Hugging Face's logo

stabilityai
/
japanese-stable-vlm

Image-to-Text
Transformers
Safetensors
Japanese
llava
image-text-to-text
vision-language
image-captioning
japanese-stable-vlm
custom_code
Inference Endpoints
Model card Files Community
3
Edit model card

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

By clicking "Agree", you agree to the License Agreement and acknowledge Stability AI's Privacy Policy.

Log in or Sign Up to review the conditions and access this model content.

Japanese Stable VLM

Please note: for commercial usage of this model, please see https://stability.ai/license

商用利用に関する日本語での問い合わせは　[email protected] までお願い致します。

Model Details

Japanese Stable VLM is a vision-language instruction-following model that enables to generate Japanese descriptions for input images and optionally input texts such as questions.

Usage 

import torch
from transformers import AutoTokenizer, AutoModelForVision2Seq, AutoImageProcessor
from PIL import Image
import requests

# helper function to format input prompts
TASK2INSTRUCTION = {
    "caption": "画像を詳細に述べてください。",
    "tag": "与えられた単語を使って、画像を詳細に述べてください。",
    "vqa": "与えられた画像を下に、質問に答えてください。",
}


def build_prompt(task="caption", input=None, sep="\n\n### "):
    assert (
        task in TASK2INSTRUCTION
    ), f"Please choose from {list(TASK2INSTRUCTION.keys())}"
    if task in ["tag", "vqa"]:
        assert input is not None, "Please fill in `input`!"
        if task == "tag" and isinstance(input, list):
            input = "、".join(input)
    else:
        assert input is None, f"`{task}` mode doesn't support to input questions"
    sys_msg = "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。"
    p = sys_msg
    roles = ["指示", "応答"]
    instruction = TASK2INSTRUCTION[task]
    msgs = [": \n" + instruction, ": \n"]
    if input:
        roles.insert(1, "入力")
        msgs.insert(1, ": \n" + input)
    for role, msg in zip(roles, msgs):
        p += sep + role + msg
    return p

# load model
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForVision2Seq.from_pretrained("stabilityai/japanese-stable-vlm", trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained("stabilityai/japanese-stable-vlm")
tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stable-vlm")
model.to(device)

# prepare inputs
url = "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
prompt = build_prompt(task="caption")
# prompt = build_prompt(task="tag", input=["河津桜", "青空"])
# prompt = build_prompt(task="vqa", input="季節はいつですか？")

inputs = processor(images=image, return_tensors="pt")
text_encoding = tokenizer(prompt, add_special_tokens=False, return_tensors="pt")
inputs.update(text_encoding)

# generate
outputs = model.generate(
    **inputs.to(device, dtype=model.dtype),
    do_sample=False,
    num_beams=5,
    max_new_tokens=128,
    min_length=1,
    repetition_penalty=1.5,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)
# 桜越しの東京スカイツリー

Model Details

Training

This model is a vision-language instruction-following model with the LLaVA 1.5 architecture. It uses stabilityai/japanese-stablelm-instruct-gamma-7b as a language model and openai/clip-vit-large-patch14 as an image encoder. During training, the MLP projection was trained from scratch at the first stage and the language model and the MLP projection were further trained at the second stage.

Training Dataset

The training dataset includes the following public datasets:

Use and Limitations

Intended Use

This model is intended to be used by the open-source community in vision-language applications.

Limitations and bias

The training dataset may have contained offensive or inappropriate content even though we applied data filters. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.

How to cite 

@misc{JapaneseStableVLM, 
    url    = {[https://huggingface.co/stabilityai/japanese-stable-vlm](https://huggingface.co/stabilityai/japanese-stable-vlm)}, 
    title  = {Japanese Stable VLM}, 
    author = {Shing, Makoto and Akiba, Takuya}
}

Contact

Downloads last month
169
Safetensors
Model size
7.57B params
Tensor type
F32
·
Inference Examples
Image-to-Text
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Spaces using stabilityai/japanese-stable-vlm 2

Collection including stabilityai/japanese-stable-vlm