Multimodal

Description

Adds support for multimodality (text+images) to text-generation-webui.

Note: multimodal currently only works for transformers, AutoGPTQ, and GPTQ-for-LLaMa loaders. ExLlama (v1 and v2) and llama.cpp support are planned.

https://user-images.githubusercontent.com/3718215/233817203-69b57e77-0c55-4fd6-b742-3204bb13b8fc.mp4

Usage

To run this extension, download a LLM that supports multimodality, and then start server.py with the appropriate --multimodal-pipeline argument. Examples:

# LLaVA 1.5 13B has the best performance
python server.py --model liuhaotian_llava-v1.5-13b --multimodal-pipeline llava-v1.5-13b --load-in-4bit
# LLaVA 1.5 7B is relatively weaker, but requires less memory
python server.py --model liuhaotian_llava-v1.5-7b --multimodal-pipeline llava-v1.5-7b --load-in-4bit
python server.py --model TheBloke_llava-v1.5-13B-GPTQ_gptq-4bit-32g-actorder_True --multimodal-pipeline llava-v1.5-13b --disable_exllama --loader autogptq
python server.py --model wojtab_llava-7b-v0-4bit-128g --multimodal-pipeline llava-7b
python server.py --model wojtab_llava-13b-v0-4bit-128g --multimodal-pipeline llava-13b
python server.py --model anon8231489123_vicuna-13b-GPTQ-4bit-128g --multimodal-pipeline minigpt4-13b
python server.py --model llama-7b-4bit --multimodal-pipeline minigpt4-7b

There is built-in support for LLaVA-v0-13B, LLaVA-v0-7b, and LLaVA-v1.5-13B. To install minigpt4:

clone https://github.com/Wojtab/minigpt-4-pipeline into extensions/multimodal/pipelines
install the requirements.txt

The same procedure should be used to install other pipelines, which can then be used with --multimodal-pipeline [pipeline name]. For additional multimodal pipelines refer to the compatibility section below.

Do note, that each image takes up a considerable amount of tokens, so adjust max_new_tokens to be at most 1700 (recommended value is between 200 to 500), so the images don't get truncated.

To send an image, just upload it to the extension field below chat, and send a prompt as always. The image will be added to the end of your message. If you wish to modify the placement, include a string <image> in your prompt.

Additionally, there is Embed all images, not only the last one checkbox. It modifies the image embeddings, by default (if it's unchecked), all but the most recent images have their embeddings empty, so they are not fed to the network. It seems as if some multimodal networks consider the features in all images at the same time as if they were a single image. Due to this behavior, by default, the extension skips previous images. However, it can lead to sub-par generation on other pipelines. If you want to include all images, just tick this checkbox.

Compatibility

As of now, the following multimodal pipelines are supported:

Pipeline	`--multimodal-pipeline`	Default LLM	LLM info(for the linked model)	Pipeline repository
LLaVA 13B	`llava-13b`	LLaVA 13B	GPTQ 4-bit quant, old CUDA	built-in
LLaVA 7B	`llava-7b`	LLaVA 7B	GPTQ 4-bit quant, old CUDA	built-in
MiniGPT-4 7B	`minigpt4-7b`	Vicuna v0 7B	GPTQ 4-bit quant, new format	Wojtab/minigpt-4-pipeline
MiniGPT-4 13B	`minigpt4-13b`	Vicuna v0 13B	GPTQ 4-bit quant, old CUDA	Wojtab/minigpt-4-pipeline
InstructBLIP 7B	`instructblip-7b`	Vicuna v1.1 7B	GPTQ 4-bit quant	kjerk/instructblip-pipeline
InstructBLIP 13B	`instructblip-13b`	Vicuna v1.1 13B	GPTQ 4-bit quant	kjerk/instructblip-pipeline

Some pipelines could support different LLMs but do note that while it might work, it isn't a supported configuration.

DO NOT report bugs if you are using a different LLM.

DO NOT report bugs with pipelines in this repository (unless they are built-in)

Extension config

This extension uses the following parameters (from settings.json):

Parameter	Description
`multimodal-vision_bits`	Number of bits to load vision models (CLIP/ViT) feature extractor in (most pipelines should support either 32 or 16, default=32)
`multimodal-vision_device`	Torch device to run the feature extractor on, for example, `cpu` or `cuda:0`, by default `cuda:0` if available
`multimodal-projector_bits`	Number of bits to load feature projector model(s) in (most pipelines should support either 32 or 16, default=32)
`multimodal-projector_device`	Torch device to run the feature projector model(s) on, for example `cpu` or `cuda:0`, by default `cuda:0` if available
`multimodal-add_all_images_to_prompt`	Default value of "Embed all images, not only the last one" checkbox

Usage through API

Chat completions endpoint

With an image URL

curl http://127.0.0.1:5000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "image_url": "https://avatars.githubusercontent.com/u/112222186?v=4"
      },
      {
        "role": "user",
        "content": "What is unusual about this image?"
      }
    ]
  }'

With a Base64 image

import base64
import json
import requests

img = open('image.jpg', 'rb')
img_bytes = img.read()
img_base64 = base64.b64encode(img_bytes).decode('utf-8')
data = { "messages": [
        {
            "role": "user",
            "image_url": f"data:image/jpeg;base64,{img_base64}"
        },
        {
            "role": "user",
            "content": "what is unusual about this image?"
        }
    ]
}
response = requests.post('http://127.0.0.1:5000/v1/chat/completions', json=data)
print(response.text)

You can run the multimodal inference through API, by inputting the images to prompt. Images are embedded like so: f'<img src="data:image/jpeg;base64,{img_str}">', where img_str is base-64 jpeg data. Note that you will need to launch server.py with the arguments --api --extensions multimodal.

Completions endpoint

Python example:

import base64
import requests

CONTEXT = "You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. Follow the instructions carefully and explain your answers in detail.### Human: Hi!### Assistant: Hi there! How can I help you today?\n"

with open('extreme_ironing.jpg', 'rb') as f:
    img_str = base64.b64encode(f.read()).decode('utf-8')
    prompt = CONTEXT + f'### Human: What is unusual about this image: \n<img src="data:image/jpeg;base64,{img_str}">### Assistant: '
    print(requests.post('http://127.0.0.1:5000/v1/completions', json={'prompt': prompt, 'max_tokens': 200, 'stop': ['\n###']}).json())

script output:

{'results': [{'text': "The unusual aspect of this image is that a man is standing on top of a yellow minivan while doing his laundry. He has set up a makeshift clothes line using the car's rooftop as an outdoor drying area. This scene is uncommon because people typically do their laundry indoors, in a dedicated space like a laundromat or a room in their home, rather than on top of a moving vehicle. Additionally, hanging clothes on the car could be potentially hazardous or illegal in some jurisdictions due to the risk of damaging the vehicle or causing accidents on the road.\n##"}]}

For pipeline developers/technical description

see DOCS.md

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