Janus 1.3B: A Unified Model for Multimodal Understanding and Generation Tasks

Janus 1.3B

Janus is a new autoregressive framework that integrates multimodal understanding and generation. Unlike previous models, which used a single visual encoder for both understanding and generation tasks, Janus introduces two separate visual encoding pathways for these functions.

Differences in Encoding for Understanding and Generation

• In multimodal understanding tasks, the visual encoder extracts high-level semantic information such as object categories and visual attributes. This encoder focuses on inferring complex meanings, emphasizing higher-dimensional semantic elements.
• On the other hand, in visual generation tasks, emphasis is placed on generating fine details and maintaining overall consistency. As a result, lower-dimensional encoding that can capture spatial structures and textures is required.

Setting Up the Environment

Here are the steps to run Janus in Google Colab:

git clone https://github.com/deepseek-ai/Janus
cd Janus
pip install -e .
# If needed, install the following as well
# pip install wheel
# pip install flash-attn --no-build-isolation

Vision Tasks

Loading the Model

Use the following code to load the necessary model for vision tasks:

import torch
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from janus.utils.io import load_pil_images

# Specify the model path
model_path = "deepseek-ai/Janus-1.3B"
vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer

vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()

Loading and Preparing Images for Encoding

Next, load the image and convert it into a format that the model can understand:

conversation = [
    {
        "role": "User",
        "content": "<image_placeholder>\nDescribe this chart.",
        "images": ["images/pie_chart.png"],
    },
    {"role": "Assistant", "content": ""},
]

# Load the image and prepare input
pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(
    conversations=conversation, images=pil_images, force_batchify=True
).to(vl_gpt.device)

# Run the image encoder and obtain image embeddings
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

Generating a Response

Finally, run the model to generate a response:

# Run the model and generate a 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)

Example Output

The image depicts a pie chart that illustrates the distribution of four different categories among four distinct groups. The chart is divided into four segments, each representing a category with a specific percentage. The categories and their corresponding percentages are as follows:

1. **Hogs**: This segment is colored in orange and represents 30.0% of the total.
2. **Frog**: This segment is colored in blue and represents 15.0% of the total.
3. **Logs**: This segment is colored in red and represents 10.0% of the total.
4. **Dogs**: This segment is colored in green and represents 45.0% of the total.

The pie chart is visually divided into four segments, each with a different color and corresponding percentage. The segments are arranged in a clockwise manner starting from the top-left, moving clockwise. The percentages are clearly labeled next to each segment.

The chart is a simple visual representation of data, where the size of each segment corresponds to the percentage of the total category it represents. This type of chart is commonly used to compare the proportions of different categories in a dataset.

To summarize, the pie chart shows the following:
- Hogs: 30.0%
- Frog: 15.0%
- Logs: 10.0%
- Dogs: 45.0%

This chart can be used to understand the relative proportions of each category in the given dataset.

The output demonstrates an appropriate understanding of the image, including its colors and text.

Image Generation Tasks

Loading the Model

Load the necessary model for image generation tasks with the following code:

import os
import PIL.Image
import torch
import numpy as np
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor

# Specify the model path
model_path = "deepseek-ai/Janus-1.3B"
vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer

vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()

Preparing the Prompt

Next, prepare the prompt based on the user’s request:

# Set up the prompt
conversation = [
    {
        "role": "User",
        "content": "cute japanese girl, wearing a bikini, in a beach",
    },
    {"role": "Assistant", "content": ""},
]

# Convert the prompt into the appropriate format
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

Generating the Image

The following function is used to generate images. By default, 16 images are generated:

@torch.inference_mode()
def generate(
    mmgpt: MultiModalityCausalLM,
    vl_chat_processor: VLChatProcessor,
    prompt: str,
    temperature: float = 1,
    parallel_size: int = 16,
    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)

    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")
        PIL.Image.fromarray(visual_img[i]).save(save_path)

# Run the image generation
generate(vl_gpt, vl_chat_processor, prompt)

The generated images will be saved in the generated_samples folder.

Sample of Generated Results

Below is an example of a generated image:

• Dogs are relatively well depicted.
• Buildings maintain overall shape, though some details, like windows, may appear unrealistic.
• Humans, however, are challenging to generate well, with notable distortions in both photo-realistic and anime-like styles.