v_1.3B", "phantom_1.3B","phantom_14B"],
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"i2v" : [ "fantasy", "multitalk", "flf2v_720p" ],
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"fantasy": ["multitalk"],
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}
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return models_eqv_map, models_comp_map
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@staticmethod
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def query_model_family():
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return "wan"
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@staticmethod
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def query_family_infos():
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return {"wan":(0, "Wan2.1"), "wan2_2":(1, "Wan2.2") }
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@staticmethod
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def get_vae_block_size(base_model_type):
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return 32 if base_model_type == "ti2v_2_2" else 16
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@staticmethod
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def get_rgb_factors(model_type):
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from shared.RGB_factors import get_rgb_factors
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if model_type == "ti2v_2_2": return None, None
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latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("wan")
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return latent_rgb_factors, latent_rgb_factors_bias
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@staticmethod
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def query_model_files(computeList, base_model_type, model_filename, text_encoder_quantization):
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text_encoder_filename = family_handler.get_wan_text_encoder_filename(text_encoder_quantization)
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vae_filepath = "Wan2.2_VAE.safetensors" if base_model_type == "ti2v_2_2" else "Wan2.1_VAE.safetensors"
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return {
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"repoId" : "DeepBeepMeep/Wan2.1",
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"sourceFolderList" : ["xlm-roberta-large", "umt5-xxl", "" ],
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"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"] + computeList(text_encoder_filename) , [vae_filepath, "fantasy_proj_model.safetensors" ] + computeList(model_filename) ]
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}
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@staticmethod
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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):
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from .configs import WAN_CONFIGS
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if test_class_i2v(base_model_type):
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cfg = WAN_CONFIGS['i2v-14B']
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else:
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cfg = WAN_CONFIGS['t2v-14B']
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# cfg = WAN_CONFIGS['t2v-1.3B']
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from . import WanAny2V
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wan_model = WanAny2V(
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config=cfg,
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checkpoint_dir="ckpts",
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model_filename=model_filename,
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model_type = model_type,
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model_def = model_def,
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base_model_type=base_model_type,
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text_encoder_filename= family_handler.get_wan_text_encoder_filename(text_encoder_quantization),
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quantizeTransformer = quantizeTransformer,
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dtype = dtype,
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VAE_dtype = VAE_dtype,
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mixed_precision_transformer = mixed_precision_transformer,
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save_quantized = save_quantized
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)
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pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model }
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if hasattr(wan_model,"model2") and wan_model.model2 is not None:
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pipe["transformer2"] = wan_model.model2
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if hasattr(wan_model, "clip"):
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pipe["text_encoder_2"] = wan_model.clip.model
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return wan_model, pipe
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@staticmethod
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def update_default_settings(base_model_type, model_def, ui_defaults):
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if base_model_type in ["fantasy"]:
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ui_defaults.update({
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"audio_guidance_scale": 5.0,
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"sliding_window_size": 1,
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})
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elif base_model_type in ["multitalk"]:
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ui_defaults.update({
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"guidance_scale": 5.0,
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"flow_shift": 7, # 11 for 720p
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"audio_guidance_scale": 4,
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"sliding_window_discard_last_frames" : 4,
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"sample_solver" : "euler",
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"adaptive_switch" : 1,
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})
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elif base_model_type in ["phantom_1.3B", "phantom_14B"]:
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ui_defaults.update({
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"guidance_scale": 7.5,
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"flow_shift": 5,
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"remove_background_images_ref": 1,
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"video_prompt_type": "I",
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# "resolution": "1280x720"
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})
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elif base_model_type in ["vace_14B", "vace_multitalk_14B"]:
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ui_defaults.update({
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"sliding_window_discard_last_frames": 0,
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})
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elif base_model_type in ["ti2v_2_2"]:
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ui_defaults.update({
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"image_prompt_type": "T",
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})
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