deepseek

Running DeepSeek Janus Pro 1B on Hugging Face

Anas Mohammad

Jan 29, 2025 • 2 min read

DeepSeek Janus Pro 1B is a cutting-edge multimodal model capable of text-to-image generation and text understanding. This guide walks you through setting it up on Hugging Face and leveraging its advanced capabilities.

1. Environment Setup

Prerequisites

Python 3.8+ and pip installed.
A Hugging Face account (to access models and libraries).
GPU recommended (for faster inference).

Step 1: Install Libraries

Install core dependencies via pip:

pip install transformers torch accelerate diffusers  # Base libraries
pip install -U datasets huggingface_hub  # Optional for data handling

Step 2: Clone the Repository (Optional)

For example scripts and custom utilities, clone the DeepSeek repository:

git clone https://github.com/deepseek-ai/Janus.git
cd Janus && pip install -r requirements.txt  # Install model-specific dependencies

2. Running the Model

Text-to-Image Generation

Use the MultiModalityCausalLM class for multimodal tasks:

from transformers import AutoProcessor, MultiModalityCausalLM

# Load model and processor
processor = AutoProcessor.from_pretrained("deepseek-ai/Janus-Pro-1B")
model = MultiModalityCausalLM.from_pretrained(
    "deepseek-ai/Janus-Pro-1B", 
    device_map="auto",  # Auto-detects GPU/CPU
    torch_dtype="auto"  # Optimizes precision (float16/32)
)

# Generate an image from text
prompt = "A futuristic cityscape at sunset"
inputs = processor(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs)

# Save the generated image
outputs.images[0].save("cityscape.png")

Text Generation (Chat/QA)

For text-only tasks, use the standard text-generation pipeline:

from transformers import pipeline

pipe = pipeline("text-generation", model="deepseek-ai/Janus-Pro-1B")
response = pipe("Explain quantum computing simply:", max_length=200, temperature=0.7)
print(response[0]['generated_text'])

3. Advanced Features & Optimization

WebGPU Browser Integration

Run Janus Pro 1B in-browser using Transformers.js:

import { AutoProcessor, MultiModalityCausalLM } from '@xenova/transformers';

const model = await MultiModalityCausalLM.from_pretrained("deepseek-ai/Janus-Pro-1B");
const processor = await AutoProcessor.from_pretrained(model);
// Generate images/text directly in the browser

Quantization for Low-Memory Devices

Reduce VRAM usage with 4-bit quantization:

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4"
)
model = MultiModalityCausalLM.from_pretrained(
    "deepseek-ai/Janus-Pro-1B", 
    quantization_config=bnb_config
)

4. Troubleshooting

Issue	Solution
CUDA Out of Memory	Use `fp16` or 4-bit quantization.
Slow Inference	Enable `device_map="auto"` and `torch.compile(model)`.
Model Not Found	Ensure you’re logged into Hugging Face: `huggingface-cli login`.

5. Key Notes

Hardware Requirements:
- The 1B parameter model runs on 8GB+ VRAM GPUs (e.g., NVIDIA T4, RTX 3060).
- For CPU-only setups, expect slower performance (~10 sec/token).
Multimodal Flexibility:
- Use processor(images=..., text=...) for image-to-text tasks (e.g., captioning).
- Adjust temperature (0.1–1.0) to balance creativity vs. determinism.
Ethical Use:
- Comply with Hugging Face’s Model License and avoid harmful content generation.
Hugging Face Spaces Demo:
- Test the model without local setup:

Additional Resources

For further assistance or updates:

Check out Janus-Pro-1B on Hugging Face and DeepSeek Official GitHub for detailed usage instructions.
Join community forums or Discord channels related to AI art generation for tips and troubleshooting help.

By following these detailed steps, you should be able to successfully install and run DeepSeek Janus-Pro 1B on Hugging Face!