479 lines
19 KiB
Python
479 lines
19 KiB
Python
import json
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import os.path as osp
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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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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,
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HM3ImagePipeline, HM3VideoPipeline,
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HM5ImagePipeline, HM5VideoPipeline,
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download_file_from_cloud,
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creat_model_from_cloud)
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cur_dir = osp.dirname(osp.abspath(__file__))
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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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DEFAULT_PROMPT_NEW = MODEL_CONFIG['prompt_new']
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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, vae, lora, stylize, lora_scale, official_id, deployment):
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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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lora_path = download_file_from_cloud(tmp_lora_info[0], tmp_lora_info[1], modelscope=deployment == 'modelscope')
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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, lora_path=lora_path, vae_path=vae_path,
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stylize=stylize, lora_scale=lora_scale,
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official_id=official_id,
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modelscope=deployment == 'modelscope')
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def get_checkpoint_path(config, checkpoint):
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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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checkpoint_path = config['sd15']['checkpoints'][checkpoint]
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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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return checkpoint_path
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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": (['v5c', 'v5b', 'v5', 'v4', 'v3', 'v2', 'v1'], ),
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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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"dtype": (['fp32', 'fp16'], ),
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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, dtype='fp32'):
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dtype = torch.float32 if dtype == 'fp32' else torch.float16
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checkpoint_path = get_checkpoint_path(MODEL_CONFIG, checkpoint)
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if version in ['v3', 'v4']:
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pipeline = creat_model_from_cloud(HM3ImagePipeline, checkpoint_path,
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modelscope=deployment == 'modelscope')
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elif version in ['v5', 'v5b', 'v5c']:
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pipeline = creat_model_from_cloud(HM5ImagePipeline, checkpoint_path,
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modelscope=deployment == 'modelscope')
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else:
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pipeline = creat_model_from_cloud(HMImagePipeline, checkpoint_path,
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modelscope=deployment == 'modelscope')
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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, vae, lora, stylize, lora_scale,
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'songkey/stable-diffusion-v1-5', 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": (['v5', 'v4', 'v3', 'v2', 'v1'], ),
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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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"dtype": (['fp32', 'fp16'], ),
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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, dtype='fp32'):
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dtype = torch.float32 if dtype == 'fp32' else torch.float16
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checkpoint_path = get_checkpoint_path(MODEL_CONFIG, checkpoint)
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if version in ['v3', 'v4']:
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pipeline = creat_model_from_cloud(HM3VideoPipeline, checkpoint_path,
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modelscope=deployment == 'modelscope')
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elif version in ['v5', 'v5b', 'v5c']:
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pipeline = creat_model_from_cloud(HM5VideoPipeline, checkpoint_path,
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modelscope=deployment == 'modelscope')
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else:
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pipeline = creat_model_from_cloud(HMVideoPipeline, checkpoint_path,
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modelscope=deployment == 'modelscope')
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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, vae, lora, stylize, lora_scale,
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'songkey/stable-diffusion-v1-5', 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.float32
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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 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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assert frame_num > 0, 'No image detected'
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_, landmark_list = det_landmarks(face_toolkits['face_aligner'], frame_list)
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assert len(frame_list) == frame_num, 'Not all images have face detected!'
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return (torch.from_numpy(landmark_list).float(), )
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class GetHeadPose:
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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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"crop": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
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}
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}
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RETURN_TYPES = ("HEAD_POSE",)
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RETURN_NAMES = ("head_pose",)
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FUNCTION = "get_head_pose"
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CATEGORY = "hellomeme"
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def get_head_pose(self, face_toolkits, images, landmarks, crop):
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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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new_frames, new_landmarks, rot_list, trans_list = get_drive_pose(face_toolkits, frame_list, landmarks, save_size=512, crop=crop)
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return (dict(rot=np.stack(rot_list), trans=np.stack(trans_list), frame=np.stack(new_frames), landmarks=np.stack(new_landmarks)), )
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class GetExpression:
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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 = ("EXPRESSION",)
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RETURN_NAMES = ("expression",)
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FUNCTION = "get_expression"
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CATEGORY = "hellomeme"
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def get_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 GetExpression2:
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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 = ("EXPRESSION",)
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RETURN_NAMES = ("expression",)
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FUNCTION = "get_expression"
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CATEGORY = "hellomeme"
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def get_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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"ref_head_pose": ("HEAD_POSE",),
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"ref_expression": ("EXPRESSION",),
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"drive_head_pose": ("HEAD_POSE",),
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"drive_expression": ("EXPRESSION",),
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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": ''}),
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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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}
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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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ref_head_pose,
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ref_expression,
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drive_head_pose,
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drive_expression,
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trans_ratio='0.0',
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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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dtype = hm_image_pipeline.dtype
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PROMPT = DEFAULT_PROMPT_NEW if hm_image_pipeline.version in ['v5b', 'v5c'] else DEFAULT_PROMPT
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prompt = PROMPT if prompt == '' else prompt + ", " + PROMPT
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image_np = cv2.cvtColor(ref_head_pose['frame'][0], cv2.COLOR_BGR2RGB)
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image_pil = Image.fromarray(image_np)
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ref_rot, ref_trans = ref_head_pose['rot'], ref_head_pose['trans']
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drive_rot, drive_trans = drive_head_pose['rot'], drive_head_pose['trans']
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condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans[0], 512, trans_ratio)
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ref_condition = gen_control_heatmaps(ref_rot, ref_trans, ref_trans[0], 512, 0.0)
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drive_params = dict(condition=condition.unsqueeze(0))
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ref_params = dict(condition=ref_condition.unsqueeze(0))
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drive_params.update(drive_expression)
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ref_params.update(ref_expression)
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for k, v in drive_params.items():
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drive_params[k] = v.to(dtype=dtype)
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for k, v in ref_params.items():
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ref_params[k] = v.to(dtype=dtype, device='cpu')
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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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ref_params=ref_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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"ref_head_pose": ("HEAD_POSE",),
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"ref_expression": ("EXPRESSION",),
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"drive_head_pose": ("HEAD_POSE",),
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"drive_expression": ("EXPRESSION",),
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"trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1}),
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"patch_overlap": ("INT", {"default": 4, "min": 0, "max": 5}),
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"prompt": ("STRING", {"default": ''}),
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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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}
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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_video_pipeline,
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ref_head_pose,
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ref_expression,
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drive_head_pose,
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drive_expression,
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trans_ratio=0.0,
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patch_overlap=4,
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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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dtype = hm_video_pipeline.dtype
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prompt = DEFAULT_PROMPT if prompt == '' else prompt + ", " + DEFAULT_PROMPT
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image_np = cv2.cvtColor(ref_head_pose['frame'][0], cv2.COLOR_BGR2RGB)
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image_pil = Image.fromarray(image_np)
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ref_rot, ref_trans = ref_head_pose['rot'], ref_head_pose['trans']
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generator = torch.Generator().manual_seed(seed)
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drive_rot, drive_trans = drive_head_pose['rot'], drive_head_pose['trans']
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condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans[0], 512, trans_ratio)
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ref_condition = gen_control_heatmaps(ref_rot, ref_trans, ref_trans[0], 512, 0.0)
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drive_params = dict(condition=condition.unsqueeze(0))
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ref_params = dict(condition=ref_condition.unsqueeze(0))
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drive_params.update(drive_expression)
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ref_params.update(ref_expression)
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for k, v in drive_params.items():
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drive_params[k] = v.to(dtype=dtype)
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for k, v in ref_params.items():
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ref_params[k] = v.to(dtype=dtype, device='cpu')
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res_frames, latents = hm_video_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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chunk_overlap=patch_overlap,
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|
ref_params=ref_params,
|
|
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,
|
|
"GetFaceLandmarks": GetFaceLandmarks,
|
|
"GetHeadPose": GetHeadPose,
|
|
"GetExpression": GetExpression,
|
|
"GetExpression2": GetExpression2,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"HMImagePipelineLoader": "Load HelloMemeImage Pipeline",
|
|
"HMVideoPipelineLoader": "Load HelloMemeVideo Pipeline",
|
|
"HMFaceToolkitsLoader": "Load Face Toolkits",
|
|
"HMPipelineImage": "HelloMeme Image Pipeline",
|
|
"HMPipelineVideo": "HelloMeme Video Pipeline",
|
|
"GetFaceLandmarks": "Get Face Landmarks",
|
|
"GetHeadPose": "Get Head Pose",
|
|
"GetExpression": "Get Face Expression",
|
|
"GetExpression2": "Get Face Expression V2",
|
|
}
|