最新融合多模态的理解和生成的大一统transform架构，show-o模型部署

Show-o是由字节跳动和新加坡国立大学Show Lab共同研发的一个多模态大模型，统一了多模态理解和生成。

Show-o的创新之处在于它将自回归和离散扩散建模相结合，以适应不同和混合模态的输入和输出。

Show-o模型的架构基于预训练的大型语言模型（LLM），并采用了离散去噪扩散来模拟离散图像标记，简化了额外文本编码器的需求。

Show-o采用了统一的提示策略，将图像和文本标记化后形成输入序列，并通过全方位的注意机制处理不同类型的信号，结合了因果注意和全面注意，以适应性地混合和变化。

Show-o在自回归生成图像时所需的采样步骤大约减少了20倍，显著减少了计算资源的消耗，并提高了模型的应用灵活性。

Show-o还天然支持多种下游应用，如文本引导的图像修复和外推，而无需任何微调，进一步展示了其作为下一代基础模型的潜力。

其中github项目地址：https://github.com/showlab/Show-o。

一、环境安装

1、python环境

建议安装python版本在3.10以上。

2、pip库安装

pip install torch==2.2.1+cu118 torchvision==0.17.1+cu118 torchaudio==2.2.1 --extra-index-url https://download.pytorch.org/whl/cu118

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

3、show-o模型下载：

git lfs install

git clone https://huggingface.co/showlab/show-o

4、show-o-w-clip-vit模型下载：

git lfs install

git clone https://huggingface.co/showlab/show-o-w-clip-vit

5、magvitv2模型下载：

git lfs install

git clone https://huggingface.co/showlab/magvitv2

二、功能测试

1、运行测试：

（1）图片理解的python代码
 

import os
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
import wandb
from models import Showo, MAGVITv2
from prompting_utils import UniversalPrompting, create_attention_mask_for_mmu, create_attention_mask_for_mmu_vit
from utils import get_config, flatten_omega_conf, image_transform
from transformers import AutoTokenizer
from models.clip_encoder import CLIPVisionTower
from transformers import CLIPImageProcessor
import conversation as conversation_lib# Set environment variables
os.environ["TOKENIZERS_PARALLELISM"] = "true"# Set up conversation template
conversation_lib.default_conversation = conversation_lib.conv_templates["phi1.5"]
SYSTEM_PROMPT = ("A chat between a curious user and an artificial intelligence assistant. ""The assistant gives helpful, detailed, and polite answers to the user's questions.")
SYSTEM_PROMPT_LEN = 28def get_vq_model_class(model_type):if model_type == "magvitv2":return MAGVITv2else:raise ValueError(f"model_type {model_type} not supported.")def initialize_wandb(config):resume_wandb_run = config.wandb.resumerun_id = config.wandb.get("run_id", None)if run_id is None:resume_wandb_run = Falserun_id = wandb.util.generate_id()config.wandb.run_id = run_idwandb_config = {k: v for k, v in flatten_omega_conf(config, resolve=True)}wandb.init(project="demo",name=config.experiment.name + '_mmu',config=wandb_config,)return run_iddef prepare_model_and_tokenizer(config, device):tokenizer = AutoTokenizer.from_pretrained(config.model.showo.llm_model_path, padding_side="left")uni_prompting = UniversalPrompting(tokenizer, max_text_len=config.dataset.preprocessing.max_seq_length,special_tokens=("<|soi|>", "<|eoi|>", "<|sov|>", "<|eov|>", "<|t2i|>", "<|mmu|>", "<|t2v|>", "<|v2v|>", "<|lvg|>"),ignore_id=-100, cond_dropout_prob=config.training.cond_dropout_prob)vq_model = get_vq_model_class(config.model.vq_model.type)vq_model = vq_model.from_pretrained(config.model.vq_model.vq_model_name).to(device).eval()vision_tower = CLIPVisionTower("openai/clip-vit-large-patch14-336").to(device)clip_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14-336")model = Showo.from_pretrained(config.model.showo.pretrained_model_path).to(device).eval()return tokenizer, uni_prompting, vq_model, vision_tower, clip_processor, modeldef process_image(image_path, config, device):image_ori = Image.open(image_path).convert("RGB")image = image_transform(image_ori, resolution=config.dataset.params.resolution).to(device).unsqueeze(0)pixel_values = CLIPImageProcessor.preprocess(image_ori, return_tensors="pt")["pixel_values"]return image, pixel_values.squeeze(0)def generate_response(model, tokenizer, uni_prompting, vision_tower, pixel_values, image_tokens, question, device, configs):batch_size = 1if configs.model.showo.w_clip_vit:conv = conversation_lib.default_conversation.copy()conv.append_message(conv.roles[0], question)conv.append_message(conv.roles[1], None)prompt_question = conv.get_prompt().strip()input_ids_system = tokenizer(SYSTEM_PROMPT, return_tensors="pt", padding="longest").input_idsassert input_ids_system.shape[-1] == 28input_ids_system = input_ids_system.to(device)input_ids = tokenizer(prompt_question, return_tensors="pt", padding="longest").input_ids.squeeze(0)input_ids_combined = torch.cat([(torch.ones(1, 1) * uni_prompting.sptids_dict['<|mmu|>']).to(device),input_ids_system,(torch.ones(1, 1) * uni_prompting.sptids_dict['<|soi|>']).to(device),(torch.ones(1, 1) * uni_prompting.sptids_dict['<|eoi|>']).to(device),input_ids], dim=1).long()image_embeddings = vision_tower(pixel_values[None]).squeeze(0)image_embeddings = model.mm_projector(image_embeddings)text_embeddings = model.showo.model.embed_tokens(input_ids_combined)input_embeddings = torch.cat([text_embeddings[:, :2 + SYSTEM_PROMPT_LEN, :],image_embeddings,text_embeddings[:, 2 + SYSTEM_PROMPT_LEN:, :]], dim=1)attention_mask = create_attention_mask_for_mmu_vit(input_embeddings, system_prompt_len=SYSTEM_PROMPT_LEN)[0].unsqueeze(0)cont_toks_list = model.mmu_generate(input_embeddings=input_embeddings,attention_mask=attention_mask,max_new_tokens=100,top_k=1,eot_token=tokenizer.eos_token_id)else:input_ids = tokenizer('USER: \n' + question + ' ASSISTANT:', return_tensors='pt')['input_ids']input_ids = torch.cat([(torch.ones(1, 1) * uni_prompting.sptids_dict['<|mmu|>']).to(device),(torch.ones(1, 1) * uni_prompting.sptids_dict['<|soi|>']).to(device),image_tokens,(torch.ones(1, 1) * uni_prompting.sptids_dict['<|eoi|>']).to(device),(torch.ones(1, 1) * uni_prompting.sptids_dict['<|sot|>']).to(device),input_ids], dim=1).long()attention_mask = create_attention_mask_for_mmu(input_ids, int(uni_prompting.sptids_dict['<|eoi|>']))cont_toks_list = model.mmu_generate(input_ids=input_ids,attention_mask=attention_mask,max_new_tokens=100,top_k=1,eot_token=uni_prompting.sptids_dict['<|eot|>'])cont_toks_list = torch.stack(cont_toks_list).squeeze()[None]text_response = tokenizer.batch_decode(cont_toks_list, skip_special_tokens=True)[0]return text_responseif __name__ == '__main__':config = get_config()run_id = initialize_wandb(config)device = torch.device("cuda" if torch.cuda.is_available() else "cpu")tokenizer, uni_prompting, vq_model, vision_tower, clip_processor, model = prepare_model_and_tokenizer(config, device)file_list = os.listdir(config.mmu_image_root)responses = ['' for _ in range(len(file_list))]images = []for i, file_name in enumerate(tqdm(file_list)):image_path = os.path.join(config.mmu_image_root, file_name)image, pixel_values = process_image(image_path, config, device)images.append(image)image_tokens = vq_model.get_code(image) + len(uni_prompting.text_tokenizer)for question in config.question.split(' *** '):text_response = generate_response(model, tokenizer, uni_prompting, vision_tower, pixel_values, image_tokens, question, device, config)responses[i] += f'User: {question}\nAnswer: {text_response}\n'images = torch.cat(images, dim=0)images = torch.clamp((images + 1.0) / 2.0, 0.0, 1.0) * 255.0images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)pil_images = [Image.fromarray(image) for image in images]wandb_images = [wandb.Image(image, caption=responses[i]) for i, image in enumerate(pil_images)]wandb.log({"multimodal understanding": wandb_images}, step=0)

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