562 lines
21 KiB
Python
562 lines
21 KiB
Python
import json
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import os
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import os.path as osp
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import random
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import sys
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import subprocess
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import importlib.metadata
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cur_dir = osp.dirname(osp.abspath(__file__))
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installed_packages = [package.name for package in importlib.metadata.distributions()]
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REQUIRED = {
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'diffusers':'0.31.0', 'transformers':'4.46.3', 'einops':'0.8.0', 'opencv-python':'4.10.0.84', 'tqdm':'4.67.0',
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'pillow':'10.2.0', 'onnxruntime-gpu':'1.18.1', 'onnx':'1.17.0', 'safetensors':'0.4.5',
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'accelerate':'1.1.1', 'peft':'0.13.2', 'modelscope':'', 'huggingface-hub':''
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}
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missing = [name for name in REQUIRED.keys() if name not in installed_packages]
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missing_params = ' '.join([f'{k}' for k in missing])
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print("missing pkgs", missing_params)
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if missing:
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python = sys.executable
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os.system(f'{sys.executable} -m pip install {missing_params}')
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# subprocess.check_call([python, '-m', 'pip', 'install', missing_params], stdout=subprocess.DEVNULL)
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try:
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from huggingface_hub import login
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# Model read permission
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login(token='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')
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except:
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print("login error!")
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import numpy as np
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import cv2
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from PIL import Image
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import torch
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from einops import rearrange
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import folder_paths
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from .hellomeme.utils import (get_drive_expression,
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get_drive_expression_pd_fgc,
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gen_control_heatmaps,
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get_drive_pose,
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crop_and_resize,
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det_landmarks,
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get_torch_device,
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append_pipline_weights,
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load_face_toolkits
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)
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from .hellomeme import HMImagePipeline, HMVideoPipeline, HM3ImagePipeline, HM3VideoPipeline
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config_path = osp.join(cur_dir, 'hellomeme', 'model_config.json')
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with open(config_path, 'r') as f:
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MODEL_CONFIG = json.load(f)
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DEFAULT_PROMPT = MODEL_CONFIG['prompt']
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def get_models_files():
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checkpoint_files = folder_paths.get_filename_list("checkpoints")
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checkpoint_files = list(MODEL_CONFIG['sd15']['checkpoints'].keys()) + checkpoint_files
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vae_files = folder_paths.get_filename_list("vae")
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vae_files = ["[vae] " + x for x in vae_files] + \
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["[checkpoint] " + x for x in checkpoint_files]
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vae_files = ['same as checkpoint', 'SD1.5 default vae'] + vae_files
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lora_files = folder_paths.get_filename_list("loras")
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lora_files = ['None'] + list(MODEL_CONFIG['sd15']['loras'].keys()) + lora_files
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return checkpoint_files, vae_files, lora_files
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def format_model_path(pipeline, config, checkpoint, vae, lora, stylize, lora_scale, deployment):
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if checkpoint and not checkpoint.startswith('SD1.5'):
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if checkpoint in config['sd15']['checkpoints']:
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tmp_checkpoint_name = config['sd15']['checkpoints'][checkpoint]
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if deployment == 'modelscope':
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from modelscope import snapshot_download
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checkpoint_path = snapshot_download(tmp_checkpoint_name)
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else:
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checkpoint_path = tmp_checkpoint_name
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else:
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checkpoint_path = folder_paths.get_full_path_or_raise("checkpoints", checkpoint)
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else:
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checkpoint_path = checkpoint
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if vae and vae.startswith("[checkpoint] "):
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vae_path = folder_paths.get_full_path_or_raise("checkpoints", vae.replace("[checkpoint] ", ""))
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elif vae and vae.startswith("[vae] "):
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vae_path = folder_paths.get_full_path_or_raise("vae", vae.replace("[vae] ", ""))
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else:
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vae_path = vae
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if lora and not lora.startswith('None'):
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if lora in config['sd15']['loras']:
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tmp_lora_info = config['sd15']['loras'][lora]
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if deployment == 'modelscope':
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from modelscope import snapshot_download
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lora_path = osp.join(snapshot_download(tmp_lora_info[0]), tmp_lora_info[1])
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else:
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from huggingface_hub import hf_hub_download
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lora_path = hf_hub_download(tmp_lora_info[0], filename=tmp_lora_info[1])
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else:
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lora_path = folder_paths.get_full_path_or_raise("loras", lora)
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else:
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lora_path = lora
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append_pipline_weights(pipeline, checkpoint_path=checkpoint_path, lora_path=lora_path, vae_path=vae_path,
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stylize=stylize, lora_scale=lora_scale)
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class HMImagePipelineLoader:
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@classmethod
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def INPUT_TYPES(s):
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checkpoint_files, vae_files, lora_files = get_models_files()
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return {
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"optional": {
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"checkpoint": (checkpoint_files, ),
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"lora": (lora_files, ),
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"vae": (vae_files, ),
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"version": (['v1', 'v2', 'v3'], ),
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"stylize": (['x1', 'x2'], ),
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"deployment": (['huggingface', 'modelscope'], ),
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"lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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}
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}
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RETURN_TYPES = ("HMIMAGEPIPELINE", )
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RETURN_NAMES = ("hm_image_pipeline", )
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FUNCTION = "load_pipeline"
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CATEGORY = "hellomeme"
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def load_pipeline(self, checkpoint=None, lora=None, vae=None,
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version='v2', stylize='x1', deployment='huggingface', lora_scale=1.0):
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dtype = torch.float16
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if deployment == 'modelscope':
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from modelscope import snapshot_download
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sd1_5_dir = snapshot_download('songkey/stable-diffusion-v1-5')
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else:
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sd1_5_dir = "songkey/stable-diffusion-v1-5"
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if version == 'v3':
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pipeline = HM3ImagePipeline.from_pretrained(sd1_5_dir)
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else:
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pipeline = HMImagePipeline.from_pretrained(sd1_5_dir)
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pipeline.to(dtype=dtype)
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pipeline.caryomitosis(version=version, modelscope=deployment=='modelscope')
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format_model_path(pipeline, MODEL_CONFIG, checkpoint, vae, lora, stylize, lora_scale, deployment)
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pipeline.insert_hm_modules(version=version, dtype=dtype, modelscope=deployment=='modelscope')
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return (pipeline, )
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class HMVideoPipelineLoader:
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@classmethod
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def INPUT_TYPES(s):
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checkpoint_files, vae_files, lora_files = get_models_files()
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return {
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"optional": {
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"checkpoint": (checkpoint_files, ),
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"lora": (lora_files, ),
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"vae": (vae_files, ),
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"version": (['v1', 'v2', 'v3'], ),
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"stylize": (['x1', 'x2'], ),
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"deployment": (['huggingface', 'modelscope'], ),
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"lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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}
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}
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RETURN_TYPES = ("HMVIDEOPIPELINE",)
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RETURN_NAMES = ("hm_video_pipeline",)
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FUNCTION = "load_pipeline"
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CATEGORY = "hellomeme"
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def load_pipeline(self, checkpoint=None, lora=None, vae=None,
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version='v2', stylize='x1', deployment='huggingface', lora_scale=1.0):
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dtype = torch.float16
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if deployment == 'modelscope':
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from modelscope import snapshot_download
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sd1_5_dir = snapshot_download('songkey/stable-diffusion-v1-5')
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else:
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sd1_5_dir = "songkey/stable-diffusion-v1-5"
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if version == 'v3':
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pipeline = HM3VideoPipeline.from_pretrained(sd1_5_dir)
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else:
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pipeline = HMVideoPipeline.from_pretrained(sd1_5_dir)
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pipeline.to(dtype=dtype)
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pipeline.caryomitosis(version=version, modelscope=deployment=='modelscope')
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format_model_path(pipeline, MODEL_CONFIG, checkpoint, vae, lora, stylize, lora_scale, deployment)
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pipeline.insert_hm_modules(version=version, dtype=dtype, modelscope=deployment=='modelscope')
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return (pipeline,)
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class HMFaceToolkitsLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"gpu_id": ("INT", {"default": 0, "min": -1, "max": 16}, ),
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"deployment": (['huggingface', 'modelscope'], ),
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}
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}
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RETURN_TYPES = ("FACE_TOOLKITS",)
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RETURN_NAMES = ("face_toolkits",)
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FUNCTION = "load_face_toolkits"
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CATEGORY = "hellomeme"
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def load_face_toolkits(self, gpu_id, deployment='huggingface'):
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dtype = torch.float16
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face_toolkits = load_face_toolkits(dtype=dtype, gpu_id=gpu_id, modelscope=deployment=='modelscope')
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return (face_toolkits, )
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class CropPortrait:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"face_toolkits": ("FACE_TOOLKITS",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "crop_portrait"
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CATEGORY = "hellomeme"
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def crop_portrait(self, image, face_toolkits):
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image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
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if min(image_np.shape[:2]) < 512:
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raise Exception(f'Image size is too small -> min{image_np.shape[:2]} < 512')
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face_toolkits['face_aligner'].reset_track()
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faces = face_toolkits['face_aligner'].forward(image_np)
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if len(faces) > 0:
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face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
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x['face_rect'][3] - x['face_rect'][1]))[-1]
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ref_landmark = face['pre_kpt_222']
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new_image, new_landmark = crop_and_resize(image_np[np.newaxis, :,:,:], ref_landmark[np.newaxis, :,:], 512, crop=True)
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# for x, y in new_landmark[0]:
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# cv2.circle(new_image[0], (int(x), int(y)), 2, (0, 255, 0), -1)
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else:
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raise Exception('No face detected')
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new_image = cv2.cvtColor(new_image[0], cv2.COLOR_RGB2BGR)
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return (torch.from_numpy(new_image[np.newaxis, :,:,:]).float() / 255., )
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class GetFaceLandmarks:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"face_toolkits": ("FACE_TOOLKITS",),
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"images": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("FACELANDMARKS222",)
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RETURN_NAMES = ("landmarks",)
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FUNCTION = "get_face_landmarks"
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CATEGORY = "hellomeme"
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def get_face_landmarks(self, face_toolkits, images):
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frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
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frame_num = len(frame_list)
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if frame_num == 0:
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raise Exception('No image detected')
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_, landmark_list = det_landmarks(face_toolkits['face_aligner'], frame_list)
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if len(frame_list) != frame_num:
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raise Exception('Not all images have face detected!')
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return (torch.from_numpy(landmark_list).float(), )
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class GetDrivePose:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"face_toolkits": ("FACE_TOOLKITS",),
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"images": ("IMAGE",),
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"landmarks": ("FACELANDMARKS222",),
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}
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}
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RETURN_TYPES = ("DRIVE_POSE",)
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RETURN_NAMES = ("drive_pose",)
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FUNCTION = "get_drive_pose"
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CATEGORY = "hellomeme"
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def get_drive_pose(self, face_toolkits, images, landmarks):
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frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
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landmarks = landmarks.cpu().numpy()
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rot_list, trans_list = get_drive_pose(face_toolkits, frame_list, landmarks, save_size=512)
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return (dict(rot=np.stack(rot_list), trans=np.stack(trans_list)), )
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class GetDriveExpression:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"face_toolkits": ("FACE_TOOLKITS",),
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"images": ("IMAGE",),
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"landmarks": ("FACELANDMARKS222",),
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}
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}
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RETURN_TYPES = ("DRIVE_EXPRESSION",)
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RETURN_NAMES = ("drive_exp",)
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FUNCTION = "get_drive_expression"
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CATEGORY = "hellomeme"
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def get_drive_expression(self, face_toolkits, images, landmarks):
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frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
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landmarks = landmarks.cpu().numpy()
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exp_dict = get_drive_expression(face_toolkits, frame_list, landmarks)
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return (exp_dict, )
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class GetDriveExpression2:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"face_toolkits": ("FACE_TOOLKITS",),
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"images": ("IMAGE",),
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"landmarks": ("FACELANDMARKS222",),
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}
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}
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RETURN_TYPES = ("DRIVE_EXPRESSION2",)
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RETURN_NAMES = ("drive_exp2",)
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FUNCTION = "get_drive_expression"
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CATEGORY = "hellomeme"
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def get_drive_expression(self, face_toolkits, images, landmarks):
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frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
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landmarks = landmarks.cpu().numpy()
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exp_dict = get_drive_expression_pd_fgc(face_toolkits, frame_list, landmarks)
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return (exp_dict, )
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class HMPipelineImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"hm_image_pipeline": ("HMIMAGEPIPELINE",),
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"face_toolkits": ("FACE_TOOLKITS",),
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"ref_image": ("IMAGE",),
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"drive_pose": ("DRIVE_POSE",),
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"trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1}),
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"prompt": ("STRING", {"default": DEFAULT_PROMPT}),
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"negative_prompt": ("STRING", {"default": ''}),
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"steps": ("INT", {"default": 25, "min": 1, "max": 10000,
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"tooltip": "The number of steps used in the denoising process."}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff,
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"tooltip": "The random seed used for creating the noise."}),
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"guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1}),
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"gpu_id": ("INT", {"default": 0, "min": -1, "max": 16}, ),
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},
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"optional": {
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"drive_exp": ("DRIVE_EXPRESSION", {"default": None},),
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"drive_exp2": ("DRIVE_EXPRESSION2", {"default": None},),
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}
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}
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RETURN_TYPES = ("IMAGE", "LATENT", )
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FUNCTION = "sample"
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CATEGORY = "hellomeme"
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def sample(self,
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hm_image_pipeline,
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face_toolkits,
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ref_image,
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drive_pose,
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drive_exp=None,
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drive_exp2=None,
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trans_ratio='0.0',
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prompt=DEFAULT_PROMPT,
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negative_prompt='',
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steps=25,
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seed=0,
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guidance_scale=2.0,
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gpu_id=0
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):
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device = get_torch_device(gpu_id)
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image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
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if min(image_np.shape[:2]) < 512:
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raise Exception(f'Reference image size is too small -> min{image_np.shape[:2]} < 512')
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image_np = cv2.resize(image_np, (512, 512))
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image_pil = Image.fromarray(image_np)
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face_toolkits['face_aligner'].reset_track()
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faces = face_toolkits['face_aligner'].forward(image_np)
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if len(faces) == 0: raise Exception('No face detected')
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face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
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x['face_rect'][3] - x['face_rect'][1]))[-1]
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ref_landmark = face['pre_kpt_222']
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_, ref_trans = face_toolkits['h3dmm'].forward_params(image_np, ref_landmark)
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drive_rot, drive_trans = drive_pose['rot'], drive_pose['trans']
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condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans, 512, trans_ratio)
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drive_params = dict(condition=condition.unsqueeze(0).to(dtype=torch.float16, device='cpu'))
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if isinstance(drive_exp, dict):
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drive_params.update(drive_exp)
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if isinstance(drive_exp2, dict):
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drive_params.update(drive_exp2)
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generator = torch.Generator().manual_seed(seed)
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result_img, latents = hm_image_pipeline(
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prompt=[prompt],
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strength=1.0,
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image=image_pil,
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drive_params=drive_params,
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num_inference_steps=steps,
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negative_prompt=[negative_prompt],
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guidance_scale=guidance_scale,
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generator=generator,
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device=device,
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output_type='np'
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)
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return (torch.from_numpy(np.clip(result_img[0], 0, 1)), dict(samples=latents), )
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class HMPipelineVideo:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required":{
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"hm_video_pipeline": ("HMVIDEOPIPELINE",),
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"face_toolkits": ("FACE_TOOLKITS",),
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"ref_image": ("IMAGE",),
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"drive_pose": ("DRIVE_POSE",),
|
|
"trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1}),
|
|
"patch_overlap": ("INT", {"default": 4, "min": 0, "max": 5}),
|
|
"prompt": ("STRING", {"default": DEFAULT_PROMPT}),
|
|
"negative_prompt": ("STRING", {"default": ''}),
|
|
"steps": ("INT", {"default": 25, "min": 1, "max": 10000,
|
|
"tooltip": "The number of steps used in the denoising process."}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff,
|
|
"tooltip": "The random seed used for creating the noise."}),
|
|
"guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1}),
|
|
"gpu_id": ("INT", {"default": 0, "min": -1, "max": 16}, ),
|
|
},
|
|
"optional": {
|
|
"drive_exp": ("DRIVE_EXPRESSION", {"default": None},),
|
|
"drive_exp2": ("DRIVE_EXPRESSION2", {"default": None},),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "LATENT", )
|
|
FUNCTION = "sample"
|
|
CATEGORY = "hellomeme"
|
|
|
|
def sample(self,
|
|
hm_video_pipeline,
|
|
face_toolkits,
|
|
ref_image,
|
|
drive_pose,
|
|
drive_exp=None,
|
|
drive_exp2=None,
|
|
trans_ratio=0.0,
|
|
patch_overlap=4,
|
|
prompt=DEFAULT_PROMPT,
|
|
negative_prompt="",
|
|
steps=25,
|
|
seed=0,
|
|
guidance_scale=2.0,
|
|
gpu_id=0
|
|
):
|
|
device = get_torch_device(gpu_id)
|
|
|
|
image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
|
|
if min(image_np.shape[:2]) < 512:
|
|
raise Exception(f'Reference image size is too small -> min{image_np.shape[:2]} < 512')
|
|
|
|
image_np = cv2.resize(image_np, (512, 512))
|
|
image_pil = Image.fromarray(image_np)
|
|
|
|
face_toolkits['face_aligner'].reset_track()
|
|
faces = face_toolkits['face_aligner'].forward(image_np)
|
|
face_toolkits['face_aligner'].reset_track()
|
|
if len(faces) == 0: raise Exception('No face detected')
|
|
|
|
face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
|
|
x['face_rect'][3] - x['face_rect'][1]))[-1]
|
|
ref_landmark = face['pre_kpt_222']
|
|
|
|
_, ref_trans = face_toolkits['h3dmm'].forward_params(image_np, ref_landmark)
|
|
|
|
generator = torch.Generator().manual_seed(seed)
|
|
|
|
drive_rot, drive_trans = drive_pose['rot'], drive_pose['trans']
|
|
condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans, 512, trans_ratio)
|
|
drive_params = dict(condition=condition.unsqueeze(0).to(dtype=torch.float16, device='cpu'))
|
|
|
|
if isinstance(drive_exp, dict):
|
|
drive_params.update(drive_exp)
|
|
if isinstance(drive_exp2, dict):
|
|
drive_params.update(drive_exp2)
|
|
|
|
res_frames, latents = hm_video_pipeline(
|
|
prompt=[prompt],
|
|
strength=1.0,
|
|
image=image_pil,
|
|
chunk_overlap=patch_overlap,
|
|
drive_params=drive_params,
|
|
num_inference_steps=steps,
|
|
negative_prompt=[negative_prompt],
|
|
guidance_scale=guidance_scale,
|
|
generator=generator,
|
|
device=device,
|
|
output_type='np'
|
|
)
|
|
res_frames = [np.clip(x[0], 0, 1) for x in res_frames]
|
|
latents = rearrange(latents[0], 'c f h w -> f c h w')
|
|
|
|
return (torch.from_numpy(np.array(res_frames)), dict(samples=latents), )
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"HMImagePipelineLoader": HMImagePipelineLoader,
|
|
"HMVideoPipelineLoader": HMVideoPipelineLoader,
|
|
"HMFaceToolkitsLoader": HMFaceToolkitsLoader,
|
|
"HMPipelineImage": HMPipelineImage,
|
|
"HMPipelineVideo": HMPipelineVideo,
|
|
"CropPortrait": CropPortrait,
|
|
"GetFaceLandmarks": GetFaceLandmarks,
|
|
"GetDrivePose": GetDrivePose,
|
|
"GetDriveExpression": GetDriveExpression,
|
|
"GetDriveExpression2": GetDriveExpression2,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"HMImagePipelineLoader": "Load HelloMemeImage Pipeline",
|
|
"HMVideoPipelineLoader": "Load HelloMemeVideo Pipeline",
|
|
"HMFaceToolkitsLoader": "Load Face Toolkits",
|
|
"HMPipelineImage": "HelloMeme Image Pipeline",
|
|
"HMPipelineVideo": "HelloMeme Video Pipeline",
|
|
"CropPortrait": "Crop Portrait",
|
|
"GetFaceLandmarks": "Get Face Landmarks",
|
|
"GetDrivePose": "Get Drive Pose",
|
|
"GetDriveExpression": "Get Drive Expression",
|
|
"GetDriveExpression2": "Get Drive Expression V2",
|
|
}
|