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A family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic images from text descriptions. 👋 join our WeChat
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[2024-08-11] 🎉🎉🎉 Training codes and documents are released! 🎉🎉🎉
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[2024-07-08] 🎉🎉🎉 Lumina-mGPT is released! 🎉🎉🎉
See INSTALL.md for detailed instructions.
Note that the Lumina-mGPT implementation heavily relies on the xllmx module, which is evolved from LLaMA2-Accessory for supporting LLM-centered multimodal tasks. Make sure it is installed correctly as a python package before going on.
Note
Before using the Lumina-mGPT model, run
# bash
cd lumina_mgpt
to enter the directory of the Lumina-mGPT implementation.
Since currently the Chameleon implementation in transformers does not contain the VQ-VAE decoder, please manually download the original VQ-VAE weights provided by Meta and put them to the following directory:
Lumina-mGPT
- lumina_mgpt/
- ckpts/
- chameleon/
- tokenizer/
- text_tokenizer.json
- vqgan.yaml
- vqgan.ckpt
- xllmx/
- ...
We have prepared three different Gradio demos, each showcasing unique functionalities, to help you quickly become familiar with the capabilities of the Lumina-mGPT models.
This demo is customized for Image Generation tasks, where you can input a text description and generate a corresponding image. To host this demo, run:
# Note to set the `--target_size` argument consistent with the checkpoint
python -u demos/demo_image_generation.py \
--pretrained_path Alpha-VLLM/Lumina-mGPT-7B-768 \
--target_size 768
This demo is designed for models trained with Omni-SFT. you can conveniently switch between the multiple downstream tasks using this demo.
# Note to set the `--target_size` argument consistent with the checkpoint
python -u demos/demo_image2image.py \
--pretrained_path Alpha-VLLM/Lumina-mGPT-7B-768-Omni \
--target_size 768
This is a powerful demo with minimal constraint on the input format. It supports flexible interation and is suitable for in-deep exploration.
# Note to set the `--target_size` argument consistent with the checkpoint
python -u demos/demo_freeform.py \
--pretrained_path Alpha-VLLM/Lumina-mGPT-7B-768-Omni \
--target_size 768
The simplest code for Lumina-mGPT inference:
from inference_solver import FlexARInferenceSolver
from PIL import Image
# ******************** Image Generation ********************
inference_solver = FlexARInferenceSolver(
model_path="Alpha-VLLM/Lumina-mGPT-7B-768",
precision="bf16",
target_size=768,
)
q1 = f"Generate an image of 768x768 according to the following prompt:\n"
f"Image of a dog playing water, and a waterfall is in the background."
# generated: tuple of (generated response, list of generated images)
generated = inference_solver.generate(
images=[],
qas=[[q1, None]],
max_gen_len=8192,
temperature=1.0,
logits_processor=inference_solver.create_logits_processor(cfg=4.0, image_top_k=2000),
)
a1, new_image = generated[0], generated[1][0]
# ******************* Image Understanding ******************
inference_solver = FlexARInferenceSolver(
model_path="Alpha-VLLM/Lumina-mGPT-7B-512",
precision="bf16",
target_size=512,
)
# "<|image|>" symbol will be replaced with sequence of image tokens before fed to LLM
q1 = "Describe the image in detail. <|image|>"
images = [Image.open("image.png")]
qas = [[q1, None]]
# `len(images)` should be equal to the number of appearance of "<|image|>" in qas
generated = inference_solver.generate(
images=images,
qas=qas,
max_gen_len=8192,
temperature=1.0,
logits_processor=inference_solver.create_logits_processor(cfg=4.0, image_top_k=2000),
)
a1 = generated[0]
# generated[1], namely the list of newly generated images, should typically be empty in this case.
# ********************* Omni-Potent *********************
inference_solver = FlexARInferenceSolver(
model_path="Alpha-VLLM/Lumina-mGPT-7B-768-Omni",
precision="bf16",
target_size=768,
)
# Example: Depth Estimation
# For more instructions, see demos/demo_image2image.py
q1 = "Depth estimation. <|image|>"
images = [Image.open("image.png")]
qas = [[q1, None]]
generated = inference_solver.generate(
images=images,
qas=qas,
max_gen_len=8192,
temperature=1.0,
logits_processor=inference_solver.create_logits_processor(cfg=1.0, image_top_k=200),
)
a1 = generated[0]
new_image = generated[1][0]
Configurations
7B models
Model | Size | Huggingface |
---|---|---|
FP-SFT@512 | 7B | Alpha-VLLM/Lumina-mGPT-7B-512 |
FP-SFT@768 | 7B | Alpha-VLLM/Lumina-mGPT-7B-768 |
Omni-SFT@768 | 7B | Alpha-VLLM/Lumina-mGPT-7B-768-Omni |
FP-SFT@1024 | 7B | Alpha-VLLM/Lumina-mGPT-7B-1024 |
34B models
Model | Size | Huggingface |
---|---|---|
FP-SFT@512 | 34B | Alpha-VLLM/Lumina-mGPT-34B-512 |
More checkpoints coming soon.
- Inference code
- Training code
We are hiring interns, postdocs, and full-time researchers at the General Vision Group, Shanghai AI Lab, with a focus on multi-modality and vision foundation models. If you are interested, please contact gaopengcuhk@gmail.com.
@misc{liu2024lumina-mgpt,
title={Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining},
author={Dongyang Liu and Shitian Zhao and Le Zhuo and Weifeng Lin and Yu Qiao and Hongsheng Li and Peng Gao},
year={2024},
eprint={2408.02657},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.02657},
}