import os import glob from typing import List import torch import torch.nn as nn from diffusers import StableDiffusionPipeline from diffusers.pipelines.controlnet import MultiControlNetModel from PIL import Image from safetensors import safe_open from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from .utils import is_torch2_available, get_generator L = 4 def pos_encode(x, L): pos_encode = [] for freq in range(L): pos_encode.append(torch.cos(2**freq * torch.pi * x)) pos_encode.append(torch.sin(2**freq * torch.pi * x)) pos_encode = torch.cat(pos_encode, dim=1) return pos_encode 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 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, num_tokens=4, target_blocks=["block"], ): 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 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=torch.float16) self.clip_image_processor = CLIPImageProcessor() # image proj model self.image_proj_model = self.init_proj() self.load_ip_adapter() 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.float16) return image_proj_model 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] = AttnProcessor() else: selected = False for block_name in self.target_blocks: if block_name in name: selected = True break if selected: 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=torch.float16) else: attn_procs[name] = IPAttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, num_tokens=self.num_tokens, skip=True, ).to(self.device, dtype=torch.float16) 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.float16) ).image_embeds else: clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16) if content_prompt_embeds is not None: print(clip_image_embeds.shape) print(content_prompt_embeds.shape) clip_image_embeds = clip_image_embeds - content_prompt_embeds image_prompt_embeds = self.image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.image_proj_model( torch.zeros_like(clip_image_embeds) ) return image_prompt_embeds, uncond_image_prompt_embeds @torch.inference_mode() def generate_image_edit_dir( self, pil_image=None, content_prompt_embeds=None, edit_mlps: dict[torch.nn.Module, float] = None, ): print("Combining multiple MLPs!") 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.float16) ).image_embeds pred_editing_dirs = [ net( clip_image_embeds, torch.Tensor([strength]).to(self.device, dtype=torch.float16), ) for net, strength in edit_mlps.items() ] clip_image_embeds = clip_image_embeds + sum(pred_editing_dirs) if content_prompt_embeds is not None: clip_image_embeds = clip_image_embeds - content_prompt_embeds image_prompt_embeds = self.image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.image_proj_model( torch.zeros_like(clip_image_embeds) ) return image_prompt_embeds, uncond_image_prompt_embeds @torch.inference_mode() def get_image_edit_dir( self, start_image=None, pil_image=None, pil_image2=None, content_prompt_embeds=None, edit_strength=1.0, ): print("Blending Two Materials!") 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.float16) ).image_embeds if pil_image2 is not None: if isinstance(pil_image2, Image.Image): pil_image2 = [pil_image2] clip_image2 = self.clip_image_processor( images=pil_image2, return_tensors="pt" ).pixel_values clip_image_embeds2 = self.image_encoder( clip_image2.to(self.device, dtype=torch.float16) ).image_embeds if start_image is not None: if isinstance(start_image, Image.Image): start_image = [start_image] clip_image_start = self.clip_image_processor( images=start_image, return_tensors="pt" ).pixel_values clip_image_embeds_start = self.image_encoder( clip_image_start.to(self.device, dtype=torch.float16) ).image_embeds if content_prompt_embeds is not None: clip_image_embeds = clip_image_embeds - content_prompt_embeds clip_image_embeds2 = clip_image_embeds2 - content_prompt_embeds # clip_image_embeds += edit_strength * (clip_image_embeds2 - clip_image_embeds) clip_image_embeds = clip_image_embeds_start + edit_strength * ( clip_image_embeds2 - clip_image_embeds ) image_prompt_embeds = self.image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.image_proj_model( torch.zeros_like(clip_image_embeds) ) 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 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, 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, ): 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, 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, clip_strength=1.0, **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 if neg_content_emb is None: if neg_content_prompt is not None: with torch.inference_mode(): ( prompt_embeds_, # torch.Size([1, 77, 2048]) negative_prompt_embeds_, pooled_prompt_embeds_, # torch.Size([1, 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 image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds( pil_image, content_prompt_embeds=pooled_prompt_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 ) print("CLIP Strength is {}".format(clip_strength)) image_prompt_embeds *= clip_strength uncond_image_prompt_embeds *= clip_strength 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 ) 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, num_inference_steps=num_inference_steps, generator=self.generator, **kwargs, ).images return images def generate_parametric_edits( self, pil_image, edit_mlps: dict[torch.nn.Module, float], 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, ): 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_, # torch.Size([1, 77, 2048]) negative_prompt_embeds_, pooled_prompt_embeds_, # torch.Size([1, 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 image_prompt_embeds, uncond_image_prompt_embeds = self.generate_image_edit_dir( pil_image, content_prompt_embeds=pooled_prompt_embeds_, edit_mlps=edit_mlps ) 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 ) 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, num_inference_steps=num_inference_steps, generator=self.generator, **kwargs, ).images return images def generate_edit( self, start_image, pil_image, pil_image2, 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, edit_strength=1.0, **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 if neg_content_emb is None: if neg_content_prompt is not None: with torch.inference_mode(): ( prompt_embeds_, # torch.Size([1, 77, 2048]) negative_prompt_embeds_, pooled_prompt_embeds_, # torch.Size([1, 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 image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_edit_dir( start_image, pil_image, pil_image2, content_prompt_embeds=pooled_prompt_embeds_, edit_strength=edit_strength, ) 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 ) 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, num_inference_steps=num_inference_steps, generator=self.generator, **kwargs, ).images 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.float16) 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.float16) 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.float16) 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.float16) 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.float16) 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, 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