Add pipeline tag, license, and link to Github repository

This PR adds the `pipeline_tag` as `text-to-image` to the model card, adds license information, and links to the Github repository.

README.md

@@ -1,10 +1,10 @@
 ---
-pipeline_tag: any-to-any
+pipeline_tag: text-to-image
+license: apache-2.0
 tags:
 - Any2Any
 ---
 
-
 **Lumina-mGPT** is a family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic images from text descriptions.
 
 [![Lumina-mGPT](https://img.shields.io/badge/Paper-Lumina--mGPT-2b9348.svg?logo=arXiv)](https://arxiv.org/abs/2408.02657)
@@ -12,4 +12,236 @@ tags:
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6358a167f56b03ec9147074d/hgaCZdtmdlCDcZ8tb4Rme.png)
 
 # Usage
-We provide the implementation of Lumina-mGPT, as well as sampling code, in our [github repository](https://github.com/Alpha-VLLM/Lumina-mGPT).
+We provide the implementation of Lumina-mGPT, as well as sampling code, in our [github repository](https://github.com/Alpha-VLLM/Lumina-mGPT).
+
+<div align="center">
+
+<img src="assets/logo.png" width="30%"/>
+
+# Lumina-mGPT
+
+<b> 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 <a href="http://imagebind-llm.opengvlab.com/qrcode/" target="_blank">WeChat</a> </b>
+
+[![Lumina-mGPT](https://img.shields.io/badge/Paper-Lumina--mGPT-2b9348.svg?logo=arXiv)](https://arxiv.org/abs/2408.02657)&#160;
+
+[![Static Badge](https://img.shields.io/badge/Official(node1)-6B88E3?logo=youtubegaming&label=Demo%20Lumina-mGPT)](http://106.14.2.150:10020/)&#160;
+[![Static Badge](https://img.shields.io/badge/Official(node2)-6B88E3?logo=youtubegaming&label=Demo%20Lumina-mGPT)](http://106.14.2.150:10021/)&#160;
+
+</div>
+
+<img src="assets/demos.png">
+
+## 📰 News
+
+- **[2024-08-11] 🎉🎉🎉 [Training codes and documents](./lumina_mgpt/TRAIN.md) are released! 🎉🎉🎉**
+
+- **[2024-07-08] 🎉🎉🎉 Lumina-mGPT is released! 🎉🎉🎉**
+
+## ⚙️ Installation
+
+See [INSTALL.md](./INSTALL.md) for detailed instructions.
+
+Note that the Lumina-mGPT implementation heavily relies on
+the [xllmx](./xllmx) module, which is evolved from [LLaMA2-Accessory](https://github.com/Alpha-VLLM/LLaMA2-Accessory) for supporting
+LLM-centered multimodal tasks. Make sure it is installed correctly as a python package before going on.
+
+## ⛽ Training
+See [lumina_mgpt/TRAIN.md](lumina_mgpt/TRAIN.md)
+
+## 📽️ Inference
+
+> [!Note]
+>
+> Before using the Lumina-mGPT model, run
+>
+> ```bash
+> # bash
+> cd lumina_mgpt
+> ```
+>
+> to enter the directory of the Lumina-mGPT implementation.
+
+### Perpetration
+
+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](https://github.com/facebookresearch/chameleon) and
+put them to the following directory:
+
+```
+Lumina-mGPT
+- lumina_mgpt/
+    - ckpts/
+        - chameleon/
+            - tokenizer/
+                - text_tokenizer.json
+                - vqgan.yaml
+                - vqgan.ckpt
+- xllmx/
+- ...
+```
+
+### Local Gradio Demos
+
+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.
+
+#### 1. [demos/demo_image_generation.py](./Lumina-mGPT/demos/demo_image_generation.py)
+
+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:
+
+```bash
+# 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
+```
+
+#### 2. [demos/demo_image2image.py](./Lumina-mGPT/demos/demo_image2image.py)
+
+This demo is designed for models trained with Omni-SFT. you can conveniently switch between the multiple downstream tasks using this demo.
+
+```bash
+# 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
+```
+
+#### 3. [demos/demo_freeform.py](./Lumina-mGPT/demos/demo_freeform.py)
+
+This is a powerful demo with minimal constraint on the input format. It supports flexible interation and is suitable for in-deep exploration.
+
+```bash
+# 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
+```
+
+### Simple Inference
+
+The simplest code for Lumina-mGPT inference:
+
+```python
+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:
+"
+     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]
+
+```
+
+## 🤗 Checkpoints
+
+**Configurations**
+
+<img src="assets/config2.jpg">
+<img src="assets/config1.jpg">
+
+**7B models**
+
+| Model        | Size | Huggingface                                                                              |
+| ------------ | ---- | ---------------------------------------------------------------------------------------- |
+| FP-SFT@512   | 7B   | [Alpha-VLLM/Lumina-mGPT-7B-512](https://huggingface.co/Alpha-VLLM/Lumina-mGPT-7B-512)       |
+| FP-SFT@768   | 7B   | [Alpha-VLLM/Lumina-mGPT-7B-768](https://huggingface.co/Alpha-VLLM/Lumina-mGPT-7B-768)       |
+| Omni-SFT@768 | 7B   | [Alpha-VLLM/Lumina-mGPT-7B-768-Omni](https://huggingface.co/Alpha-VLLM/Lumina-mGPT-7B-768-Omni) |
+| FP-SFT@1024  | 7B   | [Alpha-VLLM/Lumina-mGPT-7B-1024](https://huggingface.co/Alpha-VLLM/Lumina-mGPT-7B-1024)     |
+
+**34B models**
+
+| Model      | Size | Huggingface                                                                          |
+| ---------- | ---- | ------------------------------------------------------------------------------------ |
+| FP-SFT@512 | 34B  | [Alpha-VLLM/Lumina-mGPT-34B-512](https://huggingface.co/Alpha-VLLM/Lumina-mGPT-34B-512) |
+
+More checkpoints coming soon.
+
+## 📑 Open-source Plan
+
+- [X] Inference code
+- [X] Training code
+
+## 🔥 Open positions
+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.
+
+## 📄 Citation
+
+```
+@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},
+}
+```