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import os
import torch
import gc
from ..utils import log
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
import comfy.model_management as mm
from comfy.utils import load_torch_file
import folder_paths
script_directory = os.path.dirname(os.path.abspath(__file__))
class DownloadAndLoadWav2VecModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (["facebook/wav2vec2-base-960h"],),
"base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}),
},
}
RETURN_TYPES = ("WAV2VECMODEL",)
RETURN_NAMES = ("wav2vec_model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
def loadmodel(self, model, base_precision, load_device):
from transformers import Wav2Vec2Model, Wav2Vec2Processor
base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision]
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
if load_device == "offload_device":
transfomer_load_device = offload_device
else:
transfomer_load_device = device
model_path = os.path.join(folder_paths.models_dir, "transformers", model)
if not os.path.exists(model_path):
log.info(f"Downloading Qwen model to: {model_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=model,
ignore_patterns=["*.bin", "*.h5"],
local_dir=model_path,
local_dir_use_symlinks=False,
)
wav2vec_processor = Wav2Vec2Processor.from_pretrained(model_path)
wav2vec = Wav2Vec2Model.from_pretrained(model_path).to(base_dtype).to(transfomer_load_device).eval()
wav2vec_processor_model = {
"processor": wav2vec_processor,
"model": wav2vec,
"dtype": base_dtype,}
return (wav2vec_processor_model,)
class FantasyTalkingModelLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
"base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}),
},
}
RETURN_TYPES = ("FANTASYTALKINGMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
def loadmodel(self, model, base_precision):
from .model import FantasyTalkingAudioConditionModel
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision]
model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
sd = load_torch_file(model_path, device=offload_device, safe_load=True)
with init_empty_weights():
fantasytalking_proj_model = FantasyTalkingAudioConditionModel(audio_in_dim=768, audio_proj_dim=2048)
#fantasytalking_proj_model.load_state_dict(sd, strict=False)
for name, param in fantasytalking_proj_model.named_parameters():
set_module_tensor_to_device(fantasytalking_proj_model, name, device=offload_device, dtype=base_dtype, value=sd[name])
fantasytalking = {
"proj_model": fantasytalking_proj_model,
"sd": sd,
}
return (fantasytalking,)
class FantasyTalkingWav2VecEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"wav2vec_model": ("WAV2VECMODEL",),
"fantasytalking_model": ("FANTASYTALKINGMODEL",),
"audio": ("AUDIO",),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 1000, "step": 1}),
"fps": ("FLOAT", {"default": 23.0, "min": 1.0, "max": 60.0, "step": 0.1}),
"audio_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "Strength of the audio conditioning"}),
"audio_cfg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "When not 1.0, an extra model pass without audio conditioning is done: slower inference but more motion is allowed"}),
},
}
RETURN_TYPES = ("FANTASYTALKING_EMBEDS", )
RETURN_NAMES = ("fantasytalking_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, wav2vec_model, fantasytalking_model, fps, num_frames, audio_scale, audio_cfg_scale, audio):
import torchaudio
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
dtype = wav2vec_model["dtype"]
wav2vec = wav2vec_model["model"]
wav2vec_processor = wav2vec_model["processor"]
audio_proj_model = fantasytalking_model["proj_model"]
sr = 16000
audio_input = audio["waveform"]
sample_rate = audio["sample_rate"]
if sample_rate != sr:
audio_input = torchaudio.functional.resample(audio_input, sample_rate, sr)
audio_input = audio_input[0][0]
start_time = 0
end_time = num_frames / fps
start_sample = int(start_time * sr)
end_sample = int(end_time * sr)
try:
audio_segment = audio_input[start_sample:end_sample]
except:
audio_segment = audio_input
print("audio_segment.shape", audio_segment.shape)
input_values = wav2vec_processor(
audio_segment.numpy(), sampling_rate=sr, return_tensors="pt"
).input_values.to(dtype).to(device)
audio_features = wav2vec(input_values).last_hidden_state
audio_proj_model.proj_model.to(device)
audio_proj_fea = audio_proj_model.get_proj_fea(audio_features)
pos_idx_ranges = audio_proj_model.split_audio_sequence(
audio_proj_fea.size(1), num_frames=num_frames
)
audio_proj_split, audio_context_lens = audio_proj_model.split_tensor_with_padding(
audio_proj_fea, pos_idx_ranges, expand_length=4
) # [b,21,9+8,768]
audio_proj_model.proj_model.to(offload_device)
mm.soft_empty_cache()
out = {
"audio_proj": audio_proj_split,
"audio_context_lens": audio_context_lens,
"audio_scale": audio_scale,
"audio_cfg_scale": audio_cfg_scale
}
return (out,)
NODE_CLASS_MAPPINGS = {
"DownloadAndLoadWav2VecModel": DownloadAndLoadWav2VecModel,
"FantasyTalkingModelLoader": FantasyTalkingModelLoader,
"FantasyTalkingWav2VecEmbeds": FantasyTalkingWav2VecEmbeds,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DownloadAndLoadWav2VecModel": "(Down)load Wav2Vec Model",
"FantasyTalkingModelLoader": "FantasyTalking Model Loader",
"FantasyTalkingWav2VecEmbeds": "FantasyTalking Wav2Vec Embeds",
}
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