DeepSeek 多模态模型 Janus-Pro 本地部署

# 一、概述 Janus-Pro是DeepSeek最新开源的多模态模型，是一种新颖的自回归框架，统一了多模态理解和生成。通过将视觉编码解耦为独立的路径，同时仍然使用单一的、统一的变压器架构进行处理，该框架解决了先前方法的局限性。这种解耦不仅缓解了视觉编码器在理解和生成中的角色冲突，还增强了框架的灵活性。Janus-Pro 超过了以前的统一模型，并且匹配或超过了特定任务模型的性能。 代码链接：https://github.com/deepseek-ai/Janus 模型链接：https://modelscope.cn/collections/Janus-Pro-0f5e48f6b96047 体验页面：https://modelscope.cn/studios/AI-ModelScope/Janus-Pro-7B # 二、虚拟环境 ## 环境说明 本文使用WSL2运行的ubuntu系统来进行演示，参考链接：https://www.cnblogs.com/xiao987334176/p/18864140 ## 创建虚拟环境 ```csharp conda create --name vll-Janus-Pro-7B python=3.12.7 ``` 激活虚拟环境，执行命令： ```csharp conda activate vll-Janus-Pro-7B ``` 查看CUDA版本，执行命令： ```csharp # nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jan_15_19:20:09_PST_2025 Cuda compilation tools, release 12.8, V12.8.61 Build cuda_12.8.r12.8/compiler.35404655_0 ``` # 三、安装Janus-Pro 创建项目目录 ```csharp mkdir vllm cd vllm ``` 克隆代码 ```csharp git clone https://github.com/deepseek-ai/Janus ``` 安装依赖包，注意：这里要手动安装pytorch，指定版本。 ```csharp pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 ``` 安装其他依赖组件 ```csharp pip3 install transformers attrdict einops timm ``` 下载模型 可以用modelscope下载，安装modelscope，命令如下： ```csharp pip install modelscope modelscope download --model deepseek-ai/Janus-Pro-7B ``` 效果如下： ```csharp # modelscope download --model deepseek-ai/Janus-Pro-7B Downloading Model from https://www.modelscope.cn to directory: /root/.cache/modelscope/hub/models/deepseek-ai/Janus-Pro-7B Downloading [config.json]: 100%|███████████████████████████████████████████████████| 1.42k/1.42k [00:00<00:00, 5.29kB/s] Downloading [configuration.json]: 100%|████████████████████████████████████████████████| 68.0/68.0 [00:00<00:00, 221B/s] Downloading [README.md]: 100%|█████████████████████████████████████████████████████| 2.49k/2.49k [00:00<00:00, 7.20kB/s] Downloading [processor_config.json]: 100%|███████████████████████████████████████████████| 210/210 [00:00<00:00, 590B/s] Downloading [janus_pro_teaser1.png]: 100%|██████████████████████████████████████████| 95.7k/95.7k [00:00<00:00, 267kB/s] Downloading [preprocessor_config.json]: 100%|████████████████████████████████████████████| 346/346 [00:00<00:00, 867B/s] Downloading [janus_pro_teaser2.png]: 100%|███████████████████████████████████████████| 518k/518k [00:00<00:00, 1.18MB/s] Downloading [special_tokens_map.json]: 100%|███████████████████████████████████████████| 344/344 [00:00<00:00, 1.50kB/s] Downloading [tokenizer_config.json]: 100%|███████████████████████████████████████████████| 285/285 [00:00<00:00, 926B/s] Downloading [pytorch_model.bin]: 0%|▏ | 16.0M/3.89G [00:00<03:55, 17.7MB/s] Downloading [tokenizer.json]: 100%|████████████████████████████████████████████████| 4.50M/4.50M [00:00<00:00, 6.55MB/s] Processing 11 items: 91%|█████████████████████████████████████████████████████▋ | 10.0/11.0 [00:19<00:00, 14.1it/s]Downloading [pytorch_model.bin]: 100%|█████████████████████████████████████████████| 3.89G/3.89G [09:18<00:00, 7.48MB/s] Processing 11 items: 100%|███████████████████████████████████████████████████████████| 11.0/11.0 [09:24<00:00, 51.3s/it] ``` 可以看到下载目录为/root/.cache/modelscope/hub/models/deepseek-ai/Janus-Pro-7B 把下载的模型移动到vllm目录里面 ```csharp mv /root/.cache/modelscope/hub/models/deepseek-ai /home/xiao/vllm ``` # 四、测试图片理解 vllm目录有2个文件夹，结构如下： ```csharp # ll total 20 drwxr-xr-x 4 root root 4096 May 8 18:59 ./ drwxr-x--- 5 xiao xiao 4096 May 8 14:50 ../ drwxr-xr-x 8 root root 4096 May 8 18:59 Janus/ drwxr-xr-x 4 root root 4096 May 8 16:01 deepseek-ai/ ``` 进入deepseek-ai目录，会看到一个文件夹Janus-Pro-7B ```csharp # ll total 16 drwxr-xr-x 4 root root 4096 May 8 16:01 ./ drwxr-xr-x 4 root root 4096 May 8 18:59 ../ drwxr-xr-x 2 root root 4096 May 7 18:32 Janus-Pro-7B/ ``` 返回上一级，在Janus目录，创建image\_understanding.py文件，代码如下： ```csharp import torch from transformers import AutoModelForCausalLM from janus.models import MultiModalityCausalLM, VLChatProcessor from janus.utils.io import load_pil_images model_path = "../deepseek-ai/Janus-Pro-7B" image='aa.jpeg' question='请说明一下这张图片' vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path) tokenizer = vl_chat_processor.tokenizer vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True ) vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval() conversation = [ { "role": "<|User|>", "content": f"\n{question}", "images": [image], }, {"role": "<|Assistant|>", "content": ""}, ] # load images and prepare for inputs pil_images = load_pil_images(conversation) prepare_inputs = vl_chat_processor( conversations=conversation, images=pil_images, force_batchify=True ).to(vl_gpt.device) # # run image encoder to get the image embeddings inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs) # # run the model to get the response outputs = vl_gpt.language_model.generate( inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask, pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=512, do_sample=False, use_cache=True, ) answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True) print(f"{prepare_inputs['sft_format'][0]}", answer) ``` 下载一张图片，地址：https://pics6.baidu.com/feed/09fa513d269759ee74c8d049640fcc1b6f22df9e.jpeg  将此图片，重命名为aa.jpeg，存放在Janus目录 最终Janus目录，文件如下： ```csharp # ll total 2976 drwxr-xr-x 8 root root 4096 May 8 18:59 ./ drwxr-xr-x 4 root root 4096 May 8 18:59 ../ drwxr-xr-x 8 root root 4096 May 7 18:11 .git/ -rw-r--r-- 1 root root 115 May 7 18:11 .gitattributes -rw-r--r-- 1 root root 7301 May 7 18:11 .gitignore -rw-r--r-- 1 root root 1065 May 7 18:11 LICENSE-CODE -rw-r--r-- 1 root root 13718 May 7 18:11 LICENSE-MODEL -rw-r--r-- 1 root root 3069 May 7 18:11 Makefile -rwxr-xr-x 1 root root 26781 May 7 18:11 README.md* -rw-r--r-- 1 root root 62816 May 8 14:59 aa.jpeg drwxr-xr-x 2 root root 4096 May 7 18:11 demo/ drwxr-xr-x 2 root root 4096 May 8 17:19 generated_samples/ -rw-r--r-- 1 root root 4515 May 7 18:11 generation_inference.py -rw-r--r-- 1 xiao xiao 4066 May 8 18:50 image_generation.py -rw-r--r-- 1 root root 1594 May 8 18:58 image_understanding.py drwxr-xr-x 2 root root 4096 May 7 18:11 images/ -rw-r--r-- 1 root root 2642 May 7 18:11 inference.py -rw-r--r-- 1 root root 5188 May 7 18:11 interactivechat.py drwxr-xr-x 6 root root 4096 May 7 19:01 janus/ drwxr-xr-x 2 root root 4096 May 7 18:11 janus.egg-info/ -rw-r--r-- 1 root root 2846268 May 7 18:11 janus_pro_tech_report.pdf -rw-r--r-- 1 root root 1111 May 7 18:11 pyproject.toml -rw-r--r-- 1 root root 278 May 7 18:11 requirements.txt ``` 运行代码，效果如下： ```csharp # python image_understanding.py Python version is above 3.10, patching the collections module. /root/anaconda3/envs/vll-Janus-Pro-7B/lib/python3.12/site-packages/transformers/models/auto/image_processing_auto.py:604: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead warnings.warn( Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`. You are using the default legacy behaviour of the . This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message. Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████| 2/2 [00:10<00:00, 5.18s/it] You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. <|User|>: 请说明一下这张图片 <|Assistant|>: 这张图片展示了一位身穿传统服饰的女性，她正坐在户外，双手合十，闭着眼睛，似乎在进行冥想或祈祷。背景是绿色的树木和植物，阳光透过树叶洒在她的身上，营造出一种宁静、祥和的氛围。她的服装以淡雅的白色和粉色为主，带有精致的花纹，整体风格非常优雅。 ``` 描述还是比较准确的 # 五、测试图片生成 在Janus目录，新建image\_generation.py脚本，代码如下： ```csharp import os import torch import numpy as np from PIL import Image from transformers import AutoModelForCausalLM from janus.models import MultiModalityCausalLM, VLChatProcessor model_path = "../deepseek-ai/Janus-Pro-7B" vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path) tokenizer = vl_chat_processor.tokenizer vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True ) vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval() conversation = [ {"role": "<|User|>", "content": "超写实8K渲染，一位具有东方古典美的中国女性，瓜子脸，西昌的眉毛如弯弯的月牙，双眼明亮而深邃，犹如夜空中闪烁的星星。高挺的鼻梁，樱桃小嘴微微上扬，透露出一丝诱人的微笑。她的头发如黑色的瀑布般垂直落在减胖两侧，微风轻轻浮动发色。肌肤白皙如雪，在阳光下泛着微微的光泽。她身着乙烯白色的透薄如纱的连衣裙，裙摆在海风中轻轻飘动。"}, {"role": "<|Assistant|>", "content": ""}, ] sft_format = vl_chat_processor.apply_sft_template_for_multi_turn_prompts( conversations=conversation, sft_format=vl_chat_processor.sft_format, system_prompt="" ) prompt = sft_format + vl_chat_processor.image_start_tag @torch.inference_mode() def generate( mmgpt: MultiModalityCausalLM, vl_chat_processor: VLChatProcessor, prompt: str, temperature: float = 1, parallel_size: int = 1, # 减小 parallel_size cfg_weight: float = 5, image_token_num_per_image: int = 576, img_size: int = 384, patch_size: int = 16, ): input_ids = vl_chat_processor.tokenizer.encode(prompt) input_ids = torch.LongTensor(input_ids) tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).cuda() for i in range(parallel_size * 2): tokens[i, :] = input_ids if i % 2 != 0: tokens[i, 1:-1] = vl_chat_processor.pad_id inputs_embeds = mmgpt.language_model.get_input_embeddings()(tokens) generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda() for i in range(image_token_num_per_image): outputs = mmgpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=outputs.past_key_values if i != 0 else None) hidden_states = outputs.last_hidden_state logits = mmgpt.gen_head(hidden_states[:, -1, :]) logit_cond = logits[0::2, :] logit_uncond = logits[1::2, :] logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond) probs = torch.softmax(logits / temperature, dim=-1) next_token = torch.multinomial(probs, num_samples=1) generated_tokens[:, i] = next_token.squeeze(dim=-1) next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1) img_embeds = mmgpt.prepare_gen_img_embeds(next_token) inputs_embeds = img_embeds.unsqueeze(dim=1) # 添加显存清理 del logits, logit_cond, logit_uncond, probs torch.cuda.empty_cache() dec = mmgpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int), shape=[parallel_size, 8, img_size // patch_size, img_size // patch_size]) dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1) dec = np.clip((dec + 1) / 2 * 255, 0, 255) visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8) visual_img[:, :, :] = dec os.makedirs('generated_samples', exist_ok=True) for i in range(parallel_size): save_path = os.path.join('generated_samples', f"img_{i}.jpg") img = Image.fromarray(visual_img[i]) img.save(save_path) generate( vl_gpt, vl_chat_processor, prompt, ) ``` **注意：提示词是可以写中文的，不一定非要是英文。** **代码在默认的基础上做了优化，否则运行会导致英伟达5080显卡直接卡死。** 运行代码，效果如下： ```csharp # python image_generation.py Python version is above 3.10, patching the collections module. /root/anaconda3/envs/vll-Janus-Pro-7B/lib/python3.12/site-packages/transformers/models/auto/image_processing_auto.py:604: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead warnings.warn( Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`. You are using the default legacy behaviour of the . This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message. Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████| 1/1 [00:09<00:00, 4.58s/it] ``` 注意观察一下GPU使用情况，这里会很高。   RTX 5080显卡，16GB显存，几乎已经占满了。 等待30秒左右，就会生成一张图片。 打开小企鹅  进入目录\home\xiao\vllm\Janus\generated\_samples 这里会出现一张图片   打开图片，效果如下：  效果还算可以，距离真正的8k画质，还是有点差距的。 **注意提示词，尽量丰富一点，生成的图片，才符合要求。** 如果不会写提示词，可以让deepseek帮你写一段提示词。