ip_adapter/ip_adapter.py

# -*- coding: utf-8 -*-
import os
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.controlnet import MultiControlNetModel
from PIL import Image, ImageOps
from safetensors import safe_open
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPTokenizer, CLIPTextModelWithProjection
from .utils import is_torch2_available, get_generator

if is_torch2_available():
    from .attention_processor import (
        AttnProcessor2_0 as AttnProcessor,
    )
    from .attention_processor import (
        CNAttnProcessor2_0 as CNAttnProcessor,
    )
    from .attention_processor import (
        IPAttnProcessor2_0 as IPAttnProcessor,
    )
else:
    from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
from .resampler import Resampler

import numpy as np, random


import math
import torch

import torch
import torch.nn.functional as F
import numpy as np
import cv2
from PIL import Image



def _cosine(a: torch.Tensor, b: torch.Tensor, eps: float = 1e-12) -> float:
    a = a.float(); b = b.float()
    na = a.norm(); nb = b.norm()
    if na.item() < eps or nb.item() < eps:
        return float("nan")
    return float((a @ b) / (na * nb))

def verify_style_content_embeddings(adapter, sim_threshold: float = 0.999):

    content_fps, style_fps = [], []
    wrong_source = []

    for name, proc in adapter.attn_procs.items():
        group = getattr(proc, "group", "off")
        mu    = getattr(proc, "last_ip_mu", None)
        src   = getattr(proc, "last_ip_source", None)

        if group not in ("content", "style"): 
            continue
        if mu is None:
            continue 

        if group == "content" and src != "tail":
            wrong_source.append((name, group, src))
        if group == "style" and src != "override":
            wrong_source.append((name, group, src))

        if group == "content":
            content_fps.append((name, mu))
        else:
            style_fps.append((name, mu))

    print("\n[Verify] token source check")
    if wrong_source:
        for name, grp, src in wrong_source:
            print(f" - !! {name}: group={grp} but last_ip_source={src}")
    else:
        print(" - OK: content uses 'tail', style uses 'override'")

    if not content_fps or not style_fps:
        return False

    content_mu = torch.stack([mu for _, mu in content_fps], dim=0).mean(dim=0)
    style_mu   = torch.stack([mu for _, mu in style_fps],   dim=0).mean(dim=0)

    cos = _cosine(content_mu, style_mu)
    print(f"\n[Verify] group-wise cosine(content, style) = {cos:.6f}")

    print("\n[Verify] layer-wise cosine to content-mean (lower is more different)")
    for name, mu in style_fps:
        cs = _cosine(content_mu, mu)
        print(f" - {name:<60} cos={cs:.6f}")

    ok = (not wrong_source) and (not math.isnan(cos)) and (cos < sim_threshold)


def _split_bounds(size, parts):
    bounds = np.linspace(0, size, parts + 1)
    return [int(round(b)) for b in bounds]


class ImageProjModel(torch.nn.Module):
    """Projection Model"""

    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
        super().__init__()

        self.generator = None
        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 MLPProjModel(torch.nn.Module):
    """SD model with image prompt"""
    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
        super().__init__()
        
        self.proj = torch.nn.Sequential(
            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
            torch.nn.GELU(),
            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
            torch.nn.LayerNorm(cross_attention_dim)
        )
        
    def forward(self, image_embeds):
        clip_extra_context_tokens = self.proj(image_embeds)
        return clip_extra_context_tokens
    
class IPAdapter:
    def __init__(
        self,
        sd_pipe,
        image_encoder_path,
        ip_ckpt,
        device,
        mask=None,
        sketch=None,
        num_tokens=4,
        target_blocks=None,
        # NEW: block groups & scales
        content_blocks=None,
        style_blocks=None,
        content_scale: float = 0.5,
        style_scale: float = 0.5,
        garment_images = None,
        garment_mask = None,
    ):
        self.device = device
        self.image_encoder_path = image_encoder_path
        self.ip_ckpt = ip_ckpt
        self.num_tokens = num_tokens
        self.target_blocks = target_blocks or []

        self.pipe = sd_pipe.to(self.device)
        self.mask = mask
        self.sketch = sketch

        self.garment_images = garment_images
        self.garment_mask = garment_mask

        self.content_blocks = [
            "down_blocks.2.attentions.1", 
        ]
        self.style_blocks = [
            "up_blocks.0.attentions.1",
        ]
        self.content_scale = float(content_scale)
        self.style_scale = float(style_scale)

        self.attn_procs = {}

        self.set_ip_adapter()

        self.clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
        self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
            "openai/clip-vit-large-patch14"
        ).to(self.device)

        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
            self.image_encoder_path
        ).to(self.device, dtype=torch.float32)
        self.clip_image_processor = CLIPImageProcessor()

        self.image_proj_model = self.init_proj()
        self.load_ip_adapter()

    # --- utils ---
    def _parse_block_id(self, name: str, prefix: str) -> int:
        # "up_blocks.0.attentions.1.processor" -> 0
        return int(name[len(prefix):].split(".")[0])

    def init_proj(self):
        image_proj_model = ImageProjModel(
            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
            clip_embeddings_dim=self.image_encoder.config.projection_dim,
            clip_extra_context_tokens=self.num_tokens,
        ).to(self.device, dtype=torch.float32)
        return image_proj_model


    def _apply_group_scales(self):
        for name, proc in self.attn_procs.items():
            if not isinstance(proc, IPAttnProcessor):
                continue
            if any(b in name for b in self.content_blocks):
                proc.skip = False
                proc.scale = float(self.content_scale)
            elif any(b in name for b in self.style_blocks):
                proc.skip = False
                proc.scale = float(self.style_scale)
            else:
                proc.skip = True  


    def _which_group(self, name: str) -> str:
        if any(b in name for b in self.content_blocks):
            return "content"
        if any(b in name for b in self.style_blocks):
            return "style"
        return "off"
    
    def _get_proc_tokens(self, proc):
        for key in ("image_prompt_embeds", "ip_tokens", "image_prompts"):
            t = getattr(proc, key, None)
            if t is not None:
                return t
        return None
    
    def print_block_scales(self, verbose: bool = True):
        rows = []
        for name, proc in self.attn_procs.items():
            scale = getattr(proc, "scale", None)
            skip  = getattr(proc, "skip", None)
            group = getattr(proc, "group", "self" if name.endswith("attn1.processor") else "off")
            rows.append((name, group, scale, skip))

        def _key(t):
            n = t[0]
            if n.startswith("down_blocks"): p = 0
            elif n.startswith("mid_block"): p = 1
            elif n.startswith("up_blocks"): p = 2
            else: p = 3
            # 숫자 추출
            import re
            m = re.findall(r"\d+", n)
            idx = tuple(int(x) for x in m) if m else (999,)
            return (p, idx, n)

        rows.sort(key=_key)

        if verbose:
            print("\n[IPAdapter] Block-scale report")
            for name, group, scale, skip in rows:
                tag = "ATTN2" if name.endswith("attn2.processor") else "ATTN1"
                print(f" - {name:<60} [{tag}] group={group:<7} scale={scale} skip={skip}")

        return rows

    def set_ip_adapter(self):
        unet = self.pipe.unet
        attn_procs = {}
        self.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 = self._parse_block_id(name, "up_blocks.")
                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = self._parse_block_id(name, "down_blocks.")
                hidden_size = unet.config.block_out_channels[block_id]
            else:
                hidden_size = unet.config.block_out_channels[0]

            if cross_attention_dim is None:
                proc = AttnProcessor()
                setattr(proc, "layer_name", name)
            else:
                is_content = any(b in name for b in self.content_blocks)
                is_style   = any(b in name for b in self.style_blocks)
                selected   = is_content or is_style or any(b in name for b in self.target_blocks)

                init_skip  = not selected
                init_scale = 1.0
                if is_content:
                    init_scale = float(self.content_scale)
                elif is_style:
                    init_scale = float(self.style_scale)

                proc = IPAttnProcessor(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    scale=init_scale,
                    num_tokens=self.num_tokens,
                    skip=init_skip,
                ).to(self.device, dtype=torch.float32)

                setattr(proc, "layer_name", name)
                setattr(proc, "group", "content" if is_content else ("style" if is_style else "off"))

            attn_procs[name] = proc
            self.attn_procs[name] = proc

        unet.set_attn_processor(attn_procs)

        if hasattr(self.pipe, "controlnet"):
            if isinstance(self.pipe.controlnet, MultiControlNetModel):
                for controlnet in self.pipe.controlnet.nets:
                    controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
            else:
                self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))

    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, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
        if pil_image is not None:
            if isinstance(pil_image, Image.Image):
                pil_image = [pil_image]
            clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
            clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float32)).image_embeds
        else:
            clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float32)

        image_prompt_embeds = self.image_proj_model(clip_image_embeds)  # [B, Ni, D] = [1,4,2048]

        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
        return image_prompt_embeds, uncond_image_prompt_embeds


    def generate(
        self,
        pil_image=None,
        clip_image_embeds=None,
        prompt=None,
        negative_prompt=None,
        scale=1.0,
        num_samples=4,
        seed=None,
        guidance_scale=7.5,
        num_inference_steps=30,
        neg_content_emb=None,
        **kwargs,
    ):
        if scale is not None:
            self.set_scale(scale)

        if pil_image is not None:
            num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
        else:
            num_prompts = clip_image_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(
            pil_image=pil_image, clip_image_embeds=clip_image_embeds, content_prompt_embeds=neg_content_emb
        )
        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)

        generator = get_generator(seed, self.device)

        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,
            **kwargs,
        ).images

        return images


class IPAdapterXL(IPAdapter):
    """SDXL"""

    def generate(
        self,
        pil_image,
        prompt=None,
        shape_prompt=None,
        negative_prompt=None,
        scale=1.0,
        num_samples=4,
        seed=None,
        num_inference_steps=30,
        neg_content_emb=None,
        neg_content_prompt=None,
        neg_content_scale=1.0,
        **kwargs,
    ):
        if scale is not None:
            self.set_scale(scale)


        num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)

        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


        if neg_content_emb is None:
            if neg_content_prompt is not None:
                with torch.inference_mode():
                    (
                        prompt_embeds_,                # [B, 77, 2048]
                        negative_prompt_embeds_,
                        pooled_prompt_embeds_,         # [B, 1280]
                        negative_pooled_prompt_embeds_,
                    ) = self.pipe.encode_prompt(
                        neg_content_prompt,
                        num_images_per_prompt=num_samples,
                        do_classifier_free_guidance=True,
                        negative_prompt=negative_prompt,
                    )
                    pooled_prompt_embeds_ *= neg_content_scale
            else:
                pooled_prompt_embeds_ = neg_content_emb
        else:
            pooled_prompt_embeds_ = None

        content_ip_tokens, uncond_content_ip_tokens = self.get_image_embeds(
            pil_image=pil_image,
            content_prompt_embeds=pooled_prompt_embeds_
        )
        bs_embed, seq_len, _ = content_ip_tokens.shape
        content_ip_tokens = content_ip_tokens.repeat(1, num_samples, 1).view(bs_embed * num_samples, seq_len, -1)
        uncond_content_ip_tokens = uncond_content_ip_tokens.repeat(1, num_samples, 1).view(bs_embed * num_samples, seq_len, -1)

        style_ip_tokens, uncond_style_ip_tokens = self.get_image_embeds(
            pil_image=pil_image,
            content_prompt_embeds=pooled_prompt_embeds_
        )

        bs_embed, seq_len, _ = style_ip_tokens.shape
        style_ip_tokens = style_ip_tokens.repeat(1, num_samples, 1).view(bs_embed * num_samples, seq_len, -1)
        style_ip_tokens_uncond = uncond_style_ip_tokens.repeat(1, num_samples, 1).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,
                device=self.device,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
            )
            # ★ 여기서 "콘텐츠" IP 토큰만 붙인다
            prompt_embeds = torch.cat([prompt_embeds, content_ip_tokens], dim=1)
            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_content_ip_tokens], dim=1)

        with torch.inference_mode():
            (
                shape_prompt_embeds,
                shape_negative_prompt_embeds,
                shape_pooled_prompt_embeds,
                shape_negative_pooled_prompt_embeds,
            ) = self.pipe.encode_prompt(
                shape_prompt,
                device=self.device,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
            )
            shape_prompt_embeds = torch.cat([shape_prompt_embeds, content_ip_tokens], dim=1)
            shape_negative_prompt_embeds = torch.cat([shape_negative_prompt_embeds, uncond_content_ip_tokens], dim=1)


        for name, proc in self.attn_procs.items():
            if getattr(proc, "group", "off") == "style":
                proc.ip_tokens_override = style_ip_tokens.to(self.device, dtype=torch.float32)
                proc.ip_tokens_override_uncond = style_ip_tokens_uncond.to(self.device, dtype=torch.float32)
            else:
                if hasattr(proc, "ip_tokens_override"):
                    proc.ip_tokens_override = None
                if hasattr(proc, "ip_tokens_override_uncond"):
                    proc.ip_tokens_override_uncond = None
        

        self.generator = get_generator(seed, self.device)

        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,
            shape_prompt_embeds=shape_prompt_embeds,
            shape_negative_prompt_embeds=shape_negative_prompt_embeds,
            shape_pooled_prompt_embeds=shape_pooled_prompt_embeds,
            shape_negative_pooled_prompt_embeds=shape_negative_pooled_prompt_embeds,
            num_inference_steps=num_inference_steps,
            generator=self.generator,
            mask_image=self.mask,
            sketch_image=self.sketch,
            garment_images=self.garment_images,
            garment_mask=self.garment_mask,
            **kwargs,
        ).images

        for name, proc in self.attn_procs.items():
            if hasattr(proc, "ip_tokens_override"):
                proc.ip_tokens_override = None
            if hasattr(proc, "ip_tokens_override_uncond"):
                proc.ip_tokens_override_uncond = None

        return images




class IPAdapterPlus(IPAdapter):
    """IP-Adapter with fine-grained features"""

    def init_proj(self):
        image_proj_model = Resampler(
            dim=self.pipe.unet.config.cross_attention_dim,
            depth=4,
            dim_head=64,
            heads=12,
            num_queries=self.num_tokens,
            embedding_dim=self.image_encoder.config.hidden_size,
            output_dim=self.pipe.unet.config.cross_attention_dim,
            ff_mult=4,
        ).to(self.device, dtype=torch.float32)
        return image_proj_model

    @torch.inference_mode()
    def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
        if isinstance(pil_image, Image.Image):
            pil_image = [pil_image]
        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
        clip_image = clip_image.to(self.device, dtype=torch.float32)
        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
        uncond_clip_image_embeds = self.image_encoder(
            torch.zeros_like(clip_image), output_hidden_states=True
        ).hidden_states[-2]
        uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
        return image_prompt_embeds, uncond_image_prompt_embeds


class IPAdapterFull(IPAdapterPlus):
    """IP-Adapter with full features"""

    def init_proj(self):
        image_proj_model = MLPProjModel(
            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
            clip_embeddings_dim=self.image_encoder.config.hidden_size,
        ).to(self.device, dtype=torch.float32)
        return image_proj_model


class IPAdapterPlusXL(IPAdapter):
    """SDXL"""

    def init_proj(self):
        image_proj_model = Resampler(
            dim=1280,
            depth=4,
            dim_head=64,
            heads=20,
            num_queries=self.num_tokens,
            embedding_dim=self.image_encoder.config.hidden_size,
            output_dim=self.pipe.unet.config.cross_attention_dim,
            ff_mult=4,
        ).to(self.device, dtype=torch.float32)
        return image_proj_model

    @torch.inference_mode()
    def get_image_embeds(self, pil_image):
        if isinstance(pil_image, Image.Image):
            pil_image = [pil_image]
        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
        clip_image = clip_image.to(self.device, dtype=torch.float32)
        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
        uncond_clip_image_embeds = self.image_encoder(
            torch.zeros_like(clip_image), output_hidden_states=True
        ).hidden_states[-2]
        uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
        return image_prompt_embeds, uncond_image_prompt_embeds

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

        num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)

        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(pil_image)
        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,
                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)

        generator = get_generator(seed, self.device)

        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