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 .attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor class MLPProjModel(torch.nn.Module): """SD model with image prompt""" def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4): super().__init__() self.cross_attention_dim = cross_attention_dim self.num_tokens = num_tokens self.proj = torch.nn.Sequential( torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2), torch.nn.GELU(), torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens), ) self.norm = torch.nn.LayerNorm(cross_attention_dim) def forward(self, id_embeds): x = self.proj(id_embeds) x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) x = self.norm(x) return x class IPAdapterFaceID: def __init__(self, sd_pipe, ip_ckpt, device, lora_rank=128, num_tokens=4): self.device = device self.ip_ckpt = ip_ckpt self.lora_rank = lora_rank self.num_tokens = num_tokens self.pipe = sd_pipe.to(self.device) self.set_ip_adapter() # image proj model self.image_proj_model = self.init_proj() self.load_ip_adapter() def init_proj(self): image_proj_model = MLPProjModel( cross_attention_dim=self.pipe.unet.config.cross_attention_dim, id_embeddings_dim=512, num_tokens=self.num_tokens, ).to(self.device, dtype=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] = LoRAAttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank, ).to(self.device, dtype=torch.float16) else: attn_procs[name] = LoRAIPAttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens, ).to(self.device, dtype=torch.float16) unet.set_attn_processor(attn_procs) def load_ip_adapter(self): if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors": state_dict = {"image_proj": {}, "ip_adapter": {}} with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f: for key in f.keys(): if key.startswith("image_proj."): state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key) elif key.startswith("ip_adapter."): state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key) else: state_dict = torch.load(self.ip_ckpt, map_location="cpu") self.image_proj_model.load_state_dict(state_dict["image_proj"]) ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values()) ip_layers.load_state_dict(state_dict["ip_adapter"]) @torch.inference_mode() def get_image_embeds(self, faceid_embeds): faceid_embeds = faceid_embeds.to(self.device, dtype=torch.float16) image_prompt_embeds = self.image_proj_model(faceid_embeds) uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_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, LoRAIPAttnProcessor): attn_processor.scale = scale def generate( self, faceid_embeds=None, prompt=None, negative_prompt=None, scale=1.0, num_samples=4, seed=None, guidance_scale=7.5, num_inference_steps=30, **kwargs, ): self.set_scale(scale) num_prompts = faceid_embeds.size(0) if prompt is None: prompt = "best quality, high quality" if negative_prompt is None: negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" if not isinstance(prompt, List): prompt = [prompt] * num_prompts if not isinstance(negative_prompt, List): negative_prompt = [negative_prompt] * num_prompts image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds) bs_embed, seq_len, _ = image_prompt_embeds.shape image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1) image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1) uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) with torch.inference_mode(): prompt_embeds_, negative_prompt_embeds_ = 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