481 lines
20 KiB
Python
481 lines
20 KiB
Python
import os
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import os.path as osp
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import random
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import torch
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import numpy as np
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import cv2
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import sys
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from PIL import Image
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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', 'transformers', 'einops', 'opencv-python', 'tqdm', 'pillow', 'onnxruntime-gpu', 'onnx', 'safetensors', 'accelerate', 'peft'
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}
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missing = [name for name in REQUIRED if name not in installed_packages]
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print("missing pkgs", missing)
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if missing:
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python = sys.executable
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subprocess.check_call([python, '-m', 'pip', 'install', *missing], stdout=subprocess.DEVNULL)
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from .hellomeme.utils import (face_params_to_tensor,
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gen_control_heatmaps,
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get_drive_params,
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crop_and_resize,
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get_face_params,
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load_data_list,
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load_unet_from_safetensors
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)
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from .hellomeme.tools import Hello3DMMPred, HelloARKitBSPred, HelloFaceAlignment, HelloCameraDemo
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from .hellomeme import HMImagePipeline, HMVideoPipeline, HMVideoSimplePipeline
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from transformers import CLIPVisionModelWithProjection
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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 = sorted(load_data_list(osp.join(cur_dir, '../../models/checkpoints'), '.pt;.pth;.ckpt;.safetensors'))
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lora_files = sorted(load_data_list(osp.join(cur_dir, '../../models/loras'), '.safetensors'))
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return {
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"optional": {
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"checkpoint_path": (['None'] + checkpoint_files, ),
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"lora_path": (['None'] + lora_files, ),
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"gpu_id": ("INT", {"default": 0}),
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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_path=None, lora_path=None, gpu_id=0):
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dtype = torch.float16
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if gpu_id >= 0:
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device = torch.device("cuda:{}".format(gpu_id))
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else:
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device = torch.device("cpu")
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pipeline = HMImagePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype, device=device)
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pipeline.caryomitosis()
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### load customized checkpoint or lora here:
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## checkpoints
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if checkpoint_path and osp.isfile(checkpoint_path):
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if checkpoint_path.endswith('.safetensors'):
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state_dict = load_unet_from_safetensors(checkpoint_path, pipeline.unet_ref.config)
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pipeline.unet.load_state_dict(state_dict, strict=False)
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elif osp.splitext(checkpoint_path)[-1] in ['.pt', '.pth', '.ckpt']:
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state_dict = torch.load(checkpoint_path)
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pipeline.unet.load_state_dict(state_dict, strict=False)
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else:
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print("Invalid checkpoint path", checkpoint_path)
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### lora
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if lora_path and osp.isfile(lora_path):
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pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")
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pipeline.insert_hm_modules(dtype=dtype, device=device)
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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 = sorted(load_data_list(osp.join(cur_dir, '../../models/checkpoints'), '.pt;.pth;.ckpt;.safetensors'))
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lora_files = sorted(load_data_list(osp.join(cur_dir, '../../models/loras'), '.safetensors'))
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return {
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"optional": {
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"checkpoint_path": (['None'] + checkpoint_files, ),
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"lora_path": (['None'] + lora_files, ),
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"gpu_id": ("INT", {"default": 0}),
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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_path=None, lora_path=None, gpu_id=0):
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dtype = torch.float16
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if gpu_id >= 0:
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device = torch.device("cuda:{}".format(gpu_id))
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else:
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device = torch.device("cpu")
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pipeline = HMVideoPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype,
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device=device)
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pipeline.caryomitosis()
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### load customized checkpoint or lora here:
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## checkpoints
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if checkpoint_path and osp.isfile(checkpoint_path):
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if checkpoint_path.endswith('.safetensors'):
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state_dict = load_unet_from_safetensors(checkpoint_path, pipeline.unet_ref.config)
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pipeline.unet.load_state_dict(state_dict, strict=False)
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elif osp.splitext(checkpoint_path)[-1] in ['.pt', '.pth', '.ckpt']:
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state_dict = torch.load(checkpoint_path)
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pipeline.unet.load_state_dict(state_dict, strict=False)
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else:
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print("Invalid checkpoint path", checkpoint_path)
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### lora
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if lora_path and osp.isfile(lora_path):
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pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")
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pipeline.insert_hm_modules(dtype=dtype, device=device)
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return (pipeline,)
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class HMVideoSimplePipelineLoader:
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@classmethod
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def INPUT_TYPES(s):
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checkpoint_files = sorted(load_data_list(osp.join(cur_dir, '../../models/checkpoints'), '.pt;.pth;.ckpt;.safetensors'))
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lora_files = sorted(load_data_list(osp.join(cur_dir, '../../models/loras'), '.safetensors'))
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return {
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"optional": {
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"checkpoint_path": (['None'] + checkpoint_files, ),
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"lora_path": (['None'] + lora_files, ),
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"gpu_id": ("INT", {"default": 0}),
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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_path=None, lora_path=None, gpu_id=0):
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dtype = torch.float16
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if gpu_id >= 0:
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device = torch.device("cuda:{}".format(gpu_id))
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else:
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device = torch.device("cpu")
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pipeline = HMVideoSimplePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype,
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device=device)
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pipeline.caryomitosis()
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### load customized checkpoint or lora here:
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## checkpoints
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if checkpoint_path and osp.isfile(checkpoint_path):
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if checkpoint_path.endswith('.safetensors'):
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state_dict = load_unet_from_safetensors(checkpoint_path, pipeline.unet_ref.config)
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pipeline.unet.load_state_dict(state_dict, strict=False)
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elif osp.splitext(checkpoint_path)[-1] in ['.pt', '.pth', '.ckpt']:
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state_dict = torch.load(checkpoint_path)
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pipeline.unet.load_state_dict(state_dict, strict=False)
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else:
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print("Invalid checkpoint path", checkpoint_path)
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### lora
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if lora_path and osp.isfile(lora_path):
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pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")
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pipeline.insert_hm_modules(dtype=dtype, device=device)
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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}),
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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):
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dtype = torch.float16
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device = torch.device(f'cuda:{gpu_id}')
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face_aligner = HelloCameraDemo(face_alignment_module=HelloFaceAlignment(gpu_id=gpu_id), reset=True)
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harkit_bs = HelloARKitBSPred(gpu_id=gpu_id)
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h3dmm = Hello3DMMPred(gpu_id=gpu_id)
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clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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'h94/IP-Adapter',subfolder='models/image_encoder').to(dtype=dtype, device=device)
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return (dict(face_aligner=face_aligner, harkit_bs=harkit_bs, h3dmm=h3dmm, image_encoder=clip_image_encoder), )
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class GetReferenceImageRT:
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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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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("REFRT",)
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RETURN_NAMES = ("ref_rt",)
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FUNCTION = "get_reference_image_rt"
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CATEGORY = "hellomeme"
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def get_reference_image_rt(self, face_toolkits, image):
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image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
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image_np = cv2.resize(image_np, (512, 512))
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# print(image_np.shape)
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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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assert 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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ref_rot, ref_trans = face_toolkits['h3dmm'].forward_params(image_np, ref_landmark)
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return (dict(rot=ref_rot, trans=ref_trans), )
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class CropReferenceImage:
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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_reference_image"
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CATEGORY = "hellomeme"
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def crop_reference_image(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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image_np = cv2.resize(image_np, (512, 512))
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# print(image_np.shape)
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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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assert 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 = crop_and_resize(image_np[np.newaxis, :,:,:], ref_landmark[np.newaxis, :,:], 512, crop=True)[0]
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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 GetImageDriveParams:
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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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"image": ("IMAGE",),
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"ref_rt": ("REFRT",),
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}
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}
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RETURN_TYPES = ("DRIVE_PARAMS",)
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RETURN_NAMES = ("drive_params",)
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FUNCTION = "get_face_params"
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CATEGORY = "hellomeme"
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def get_face_params(self, face_toolkits, image, ref_rt):
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dtype = torch.float16
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image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
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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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assert 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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drive_landmark = face['pre_kpt_222']
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drive_face_parts, drive_coeff, drive_rot, drive_trans = get_face_params(face_toolkits['h3dmm'],
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face_toolkits['harkit_bs'],
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[image_np],
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[drive_landmark],
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save_size=(512, 512),
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align=False)
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face_parts_embedding = face_params_to_tensor(face_toolkits['image_encoder'], drive_face_parts)
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drive_params = dict(
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face_parts=face_parts_embedding.unsqueeze(0).to(dtype=dtype, device='cpu'),
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drive_coeff=drive_coeff.unsqueeze(0).to(dtype=dtype, device='cpu'),
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drive_rot=drive_rot,
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drive_trans=drive_trans,
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)
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return (drive_params, )
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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_image": ("IMAGE",),
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"ref_rt": ("REFRT",),
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"trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"drive_params": ("DRIVE_PARAMS",),
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"prompt": ("STRING", {"default": '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'}),
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"negative_prompt": ("STRING", {"default": ''}),
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"steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
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"seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
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"guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sample"
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CATEGORY = "hellomeme"
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def sample(self, hm_image_pipeline, ref_image, ref_rt, trans_ratio, drive_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
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image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
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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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drive_rot = drive_params['drive_rot']
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drive_trans = drive_params['drive_trans']
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condition = gen_control_heatmaps(drive_rot, drive_trans, ref_rt['trans'], 512, trans_ratio)
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drive_params['condition'] = condition.unsqueeze(0).to(dtype=torch.float16, device='cpu')
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if seed < 0:
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generator = torch.Generator().manual_seed(random.randint(0, 100000))
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else:
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generator = torch.Generator().manual_seed(seed)
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result_img = 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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output_type='np'
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)
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return (torch.from_numpy(np.clip(result_img[0], 0, 1)), )
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class GetVideoDriveParams:
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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 = ("DRIVE_PARAMS",)
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RETURN_NAMES = ("drive_params",)
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FUNCTION = "get_face_params"
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CATEGORY = "hellomeme"
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def get_face_params(self, face_toolkits, images):
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dtype = torch.float16
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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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face_toolkits['face_aligner'].reset_track()
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(drive_face_parts, drive_coeff, drive_rot, drive_trans) = get_drive_params(face_toolkits['face_aligner'],
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face_toolkits['h3dmm'],
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face_toolkits['harkit_bs'],
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frame_list=frame_list,
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save_size=512,
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align=True)
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face_toolkits['face_aligner'].reset_track()
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face_parts_embedding = face_params_to_tensor(face_toolkits['image_encoder'], drive_face_parts)
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drive_params = dict(
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face_parts=face_parts_embedding.unsqueeze(0).to(dtype=dtype, device='cpu'),
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drive_coeff=drive_coeff.unsqueeze(0).to(dtype=dtype, device='cpu'),
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drive_rot=drive_rot,
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drive_trans=drive_trans,
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)
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return (drive_params, )
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class HMPipelineVideo:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"hm_video_pipeline": ("HMVIDEOPIPELINE",),
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"ref_image": ("IMAGE",),
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"ref_rt": ("REFRT",),
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"trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"drive_params": ("DRIVE_PARAMS",),
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"prompt": ("STRING", {"default": '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'}),
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"negative_prompt": ("STRING", {"default": ''}),
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"steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
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"seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
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"guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sample"
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CATEGORY = "hellomeme"
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def sample(self, hm_video_pipeline, ref_image, ref_rt, trans_ratio, drive_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
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image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
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image_np = cv2.resize(image_np, (512, 512))
|
|
image_pil = Image.fromarray(image_np)
|
|
if seed < 0:
|
|
generator = torch.Generator().manual_seed(random.randint(0, 100000))
|
|
else:
|
|
generator = torch.Generator().manual_seed(seed)
|
|
|
|
drive_rot = drive_params['drive_rot']
|
|
drive_trans = drive_params['drive_trans']
|
|
|
|
condition = gen_control_heatmaps(drive_rot, drive_trans, ref_rt['trans'], 512, trans_ratio)
|
|
drive_params['condition'] = condition.unsqueeze(0).to(dtype=torch.float16, device='cpu')
|
|
|
|
res_frames = hm_video_pipeline(
|
|
prompt=[prompt],
|
|
strength=1.0,
|
|
image=image_pil,
|
|
drive_params=drive_params,
|
|
num_inference_steps=steps,
|
|
negative_prompt=[negative_prompt],
|
|
guidance_scale=guidance_scale,
|
|
generator=generator,
|
|
output_type='np'
|
|
)
|
|
res_frames = [np.clip(x[0], 0, 1) for x in res_frames]
|
|
return (torch.from_numpy(np.array(res_frames)), )
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"HMImagePipelineLoader": HMImagePipelineLoader,
|
|
"HMVideoPipelineLoader": HMVideoPipelineLoader,
|
|
"HMVideoSimplePipelineLoader": HMVideoSimplePipelineLoader,
|
|
"HMFaceToolkitsLoader": HMFaceToolkitsLoader,
|
|
"GetReferenceImageRT": GetReferenceImageRT,
|
|
"GetImageDriveParams": GetImageDriveParams,
|
|
"GetVideoDriveParams": GetVideoDriveParams,
|
|
"HMPipelineImage": HMPipelineImage,
|
|
"HMPipelineVideo": HMPipelineVideo,
|
|
"CropReferenceImage": CropReferenceImage,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"HMImagePipelineLoader": "Load HelloMemeImage Pipeline",
|
|
"HMVideoPipelineLoader": "Load HelloMemeVideo Pipeline",
|
|
"HMVideoSimplePipelineLoader": "Load HelloMemeVideoSimple Pipeline",
|
|
"HMFaceToolkitsLoader": "Load Face Toolkits",
|
|
"GetReferenceImageT": "Get Reference Image Translation",
|
|
"GetImageDriveParams": "Get Drive Image Parameters",
|
|
"GetVideoDriveParams": "Get Drive Video Parameters",
|
|
"HMPipelineImage": "HelloMeme Image Pipeline",
|
|
"HMPipelineVideo": "HelloMeme Video Pipeline",
|
|
"CropReferenceImage": "Crop Reference Image",
|
|
}
|