README.md

---
license: apache-2.0
---
# OmniFusion

**OmniFusion** is an advanced multimodal AI model designed to extend the capabilities of traditional language processing systems by integrating additional data modalities such as images, and potentially audio, 3D and video content.

### Architecture

<p align="left">
<img src="https://raw.githubusercontent.com/AIRI-Institute/OmniFusion/main/content/architecture.png" width="100%">
</p>


OmniFusion open source version core is Mistral-7B. Initially focusing on images, we selected the CLIP-ViT-L as the visual encoder for its efficient information transfer capabilities. The most important component of OmniFusion is its adapter, a mechanism allowing the language model to interpret and incorporate information from different modalities. The adapter is a single-layer, four-headed transformer, which has shown superior performance compared to simpler linear layers or MLP structures.

This adapter takes embeddings from the visual encoder (excluding the CLS token) and maps them into textual embeddings compatible with the language model.

To further enhance the model's multimodal capabilities, we employ trainable special tokens to mark the beginning and end of visual data within the text sequence.


### Training Process consists of two stages

1. Pre-training the adapter on Image Captioning tasks (LAION, CC-4M).
2. Once the adapter has learned to map ViT's visual embeddings to the language model's textual space, we proceed to unfreeze Mistral for improved understanding of dialog formats and complex queries.

<p align="left">
<img src="https://raw.githubusercontent.com/AIRI-Institute/OmniFusion/main/content/datasets.png" width="70%">
</p>

### Results

OmniFusion was benchmarked against the latest multimodal SOTA models. It excelled in generative metrics and classification benchmarks like VisualDialog.
<p align="left">
<img src="https://raw.githubusercontent.com/AIRI-Institute/OmniFusion/main/content/radar.png" width="70%">
</p>

Model Performance on Visual Dialog Benchmark

| Model        | NDCG | MRR  | Recall@1 | Recall@5 | Recall@10 |
| ------------ | ---- | ---- | -------- | -------- | --------- |
| OmniFusion   | 25.91| 10.78| 4.74     | 13.80    | 20.53     |
| LLaVA-13B    | 24.74| 8.91 | 2.98     | 10.80    | 18.02     |

### Examples

<p align="left">
<img src="https://raw.githubusercontent.com/AIRI-Institute/OmniFusion/main/content/examples.png" width="100%">
</p>

### How to Use

```python
import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from urllib.request import urlopen
import torch.nn as nn
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig

DEVICE = "cuda:0"
PROMPT = "This is a dialog with AI assistant.\n"
tokenizer = AutoTokenizer.from_pretrained("OmniMistral-tokenizer", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("OmniMistral-model", torch_dtype=torch.bfloat16, device_map=DEVICE)

projection = torch.load("projection", map_location=DEVICE)
special_embs = torch.load("special_embeddings.pt", map_location=DEVICE)





class CLIPVisionTower(nn.Module):
    def __init__(self, vision_tower, args, delay_load=False):
        super().__init__()

        self.is_loaded = False

        self.vision_tower_name = vision_tower
        self.select_layer = args.mm_vision_select_layer
        self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')

        if not delay_load:
            self.load_model()
        else:
            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)

    def load_model(self):
        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
        self.vision_tower.requires_grad_(False)

        self.is_loaded = True

    def feature_select(self, image_forward_outs):
        image_features = image_forward_outs.hidden_states[self.select_layer]
        if self.select_feature == 'patch':
            image_features = image_features[:, 1:]
        elif self.select_feature == 'cls_patch':
            image_features = image_features
        else:
            raise ValueError(f'Unexpected select feature: {self.select_feature}')
        return image_features

    @torch.no_grad()
    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
                image_feature = self.feature_select(image_forward_out).to(image.dtype)
                image_features.append(image_feature)
        else:
            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
            image_features = self.feature_select(image_forward_outs).to(images.dtype)

        return image_features

    @property
    def dummy_feature(self):
        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def config(self):
        if self.is_loaded:
            return self.vision_tower.config
        else:
            return self.cfg_only

    @property
    def hidden_size(self):
        return self.config.hidden_size


class ClipTowerCfg:
    def __init__(self):
        self.mm_vision_select_feature = 'patch'
        self.mm_vision_select_layer =  -2

clip = CLIPVisionTower("openai/clip-vit-large-patch14-336", ClipTowerCfg())
clip.load_model()
clip = clip.to(device=DEVICE, dtype=torch.bfloat16)

def gen_answer(model, tokenizer, clip, projection, query, special_embs, image=None):
    bad_words_ids = tokenizer(["\n", "</s>", ":"], add_special_tokens=False).input_ids + [[13]]
    gen_params = {
        "do_sample": False,
        "max_new_tokens": 50,
        "early_stopping": True,
        "num_beams": 3,
        "repetition_penalty": 1.0,
        "remove_invalid_values": True,
        "eos_token_id": 2,
        "pad_token_id": 2,
        "forced_eos_token_id": 2,
        "use_cache": True,
        "no_repeat_ngram_size": 4,
        "bad_words_ids": bad_words_ids,
        "num_return_sequences": 1,
    }
    with torch.no_grad():
        image_features = clip.image_processor(image, return_tensors='pt')
        image_embedding = clip(image_features['pixel_values']).to(device=DEVICE, dtype=torch.bfloat16)
        
        projected_vision_embeddings = projection(image_embedding).to(device=DEVICE, dtype=torch.bfloat16)
        prompt_ids = tokenizer.encode(f"{PROMPT}", add_special_tokens=False, return_tensors="pt").to(device=DEVICE)
        question_ids = tokenizer.encode(query, add_special_tokens=False, return_tensors="pt").to(device=DEVICE)
        
        prompt_embeddings = model.model.embed_tokens(prompt_ids).to(torch.bfloat16)
        question_embeddings = model.model.embed_tokens(question_ids).to(torch.bfloat16)

        embeddings = torch.cat(
            [
                prompt_embeddings,
                special_embs['SOI'][None, None, ...],
                projected_vision_embeddings,
                special_embs['EOI'][None, None, ...],
                special_embs['USER'][None, None, ...],
                question_embeddings,
                special_embs['BOT'][None, None, ...]
            ],
            dim=1,
        ).to(dtype=torch.bfloat16, device=DEVICE)
        out = model.generate(inputs_embeds=embeddings, **gen_params)
    out = out[:, 1:]
    generated_texts = tokenizer.batch_decode(out)[0]
    return generated_texts

img_url = "https://i.pinimg.com/originals/32/c7/81/32c78115cb47fd4825e6907a83b7afff.jpg"
question = "who is the author?"
img = Image.open(urlopen(img_url))

answer = gen_answer(
    model,
    tokenizer, 
    clip, 
    projection, 
    query=question, 
    special_embs=special_embs, 
    image=img
)

img.show()
print(question)
print(answer)
```

### Future Plans

Work is underway on a version that understands Russian, uses ImageBind encoders, and accepts more modalities (sound, 3D, video). Stay tuned for updates on GitHub!

### Authors

The FusionBrain scientific group from the AIRI Institute, in collaboration with scientists from Sber AI, led the model's development.

Main contributors:
+ Anton Razzhigaev: [Blog](https://t.me/abstractDL)
+ Elizaveta Goncharova
+ Matvey Mihkalchuk
+ Maxim Kurkin
+ Irina Abdullaeva
+ Denis Dimitrov [Blog](https://t.me/dendi_math_ai)
+ Andrey Kuznetsov [Blog](https://t.me/complete_ai)