CSGO简介

CSGO: Content-Style Composition in Text-to-Image Generation（风格迁移）
本文是一篇风格迁移的论文：将内容参考图像和风格参考图像分别投影，然后注入到内容模块和风格模块，同时采用controlnet的方法将内容参考图像注入unet的上采样块当中。

github中项目的地址

论文的代码部署

需要下载的模型权重：

我们的方法与 SDXL、VAE、ControlNet 和图像编码器完全兼容。请下载它们并将它们放在 ./base_models 文件夹中。
按照readme里面的指引，下载到如下文件夹里面：

复现中存在的一些问题

①需要保证如下包的版本与readme一致

diffusers==0.25.1
torch==2.0.1
torchaudio==2.0.2
torchvision==0.15.2
transformers==4.40.2

② NotImplementedError: Cannot copy out of meta tensor; no data!
参考知乎这篇
大语言模型调用踩坑点记录
数据在显存和内存中切换，导致出问题（显存不够）

部分参数从gpu拷贝到cpu会报错，将改成low_cpu_mem_usage=False，可以正常推理

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(base_model_path,controlnet=controlnet,torch_dtype=torch.float16,add_watermarker=False,use_safetensors=True,vae=vae,revision="fp16",##这个参数low_cpu_mem_usage=False)

③模型加载的问题
由于下载的模型权重都是fp16的格式的，然而这里模型的加载方式的参数是统一在最外面控制的，导致不同模型加载时，识别不了对应的模型文件：
加载模型是一些参数的设定

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(base_model_path,controlnet=controlnet,torch_dtype=torch.float16,add_watermarker=False,#用safetensors格式的权重文件use_safetensors=True,vae=vae,revision="fp16",#device_map="auto"low_cpu_mem_usage=False)

#这两个参数同时为fp16才会去读fp16的文件revision="fp16"variant= "fp16"

④需要统一数据的数据类型
由于之前的文本编码器的权重读取的是fp32，导致后续出现数据的类型不相同不能做运算的情况。
将pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
add_watermarker=False,
use_safetensors=True,
vae=vae,
revision=“fp16”,
#device_map=“auto”
low_cpu_mem_usage=False

)中的.from_pretrained函数（这个函数在pipeline_utils.py文件夹里）进行修改，当模型是文本编码器时，修改传入的一些参数

 if name == "text_encoder":#如果是文本编码器，将varient设置为fp16variant = "fp16"if variant is not None:# for folder in os.listdir(cached_folder):folder_path = os.path.join(cached_folder, "text_encoder")is_folder = os.path.isdir(folder_path) and "text_encoder" in config_dictvariant_exists = is_folder and any(p.split(".")[1].startswith(variant) for p in os.listdir(folder_path))if variant_exists:model_variants["text_encoder"] = variantif name == "text_encoder_2":variant = "fp16"if variant is not None:# for folder in os.listdir(cached_folder):folder_path = os.path.join(cached_folder, "text_encoder_2")is_folder = os.path.isdir(folder_path) and "text_encoder_2" in config_dictvariant_exists = is_folder and any(p.split(".")[1].startswith(variant) for p in os.listdir(folder_path))if variant_exists:model_variants["text_encoder_2"] = variantloaded_sub_model = load_sub_model(library_name=library_name,class_name=class_name,importable_classes=importable_classes,pipelines=pipelines,is_pipeline_module=is_pipeline_module,pipeline_class=pipeline_class,torch_dtype=torch_dtype,provider=provider,sess_options=sess_options,device_map=device_map,max_memory=max_memory,offload_folder=offload_folder,offload_state_dict=offload_state_dict,model_variants=model_variants,name=name,from_flax=from_flax,variant=variant,low_cpu_mem_usage=low_cpu_mem_usage,cached_folder=cached_folder,revision=revision,)logger.info(f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}.")

⑤推理代码中的风格图像和内容图像都要是两个图像，而给的代码中是一个文本，一个图像

## 注意这里的两个图片都要转化为图片的格式，论文给的推理代码一个是文本，另一个是
style_image = Image.open("/mnt/CSGO-main/assets/{}".format(style_name)).convert('RGB')
content_image = Image.open('/mnt/test/image/{}'.format(content_name)).convert('RGB')

推理代码

import randomimport torch
from ip_adapter.utils import BLOCKS as BLOCKS
from ip_adapter.utils import controlnet_BLOCKS as controlnet_BLOCKS
from PIL import Image
from diffusers import (AutoencoderKL,ControlNetModel,StableDiffusionXLControlNetPipeline,)
from ip_adapter import CSGO#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")device = 'cuda:0'base_model_path =  "./base_models/stable-diffusion-xl-base-1.0"  
image_encoder_path = "./base_models/IP-Adapter/sdxl_models/image_encoder"
csgo_ckpt = "./CSGO/csgo4_32.bin"
pretrained_vae_name_or_path ='./base_models/vae'
controlnet_path = "./base_models/TTPLanet_SDXL_Controlnet_Tile_Realistic"
weight_dtype = torch.float16
weight_dtype = torch.float16vae = AutoencoderKL.from_pretrained(pretrained_vae_name_or_path,torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16,use_safetensors=True)def get_device_map():return 'cuda' if torch.cuda.is_available() else 'cpu'device = get_device_map()pipe = StableDiffusionXLControlNetPipeline.from_pretrained(base_model_path,controlnet=controlnet,torch_dtype=torch.float16,add_watermarker=False,use_safetensors=True,vae=vae,revision="fp16",## 这里要加这个代码，不然会报错，因为显存不够，然后导致数据在显存和内存之间转换，报错low_cpu_mem_usage=False
)
pipe.enable_vae_tiling()target_content_blocks = BLOCKS['content']
target_style_blocks = BLOCKS['style']
controlnet_target_content_blocks = controlnet_BLOCKS['content']
controlnet_target_style_blocks = controlnet_BLOCKS['style']csgo = CSGO(pipe, image_encoder_path, csgo_ckpt, device, num_content_tokens=4,num_style_tokens=32,target_content_blocks=target_content_blocks, target_style_blocks=target_style_blocks,controlnet_adapter=True,controlnet_target_content_blocks=controlnet_target_content_blocks,controlnet_target_style_blocks=controlnet_target_style_blocks,content_model_resampler=True,style_model_resampler=True,)style_name = 'img_0.png'
content_name = 's_01_e_26_shot_005126_005200.png'## 注意这里的两个图片都要转化为图片的格式，论文给的推理代码一个是文本，另一个是
style_image = Image.open("/mnt/CSGO-main/assets/{}".format(style_name)).convert('RGB')
content_image = Image.open('/mnt/test/image/{}'.format(content_name)).convert('RGB')num_sample=1
caption = ''
#写个循环，看看各个参数对生成图片的影响
while True:tem = 0for ccs in range(5, 11, 1):ccs = ccs * 0.1content_scale = random.uniform(0.6, 1.5)style_scale = random.uniform(0.5, 1)images = csgo.generate(pil_content_image= content_image, pil_style_image=style_image,prompt=caption,negative_prompt= "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",content_scale=1.0,style_scale=1.0,guidance_scale=10,num_images_per_prompt=num_sample,num_samples=1,num_inference_steps=50,seed=42,image=content_image.convert('RGB'),controlnet_conditioning_scale=0.6,)formatted_ccs = "{:.2f}".format(ccs)formatted_content_scale = "{:.2f}".format(content_scale)formatted_style_scale = "{:.2f}".format(style_scale)images[0].save(f"inference/ccs:{formatted_ccs}-cs:{formatted_content_scale}-ss:{formatted_style_scale}.png")tem = tem + 1if tem >= 100:break

生成结果示意图

风格参考图像

文本： a cat

生成的图像