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 .utils import is_torch2_available 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 IPAttnProcessor2_0_Lora # else: # from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor from .resampler import Resampler from diffusers.models.lora import LoRALinearLayer 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.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): self.device = device self.image_encoder_path = image_encoder_path self.ip_ckpt = ip_ckpt self.num_tokens = num_tokens 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: 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) 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 self.ip_ckpt is not None: 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"]) # def load_ip_adapter(self): # if self.ip_ckpt is not None: # if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors": # state_dict = {"image_proj_model": {}, "ip_adapter": {}} # with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f: # for key in f.keys(): # if key.startswith("image_proj_model."): # state_dict["image_proj_model"][key.replace("image_proj_model.", "")] = 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") # tmp1 = {} # for k,v in state_dict.items(): # if 'image_proj_model' in k: # tmp1[k.replace('image_proj_model.','')] = v # self.image_proj_model.load_state_dict(tmp1, strict=True) # # ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values()) # tmp2 = {} # for k,v in state_dict.ites(): # if 'adapter_mode' in k: # tmp1[k] = v # print(ip_layers.state_dict()) # ip_layers.load_state_dict(state_dict,strict=False) @torch.inference_mode() def get_image_embeds(self, pil_image=None, clip_image_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) 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 get_image_embeds_train(self, pil_image=None, clip_image_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) 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=50, **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 ) 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 = 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, **kwargs, ).images return images class IPAdapterXL(IPAdapter): """SDXL""" def generate_test( 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 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, ) 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 # with torch.autocast("cuda"): # 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 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 = 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 # with torch.autocast("cuda"): # 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 IPAdapterPlus(IPAdapter): """IP-Adapter with fine-grained features""" 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=50, **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=clip_image_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) 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, **kwargs, ).images return images 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=None, uncond= 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 = clip_image.to(self.device, dtype=torch.float16) clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2] else: 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 IPAdapterPlus_Lora(IPAdapter): """IP-Adapter with fine-grained features""" def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, rank=32): self.rank = rank super().__init__(sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens) 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=50, **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=clip_image_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) 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, **kwargs, ).images return images 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=None, uncond= 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 = clip_image.to(self.device, dtype=torch.float16) clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2] else: 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 set_ip_adapter(self): unet = self.pipe.unet attn_procs = {} unet_sd = unet.state_dict() for attn_processor_name, attn_processor in unet.attn_processors.items(): # Parse the attention module. cross_attention_dim = None if attn_processor_name.endswith("attn1.processor") else unet.config.cross_attention_dim if attn_processor_name.startswith("mid_block"): hidden_size = unet.config.block_out_channels[-1] elif attn_processor_name.startswith("up_blocks"): block_id = int(attn_processor_name[len("up_blocks.")]) hidden_size = list(reversed(unet.config.block_out_channels))[block_id] elif attn_processor_name.startswith("down_blocks"): block_id = int(attn_processor_name[len("down_blocks.")]) hidden_size = unet.config.block_out_channels[block_id] if cross_attention_dim is None: attn_procs[attn_processor_name] = AttnProcessor() else: layer_name = attn_processor_name.split(".processor")[0] weights = { "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"], "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"], } attn_procs[attn_processor_name] = IPAttnProcessor2_0_Lora(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_tokens=self.num_tokens) attn_procs[attn_processor_name].load_state_dict(weights,strict=False) attn_module = unet for n in attn_processor_name.split(".")[:-1]: attn_module = getattr(attn_module, n) attn_module.q_lora = LoRALinearLayer(in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=self.rank) attn_module.k_lora = LoRALinearLayer(in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=self.rank) attn_module.v_lora = LoRALinearLayer(in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=self.rank) attn_module.out_lora = LoRALinearLayer(in_features=attn_module.to_out[0].in_features, out_features=attn_module.to_out[0].out_features, rank=self.rank) 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)) class IPAdapterPlus_Lora_up(IPAdapter): """IP-Adapter with fine-grained features""" def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, rank=32): self.rank = rank super().__init__(sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens) 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=50, **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=clip_image_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) 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, **kwargs, ).images return images 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=None, uncond= 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 = clip_image.to(self.device, dtype=torch.float16) clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2] else: 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 set_ip_adapter(self): unet = self.pipe.unet attn_procs = {} unet_sd = unet.state_dict() for attn_processor_name, attn_processor in unet.attn_processors.items(): # Parse the attention module. cross_attention_dim = None if attn_processor_name.endswith("attn1.processor") else unet.config.cross_attention_dim if attn_processor_name.startswith("mid_block"): hidden_size = unet.config.block_out_channels[-1] elif attn_processor_name.startswith("up_blocks"): block_id = int(attn_processor_name[len("up_blocks.")]) hidden_size = list(reversed(unet.config.block_out_channels))[block_id] elif attn_processor_name.startswith("down_blocks"): block_id = int(attn_processor_name[len("down_blocks.")]) hidden_size = unet.config.block_out_channels[block_id] if cross_attention_dim is None: attn_procs[attn_processor_name] = AttnProcessor() else: layer_name = attn_processor_name.split(".processor")[0] weights = { "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"], "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"], } attn_procs[attn_processor_name] = IPAttnProcessor2_0_Lora(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_tokens=self.num_tokens) attn_procs[attn_processor_name].load_state_dict(weights,strict=False) attn_module = unet for n in attn_processor_name.split(".")[:-1]: attn_module = getattr(attn_module, n) if "up_blocks" in attn_processor_name: attn_module.q_lora = LoRALinearLayer(in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=self.rank) attn_module.k_lora = LoRALinearLayer(in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=self.rank) attn_module.v_lora = LoRALinearLayer(in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=self.rank) attn_module.out_lora = LoRALinearLayer(in_features=attn_module.to_out[0].in_features, out_features=attn_module.to_out[0].out_features, rank=self.rank) 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)) 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=None, clip_image_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 = clip_image.to(self.device, dtype=torch.float16) clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2] else: clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16) 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 = 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