garment_adapter/garment_ipadapter_faceid.py

import os
import pdb
from typing import List

import numpy as np
import torch
from safetensors import safe_open
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from garment_seg.process import load_seg_model, generate_mask

from utils.utils import is_torch2_available, prepare_image, prepare_mask
import copy
from utils.resampler import PerceiverAttention, FeedForward
from insightface.utils import face_align
from insightface.app import FaceAnalysis
import cv2

USE_DAFAULT_ATTN = False  # should be True for visualization_attnmap
if is_torch2_available() and (not USE_DAFAULT_ATTN):
    from .attention_processor import AttnProcessor2_0 as AttnProcessor
    from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor
    from .attention_processor import REFAttnProcessor2_0 as REFAttnProcessor
else:
    from .attention_processor import AttnProcessor, IPAttnProcessor, REFAttnProcessor


class FacePerceiverResampler(torch.nn.Module):
    def __init__(
            self,
            *,
            dim=768,
            depth=4,
            dim_head=64,
            heads=16,
            embedding_dim=1280,
            output_dim=768,
            ff_mult=4,
    ):
        super().__init__()

        self.proj_in = torch.nn.Linear(embedding_dim, dim)
        self.proj_out = torch.nn.Linear(dim, output_dim)
        self.norm_out = torch.nn.LayerNorm(output_dim)
        self.layers = torch.nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                torch.nn.ModuleList(
                    [
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        FeedForward(dim=dim, mult=ff_mult),
                    ]
                )
            )

    def forward(self, latents, x):
        x = self.proj_in(x)
        for attn, ff in self.layers:
            latents = attn(x, latents) + latents
            latents = ff(latents) + latents
        latents = self.proj_out(latents)
        return self.norm_out(latents)


class MLPProjModel(torch.nn.Module):
    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
        super().__init__()

        self.cross_attention_dim = cross_attention_dim
        self.num_tokens = num_tokens

        self.proj = torch.nn.Sequential(
            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
            torch.nn.GELU(),
            torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
        )
        self.norm = torch.nn.LayerNorm(cross_attention_dim)

    def forward(self, id_embeds):
        x = self.proj(id_embeds)
        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
        x = self.norm(x)
        return x


class ProjPlusModel(torch.nn.Module):
    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4):
        super().__init__()

        self.cross_attention_dim = cross_attention_dim
        self.num_tokens = num_tokens

        self.proj = torch.nn.Sequential(
            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
            torch.nn.GELU(),
            torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
        )
        self.norm = torch.nn.LayerNorm(cross_attention_dim)

        self.perceiver_resampler = FacePerceiverResampler(
            dim=cross_attention_dim,
            depth=4,
            dim_head=64,
            heads=cross_attention_dim // 64,
            embedding_dim=clip_embeddings_dim,
            output_dim=cross_attention_dim,
            ff_mult=4,
        )

    def forward(self, id_embeds, clip_embeds, shortcut=False, scale=1.0):
        x = self.proj(id_embeds)
        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
        x = self.norm(x)
        out = self.perceiver_resampler(x, clip_embeds)
        if shortcut:
            out = x + scale * out
        return out


class IPAdapterFaceID:
    def __init__(self, sd_pipe, ref_path, ip_ckpt, device, enable_cloth_guidance, num_tokens=4, n_cond=1, torch_dtype=torch.float16, set_seg_model=True):
        self.enable_cloth_guidance = enable_cloth_guidance
        self.device = device
        self.ip_ckpt = ip_ckpt
        self.num_tokens = num_tokens
        self.n_cond = n_cond
        self.torch_dtype = torch_dtype

        self.pipe = sd_pipe.to(self.device)
        self.set_ip_adapter()

        # image proj model
        self.image_proj_model = self.init_proj()

        self.load_ip_adapter()

        self.set_insightface()

        ref_unet = copy.deepcopy(sd_pipe.unet)
        state_dict = {}
        with safe_open(ref_path, framework="pt", device="cpu") as f:
            for key in f.keys():
                state_dict[key] = f.get_tensor(key)
        ref_unet.load_state_dict(state_dict, strict=False)

        self.ref_unet = ref_unet.to(self.device)
        self.set_ref_adapter()
        if set_seg_model:
            self.set_seg_model()
        self.attn_store = {}

    def set_insightface(self):
        self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
        self.app.prepare(ctx_id=0, det_size=(640, 640))

    def set_seg_model(self, ):
        checkpoint_path = 'checkpoints/cloth_segm.pth'
        self.seg_net = load_seg_model(checkpoint_path, device=self.device)

    def init_proj(self):
        image_proj_model = MLPProjModel(
            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
            id_embeddings_dim=512,
            num_tokens=self.num_tokens,
        ).to(self.device, dtype=self.torch_dtype)
        return image_proj_model

    def set_ref_adapter(self):
        attn_procs = {}
        for name in self.ref_unet.attn_processors.keys():
            if "attn1" in name:
                attn_procs[name] = REFAttnProcessor(name=name, type="read")
            else:
                attn_procs[name] = AttnProcessor()
        self.ref_unet.set_attn_processor(attn_procs)

    def set_ip_adapter(self):
        unet = self.pipe.unet
        attn_procs = {}
        for name in unet.attn_processors.keys():
            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = unet.config.block_out_channels[block_id]
            if cross_attention_dim is None:
                attn_procs[name] = REFAttnProcessor(name=name, type="write")
            else:
                attn_procs[name] = IPAttnProcessor(
                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, num_tokens=self.num_tokens * self.n_cond,
                ).to(self.device, dtype=self.torch_dtype)
        unet.set_attn_processor(attn_procs)

    def load_ip_adapter(self):
        if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
            state_dict = {"image_proj": {}, "ip_adapter": {}}
            with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
                for key in f.keys():
                    if key.startswith("image_proj."):
                        state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
                    elif key.startswith("ip_adapter."):
                        state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
        else:
            state_dict = torch.load(self.ip_ckpt, map_location="cpu")
        self.image_proj_model.load_state_dict(state_dict["image_proj"])
        ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
        ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)

    @torch.inference_mode()
    def get_image_embeds(self, faceid_embeds):

        multi_face = False
        if faceid_embeds.dim() == 3:
            multi_face = True
            b, n, c = faceid_embeds.shape
            faceid_embeds = faceid_embeds.reshape(b * n, c)

        faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
        image_prompt_embeds = self.image_proj_model(faceid_embeds)
        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds))
        if multi_face:
            c = image_prompt_embeds.size(-1)
            image_prompt_embeds = image_prompt_embeds.reshape(b, -1, c)
            uncond_image_prompt_embeds = uncond_image_prompt_embeds.reshape(b, -1, c)

        return image_prompt_embeds, uncond_image_prompt_embeds

    def set_scale(self, scale):
        for attn_processor in self.pipe.unet.attn_processors.values():
            if isinstance(attn_processor, IPAttnProcessor):
                attn_processor.scale = scale

    def generate(
            self,
            cloth_image,
            face_image,
            cloth_mask=None,
            prompt=None,
            a_prompt="best quality, high quality",
            negative_prompt=None,
            num_samples=4,
            seed=None,
            guidance_scale=3.,
            cloth_guidance_scale=3.,
            num_inference_steps=30,
            height=512,
            width=384,
            scale=1.0,
            **kwargs,
    ):
        faces = self.app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
        try:
            faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
        except:
            return None

        if cloth_mask is None:
            cloth_mask_image = generate_mask(cloth_image, net=self.seg_net, device=self.device)

        cloth = prepare_image(cloth_image, height, width)
        cloth_mask = prepare_mask(cloth_mask_image, height, width)
        cloth = (cloth * cloth_mask).to(self.device, dtype=torch.float16)

        self.set_scale(scale)

        num_prompts = faceid_embeds.size(0)

        if prompt is None:
            prompt = "a photography of a model"
        prompt = prompt + ", " + a_prompt
        if negative_prompt is None:
            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

        if not isinstance(prompt, List):
            prompt = [prompt] * num_prompts
        if not isinstance(negative_prompt, List):
            negative_prompt = [negative_prompt] * num_prompts

        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds)

        bs_embed, seq_len, _ = image_prompt_embeds.shape
        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)

        with torch.inference_mode():
            prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
                prompt,
                device=self.device,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
            )
            prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
            negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)

            prompt_embeds_null = self.pipe.encode_prompt([""], device=self.device, num_images_per_prompt=num_samples, do_classifier_free_guidance=False)[0]
            cloth_embeds = self.pipe.vae.encode(cloth).latent_dist.mode() * self.pipe.vae.config.scaling_factor
            self.ref_unet(torch.cat([cloth_embeds] * num_samples), 0, prompt_embeds_null, cross_attention_kwargs={"attn_store": self.attn_store})

        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
        images = self.pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            generator=generator,
            height=height,
            width=width,
            cross_attention_kwargs={"attn_store": self.attn_store, "do_classifier_free_guidance": guidance_scale > 1.0, "enable_cloth_guidance": self.enable_cloth_guidance},
            **kwargs,
        ).images

        return images, cloth_mask_image


class IPAdapterFaceIDPlus:
    def __init__(self, sd_pipe, ref_path, image_encoder_path, ip_ckpt, device, enable_cloth_guidance, num_tokens=4, torch_dtype=torch.float16, set_seg_model=True):
        self.enable_cloth_guidance = enable_cloth_guidance
        self.device = device
        self.image_encoder_path = image_encoder_path
        self.ip_ckpt = ip_ckpt
        self.num_tokens = num_tokens
        self.torch_dtype = torch_dtype

        self.pipe = sd_pipe.to(self.device)
        self.set_ip_adapter()

        # load image encoder
        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
            self.device, dtype=self.torch_dtype
        )
        self.clip_image_processor = CLIPImageProcessor()
        # image proj model
        self.image_proj_model = self.init_proj()

        self.load_ip_adapter()

        self.set_insightface()

        ref_unet = copy.deepcopy(sd_pipe.unet)
        state_dict = {}
        with safe_open(ref_path, framework="pt", device="cpu") as f:
            for key in f.keys():
                state_dict[key] = f.get_tensor(key)
        ref_unet.load_state_dict(state_dict, strict=False)

        self.ref_unet = ref_unet.to(self.device)
        self.set_ref_adapter()
        if set_seg_model:
            self.set_seg_model()
        self.attn_store = {}

    def set_insightface(self):
        self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
        self.app.prepare(ctx_id=0, det_size=(640, 640))

    def set_seg_model(self, ):
        checkpoint_path = 'checkpoints/cloth_segm.pth'
        self.seg_net = load_seg_model(checkpoint_path, device=self.device)

    def init_proj(self):
        image_proj_model = ProjPlusModel(
            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
            id_embeddings_dim=512,
            clip_embeddings_dim=self.image_encoder.config.hidden_size,
            num_tokens=self.num_tokens,
        ).to(self.device, dtype=self.torch_dtype)
        return image_proj_model

    def set_ref_adapter(self):
        attn_procs = {}
        for name in self.ref_unet.attn_processors.keys():
            if "attn1" in name:
                attn_procs[name] = REFAttnProcessor(name=name, type="read")
            else:
                attn_procs[name] = AttnProcessor()
        self.ref_unet.set_attn_processor(attn_procs)

    def set_ip_adapter(self):
        unet = self.pipe.unet
        attn_procs = {}
        for name in unet.attn_processors.keys():
            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = unet.config.block_out_channels[block_id]
            if cross_attention_dim is None:
                attn_procs[name] = REFAttnProcessor(name=name, type="write")
            else:
                attn_procs[name] = IPAttnProcessor(
                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, num_tokens=self.num_tokens,
                ).to(self.device, dtype=self.torch_dtype)
        unet.set_attn_processor(attn_procs)

    def load_ip_adapter(self):
        if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
            state_dict = {"image_proj": {}, "ip_adapter": {}}
            with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
                for key in f.keys():
                    if key.startswith("image_proj."):
                        state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
                    elif key.startswith("ip_adapter."):
                        state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
        else:
            state_dict = torch.load(self.ip_ckpt, map_location="cpu")
        self.image_proj_model.load_state_dict(state_dict["image_proj"])
        ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
        ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)

    @torch.inference_mode()
    def get_image_embeds(self, faceid_embeds, face_image, s_scale, shortcut):
        clip_image = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values
        clip_image = clip_image.to(self.device, dtype=self.torch_dtype)
        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
        uncond_clip_image_embeds = self.image_encoder(
            torch.zeros_like(clip_image), output_hidden_states=True
        ).hidden_states[-2]

        faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
        image_prompt_embeds = self.image_proj_model(faceid_embeds, clip_image_embeds, shortcut=shortcut, scale=s_scale)
        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds), uncond_clip_image_embeds, shortcut=shortcut, scale=s_scale)
        return image_prompt_embeds, uncond_image_prompt_embeds

    def set_scale(self, scale):
        for attn_processor in self.pipe.unet.attn_processors.values():
            if isinstance(attn_processor, IPAttnProcessor):
                attn_processor.scale = scale

    def generate(
            self,
            cloth_image,
            face_image,
            cloth_mask=None,
            prompt=None,
            a_prompt="best quality, high quality",
            negative_prompt=None,
            num_samples=4,
            seed=None,
            guidance_scale=2.5,
            cloth_guidance_scale=2.5,
            num_inference_steps=20,
            height=512,
            width=384,
            scale=1.0,
            s_scale=1.,
            shortcut=False,
            **kwargs,
    ):
        face_image = cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)
        faces = self.app.get(face_image)
        try:
            faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
            face_image = face_align.norm_crop(face_image, landmark=faces[0].kps, image_size=224)
        except:
            return None

        if cloth_mask is None:
            cloth_mask_image = generate_mask(cloth_image, net=self.seg_net, device=self.device)

        cloth = prepare_image(cloth_image, height, width)
        cloth_mask = prepare_mask(cloth_mask_image, height, width)
        cloth = (cloth * cloth_mask).to(self.device, dtype=torch.float16)
        self.set_scale(scale)

        num_prompts = faceid_embeds.size(0)

        if prompt is None:
            prompt = "a photography of a model"
        prompt = prompt + ", " + a_prompt
        if negative_prompt is None:
            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

        if not isinstance(prompt, List):
            prompt = [prompt] * num_prompts
        if not isinstance(negative_prompt, List):
            negative_prompt = [negative_prompt] * num_prompts

        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds, face_image, s_scale, shortcut)

        bs_embed, seq_len, _ = image_prompt_embeds.shape
        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)

        with torch.inference_mode():
            prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
                prompt,
                device=self.device,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
            )
            prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
            negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)

            prompt_embeds_null = self.pipe.encode_prompt([""], device=self.device, num_images_per_prompt=num_samples, do_classifier_free_guidance=False)[0]
            cloth_embeds = self.pipe.vae.encode(cloth).latent_dist.mode() * self.pipe.vae.config.scaling_factor
            self.ref_unet(torch.cat([cloth_embeds] * num_samples), 0, prompt_embeds_null, cross_attention_kwargs={"attn_store": self.attn_store})

        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
        if self.enable_cloth_guidance:
            images = self.pipe(
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=negative_prompt_embeds,
                guidance_scale=guidance_scale,
                cloth_guidance_scale=cloth_guidance_scale,
                num_inference_steps=num_inference_steps,
                generator=generator,
                height=height,
                width=width,
                cross_attention_kwargs={"attn_store": self.attn_store, "do_classifier_free_guidance": guidance_scale > 1.0, "enable_cloth_guidance": self.enable_cloth_guidance},
                **kwargs,
            ).images
        else:
            images = self.pipe(
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=negative_prompt_embeds,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                generator=generator,
                height=height,
                width=width,
                cross_attention_kwargs={"attn_store": self.attn_store, "do_classifier_free_guidance": guidance_scale > 1.0, "enable_cloth_guidance": self.enable_cloth_guidance},
                **kwargs,
            ).images

        return images, cloth_mask_image


class IPAdapterFaceIDXL(IPAdapterFaceID):
    """SDXL"""

    def generate(
            self,
            faceid_embeds=None,
            prompt=None,
            negative_prompt=None,
            scale=1.0,
            num_samples=4,
            seed=None,
            num_inference_steps=30,
            **kwargs,
    ):
        self.set_scale(scale)

        num_prompts = faceid_embeds.size(0)

        if prompt is None:
            prompt = "best quality, high quality"
        if negative_prompt is None:
            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

        if not isinstance(prompt, List):
            prompt = [prompt] * num_prompts
        if not isinstance(negative_prompt, List):
            negative_prompt = [negative_prompt] * num_prompts

        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds)

        bs_embed, seq_len, _ = image_prompt_embeds.shape
        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)

        with torch.inference_mode():
            (
                prompt_embeds,
                negative_prompt_embeds,
                pooled_prompt_embeds,
                negative_pooled_prompt_embeds,
            ) = self.pipe.encode_prompt(
                prompt,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
            )
            prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)

        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
        images = self.pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            num_inference_steps=num_inference_steps,
            generator=generator,
            **kwargs,
        ).images

        return images


class IPAdapterFaceIDPlusXL(IPAdapterFaceIDPlus):
    """SDXL"""

    def generate(
            self,
            face_image=None,
            faceid_embeds=None,
            prompt=None,
            negative_prompt=None,
            scale=1.0,
            num_samples=4,
            seed=None,
            guidance_scale=7.5,
            num_inference_steps=30,
            s_scale=1.0,
            shortcut=True,
            **kwargs,
    ):
        self.set_scale(scale)

        num_prompts = faceid_embeds.size(0)

        if prompt is None:
            prompt = "best quality, high quality"
        if negative_prompt is None:
            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

        if not isinstance(prompt, List):
            prompt = [prompt] * num_prompts
        if not isinstance(negative_prompt, List):
            negative_prompt = [negative_prompt] * num_prompts

        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds, face_image, s_scale, shortcut)

        bs_embed, seq_len, _ = image_prompt_embeds.shape
        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)

        with torch.inference_mode():
            (
                prompt_embeds,
                negative_prompt_embeds,
                pooled_prompt_embeds,
                negative_pooled_prompt_embeds,
            ) = self.pipe.encode_prompt(
                prompt,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
            )
            prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)

        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
        images = self.pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            num_inference_steps=num_inference_steps,
            generator=generator,
            guidance_scale=guidance_scale,
            **kwargs,
        ).images

        return images