InstantID.py

import torch
import os
import comfy.utils
import folder_paths
import numpy as np
import math
import cv2
import PIL.Image
from comfy.ldm.modules.attention import optimized_attention
from .resampler import Resampler

from insightface.app import FaceAnalysis

import torchvision.transforms.v2 as T
import torch.nn.functional as F

MODELS_DIR = os.path.join(folder_paths.models_dir, "instantid")
if "instantid" not in folder_paths.folder_names_and_paths:
    current_paths = [MODELS_DIR]
else:
    current_paths, _ = folder_paths.folder_names_and_paths["instantid"]
folder_paths.folder_names_and_paths["instantid"] = (current_paths, folder_paths.supported_pt_extensions)

INSIGHTFACE_DIR = os.path.join(folder_paths.models_dir, "insightface")

def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
    stickwidth = 4
    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
    kps = np.array(kps)

    h, w, _ = image_pil.shape
    out_img = np.zeros([h, w, 3])

    for i in range(len(limbSeq)):
        index = limbSeq[i]
        color = color_list[index[0]]

        x = kps[index][:, 0]
        y = kps[index][:, 1]
        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
    out_img = (out_img * 0.6).astype(np.uint8)

    for idx_kp, kp in enumerate(kps):
        color = color_list[idx_kp]
        x, y = kp
        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)

    out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
    return out_img_pil

# All this mess to keep compatibility with IPAdapter, it will be helpful in case we want AnimateDiff to work with InstantID
class CrossAttentionPatch:
    # forward for patching
    def __init__(self, weight, ipadapter, number, cond, uncond, weight_type="original", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
        self.weights = [weight]
        self.ipadapters = [ipadapter]
        self.conds = [cond]
        self.unconds = [uncond]
        self.number = number
        self.weight_type = [weight_type]
        self.masks = [mask]
        self.sigma_start = [sigma_start]
        self.sigma_end = [sigma_end]
        self.unfold_batch = [unfold_batch]

        self.k_key = str(self.number*2+1) + "_to_k_ip"
        self.v_key = str(self.number*2+1) + "_to_v_ip"
    
    def set_new_condition(self, weight, ipadapter, number, cond, uncond, weight_type="original", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
        self.weights.append(weight)
        self.ipadapters.append(ipadapter)
        self.conds.append(cond)
        self.unconds.append(uncond)
        self.masks.append(mask)
        self.weight_type.append(weight_type)
        self.sigma_start.append(sigma_start)
        self.sigma_end.append(sigma_end)
        self.unfold_batch.append(unfold_batch)

    def __call__(self, n, context_attn2, value_attn2, extra_options):
        org_dtype = n.dtype
        cond_or_uncond = extra_options["cond_or_uncond"]
        sigma = extra_options["sigmas"][0] if 'sigmas' in extra_options else None
        sigma = sigma.item() if sigma is not None else 999999999.9

        # extra options for AnimateDiff
        ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None

        q = n
        k = context_attn2
        v = value_attn2
        b = q.shape[0]
        qs = q.shape[1]
        batch_prompt = b // len(cond_or_uncond)
        out = optimized_attention(q, k, v, extra_options["n_heads"])
        _, _, lh, lw = extra_options["original_shape"]
        
        for weight, cond, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_type, self.sigma_start, self.sigma_end, self.unfold_batch):
            if sigma > sigma_start or sigma < sigma_end:
                continue

            if unfold_batch and cond.shape[0] > 1:
                # Check AnimateDiff context window
                if ad_params is not None and ad_params["sub_idxs"] is not None:
                    # if images length matches or exceeds full_length get sub_idx images
                    if cond.shape[0] >= ad_params["full_length"]:
                        cond = torch.Tensor(cond[ad_params["sub_idxs"]])
                        uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
                    # otherwise, need to do more to get proper sub_idxs masks
                    else:
                        # check if images length matches full_length - if not, make it match
                        if cond.shape[0] < ad_params["full_length"]:
                            cond = torch.cat((cond, cond[-1:].repeat((ad_params["full_length"]-cond.shape[0], 1, 1))), dim=0)
                            uncond = torch.cat((uncond, uncond[-1:].repeat((ad_params["full_length"]-uncond.shape[0], 1, 1))), dim=0)
                        # if we have too many remove the excess (should not happen, but just in case)
                        if cond.shape[0] > ad_params["full_length"]:
                            cond = cond[:ad_params["full_length"]]
                            uncond = uncond[:ad_params["full_length"]]
                        cond = cond[ad_params["sub_idxs"]]
                        uncond = uncond[ad_params["sub_idxs"]]

                # if we don't have enough reference images repeat the last one until we reach the right size
                if cond.shape[0] < batch_prompt:
                    cond = torch.cat((cond, cond[-1:].repeat((batch_prompt-cond.shape[0], 1, 1))), dim=0)
                    uncond = torch.cat((uncond, uncond[-1:].repeat((batch_prompt-uncond.shape[0], 1, 1))), dim=0)
                # if we have too many remove the exceeding
                elif cond.shape[0] > batch_prompt:
                    cond = cond[:batch_prompt]
                    uncond = uncond[:batch_prompt]

                k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond)
                k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond)
                v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond)
                v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond)
            else:
                k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
                k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
                v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
                v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)

            if weight_type.startswith("linear"):
                ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0) * weight
                ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0) * weight
            else:
                ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
                ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)

                if weight_type.startswith("channel"):
                    # code by Lvmin Zhang at Stanford University as also seen on Fooocus IPAdapter implementation
                    ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
                    ip_v_offset = ip_v - ip_v_mean
                    _, _, C = ip_k.shape
                    channel_penalty = float(C) / 1280.0
                    W = weight * channel_penalty
                    ip_k = ip_k * W
                    ip_v = ip_v_offset + ip_v_mean * W

            out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])           
            if weight_type.startswith("original"):
                out_ip = out_ip * weight

            if mask is not None:
                # TODO: needs checking
                mask_h = lh / math.sqrt(lh * lw / qs)
                mask_h = int(mask_h) + int((qs % int(mask_h)) != 0)
                mask_w = qs // mask_h

                # check if using AnimateDiff and sliding context window
                if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None):
                    # if mask length matches or exceeds full_length, just get sub_idx masks, resize, and continue
                    if mask.shape[0] >= ad_params["full_length"]:
                        mask_downsample = torch.Tensor(mask[ad_params["sub_idxs"]])
                        mask_downsample = F.interpolate(mask_downsample.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
                    # otherwise, need to do more to get proper sub_idxs masks
                    else:
                        # resize to needed attention size (to save on memory)
                        mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
                        # check if mask length matches full_length - if not, make it match
                        if mask_downsample.shape[0] < ad_params["full_length"]:
                            mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:].repeat((ad_params["full_length"]-mask_downsample.shape[0], 1, 1))), dim=0)
                        # if we have too many remove the excess (should not happen, but just in case)
                        if mask_downsample.shape[0] > ad_params["full_length"]:
                            mask_downsample = mask_downsample[:ad_params["full_length"]]
                        # now, select sub_idxs masks
                        mask_downsample = mask_downsample[ad_params["sub_idxs"]]
                # otherwise, perform usual mask interpolation
                else:
                    mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)

                # if we don't have enough masks repeat the last one until we reach the right size
                if mask_downsample.shape[0] < batch_prompt:
                    mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:, :, :].repeat((batch_prompt-mask_downsample.shape[0], 1, 1))), dim=0)
                # if we have too many remove the exceeding
                elif mask_downsample.shape[0] > batch_prompt:
                    mask_downsample = mask_downsample[:batch_prompt, :, :]
                
                # repeat the masks
                mask_downsample = mask_downsample.repeat(len(cond_or_uncond), 1, 1)
                mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1, 1).repeat(1, 1, out.shape[2])

                out_ip = out_ip * mask_downsample

            out = out + out_ip

        return out.to(dtype=org_dtype)


class InstantID(torch.nn.Module):
    def __init__(self, instantid_model, cross_attention_dim=1280, output_cross_attention_dim=1024, clip_embeddings_dim=512, clip_extra_context_tokens=16):
        super().__init__()

        self.clip_embeddings_dim = clip_embeddings_dim
        self.cross_attention_dim = cross_attention_dim
        self.output_cross_attention_dim = output_cross_attention_dim
        self.clip_extra_context_tokens = clip_extra_context_tokens

        self.image_proj_model = self.init_proj()

        self.image_proj_model.load_state_dict(instantid_model["image_proj"])
        self.ip_layers = To_KV(instantid_model["ip_adapter"])

    def init_proj(self):
        image_proj_model = Resampler(
            dim=self.cross_attention_dim,
            depth=4,
            dim_head=64,
            heads=20,
            num_queries=self.clip_extra_context_tokens,
            embedding_dim=self.clip_embeddings_dim,
            output_dim=self.output_cross_attention_dim,
            ff_mult=4
        )
        return image_proj_model

    @torch.inference_mode()
    def get_image_embeds(self, clip_embed, clip_embed_zeroed):
        #image_prompt_embeds = clip_embed.clone().detach()
        image_prompt_embeds = self.image_proj_model(clip_embed)
        #uncond_image_prompt_embeds = clip_embed_zeroed.clone().detach()
        uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed)

        return image_prompt_embeds, uncond_image_prompt_embeds

class ImageProjModel(torch.nn.Module):
    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
        super().__init__()
        
        self.cross_attention_dim = cross_attention_dim
        self.clip_extra_context_tokens = clip_extra_context_tokens
        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
        self.norm = torch.nn.LayerNorm(cross_attention_dim)
        
    def forward(self, image_embeds):
        embeds = image_embeds
        clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
        return clip_extra_context_tokens

class To_KV(torch.nn.Module):
    def __init__(self, state_dict):
        super().__init__()

        self.to_kvs = torch.nn.ModuleDict()
        for key, value in state_dict.items():
            k = key.replace(".weight", "").replace(".", "_")
            self.to_kvs[k] = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)
            self.to_kvs[k].weight.data = value

def _set_model_patch_replace(model, patch_kwargs, key):
    to = model.model_options["transformer_options"]
    if "patches_replace" not in to:
        to["patches_replace"] = {}
    if "attn2" not in to["patches_replace"]:
        to["patches_replace"]["attn2"] = {}
    if key not in to["patches_replace"]["attn2"]:
        to["patches_replace"]["attn2"][key] = CrossAttentionPatch(**patch_kwargs)
    else:
        to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs)

class InstantIDModelLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "instantid_file": (folder_paths.get_filename_list("instantid"), )}}

    RETURN_TYPES = ("INSTANTID",)
    FUNCTION = "load_model"
    CATEGORY = "InstantID"

    def load_model(self, instantid_file):
        ckpt_path = folder_paths.get_full_path("instantid", instantid_file)

        model = comfy.utils.load_torch_file(ckpt_path, safe_load=True)

        if ckpt_path.lower().endswith(".safetensors"):
            st_model = {"image_proj": {}, "ip_adapter": {}}
            for key in model.keys():
                if key.startswith("image_proj."):
                    st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
                elif key.startswith("ip_adapter."):
                    st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
            model = st_model
        
        return (model,)

def tensorToNP(image):
    out = torch.clamp(255. * image.detach().cpu(), 0, 255).to(torch.uint8)
    out = out[..., [2, 1, 0]]
    out = out.numpy()
    return out

def extractFeatures(insightface, image, extract_kps=False):
    face_img = tensorToNP(image)
    out = []

    insightface.det_model.input_size = (640,640) # reset the detection size

    for i in range(face_img.shape[0]):
        for size in [(size, size) for size in range(640, 128, -64)]:
            insightface.det_model.input_size = size # TODO: hacky but seems to be working
            face = insightface.get(face_img[i])
            if face:
                face = sorted(face, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1]

                if extract_kps:
                    out.append(draw_kps(face_img[i], face['kps']))
                else:
                    out.append(torch.from_numpy(face['embedding']).unsqueeze(0))

                if 640 not in size:
                    print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
                break

    if out:
        if extract_kps:
            out = torch.stack(T.ToTensor()(out), dim=0).permute([0,2,3,1])
        else:
            out = torch.stack(out, dim=0)
    else:
        out = None

    return out

class InstantIDFaceAnalysis:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "provider": (["CPU", "CUDA", "ROCM"], ),
            },
        }

    RETURN_TYPES = ("FACEANALYSIS",)
    FUNCTION = "load_insight_face"
    CATEGORY = "InstantID"

    def load_insight_face(self, provider):
        model = FaceAnalysis(name="antelopev2", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',]) # buffalo_l
        model.prepare(ctx_id=0, det_size=(640, 640))

        return (model,)

class FaceKeypointsPreprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "faceanalysis": ("FACEANALYSIS", ),
                "image": ("IMAGE", ),
            },
        }
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "preprocess_image"
    CATEGORY = "InstantID"

    def preprocess_image(self, faceanalysis, image):
        face_kps = extractFeatures(faceanalysis, image[0].unsqueeze(0), extract_kps=True)

        if face_kps is None:
            face_kps = torch.zeros_like(image)
            print(f"\033[33mWARNING: no face detected, unable to extract the keypoints!\033[0m")
            #raise Exception('Face Keypoints Image: No face detected.')

        return (face_kps,)

def add_noise(image, factor):
    seed = int(torch.sum(image).item()) % 1000000007
    torch.manual_seed(seed)
    mask = (torch.rand_like(image) < factor).float()
    noise = torch.rand_like(image)
    noise = torch.zeros_like(image) * (1-mask) + noise * mask
    
    return factor*noise

class ApplyInstantID:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "instantid": ("INSTANTID", ),
                "insightface": ("FACEANALYSIS", ),
                "control_net": ("CONTROL_NET", ),
                "image": ("IMAGE", ),
                "model": ("MODEL", ),
                "positive": ("CONDITIONING", ),
                "negative": ("CONDITIONING", ),
                "weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 5.0, "step": 0.01, }),
                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
            },
            "optional": {
                "image_kps": ("IMAGE",),
                "mask": ("MASK",),
            }
        }

    RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING",)
    RETURN_NAMES = ("MODEL", "positive", "negative", )
    FUNCTION = "apply_instantid"
    CATEGORY = "InstantID"

    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):
        self.dtype = torch.float16 if comfy.model_management.should_use_fp16() else torch.float32
        self.device = comfy.model_management.get_torch_device()

        ip_weight = weight if ip_weight is None else ip_weight
        cn_strength = weight if cn_strength is None else cn_strength

        output_cross_attention_dim = instantid["ip_adapter"]["1.to_k_ip.weight"].shape[1]
        is_sdxl = output_cross_attention_dim == 2048
        cross_attention_dim = 1280
        clip_extra_context_tokens = 16

        face_embed = extractFeatures(insightface, image)
        if face_embed is None:
            raise Exception('Reference Image: No face detected.')

        face_kps = extractFeatures(insightface, image_kps[0].unsqueeze(0) if image_kps is not None else image[0].unsqueeze(0), extract_kps=True)

        if face_kps is None:
            face_kps = torch.zeros_like(image) if image_kps is None else image_kps
            print(f"\033[33mWARNING: No face detected in the keypoints image!\033[0m")

        clip_embed = face_embed
        # InstantID works better with averaged embeds (TODO: needs testing)
        if clip_embed.shape[0] > 1:
            clip_embed = torch.mean(clip_embed, dim=0).unsqueeze(0)

        if noise > 0:
            seed = int(torch.sum(clip_embed).item()) % 1000000007
            torch.manual_seed(seed)
            clip_embed_zeroed = noise * torch.rand_like(clip_embed)
            #clip_embed_zeroed = add_noise(clip_embed, noise)
        else:
            clip_embed_zeroed = torch.zeros_like(clip_embed)

        clip_embeddings_dim = face_embed.shape[-1]

        # 1: patch the attention
        self.instantid = InstantID(
            instantid,
            cross_attention_dim=cross_attention_dim,
            output_cross_attention_dim=output_cross_attention_dim,
            clip_embeddings_dim=clip_embeddings_dim,
            clip_extra_context_tokens=clip_extra_context_tokens,
        )

        self.instantid.to(self.device, dtype=self.dtype)

        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))

        image_prompt_embeds = image_prompt_embeds.to(self.device, dtype=self.dtype)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device, dtype=self.dtype)

        work_model = model.clone()

        sigma_start = work_model.model.model_sampling.percent_to_sigma(start_at)
        sigma_end = work_model.model.model_sampling.percent_to_sigma(end_at)

        if mask is not None:
            mask = mask.to(self.device)

        patch_kwargs = {
            "number": 0,
            "weight": ip_weight,
            "ipadapter": self.instantid,
            "cond": image_prompt_embeds,
            "uncond": uncond_image_prompt_embeds,
            "mask": mask,
            "sigma_start": sigma_start,
            "sigma_end": sigma_end,
            "weight_type": "original",
        }

        if not is_sdxl:
            for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention
                _set_model_patch_replace(work_model, patch_kwargs, ("input", id))
                patch_kwargs["number"] += 1
            for id in [3,4,5,6,7,8,9,10,11]: # id of output_blocks that have cross attention
                _set_model_patch_replace(work_model, patch_kwargs, ("output", id))
                patch_kwargs["number"] += 1
            _set_model_patch_replace(work_model, patch_kwargs, ("middle", 0))
        else:
            for id in [4,5,7,8]: # id of input_blocks that have cross attention
                block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
                for index in block_indices:
                    _set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
                    patch_kwargs["number"] += 1
            for id in range(6): # id of output_blocks that have cross attention
                block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth
                for index in block_indices:
                    _set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
                    patch_kwargs["number"] += 1
            for index in range(10):
                _set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index))
                patch_kwargs["number"] += 1

        # 2: do the ControlNet
        if mask is not None and len(mask.shape) < 3:
            mask = mask.unsqueeze(0)

        cnets = {}
        cond_uncond = []

        is_cond = True
        for conditioning in [positive, negative]:
            c = []
            for t in conditioning:
                d = t[1].copy()

                prev_cnet = d.get('control', None)
                if prev_cnet in cnets:
                    c_net = cnets[prev_cnet]
                else:
                    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
                d['cross_attn_controlnet'] = image_prompt_embeds.to(comfy.model_management.intermediate_device()) if is_cond else uncond_image_prompt_embeds.to(comfy.model_management.intermediate_device())

                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

        return(work_model, cond_uncond[0], cond_uncond[1], )

class ApplyInstantIDAdvanced(ApplyInstantID):
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "instantid": ("INSTANTID", ),
                "insightface": ("FACEANALYSIS", ),
                "control_net": ("CONTROL_NET", ),
                "image": ("IMAGE", ),
                "model": ("MODEL", ),
                "positive": ("CONDITIONING", ),
                "negative": ("CONDITIONING", ),
                "ip_weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 3.0, "step": 0.01, }),
                "cn_strength": ("FLOAT", {"default": .8, "min": 0.0, "max": 10.0, "step": 0.01, }),
                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
                "noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1, }),
            },
            "optional": {
                "image_kps": ("IMAGE",),
                "mask": ("MASK",),
            }
        }

class InstantIDAttentionPatch:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "instantid": ("INSTANTID", ),
                "insightface": ("FACEANALYSIS", ),
                "image": ("IMAGE", ),
                "model": ("MODEL", ),
                "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 3.0, "step": 0.01, }),
                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
                "noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1, }),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }

    RETURN_TYPES = ("MODEL", "FACE_EMBEDS")
    FUNCTION = "patch_attention"
    CATEGORY = "InstantID"

    def patch_attention(self, instantid, insightface, image, model, weight, start_at, end_at, noise=0.0, mask=None):       
        self.dtype = torch.float16 if comfy.model_management.should_use_fp16() else torch.float32
        self.device = comfy.model_management.get_torch_device()

        output_cross_attention_dim = instantid["ip_adapter"]["1.to_k_ip.weight"].shape[1]
        is_sdxl = output_cross_attention_dim == 2048
        cross_attention_dim = 1280
        clip_extra_context_tokens = 16

        face_embed = extractFeatures(insightface, image)
        if face_embed is None:
            raise Exception('Reference Image: No face detected.')

        clip_embed = face_embed
        # InstantID works better with averaged embeds (TODO: needs testing)
        if clip_embed.shape[0] > 1:
            clip_embed = torch.mean(clip_embed, dim=0).unsqueeze(0)

        if noise > 0:
            seed = int(torch.sum(clip_embed).item()) % 1000000007
            torch.manual_seed(seed)
            clip_embed_zeroed = noise * torch.rand_like(clip_embed)
        else:
            clip_embed_zeroed = torch.zeros_like(clip_embed)

        clip_embeddings_dim = face_embed.shape[-1]

        # 1: patch the attention
        self.instantid = InstantID(
            instantid,
            cross_attention_dim=cross_attention_dim,
            output_cross_attention_dim=output_cross_attention_dim,
            clip_embeddings_dim=clip_embeddings_dim,
            clip_extra_context_tokens=clip_extra_context_tokens,
        )

        self.instantid.to(self.device, dtype=self.dtype)

        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))

        image_prompt_embeds = image_prompt_embeds.to(self.device, dtype=self.dtype)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device, dtype=self.dtype)

        if weight == 0:
            return (model, { "cond": image_prompt_embeds, "uncond": uncond_image_prompt_embeds } )

        work_model = model.clone()

        sigma_start = work_model.model.model_sampling.percent_to_sigma(start_at)
        sigma_end = work_model.model.model_sampling.percent_to_sigma(end_at)

        if mask is not None:
            mask = mask.to(self.device)

        patch_kwargs = {
            "number": 0,
            "weight": weight,
            "ipadapter": self.instantid,
            "cond": image_prompt_embeds,
            "uncond": uncond_image_prompt_embeds,
            "mask": mask,
            "sigma_start": sigma_start,
            "sigma_end": sigma_end,
            "weight_type": "original",
        }

        if not is_sdxl:
            for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention
                _set_model_patch_replace(work_model, patch_kwargs, ("input", id))
                patch_kwargs["number"] += 1
            for id in [3,4,5,6,7,8,9,10,11]: # id of output_blocks that have cross attention
                _set_model_patch_replace(work_model, patch_kwargs, ("output", id))
                patch_kwargs["number"] += 1
            _set_model_patch_replace(work_model, patch_kwargs, ("middle", 0))
        else:
            for id in [4,5,7,8]: # id of input_blocks that have cross attention
                block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
                for index in block_indices:
                    _set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
                    patch_kwargs["number"] += 1
            for id in range(6): # id of output_blocks that have cross attention
                block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth
                for index in block_indices:
                    _set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
                    patch_kwargs["number"] += 1
            for index in range(10):
                _set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index))
                patch_kwargs["number"] += 1
        
        return(work_model, { "cond": image_prompt_embeds, "uncond": uncond_image_prompt_embeds }, )

class ApplyInstantIDControlNet:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "face_embeds": ("FACE_EMBEDS", ),
                "control_net": ("CONTROL_NET", ),
                "image_kps": ("IMAGE", ),
                "positive": ("CONDITIONING", ),
                "negative": ("CONDITIONING", ),
                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, }),
                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }

    RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
    RETURN_NAMES = ("positive", "negative", )
    FUNCTION = "apply_controlnet"
    CATEGORY = "InstantID"

    def apply_controlnet(self, face_embeds, control_net, image_kps, positive, negative, strength, start_at, end_at, mask=None):
        self.device = comfy.model_management.get_torch_device()

        if strength == 0:
            return (positive, negative)

        if mask is not None:
            mask = mask.to(self.device)
        
        if mask is not None and len(mask.shape) < 3:
            mask = mask.unsqueeze(0)

        image_prompt_embeds = face_embeds['cond']
        uncond_image_prompt_embeds = face_embeds['uncond']

        cnets = {}
        cond_uncond = []
        control_hint = image_kps.movedim(-1,1)

        is_cond = True
        for conditioning in [positive, negative]:
            c = []
            for t in conditioning:
                d = t[1].copy()

                prev_cnet = d.get('control', None)
                if prev_cnet in cnets:
                    c_net = cnets[prev_cnet]
                else:
                    c_net = control_net.copy().set_cond_hint(control_hint, 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
                d['cross_attn_controlnet'] = image_prompt_embeds.to(comfy.model_management.intermediate_device()) if is_cond else uncond_image_prompt_embeds.to(comfy.model_management.intermediate_device())

                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(cond_uncond[0])

        return(cond_uncond[0], cond_uncond[1])


NODE_CLASS_MAPPINGS = {
    "InstantIDModelLoader": InstantIDModelLoader,
    "InstantIDFaceAnalysis": InstantIDFaceAnalysis,
    "ApplyInstantID": ApplyInstantID,
    "ApplyInstantIDAdvanced": ApplyInstantIDAdvanced,
    "FaceKeypointsPreprocessor": FaceKeypointsPreprocessor,

    "InstantIDAttentionPatch": InstantIDAttentionPatch,
    "ApplyInstantIDControlNet": ApplyInstantIDControlNet,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "InstantIDModelLoader": "Load InstantID Model",
    "InstantIDFaceAnalysis": "InstantID Face Analysis",
    "ApplyInstantID": "Apply InstantID",
    "ApplyInstantIDAdvanced": "Apply InstantID Advanced",
    "FaceKeypointsPreprocessor": "Face Keypoints Preprocessor",

    "InstantIDAttentionPatch": "InstantID Patch Attention",
    "ApplyInstantIDControlNet": "InstantID Apply ControlNet",
}