import os from typing import List import torch 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 torchvision import transforms from .utils import is_torch2_available, get_generator # import torchvision.transforms.functional as Func # from .clip_style_models import CSD_CLIP, convert_state_dict 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, ) from .attention_processor import IP_CS_AttnProcessor2_0 as IP_CS_AttnProcessor else: from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor from .resampler import Resampler from transformers import AutoImageProcessor, AutoModel 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 # print(clip_embeddings_dim, self.clip_extra_context_tokens, cross_attention_dim) 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: 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 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 IPAdapter_CS: def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_content_tokens=4, num_style_tokens=4, target_content_blocks=["block"], target_style_blocks=["block"], content_image_encoder_path=None, controlnet_adapter=False, controlnet_target_content_blocks=None, controlnet_target_style_blocks=None, content_model_resampler=False, style_model_resampler=False, ): self.device = device self.image_encoder_path = image_encoder_path self.ip_ckpt = ip_ckpt self.num_content_tokens = num_content_tokens self.num_style_tokens = num_style_tokens self.content_target_blocks = target_content_blocks self.style_target_blocks = target_style_blocks self.content_model_resampler = content_model_resampler self.style_model_resampler = style_model_resampler self.controlnet_adapter = controlnet_adapter self.controlnet_target_content_blocks = controlnet_target_content_blocks self.controlnet_target_style_blocks = controlnet_target_style_blocks self.pipe = sd_pipe.to(self.device) self.set_ip_adapter() self.content_image_encoder_path = content_image_encoder_path # load image encoder if content_image_encoder_path is not None: self.content_image_encoder = AutoModel.from_pretrained(content_image_encoder_path).to(self.device, dtype=torch.float16) self.content_image_processor = AutoImageProcessor.from_pretrained(content_image_encoder_path) else: self.content_image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to( self.device, dtype=torch.float16 ) self.content_image_processor = CLIPImageProcessor() # model.requires_grad_(False) self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to( self.device, dtype=torch.float16 ) # if self.use_CSD is not None: # self.style_image_encoder = CSD_CLIP("vit_large", "default",self.use_CSD+"/ViT-L-14.pt") # model_path = self.use_CSD+"/checkpoint.pth" # checkpoint = torch.load(model_path, map_location="cpu") # state_dict = convert_state_dict(checkpoint['model_state_dict']) # self.style_image_encoder.load_state_dict(state_dict, strict=False) # # normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) # self.style_preprocess = transforms.Compose([ # transforms.Resize(size=224, interpolation=Func.InterpolationMode.BICUBIC), # transforms.CenterCrop(224), # transforms.ToTensor(), # normalize, # ]) self.clip_image_processor = CLIPImageProcessor() # image proj model self.content_image_proj_model = self.init_proj(self.num_content_tokens, content_or_style_='content', model_resampler=self.content_model_resampler) self.style_image_proj_model = self.init_proj(self.num_style_tokens, content_or_style_='style', model_resampler=self.style_model_resampler) self.load_ip_adapter() def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False): # print('@@@@',self.pipe.unet.config.cross_attention_dim,self.image_encoder.config.projection_dim) if content_or_style_ == 'content' and self.content_image_encoder_path is not None: image_proj_model = ImageProjModel( cross_attention_dim=self.pipe.unet.config.cross_attention_dim, clip_embeddings_dim=self.content_image_encoder.config.projection_dim, clip_extra_context_tokens=num_tokens, ).to(self.device, dtype=torch.float16) return image_proj_model 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=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: # layername_id += 1 selected = False for block_name in self.style_target_blocks: if block_name in name: selected = True # print(name) attn_procs[name] = IP_CS_AttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, style_scale=1.0, style=True, num_content_tokens=self.num_content_tokens, num_style_tokens=self.num_style_tokens, ) for block_name in self.content_target_blocks: if block_name in name: # selected = True if selected is False: attn_procs[name] = IP_CS_AttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, content_scale=1.0, content=True, num_content_tokens=self.num_content_tokens, num_style_tokens=self.num_style_tokens, ) else: attn_procs[name].set_content_ipa(content_scale=1.0) # attn_procs[name].content=True if selected is False: attn_procs[name] = IP_CS_AttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_content_tokens=self.num_content_tokens, num_style_tokens=self.num_style_tokens, skip=True, ) attn_procs[name].to(self.device, dtype=torch.float16) unet.set_attn_processor(attn_procs) if hasattr(self.pipe, "controlnet"): if self.controlnet_adapter is False: if isinstance(self.pipe.controlnet, MultiControlNetModel): for controlnet in self.pipe.controlnet.nets: controlnet.set_attn_processor(CNAttnProcessor( num_tokens=self.num_content_tokens + self.num_style_tokens)) else: self.pipe.controlnet.set_attn_processor(CNAttnProcessor( num_tokens=self.num_content_tokens + self.num_style_tokens)) else: controlnet_attn_procs = {} controlnet_style_target_blocks = self.controlnet_target_style_blocks controlnet_content_target_blocks = self.controlnet_target_content_blocks for name in self.pipe.controlnet.attn_processors.keys(): # print(name) cross_attention_dim = None if name.endswith( "attn1.processor") else self.pipe.controlnet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = self.pipe.controlnet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(self.pipe.controlnet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = self.pipe.controlnet.config.block_out_channels[block_id] if cross_attention_dim is None: # layername_id += 1 controlnet_attn_procs[name] = AttnProcessor() else: # layername_id += 1 selected = False for block_name in controlnet_style_target_blocks: if block_name in name: selected = True # print(name) controlnet_attn_procs[name] = IP_CS_AttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, style_scale=1.0, style=True, num_content_tokens=self.num_content_tokens, num_style_tokens=self.num_style_tokens, ) for block_name in controlnet_content_target_blocks: if block_name in name: if selected is False: controlnet_attn_procs[name] = IP_CS_AttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, content_scale=1.0, content=True, num_content_tokens=self.num_content_tokens, num_style_tokens=self.num_style_tokens, ) selected = True elif selected is True: controlnet_attn_procs[name].set_content_ipa(content_scale=1.0) # if args.content_image_encoder_type !='dinov2': # weights = { # "to_k_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_k_ip.weight"], # "to_v_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_v_ip.weight"], # } # attn_procs[name].load_state_dict(weights) if selected is False: controlnet_attn_procs[name] = IP_CS_AttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_content_tokens=self.num_content_tokens, num_style_tokens=self.num_style_tokens, skip=True, ) controlnet_attn_procs[name].to(self.device, dtype=torch.float16) # layer_name = name.split(".processor")[0] # # print(state_dict["ip_adapter"].keys()) # weights = { # "to_k_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_k_ip.weight"], # "to_v_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_v_ip.weight"], # } # attn_procs[name].load_state_dict(weights) self.pipe.controlnet.set_attn_processor(controlnet_attn_procs) def load_ip_adapter(self): if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors": state_dict = {"content_image_proj": {}, "style_image_proj": {}, "ip_adapter": {}} with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f: for key in f.keys(): if key.startswith("content_image_proj."): state_dict["content_image_proj"][key.replace("content_image_proj.", "")] = f.get_tensor(key) elif key.startswith("style_image_proj."): state_dict["style_image_proj"][key.replace("style_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.content_image_proj_model.load_state_dict(state_dict["content_image_proj"]) self.style_image_proj_model.load_state_dict(state_dict["style_image_proj"]) if 'conv_in_unet_sd' in state_dict.keys(): self.pipe.unet.conv_in.load_state_dict(state_dict["conv_in_unet_sd"], strict=True) ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values()) ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False) if self.controlnet_adapter is True: print('loading controlnet_adapter') self.pipe.controlnet.load_state_dict(state_dict["controlnet_adapter_modules"], strict=False) @torch.inference_mode() def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None, content_or_style_=''): # 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: # clip_image_embeds = clip_image_embeds - content_prompt_embeds if content_or_style_ == 'content': if pil_image is not None: if isinstance(pil_image, Image.Image): pil_image = [pil_image] if self.content_image_proj_model is not None: clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values clip_image_embeds = self.content_image_encoder( clip_image.to(self.device, dtype=torch.float16)).image_embeds else: 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) image_prompt_embeds = self.content_image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds)) return image_prompt_embeds, uncond_image_prompt_embeds if content_or_style_ == 'style': if pil_image is not None: if self.use_CSD is not None: clip_image = self.style_preprocess(pil_image).unsqueeze(0).to(self.device, dtype=torch.float32) clip_image_embeds = self.style_image_encoder(clip_image) else: 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) image_prompt_embeds = self.style_image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds)) return image_prompt_embeds, uncond_image_prompt_embeds def set_scale(self, content_scale, style_scale): for attn_processor in self.pipe.unet.attn_processors.values(): if isinstance(attn_processor, IP_CS_AttnProcessor): if attn_processor.content is True: attn_processor.content_scale = content_scale if attn_processor.style is True: attn_processor.style_scale = style_scale # print('style_scale:',style_scale) if self.controlnet_adapter is not None: for attn_processor in self.pipe.controlnet.attn_processors.values(): if isinstance(attn_processor, IP_CS_AttnProcessor): if attn_processor.content is True: attn_processor.content_scale = content_scale # print(content_scale) if attn_processor.style is True: attn_processor.style_scale = style_scale def generate( self, pil_content_image=None, pil_style_image=None, clip_content_image_embeds=None, clip_style_image_embeds=None, prompt=None, negative_prompt=None, content_scale=1.0, style_scale=1.0, num_samples=4, seed=None, guidance_scale=7.5, num_inference_steps=30, neg_content_emb=None, **kwargs, ): self.set_scale(content_scale, style_scale) if pil_content_image is not None: num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image) else: num_prompts = clip_content_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 content_image_prompt_embeds, uncond_content_image_prompt_embeds = self.get_image_embeds( pil_image=pil_content_image, clip_image_embeds=clip_content_image_embeds ) style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds( pil_image=pil_style_image, clip_image_embeds=clip_style_image_embeds ) bs_embed, seq_len, _ = content_image_prompt_embeds.shape content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1) content_image_prompt_embeds = content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.repeat(1, num_samples, 1) uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) bs_style_embed, seq_style_len, _ = content_image_prompt_embeds.shape style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1) style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1) uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1) uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_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_, content_image_prompt_embeds, style_image_prompt_embeds], dim=1) negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_content_image_prompt_embeds, uncond_style_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_CS(IPAdapter_CS): """SDXL""" def generate( self, pil_content_image, pil_style_image, prompt=None, negative_prompt=None, content_scale=1.0, style_scale=1.0, num_samples=4, seed=None, content_image_embeds=None, style_image_embeds=None, num_inference_steps=30, neg_content_emb=None, neg_content_prompt=None, neg_content_scale=1.0, **kwargs, ): self.set_scale(content_scale, style_scale) num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_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 content_image_prompt_embeds, uncond_content_image_prompt_embeds = self.get_image_embeds(pil_content_image, content_image_embeds, content_or_style_='content') style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(pil_style_image, style_image_embeds, content_or_style_='style') bs_embed, seq_len, _ = content_image_prompt_embeds.shape content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1) content_image_prompt_embeds = content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.repeat(1, num_samples, 1) uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) bs_style_embed, seq_style_len, _ = style_image_prompt_embeds.shape style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1) style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1) uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1) uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_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, content_image_prompt_embeds, style_image_prompt_embeds], dim=1) negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_content_image_prompt_embeds, uncond_style_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 CSGO(IPAdapterXL_CS): """SDXL""" def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False): if content_or_style_ == 'content': if model_resampler: image_proj_model = Resampler( dim=self.pipe.unet.config.cross_attention_dim, depth=4, dim_head=64, heads=12, num_queries=num_tokens, embedding_dim=self.content_image_encoder.config.hidden_size, output_dim=self.pipe.unet.config.cross_attention_dim, ff_mult=4, ).to(self.device, dtype=torch.float16) else: 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=num_tokens, ).to(self.device, dtype=torch.float16) if content_or_style_ == 'style': if model_resampler: image_proj_model = Resampler( dim=self.pipe.unet.config.cross_attention_dim, depth=4, dim_head=64, heads=12, num_queries=num_tokens, embedding_dim=self.content_image_encoder.config.hidden_size, output_dim=self.pipe.unet.config.cross_attention_dim, ff_mult=4, ).to(self.device, dtype=torch.float16) else: 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=num_tokens, ).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, content_or_style_=''): if isinstance(pil_image, Image.Image): pil_image = [pil_image] if content_or_style_ == 'style': if self.style_model_resampler: 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), output_hidden_states=True).hidden_states[-2] image_prompt_embeds = self.style_image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds)) else: 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 image_prompt_embeds = self.style_image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds)) return image_prompt_embeds, uncond_image_prompt_embeds else: if self.content_image_encoder_path is not None: clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values outputs = self.content_image_encoder(clip_image.to(self.device, dtype=torch.float16), output_hidden_states=True) clip_image_embeds = outputs.last_hidden_state image_prompt_embeds = self.content_image_proj_model(clip_image_embeds) # uncond_clip_image_embeds = self.image_encoder( # torch.zeros_like(clip_image), output_hidden_states=True # ).last_hidden_state uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds)) return image_prompt_embeds, uncond_image_prompt_embeds else: if self.content_model_resampler: 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] # clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16) image_prompt_embeds = self.content_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.content_image_proj_model(torch.zeros_like(clip_image_embeds)) else: 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 image_prompt_embeds = self.content_image_proj_model(clip_image_embeds) uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds)) return image_prompt_embeds, uncond_image_prompt_embeds # # 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.content_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.content_image_proj_model(uncond_clip_image_embeds) # return image_prompt_embeds, uncond_image_prompt_embeds 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, **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) 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