573 lines
22 KiB
Python
573 lines
22 KiB
Python
import torch
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import os
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import comfy.utils
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import folder_paths
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import numpy as np
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import math
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import cv2
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import PIL.Image
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from .resampler import Resampler
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from .CrossAttentionPatch import Attn2Replace, instantid_attention
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from .utils import tensor_to_image
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from insightface.app import FaceAnalysis
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try:
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import torchvision.transforms.v2 as T
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except ImportError:
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import torchvision.transforms as T
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import torch.nn.functional as F
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MODELS_DIR = os.path.join(folder_paths.models_dir, "instantid")
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if "instantid" not in folder_paths.folder_names_and_paths:
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current_paths = [MODELS_DIR]
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else:
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current_paths, _ = folder_paths.folder_names_and_paths["instantid"]
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folder_paths.folder_names_and_paths["instantid"] = (current_paths, folder_paths.supported_pt_extensions)
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INSIGHTFACE_DIR = os.path.join(folder_paths.models_dir, "insightface")
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def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
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stickwidth = 4
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
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kps = np.array(kps)
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h, w, _ = image_pil.shape
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out_img = np.zeros([h, w, 3])
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for i in range(len(limbSeq)):
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index = limbSeq[i]
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color = color_list[index[0]]
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x = kps[index][:, 0]
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y = kps[index][:, 1]
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
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polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
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out_img = (out_img * 0.6).astype(np.uint8)
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for idx_kp, kp in enumerate(kps):
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color = color_list[idx_kp]
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x, y = kp
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
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out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
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return out_img_pil
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class InstantID(torch.nn.Module):
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def __init__(self, instantid_model, cross_attention_dim=1280, output_cross_attention_dim=1024, clip_embeddings_dim=512, clip_extra_context_tokens=16):
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super().__init__()
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self.clip_embeddings_dim = clip_embeddings_dim
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self.cross_attention_dim = cross_attention_dim
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self.output_cross_attention_dim = output_cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.image_proj_model = self.init_proj()
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self.image_proj_model.load_state_dict(instantid_model["image_proj"])
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self.ip_layers = To_KV(instantid_model["ip_adapter"])
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def init_proj(self):
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image_proj_model = Resampler(
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dim=self.cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=20,
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num_queries=self.clip_extra_context_tokens,
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embedding_dim=self.clip_embeddings_dim,
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output_dim=self.output_cross_attention_dim,
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ff_mult=4
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)
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return image_proj_model
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@torch.inference_mode()
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def get_image_embeds(self, clip_embed, clip_embed_zeroed):
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#image_prompt_embeds = clip_embed.clone().detach()
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image_prompt_embeds = self.image_proj_model(clip_embed)
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#uncond_image_prompt_embeds = clip_embed_zeroed.clone().detach()
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uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed)
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return image_prompt_embeds, uncond_image_prompt_embeds
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class ImageProjModel(torch.nn.Module):
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, image_embeds):
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embeds = image_embeds
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clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class To_KV(torch.nn.Module):
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def __init__(self, state_dict):
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super().__init__()
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self.to_kvs = torch.nn.ModuleDict()
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for key, value in state_dict.items():
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k = key.replace(".weight", "").replace(".", "_")
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self.to_kvs[k] = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)
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self.to_kvs[k].weight.data = value
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def _set_model_patch_replace(model, patch_kwargs, key):
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to = model.model_options["transformer_options"].copy()
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if "patches_replace" not in to:
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to["patches_replace"] = {}
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else:
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to["patches_replace"] = to["patches_replace"].copy()
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if "attn2" not in to["patches_replace"]:
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to["patches_replace"]["attn2"] = {}
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else:
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to["patches_replace"]["attn2"] = to["patches_replace"]["attn2"].copy()
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if key not in to["patches_replace"]["attn2"]:
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to["patches_replace"]["attn2"][key] = Attn2Replace(instantid_attention, **patch_kwargs)
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model.model_options["transformer_options"] = to
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else:
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to["patches_replace"]["attn2"][key].add(instantid_attention, **patch_kwargs)
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class InstantIDModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "instantid_file": (folder_paths.get_filename_list("instantid"), )}}
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RETURN_TYPES = ("INSTANTID",)
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FUNCTION = "load_model"
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CATEGORY = "InstantID"
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def load_model(self, instantid_file):
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ckpt_path = folder_paths.get_full_path("instantid", instantid_file)
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model = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
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if ckpt_path.lower().endswith(".safetensors"):
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st_model = {"image_proj": {}, "ip_adapter": {}}
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for key in model.keys():
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if key.startswith("image_proj."):
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st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
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elif key.startswith("ip_adapter."):
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st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
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model = st_model
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model = InstantID(
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model,
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cross_attention_dim=1280,
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output_cross_attention_dim=model["ip_adapter"]["1.to_k_ip.weight"].shape[1],
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clip_embeddings_dim=512,
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clip_extra_context_tokens=16,
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)
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return (model,)
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def extractFeatures(insightface, image, extract_kps=False):
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face_img = tensor_to_image(image)
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out = []
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insightface.det_model.input_size = (640,640) # reset the detection size
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for i in range(face_img.shape[0]):
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for size in [(size, size) for size in range(640, 128, -64)]:
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insightface.det_model.input_size = size # TODO: hacky but seems to be working
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face = insightface.get(face_img[i])
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if face:
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face = sorted(face, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1]
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if extract_kps:
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out.append(draw_kps(face_img[i], face['kps']))
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else:
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out.append(torch.from_numpy(face['embedding']).unsqueeze(0))
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if 640 not in size:
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print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
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break
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if out:
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if extract_kps:
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out = torch.stack(T.ToTensor()(out), dim=0).permute([0,2,3,1])
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else:
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out = torch.stack(out, dim=0)
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else:
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out = None
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return out
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class InstantIDFaceAnalysis:
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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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"provider": (["CPU", "CUDA", "ROCM", "CoreML"], ),
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},
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}
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RETURN_TYPES = ("FACEANALYSIS",)
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FUNCTION = "load_insight_face"
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CATEGORY = "InstantID"
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def load_insight_face(self, provider):
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model = FaceAnalysis(name="antelopev2", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',]) # alternative to buffalo_l
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model.prepare(ctx_id=0, det_size=(640, 640))
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return (model,)
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def add_noise(image, factor):
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seed = int(torch.sum(image).item()) % 1000000007
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torch.manual_seed(seed)
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mask = (torch.rand_like(image) < factor).float()
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noise = torch.rand_like(image)
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noise = torch.zeros_like(image) * (1-mask) + noise * mask
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return factor*noise
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class ApplyInstantID:
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"""
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应用InstantID的主要节点
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功能：将人脸身份信息应用到模型中，实现换脸效果
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参数说明：
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- image: 参考人脸图像，用于提取人脸特征嵌入
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- image_kps: 可选的关键点图像，用于生成ControlNet的控制信号
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* 如果提供：直接使用该图像提取人脸关键点
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* 如果不提供：使用参考图像(image)的第一张提取关键点
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- face_embed: 可选的预计算人脸嵌入，如果提供则跳过人脸检测和特征提取
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"""
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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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"instantid": ("INSTANTID", ), # InstantID模型
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"insightface": ("FACEANALYSIS", ), # InsightFace人脸分析模型
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"control_net": ("CONTROL_NET", ), # ControlNet模型
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"image": ("IMAGE", ), # 参考人脸图像
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"model": ("MODEL", ), # 基础扩散模型
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"positive": ("CONDITIONING", ), # 正向条件
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"negative": ("CONDITIONING", ), # 负向条件
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"weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 5.0, "step": 0.01, }), # 权重
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"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }), # 开始时间步
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"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }), # 结束时间步
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},
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"optional": {
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"image_kps": ("IMAGE",), # 可选：关键点图像，用于ControlNet
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"mask": ("MASK",), # 可选：遮罩
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"face_embed": ("FACE_EMBEDS",), # 可选：预计算的人脸嵌入
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}
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}
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RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "FACE_EMBEDS")
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RETURN_NAMES = ("MODEL", "positive", "negative", "face_embed")
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FUNCTION = "apply_instantid"
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CATEGORY = "InstantID"
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def apply_instantid(self, instantid, insightface, control_net, image, model, positive, negative, start_at, end_at, weight=.8, ip_weight=None, cn_strength=None, noise=0.35, image_kps=None, mask=None, combine_embeds='average', face_embed=None):
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"""
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应用InstantID换脸效果
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处理流程：
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1. 人脸嵌入处理：
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- 如果提供face_embed：直接使用预计算的嵌入，跳过人脸检测
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- 如果未提供：从image中检测人脸并提取特征嵌入
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2. 关键点处理：
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- 如果提供image_kps：使用该图像提取人脸关键点用于ControlNet
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- 如果未提供：使用参考图像(image)的第一张图片提取关键点
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3. 模型修补：将人脸嵌入注入到模型的注意力层中
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4. ControlNet应用：使用人脸关键点控制生成过程
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"""
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# 如果end_at为0，直接返回原始数据，跳过所有处理
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if end_at == 0 or weight == 0:
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print(f"\033[33mINFO: end_at=0 or weight=0，跳过InstantID处理\033[0m")
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return (model, positive, negative, None)
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import time
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start_total = time.time()
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print(f"\033[36m=== InstantID 处理开始 ===\033[0m")
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# 设置数据类型和设备
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start_setup = time.time()
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dtype = comfy.model_management.unet_dtype()
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if dtype not in [torch.float32, torch.float16, torch.bfloat16]:
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dtype = torch.float16 if comfy.model_management.should_use_fp16() else torch.float32
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self.dtype = dtype
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self.device = comfy.model_management.get_torch_device()
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# 设置权重参数
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ip_weight = weight if ip_weight is None else ip_weight # IP-Adapter权重
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cn_strength = weight if cn_strength is None else cn_strength # ControlNet强度
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print(f"\033[36m初始化设置耗时: {time.time() - start_setup:.3f}s\033[0m")
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# === 人脸嵌入处理 ===
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start_embed = time.time()
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# 如果提供了预计算的face_embed，直接使用；否则从图像提取
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if face_embed is not None:
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print(f"\033[32mINFO: 使用预计算的人脸嵌入\033[0m")
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image_prompt_embeds = face_embed['cond'].to(self.device, dtype=self.dtype)
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uncond_image_prompt_embeds = face_embed['uncond'].to(self.device, dtype=self.dtype)
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output_face_embed = None # 已有embed，不需要输出新的
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else:
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print(f"\033[32mINFO: 从参考图像提取人脸特征\033[0m")
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# 从参考图像中提取人脸特征
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start_face_detect = time.time()
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face_embed_raw = extractFeatures(insightface, image)
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print(f"\033[36m人脸检测耗时: {time.time() - start_face_detect:.3f}s\033[0m")
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if face_embed_raw is None:
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raise Exception('参考图像中未检测到人脸，请确保图像包含清晰的人脸')
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start_embed_process = time.time()
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clip_embed = face_embed_raw
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# InstantID使用平均嵌入效果更好
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if clip_embed.shape[0] > 1:
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if combine_embeds == 'average':
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clip_embed = torch.mean(clip_embed, dim=0).unsqueeze(0)
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elif combine_embeds == 'norm average':
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clip_embed = torch.mean(clip_embed / torch.norm(clip_embed, dim=0, keepdim=True), dim=0).unsqueeze(0)
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# 添加噪声到负向嵌入
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if noise > 0:
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seed = int(torch.sum(clip_embed).item()) % 1000000007
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torch.manual_seed(seed)
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clip_embed_zeroed = noise * torch.rand_like(clip_embed)
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else:
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clip_embed_zeroed = torch.zeros_like(clip_embed)
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print(f"\033[36m嵌入预处理耗时: {time.time() - start_embed_process:.3f}s\033[0m")
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# 使用InstantID模型处理嵌入
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start_instantid = time.time()
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self.instantid = instantid
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self.instantid.to(self.device, dtype=self.dtype)
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image_prompt_embeds, uncond_image_prompt_embeds = self.instantid.get_image_embeds(clip_embed.to(self.device, dtype=self.dtype), clip_embed_zeroed.to(self.device, dtype=self.dtype))
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image_prompt_embeds = image_prompt_embeds.to(self.device, dtype=self.dtype)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device, dtype=self.dtype)
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print(f"\033[36mInstantID模型处理耗时: {time.time() - start_instantid:.3f}s\033[0m")
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# 保存生成的face_embed用于下次使用
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output_face_embed = { "cond": image_prompt_embeds, "uncond": uncond_image_prompt_embeds }
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print(f"\033[36m人脸嵌入总耗时: {time.time() - start_embed:.3f}s\033[0m")
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# === 关键点处理 ===
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start_kps = time.time()
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# 如果没有提供关键点图像，使用参考图像的第一张提取关键点
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# image_kps用于ControlNet控制生成的人脸姿态和表情
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if image_kps is not None:
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print(f"\033[32mINFO: 使用提供的关键点图像\033[0m")
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face_kps = extractFeatures(insightface, image_kps, extract_kps=True)
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else:
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print(f"\033[32mINFO: 从参考图像提取关键点\033[0m")
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face_kps = extractFeatures(insightface, image[0].unsqueeze(0), extract_kps=True)
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# 如果关键点提取失败，使用零张量占位
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if face_kps is None:
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face_kps = torch.zeros_like(image) if image_kps is None else image_kps
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print(f"\033[33mWARNING: 关键点图像中未检测到人脸，可能影响控制效果\033[0m")
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print(f"\033[36m关键点提取耗时: {time.time() - start_kps:.3f}s\033[0m")
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# === 模型修补 ===
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start_patch = time.time()
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# 克隆模型以避免影响原始模型
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work_model = model.clone()
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# 计算时间步范围
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sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at)
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sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at)
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if mask is not None:
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mask = mask.to(self.device)
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# 准备修补参数
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patch_kwargs = {
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"ipadapter": self.instantid,
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"weight": ip_weight,
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"cond": image_prompt_embeds,
|
|
"uncond": uncond_image_prompt_embeds,
|
|
"mask": mask,
|
|
"sigma_start": sigma_start,
|
|
"sigma_end": sigma_end,
|
|
}
|
|
|
|
# 修补模型的注意力层
|
|
number = 0
|
|
# 输入块
|
|
for id in [4,5,7,8]:
|
|
block_indices = range(2) if id in [4, 5] else range(10)
|
|
for index in block_indices:
|
|
patch_kwargs["module_key"] = str(number*2+1)
|
|
_set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
|
|
number += 1
|
|
# 输出块
|
|
for id in range(6):
|
|
block_indices = range(2) if id in [3, 4, 5] else range(10)
|
|
for index in block_indices:
|
|
patch_kwargs["module_key"] = str(number*2+1)
|
|
_set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
|
|
number += 1
|
|
# 中间块
|
|
for index in range(10):
|
|
patch_kwargs["module_key"] = str(number*2+1)
|
|
_set_model_patch_replace(work_model, patch_kwargs, ("middle", 1, index))
|
|
number += 1
|
|
print(f"\033[36m模型修补耗时: {time.time() - start_patch:.3f}s\033[0m")
|
|
|
|
# === ControlNet应用 ===
|
|
start_controlnet = time.time()
|
|
# 处理遮罩维度
|
|
if mask is not None and len(mask.shape) < 3:
|
|
mask = mask.unsqueeze(0)
|
|
|
|
cnets = {}
|
|
cond_uncond = []
|
|
|
|
# 为正向和负向条件分别应用ControlNet
|
|
is_cond = True
|
|
for conditioning in [positive, negative]:
|
|
c = []
|
|
for t in conditioning:
|
|
d = t[1].copy()
|
|
|
|
# 获取或创建ControlNet
|
|
prev_cnet = d.get('control', None)
|
|
if prev_cnet in cnets:
|
|
c_net = cnets[prev_cnet]
|
|
else:
|
|
# 使用人脸关键点作为ControlNet的控制信号
|
|
c_net = control_net.copy().set_cond_hint(face_kps.movedim(-1,1), cn_strength, (start_at, end_at))
|
|
c_net.set_previous_controlnet(prev_cnet)
|
|
cnets[prev_cnet] = c_net
|
|
|
|
d['control'] = c_net
|
|
d['control_apply_to_uncond'] = False
|
|
# 设置跨注意力ControlNet嵌入
|
|
d['cross_attn_controlnet'] = image_prompt_embeds.to(comfy.model_management.intermediate_device(), dtype=c_net.cond_hint_original.dtype) if is_cond else uncond_image_prompt_embeds.to(comfy.model_management.intermediate_device(), dtype=c_net.cond_hint_original.dtype)
|
|
|
|
# 应用遮罩（仅对正向条件）
|
|
if mask is not None and is_cond:
|
|
d['mask'] = mask
|
|
d['set_area_to_bounds'] = False
|
|
|
|
n = [t[0], d]
|
|
c.append(n)
|
|
cond_uncond.append(c)
|
|
is_cond = False
|
|
print(f"\033[36mControlNet应用耗时: {time.time() - start_controlnet:.3f}s\033[0m")
|
|
|
|
total_time = time.time() - start_total
|
|
print(f"\033[36m=== InstantID 处理完成，总耗时: {total_time:.3f}s ===\033[0m")
|
|
|
|
# 优化建议
|
|
if output_face_embed is not None:
|
|
print(f"\033[33m💡 优化建议: 保存生成的face_embed可节省 {time.time() - start_embed:.3f}s 的人脸处理时间\033[0m")
|
|
|
|
return(work_model, cond_uncond[0], cond_uncond[1], output_face_embed)
|
|
|
|
|
|
class SaveFaceEmbeds:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"face_embed": ("FACE_EMBEDS",),
|
|
"name": ("STRING", {"default": "face_embed"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
RETURN_NAMES = ()
|
|
FUNCTION = "save_face_embed"
|
|
CATEGORY = "InstantID"
|
|
OUTPUT_NODE = True
|
|
|
|
def save_face_embed(self, face_embed, name):
|
|
import json
|
|
import time
|
|
|
|
# 检查face_embed是否为None或空
|
|
if face_embed is None:
|
|
print(f"\033[33mWARNING: Face embed is None, skipping save.\033[0m")
|
|
return {}
|
|
|
|
# 创建保存目录
|
|
save_dir = os.path.join(folder_paths.models_dir, "face_embeds")
|
|
|
|
os.makedirs(save_dir, exist_ok=True)
|
|
|
|
# 确保文件名以.pt结尾
|
|
filename = name + '.pt'
|
|
|
|
filepath = os.path.join(save_dir, filename)
|
|
|
|
# 检查文件是否已存在
|
|
if os.path.exists(filepath):
|
|
print(f"\033[33mWARNING: File {filepath} already exists, skipping save.\033[0m")
|
|
return {}
|
|
|
|
# 保存人脸嵌入数据
|
|
save_data = {
|
|
"cond": face_embed["cond"].cpu(),
|
|
"uncond": face_embed["uncond"].cpu(),
|
|
"timestamp": int(time.time())
|
|
}
|
|
|
|
torch.save(save_data, filepath)
|
|
print(f"\033[32mINFO: Face embed saved to {filepath}\033[0m")
|
|
|
|
return {}
|
|
|
|
class LoadFaceEmbeds:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"name": ("STRING", {"default": "face_embed"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("FACE_EMBEDS",)
|
|
RETURN_NAMES = ("face_embed",)
|
|
FUNCTION = "load_face_embed"
|
|
CATEGORY = "InstantID"
|
|
|
|
def load_face_embed(self, name):
|
|
face_embeds_dir = os.path.join(folder_paths.models_dir, "face_embeds")
|
|
|
|
# 确保文件名以.pt结尾
|
|
filename = name + '.pt'
|
|
|
|
filepath = os.path.join(face_embeds_dir, filename)
|
|
|
|
if not os.path.exists(filepath):
|
|
print(f"\033[33mWARNING: Face embed file not found: {filepath}, returning None\033[0m")
|
|
return (None,)
|
|
|
|
# 加载人脸嵌入数据
|
|
save_data = torch.load(filepath, map_location="cpu")
|
|
|
|
face_embed = {
|
|
"cond": save_data["cond"],
|
|
"uncond": save_data["uncond"]
|
|
}
|
|
|
|
print(f"\033[32mINFO: Face embed loaded from {filepath}\033[0m")
|
|
|
|
return (face_embed,)
|
|
|
|
|
|
|