---
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
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.
### Results
OmniFusion was benchmarked against the latest multimodal SOTA models. It excelled in generative metrics and classification benchmarks like VisualDialog.
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
### 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
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
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", "", ":"], 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)