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EchoMimic_node.py
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EchoMimic_node.py
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# !/usr/bin/env python
# -*- coding: UTF-8 -*-
import io
import logging
import os
import random
import numpy as np
import torch
import torchaudio
import gc
from diffusers import AutoencoderKL, DDIMScheduler
from omegaconf import OmegaConf
from .src.models.unet_2d_condition import UNet2DConditionModel
from .src.models.unet_3d_echo import EchoUNet3DConditionModel
from .src.models.whisper.audio2feature import load_audio_model
from .src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline
from .src.pipelines.pipeline_echo_mimic_acc import Audio2VideoPipeline as Audio2VideoACCPipeline
from .src.pipelines.pipeline_echo_mimic_pose import AudioPose2VideoPipeline
from .src.pipelines.pipeline_echo_mimic_pose_acc import AudioPose2VideoPipeline as AudioPose2VideoaccPipeline
from .src.models.face_locator import FaceLocator
from .src.utils.draw_utils import FaceMeshVisualizer
from .src.utils.motion_utils import motion_sync
from .utils import load_images, tensor2cv, find_directories, nomarl_upscale, download_weights, get_instance_path, \
process_video, narry_list, weight_dtype, cf_tensor2cv,process_video_v2
from .echomimic_v2.src.models.pose_encoder import PoseEncoder
from .echomimic_v2.src.pipelines.pipeline_echomimicv2 import EchoMimicV2Pipeline
from .echomimic_v2.src.models.unet_2d_condition import UNet2DConditionModel as UNet2DConditionModelV2
from .echomimic_v2.src.models.unet_3d_emo import EMOUNet3DConditionModel as EMOUNet3DConditionModelV2
from .hallo.video_sr import run_realesrgan, pre_u_loader
from pathlib import Path
from huggingface_hub import hf_hub_download
import folder_paths
import platform
import subprocess
MAX_SEED = np.iinfo(np.int32).max
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
current_path = os.path.dirname(os.path.abspath(__file__))
inference_config_path = os.path.join(current_path, "configs", "inference", "inference_v2.yaml")
infer_config = OmegaConf.load(inference_config_path)
inference_config_path_v2 = os.path.join(current_path, "echomimic_v2/configs/inference/inference_v2.yaml")
infer_config_v2 = OmegaConf.load(inference_config_path_v2)
weigths_hallo_current_path = os.path.join(folder_paths.models_dir, "Hallo")
if not os.path.exists(weigths_hallo_current_path):
os.makedirs(weigths_hallo_current_path)
try:
folder_paths.add_model_folder_path("Hallo", weigths_hallo_current_path, False)
except:
folder_paths.add_model_folder_path("Hallo", weigths_hallo_current_path)
# pre dir
weigths_current_path = os.path.join(folder_paths.models_dir, "echo_mimic")
if not os.path.exists(weigths_current_path):
os.makedirs(weigths_current_path)
weigths_uet_current_path = os.path.join(weigths_current_path, "unet")
if not os.path.exists(weigths_uet_current_path):
os.makedirs(weigths_uet_current_path)
weigths_vae_current_path = os.path.join(weigths_current_path, "vae")
if not os.path.exists(weigths_vae_current_path):
os.makedirs(weigths_vae_current_path)
weigths_au_current_path = os.path.join(weigths_current_path, "audio_processor")
if not os.path.exists(weigths_au_current_path):
os.makedirs(weigths_au_current_path)
tensorrt_lite = os.path.join(folder_paths.get_input_directory(), "tensorrt_lite")
if not os.path.exists(tensorrt_lite):
os.makedirs(tensorrt_lite)
# upscale dir
weigths_facelib_path = os.path.join(weigths_hallo_current_path, "facelib")
if not os.path.exists(weigths_hallo_current_path):
os.makedirs(weigths_facelib_path)
weigths_face_analysis_path = os.path.join(weigths_hallo_current_path, "face_analysis/models")
weigths_face_analysis_dir = os.path.join(weigths_hallo_current_path, "face_analysis")
if not os.path.exists(weigths_face_analysis_path):
os.makedirs(weigths_face_analysis_path)
# ffmpeg
ffmpeg_path = os.getenv('FFMPEG_PATH')
if ffmpeg_path is None and platform.system() in ['Linux', 'Darwin']:
try:
result = subprocess.run(['which', 'ffmpeg'], capture_output=True, text=True)
if result.returncode == 0:
ffmpeg_path = result.stdout.strip()
print(f"FFmpeg is installed at: {ffmpeg_path}")
else:
print("FFmpeg is not installed. Please download ffmpeg-static and export to FFMPEG_PATH.")
print("For example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static")
except Exception as e:
pass
if ffmpeg_path is not None and ffmpeg_path not in os.getenv('PATH'):
print("Adding FFMPEG_PATH to PATH")
os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"
# *****************mian***************
class Echo_LoadModel:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"vae": ("STRING", {"default": "stabilityai/sd-vae-ft-mse"}),
"denoising": ("BOOLEAN", {"default": True},),
"infer_mode": (["audio_drived", "audio_drived_acc", "pose_normal_dwpose","pose_normal_sapiens", "pose_acc"],),
"draw_mouse": ("BOOLEAN", {"default": False},),
"motion_sync": ("BOOLEAN", {"default": False},),
"lowvram": ("BOOLEAN", {"default": False},),
"version": (["V2", "V1", ],),
}
}
RETURN_TYPES = ("MODEL_PIPE_E", "MODEL_FACE_E", "MODEL_VISUAL_E",)
RETURN_NAMES = ("model", "face_detector", "visualizer",)
FUNCTION = "main_loader"
CATEGORY = "EchoMimic"
def main_loader(self, vae, denoising, infer_mode, draw_mouse, motion_sync, lowvram, version):
############# model_init started #############
## vae init #using local vae first
try:
vae = AutoencoderKL.from_pretrained(weigths_vae_current_path).to(device,
dtype=weight_dtype) # using local vae first
except:
try: # try downlaod model ,and load local vae
download_weights(weigths_vae_current_path, "stabilityai/sd-vae-ft-mse", subfolder="",
pt_name="diffusion_pytorch_model.safetensors")
download_weights(weigths_vae_current_path, "stabilityai/sd-vae-ft-mse", subfolder="",
pt_name="config.json")
vae = AutoencoderKL.from_pretrained(weigths_vae_current_path).to(device, dtype=weight_dtype)
except:
vae = AutoencoderKL.from_pretrained(vae).to(device, dtype=weight_dtype)
## reference net init
pretrained_base_model_path = get_instance_path(weigths_current_path)
# pre models
download_weights(weigths_current_path, "lambdalabs/sd-image-variations-diffusers", subfolder="unet",
pt_name="diffusion_pytorch_model.bin")
download_weights(weigths_current_path, "lambdalabs/sd-image-variations-diffusers", subfolder="unet",
pt_name="config.json")
audio_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic", subfolder="audio_processor",
pt_name="whisper_tiny.pt")
# pre pth
if version == "V1":
logging.info("****** refer in EchoMimic V1 mode!******")
if infer_mode == "pose_normal":
re_ckpt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="reference_unet_pose.pth")
face_locator_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="face_locator_pose.pth")
motion_path = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="motion_module_pose.pth")
denois_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="denoising_unet_pose.pth")
elif infer_mode == "pose_acc":
re_ckpt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="reference_unet_pose.pth")
motion_path = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="motion_module_pose_acc.pth")
denois_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="denoising_unet_pose_acc.pth")
face_locator_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="face_locator_pose.pth")
elif infer_mode == "audio_drived":
re_ckpt = download_weights(weigths_current_path, "BadToBest/EchoMimic", pt_name="reference_unet.pth")
face_locator_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="face_locator.pth")
motion_path = download_weights(weigths_current_path, "BadToBest/EchoMimic", pt_name="motion_module.pth")
denois_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic", pt_name="denoising_unet.pth")
else:
re_ckpt = download_weights(weigths_current_path, "BadToBest/EchoMimic", pt_name="reference_unet.pth")
face_locator_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="face_locator.pth")
motion_path = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="motion_module_acc.pth")
denois_pt = download_weights(weigths_current_path, "BadToBest/EchoMimic",
pt_name="denoising_unet_acc.pth")
else:
logging.info("****** refer in EchoMimic V2 mode!******")
weigths_current_path_v2 = os.path.join(weigths_current_path, "v2")
if not os.path.exists(weigths_current_path_v2):
os.makedirs(weigths_current_path_v2)
re_ckpt = download_weights(weigths_current_path_v2, "BadToBest/EchoMimicV2",
pt_name="reference_unet.pth")
motion_path = download_weights(weigths_current_path_v2, "BadToBest/EchoMimicV2",
pt_name="motion_module.pth")
denois_pt = download_weights(weigths_current_path_v2, "BadToBest/EchoMimicV2",
pt_name="denoising_unet.pth")
pose_encoder_pt = download_weights(weigths_current_path_v2, "BadToBest/EchoMimicV2",
pt_name="pose_encoder.pth")
# unet init
if version == "V1":
try:
reference_unet = UNet2DConditionModel.from_config(
pretrained_base_model_path,
subfolder="unet",
).to(dtype=weight_dtype)
except:
reference_unet = UNet2DConditionModel.from_pretrained(
pretrained_base_model_path,
subfolder="unet",
).to(dtype=weight_dtype)
else:
try:
reference_unet = UNet2DConditionModelV2.from_config(
pretrained_base_model_path,
subfolder="unet",
).to(dtype=weight_dtype)
except:
reference_unet = UNet2DConditionModelV2.from_pretrained(
pretrained_base_model_path,
subfolder="unet",
).to(dtype=weight_dtype)
re_state = torch.load(re_ckpt, map_location="cpu")
reference_unet.load_state_dict(re_state, strict=False)
del re_state
gc.collect()
torch.cuda.empty_cache()
## denoising net init
if version == "V1":
if denoising:
if os.path.exists(motion_path): ### stage1 + stage2
denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
pretrained_base_model_path,
motion_path,
subfolder="unet",
unet_additional_kwargs=infer_config.unet_additional_kwargs,
).to(dtype=weight_dtype)
else:
denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
pretrained_base_model_path,
"",
subfolder="unet",
unet_additional_kwargs={
"use_motion_module": False,
"unet_use_temporal_attention": False,
"cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim
}
).to(dtype=weight_dtype)
else:
### only stage1
denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
pretrained_base_model_path,
"",
subfolder="unet",
unet_additional_kwargs={
"use_motion_module": False,
"unet_use_temporal_attention": False,
"cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim
}
).to(dtype=weight_dtype, )
else: # v2
denoising_unet = EMOUNet3DConditionModelV2.from_pretrained_2d(
pretrained_base_model_path,
motion_path,
subfolder="unet",
unet_additional_kwargs=infer_config_v2.unet_additional_kwargs,
).to(dtype=weight_dtype)
denoising_state = torch.load(denois_pt, map_location="cpu")
denoising_unet.load_state_dict(denoising_state, strict=False)
del denoising_state
gc.collect()
torch.cuda.empty_cache()
if version == "V1":
if infer_mode == "pose_normal" or infer_mode == "pose_acc":
# face locator init
face_locator = FaceLocator(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to(
dtype=weight_dtype, device=device)
face_locator.load_state_dict(torch.load(face_locator_pt), strict=False)
if motion_sync:
visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=True, draw_eye=True, draw_nose=True,
draw_eyebrow=True, draw_pupil=True)
else:
visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=draw_mouse)
else:
# face locator init
face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to(
dtype=weight_dtype, device=device)
face_locator.load_state_dict(torch.load(face_locator_pt), strict=False)
visualizer = None
else: # v2
# pose net init
pose_net = PoseEncoder(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to(device=device,
dtype=weight_dtype)
pose_state = torch.load(pose_encoder_pt)
pose_net.load_state_dict(pose_state)
del pose_state
gc.collect()
torch.cuda.empty_cache()
if infer_mode == "pose_normal_dwpose":
print("using DWpose drive pose")
from .echomimic_v2.src.models.dwpose.dwpose_detector import DWposeDetector
dw_ll=download_weights(weigths_current_path, "yzd-v/DWPose", subfolder="",
pt_name="dw-ll_ucoco_384.onnx")
yolox_l = download_weights(weigths_current_path, "yzd-v/DWPose", subfolder="",
pt_name="yolox_l.onnx")
visualizer = DWposeDetector(model_det=yolox_l,model_pose=dw_ll,device=device)
elif infer_mode == "pose_normal_sapiens":
print("using Sapiens drive pose")
from .src.pose import SapiensPoseEstimation
pose_dir_32 = os.path.join(weigths_current_path,
"sapiens_1b_goliath_best_goliath_AP_639_torchscript.pt2")
pose_dir_bf16 = os.path.join(weigths_current_path,
"sapiens_1b_goliath_best_goliath_AP_639_torchscript_bf16.pt2")
dtype = torch.float32
if os.path.exists(pose_dir_bf16):
dtype = torch.float16
pose_dir = pose_dir_bf16
else:
if os.path.exists(pose_dir_32):
pose_dir = pose_dir_32
else:
pose_dir = ""
visualizer = SapiensPoseEstimation(local_pose=pose_dir, model_dir=weigths_current_path, dtype=dtype)
else:
visualizer = None
## load audio processor params
audio_processor = load_audio_model(model_path=audio_pt, device=device)
## load face detector params
if version == "V1":
from facenet_pytorch import MTCNN
face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709,
post_process=True, device=device)
else:
if infer_mode == "pose_normal_dwpose":
face_detector ="dwpose"
elif infer_mode == "pose_normal_sapiens":
face_detector = "sapiens"
else:
face_detector = None
############# model_init finished #############
if version == "V1":
sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
else:
sched_kwargs = OmegaConf.to_container(infer_config_v2.noise_scheduler_kwargs)
scheduler = DDIMScheduler(**sched_kwargs)
if version == "V1":
if infer_mode == "pose_normal":
pipe = AudioPose2VideoPipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
face_locator=face_locator,
scheduler=scheduler,
).to(dtype=weight_dtype)
elif infer_mode == "pose_acc":
pipe = AudioPose2VideoaccPipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
face_locator=face_locator,
scheduler=scheduler,
).to(dtype=weight_dtype)
elif infer_mode == "audio_drived":
pipe = Audio2VideoPipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
face_locator=face_locator,
scheduler=scheduler,
).to(dtype=weight_dtype)
else:
pipe = Audio2VideoACCPipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
face_locator=face_locator,
scheduler=scheduler,
).to(dtype=weight_dtype)
else:
pipe = EchoMimicV2Pipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
pose_encoder=pose_net,
scheduler=scheduler, )
pipe.enable_vae_slicing()
if lowvram:
pipe.enable_sequential_cpu_offload()
model = {"pipe": pipe, "lowvram": lowvram,"version":version}
return (model, face_detector, visualizer,)
class Echo_Sampler:
@classmethod
def INPUT_TYPES(s):
pose_path_list = ["pose_01","pose_02","pose_03","pose_04","pose_fight","pose_good","pose_salute","pose_ultraman"] + find_directories(tensorrt_lite) if find_directories(tensorrt_lite) else ["pose_01","pose_02","pose_03","pose_04","pose_fight","pose_good","pose_salute","pose_ultraman"]
return {
"required": {
"image": ("IMAGE",), # [B,H,W,C], C=3
"audio": ("AUDIO",),
"model": ("MODEL_PIPE_E",),
"face_detector": ("MODEL_FACE_E",),
"pose_dir": (pose_path_list,),
"seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED}),
"cfg": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.01}),
"steps": ("INT", {"default": 30, "min": 1, "max": 100}),
"fps": ("FLOAT", {"default": 25.0, "min": 5.0, "max": 120.0}),
"sample_rate": ("INT", {"default": 16000, "min": 8000, "max": 48000, "step": 1000, }),
"facemask_ratio": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
"facecrop_ratio": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
"context_frames": ("INT", {"default": 12, "min": 0, "max": 50}),
"context_overlap": ("INT", {"default": 3, "min": 0, "max": 10}),
"length": ("INT", {"default": 120, "min": 50, "max": 5000, "step": 1, "display": "number"}),
"width": ("INT", {"default": 512, "min": 128, "max": 1024, "step": 64, "display": "number"}),
"height": ("INT", {"default": 512, "min": 128, "max": 1024, "step": 64, "display": "number"}),
"save_video": ("BOOLEAN", {"default": False},), },
"optional": {
"visualizer": ("MODEL_VISUAL_E",),
"video_images": ("IMAGE",), # [B,H,W,C], C=3,B>1
}
}
RETURN_TYPES = ("IMAGE", "AUDIO", "FLOAT")
RETURN_NAMES = ("image", "audio", "frame_rate")
FUNCTION = "em_main"
CATEGORY = "EchoMimic"
def em_main(self, image, audio, model, face_detector, pose_dir, seed, cfg, steps, fps, sample_rate, facemask_ratio,
facecrop_ratio, context_frames, context_overlap, length,
width, height, save_video, **kwargs):
version= model.get("version")
pipe = model.get("pipe")
lowvram = model.get("lowvram")
if not lowvram:
pipe.to(device, torch.float16)
image = cf_tensor2cv(image, width, height) if version=="V1" else image # v1 cv ,v2 tensor
visualizer = kwargs.get("visualizer")
video_images = kwargs.get("video_images")
audio_file_prefix = ''.join(random.choice("0123456789") for _ in range(6))
audio_file = os.path.join(folder_paths.get_input_directory(), f"audio_{audio_file_prefix}_temp.wav")
# 减少音频数据传递导致的不必要文件存储
buff = io.BytesIO()
torchaudio.save(buff, audio["waveform"].squeeze(0), audio["sample_rate"], format="FLAC")
with open(audio_file, 'wb') as f:
f.write(buff.getbuffer())
if version=="V1":
output_video = process_video(image, audio_file, width, height, length, seed, facemask_ratio,
facecrop_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps,
pipe, face_detector, save_video, pose_dir, video_images, audio_file_prefix,
visualizer)
else:
output_video=process_video_v2(image, audio_file, width, height, length, seed,
context_frames, context_overlap, cfg, steps, sample_rate, fps, pipe,
save_video, pose_dir, audio_file_prefix,visualizer,video_images,face_detector )
gen = narry_list(output_video) # pil列表排序
images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
frame_rate = float(fps)
if not lowvram: # for upsacle ,need VR
pipe.to("cpu")
gc.collect()
torch.cuda.empty_cache()
return (images, audio, frame_rate)
class Echo_Upscaleloader:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
ckpt_list_filter_U = [i for i in folder_paths.get_filename_list("Hallo") if i.endswith(".pth") and "lib" in i]
upscale_list = [i for i in folder_paths.get_filename_list("upscale_models") if "x2plus" in i.lower()]
return {
"required": {
"realesrgan": (["none"] + upscale_list,),
"face_detection_model": (["none"] + ckpt_list_filter_U,),
"bg_upsampler": (['realesrgan', 'none', ],),
"face_upsample": ("BOOLEAN", {"default": False},),
"has_aligned": ("BOOLEAN", {"default": False},),
"bg_tile": ("INT", {
"default": 400,
"min": 200, # Minimum value
"max": 1000, # Maximum value
"step": 10, # Slider's step
"display": "number", # Cosmetic only: display as "number" or "slider"
}),
"upscale": ("INT", {
"default": 2,
"min": 2, # Minimum value
"max": 4, # Maximum value
"step": 2, # Slider's step
"display": "number", # Cosmetic only: display as "number" or "slider"
}),
},
}
RETURN_TYPES = ("ECHO_U_MODEL",)
RETURN_NAMES = ("model",)
FUNCTION = "Upscale_main"
CATEGORY = "EchoMimic"
def Upscale_main(self, realesrgan, face_detection_model, bg_upsampler, face_upsample, has_aligned, bg_tile,
upscale):
if realesrgan == "none":
if not "RealESRGAN_x2plus.pth" in folder_paths.get_filename_list("upscale_models"):
logging.info("NO RealESRGAN_x2plus.pth find in upscale_models dir,start auto download")
model_path = hf_hub_download(
repo_id="fudan-generative-ai/hallo2",
subfolder="realesrgan",
filename="RealESRGAN_x2plus.pth",
local_dir=os.path.join(folder_paths.models_dir, "upscale_models"),
)
else:
raise "Need choice 'RealESRGAN_x2plus.pth' at 'realesrgan' menu"
else:
model_path = folder_paths.get_full_path("upscale_models", realesrgan)
parse_model = os.path.join(weigths_facelib_path, "parsing_parsenet.pth")
if not os.path.exists(parse_model):
print(f" No 'parsing_parsenet.pth' in {parse_model} ,try download from huggingface!")
hf_hub_download(
repo_id="fudan-generative-ai/hallo2",
subfolder="facelib",
filename="parsing_parsenet.pth",
local_dir=weigths_hallo_current_path,
)
if face_detection_model == "none":
if not "detection_Resnet50_Final.pth" in folder_paths.get_filename_list("Hallo"):
logging.info("NO detection_Resnet50_Final.pth find in Hallo dir,start auto download")
face_detection_model = hf_hub_download(
repo_id="fudan-generative-ai/hallo2",
subfolder="facelib",
filename="detection_Resnet50_Final.pth",
local_dir=weigths_hallo_current_path,
)
else:
raise "need chocie a face_detection_model,resent or yolov5"
else:
face_detection_model = folder_paths.get_full_path("Hallo", face_detection_model)
hallo_model_path = os.path.join(weigths_hallo_current_path, "hallo2", "net_g.pth")
if not os.path.exists(hallo_model_path):
print(f"no net_g.pth in {weigths_hallo_current_path}/hallo2 ,try download from huggingface!")
hf_hub_download(
repo_id="fudan-generative-ai/hallo2",
subfolder="hallo2",
filename="net_g.pth",
local_dir=weigths_hallo_current_path,
)
net, face_upsampler, bg_upsampler, face_helper = pre_u_loader(bg_upsampler, model_path, bg_tile, upscale,
face_upsample, device, hallo_model_path,
face_detection_model, parse_model, has_aligned)
model = {"net": net, "face_upsampler": face_upsampler, "bg_upsampler": bg_upsampler, "upscale": upscale,
"face_helper": face_helper, "has_aligned": has_aligned, "face_upsample": face_upsample}
return (model,)
class Echo_VideoUpscale:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
input_path = folder_paths.get_input_directory()
video_files = [f for f in os.listdir(input_path) if
os.path.isfile(os.path.join(input_path, f)) and f.split('.')[-1] in ['webm', 'mp4', 'mkv',
'gif']]
return {
"required": {
"model": ("ECHO_U_MODEL",),
"video_path": (["none"] + video_files,),
"fidelity_weight": ("FLOAT", {
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.1,
"round": 0.01,
"display": "number",
}),
"only_center_face": ("BOOLEAN", {"default": False},),
"draw_box": ("BOOLEAN", {"default": False},),
"save_video": ("BOOLEAN", {"default": False},),
},
"optional": {"image": ("IMAGE",),
"audio": ("AUDIO",),
"frame_rate": ("FLOAT", {"forceInput": True, "default": 0.5, }),
"path": ("STRING", {"forceInput": True, "default": "", }),
},
}
RETURN_TYPES = ("IMAGE", "AUDIO", "FLOAT",)
RETURN_NAMES = ("image", "audio", "frame_rate")
FUNCTION = "Upscale_main"
CATEGORY = "EchoMimic"
def Upscale_main(self, model, video_path, fidelity_weight, only_center_face, draw_box, save_video, **kwargs):
# pre data
video_img = kwargs.get("image")
audio = kwargs.get("audio")
frame_rate = kwargs.get("frame_rate")
sampler_path = kwargs.get("path")
# pre model
net_g = model.get("net")
face_upsampler = model.get("face_upsampler")
bg_upsampler = model.get("bg_upsampler")
upscale = model.get("upscale")
face_helper = model.get("face_helper")
has_aligned = model.get("has_aligned")
face_upsample = model.get("face_upsample")
front_path = Path(sampler_path) if sampler_path and os.path.exists(Path(sampler_path)) else None
video_list = []
if isinstance(video_img, list):
if isinstance(video_img[0], torch.Tensor):
video_list = video_img
elif isinstance(video_img, torch.Tensor):
b, _, _, _ = video_img.size()
if b == 1:
img = [b]
while img is not []:
video_list += img
else:
video_list = torch.chunk(video_img, chunks=b)
print(len(video_list))
video_list = [tensor2cv(i) for i in video_list] if video_list else [] # tensor to np
if video_path != "none":
if front_path is not None:
path = front_path
else:
path = os.path.join(folder_paths.get_input_directory(), video_path)
else:
if front_path is not None:
path = front_path
else:
path = None
if video_list: # prior choice
path = None
if not video_list and video_path == "none" and not front_path:
raise "Need choice a video or link 'path or image' in the front!!!"
output_path = folder_paths.get_output_directory()
# infer
print("Start to video upscale processing...")
video_image, audio_form_v, fps = run_realesrgan(video_list, audio, frame_rate, fidelity_weight, path,
output_path,
has_aligned, only_center_face, draw_box, bg_upsampler,
save_video, net_g, face_upsampler, upscale, face_helper,
face_upsample, suffix="", )
if path is not None:
audio = audio_form_v
frame_rate = float(fps)
img_list = []
if isinstance(video_image, list):
for i in video_image:
for j in i:
img_list.append(j)
image = load_images(img_list)
return (image, audio, frame_rate,)
NODE_CLASS_MAPPINGS = {
"Echo_LoadModel": Echo_LoadModel,
"Echo_Sampler": Echo_Sampler,
"Echo_Upscaleloader": Echo_Upscaleloader,
"Echo_VideoUpscale": Echo_VideoUpscale
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Echo_LoadModel": "Echo_LoadModel",
"Echo_Sampler": "Echo_Sampler",
"Echo_Upscaleloader": "Echo_Upscaleloader",
"Echo_VideoUpscale": "Echo_VideoUpscale"
}