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参考文章：半小时在本地部署DeepSeek的Janus Pro，进行图片分析和文生图
我的基础环境

系统:Ubuntu 22.04
cuda ：12.3
显卡：3090 24G

下载代码

git clone https://github.com/deepseek-ai/Janus.git

下载模型文件

模型文件地址：

https://hf-mirror.com/deepseek-ai/Janus-Pro-7B
https://modelscope.cn/models/deepseek-ai/Janus-Pro-7B

也可以使用git clone克隆模型。

进入源代码目录后，执行下面的克隆命令。

git clone https://hf-mirror.com/deepseek-ai/Janus-Pro-7B

环境配置

提前安装好cuda

conda create -n janus_pro python=3.10
conda activate janus_pro

先单独安装torch==2.2.1

pip install torch==2.2.1 torchvision==0.17.1 torchaudio

然后将 requirements.txt 中下面几项改成

torch                     2.2.1
torchaudio                2.2.1
torchvision               0.17.1
transformers              4.40.0

然后再

pip install -r requirements.txt 

安装下来基本没有什么问题

Janus Pro测试

修改模型路径：
将demo/app_januspro.py 中

model_path = "deepseek-ai/Janus-Pro-7B"

改为

model_path = "/home/dell/work/python_project/Janus/Janus-Pro-7B"

运行程序

python demo/app_januspro.py

占用的显存空间得到14G，

打开网页：http://127.0.0.1:7860

http://127.0.0.1:7860

然后输入一张图像，并写一个问题，点击chat

效果演示

图像分析测试

运行结果：

也有不好的效果

文字生成图像测试

效果挺差的，分辨率一般

可能遇到的错误

1、没有模型

报错：

(janus_pro) dell@dell-Precision-7920-Tower:~/work/python_project/Janus$ python demo/app_januspro.py
Python version is above 3.10, patching the collections module.
/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torchvision/datapoints/__init__.py:12: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().warnings.warn(_BETA_TRANSFORMS_WARNING)
/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torchvision/transforms/v2/__init__.py:54: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().warnings.warn(_BETA_TRANSFORMS_WARNING)
/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:594: 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` insteadwarnings.warn(
Loading checkpoint shards:   0%|                                                                                                           | 0/2 [00:00<?, ?it/s]
Traceback (most recent call last):File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/modeling_utils.py", line 556, in load_state_dictreturn torch.load(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torch/serialization.py", line 814, in loadraise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution.Do it only if you get the file from a trusted source. WeightsUnpickler error: Unsupported operand 118During handling of the above exception, another exception occurred:Traceback (most recent call last):File "/home/dell/work/python_project/Janus/demo/app_januspro.py", line 19, in <module>vl_gpt = AutoModelForCausalLM.from_pretrained(model_path,File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrainedreturn model_class.from_pretrained(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/modeling_utils.py", line 262, in _wrapperreturn func(*args, **kwargs)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4319, in from_pretrained) = cls._load_pretrained_model(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4873, in _load_pretrained_modelstate_dict = load_state_dict(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/modeling_utils.py", line 566, in load_state_dictraise OSError(
OSError: You seem to have cloned a repository without having git-lfs installed. Please install git-lfs and run `git lfs install` followed by `git lfs pull` in the folder you cloned.

解决办法
从你给出的错误信息来看，在运行 demo/app_januspro.py 脚本时碰到了几个问题，下面为你详细分析并给出解决办法。

问题分析

OSError 错误

这是主要的错误，提示你在克隆仓库时没有安装 git-lfs。git-lfs（Git Large File Storage）用于管理大文件，许多深度学习模型的权重文件都很大，需要借助 git-lfs 来下载。

解决办法

安装 git-lfs 并下载模型权重

安装 git-lfs 并下载模型权重。
步骤如下：

1. 安装 git-lfs
   • Ubuntu/Debian：

sudo apt-get install git-lfs

2. 初始化 git-lfs
   在路径**~/work/python_project/Janus** ，运行

git lfs install

3. 进入模型所在的仓库目录并拉取大文件
   在路径/home/dell/work/python_project/Janus/Janus-Pro-7B
   运行

git lfs pull

2、RuntimeError: “triu_tril_cuda_template” not implemented for ‘BFloat16’

报错日志：

$ python demo/app_januspro.py
Python version is above 3.10, patching the collections module.
/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torchvision/datapoints/__init__.py:12: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().warnings.warn(_BETA_TRANSFORMS_WARNING)
/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torchvision/transforms/v2/__init__.py:54: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().warnings.warn(_BETA_TRANSFORMS_WARNING)
/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:594: 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` insteadwarnings.warn(
Loading checkpoint shards:   0%|                                                                                                                                          | 0/2 [00:00<?, ?it/s]/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()return self.fget.__get__(instance, owner)()
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:21<00:00, 10.59s/it]
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 v4.48, 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 <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. 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.
Some kwargs in processor config are unused and will not have any effect: add_special_token, num_image_tokens, ignore_id, sft_format, image_tag, mask_prompt. 
Running on local URL:  http://127.0.0.1:7860
IMPORTANT: You are using gradio version 3.48.0, however version 4.44.1 is available, please upgrade.
--------
Running on public URL: https://179c234a5c69fb53fa.gradio.liveThis share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
Traceback (most recent call last):File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/gradio/routes.py", line 534, in predictoutput = await route_utils.call_process_api(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/gradio/route_utils.py", line 226, in call_process_apioutput = await app.get_blocks().process_api(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/gradio/blocks.py", line 1550, in process_apiresult = await self.call_function(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/gradio/blocks.py", line 1185, in call_functionprediction = await anyio.to_thread.run_sync(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_syncreturn await get_async_backend().run_sync_in_worker_thread(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2461, in run_sync_in_worker_threadreturn await futureFile "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 962, in runresult = context.run(func, *args)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/gradio/utils.py", line 661, in wrapperresponse = f(*args, **kwargs)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_contextreturn func(*args, **kwargs)File "/home/dell/work/python_project/Janus/demo/app_januspro.py", line 60, in multimodal_understandingoutputs = vl_gpt.language_model.generate(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_contextreturn func(*args, **kwargs)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/generation/utils.py", line 2223, in generateresult = self._sample(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/generation/utils.py", line 3211, in _sampleoutputs = self(**model_inputs, return_dict=True)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_implreturn forward_call(*args, **kwargs)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/utils/deprecation.py", line 172, in wrapped_funcreturn func(*args, **kwargs)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 842, in forwardoutputs = self.model(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_implreturn forward_call(*args, **kwargs)File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 564, in forwardcausal_mask = self._update_causal_mask(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 666, in _update_causal_maskcausal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(File "/home/dell/anaconda3/envs/janus_pro/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 732, in _prepare_4d_causal_attention_mask_with_cache_positioncausal_mask = torch.triu(causal_mask, diagonal=1)
RuntimeError: "triu_tril_cuda_template" not implemented for 'BFloat16'

解决办法，升级torch版本，默认的为2.0.1，使用

pip install torch==2.2.1 torchvision==0.17.1 torchaudio