IP-Adapter/ip_adapter/ip_adapter.py

import os
from typing import List

import torch
from diffusers import StableDiffusionPipeline
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from PIL import Image

from .attention_processor import IPAttnProcessor, AttnProcessor


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 IPAdapter:
    
    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device):
        
        self.device = device
        self.image_encoder_path = image_encoder_path
        self.ip_ckpt = ip_ckpt
        
        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 = ImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024,
                clip_extra_context_tokens=4).to(self.device, dtype=torch.float16)
        
        self.load_ip_adapter()
        
    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).to(self.device, dtype=torch.float16)
        unet.set_attn_processor(attn_procs)
        
    def load_ip_adapter(self):
        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, 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_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).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 generate(
        self,
        pil_image,
        prompt=None,
        negative_prompt=None,
        scale=1.0,
        num_samples=4,
        seed=-1,
        guidance_scale=7.5,
        num_inference_steps=30,
        **kwargs,
    ):
        self.set_scale(scale)
        
        if isinstance(pil_image, Image.Image):
            num_prompts = 1
        else:
            num_prompts = 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 = self.pipe._encode_prompt(prompt, self.device, num_samples, True, negative_prompt)
            negative_prompt_embeds_, prompt_embeds_ = prompt_embeds.chunk(2)
            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 image_grid(imgs, rows, cols):
    assert len(imgs) == rows*cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols*w, rows*h))
    grid_w, grid_h = grid.size
    
    for i, img in enumerate(imgs):
        grid.paste(img, box=(i%cols*w, i//cols*h))
    return grid
    
    
if __name__ == "__main__":
    base_model_path = "/mnt/aigc_cq/shared/txt2img_models/Realistic_Vision_V5.1_noVAE/"
    image_encoder_path = "/mnt/aigc_cq/private/huye/t2i_trained_models/ip_adapter_sd15_clip-H/image_encoder/"
    ip_ckpt = "/mnt/aigc_cq/private/huye/t2i_trained_models/ip_adapter_sd15_clip-H/ip-dapter_1000000.bin"
    device = "cuda:3"
    
    
    pipe = StableDiffusionPipeline.from_pretrained(
            base_model_path,
            torch_dtype=torch.float16,
            feature_extractor=None,
            safety_checker=None,
    )
    
    
    ip_model = IPAdapter(pipe, image_encoder_path, ip_ckpt, device)
    
    image_files = ["../assets/Taylor_Swift.png", "../assets/3.png"]
    num_samples = 2
    pil_images = [Image.open(image_file) for image_file in image_files]
    
    images = ip_model.generate(pil_image=pil_images, num_samples=num_samples)
    grid = image_grid(images, 1, 4)
    grid.save("output.png")
    
    images = ip_model.generate(pil_image=pil_images, num_samples=num_samples, prompt="best quality, high quality, wearing a hat on the beach", scale=0.5)
    grid = image_grid(images, 1, 4)
    grid.save("output_hat.png")