Update README.md

README.md

@@ -6,10 +6,13 @@ license: apache-2.0
 
 Emu3.5 Team, BAAI
 
-[Project Page](https://emu.world/) | [🤗HF Models](https://huggingface.co/collections/BAAI/emu35) | [Paper](https://arxiv.org/pdf/2510.26583)
+[Project Page](https://emu.world/pages/web/landingPage) | [🤗HF Models](https://huggingface.co/collections/BAAI/emu35) | [Paper](https://arxiv.org/pdf/2510.26583) | [App](https://emu.world/pages/web/home?route=index)
 </div>
 
 
+> 🔔 **Latest**: Emu3.5 Web & Mobile Apps and vLLM offline inference are live — see [🔥 News](#news) for details.
+
+
 <div align='center'>
 <img src="https://github.com/baaivision/Emu3.5/blob/main/assets/arch.png?raw=True" class="interpolation-image" alt="arch." height="100%" width="100%" />
 </div>
@@ -33,13 +36,21 @@ Emu3.5 Team, BAAI
 |  🏆 | **Performance Benchmark**                | Matches **Gemini 2.5 Flash Image (Nano Banana)** on **image generation/editing**, and **outperforms** on **interleaved generation tasks**. |
 
 
+<a id="news"></a>
+
+## 🔥 News
+
+- **2025-11-28 · 🌐 Emu3.5 Web & Mobile Apps Live** — Official product experience is **now available** on the web at [zh.emu.world](https://zh.emu.world) (Mainland China) and [emu.world](https://emu.world) (global) 🎉 The new homepage highlights featured cases and a “Get Started” entry, while the workspace and mobile apps bring together creation, inspiration feed, history, profile, and language switch across web, Android APK, and H5. *([See more details](#official-web--mobile-apps) below.)*
+- **2025-11-19 · 🚀 vLLM Offline Inference Released** — Meet `inference_vllm.py` with a new cond/uncond batch scheduler, delivering **4–5× faster end-to-end generation** on vLLM 0.11.0 across Emu3.5 tasks. Jump to [#Run Inference with vLLM](#run-inference-with-vllm) for setup guidance and see PR [#47](https://github.com/baaivision/Emu3.5/pull/47) for full details.
+- **2025-11-17 · 🎛️ Gradio Demo (Transformers Backend)** — Introduced `gradio_demo_image.py` and `gradio_demo_interleave.py` presets for the standard Transformers runtime, providing turnkey T2I/X2I and interleaved generation experiences with streaming output. Try the commands in [#Gradio Demo](#3-gradio-demo) to launch both UIs locally.
 
 ## Table of Contents
 
 1. [Model & Weights](#1-model--weights)
 2. [Quick Start](#2-quick-start)
-3. [Schedule](#3-schedule)
-4. [Citation](#4-citation)
+3. [Gradio Demo](#3-gradio-demo)
+4. [Schedule](#4-schedule)
+5. [Citation](#5-citation)
 
 ## 1. Model & Weights
 
@@ -49,14 +60,28 @@ Emu3.5 Team, BAAI
 | Emu3.5-Image                | [🤗 HF link](https://huggingface.co/BAAI/Emu3.5-Image/tree/main) |
 | Emu3.5-VisionTokenizer     | [🤗 HF link](https://huggingface.co/BAAI/Emu3.5-VisionTokenizer/tree/main) |
 
+
+*Note:*  
+- **Emu3.5** supports general-purpose multimodal predictions, including interleaved image-text generation and single-image generation (T2I/X2I) tasks.
+- **Emu3.5-Image** is a model focused on T2I/X2I tasks for best performance on these scenarios.
+- Both models are pure next-token predictors without DiDA acceleration (each image may take several minutes to generate).  
+- ⚡ **Stay tuned for DiDA-accelerated weights.**
+
+> 💡 **Usage tip:**  
+> For **interleaved image-text generation**, use **Emu3.5**.  
+> For **single-image generation** (T2I and X2I), use **Emu3.5-Image** for the best quality.
+
+
+
 ## 2. Quick Start
 
 ### Environment Setup
 
 ```bash
+# Requires Python 3.12 or higher.
 git clone https://github.com/baaivision/Emu3.5
 cd Emu3.5
-pip install -r requirements.txt
+pip install -r requirements/transformers.txt
 pip install flash_attn==2.8.3 --no-build-isolation
 ```
 ### Configuration
@@ -64,8 +89,10 @@ pip install flash_attn==2.8.3 --no-build-isolation
 Edit `configs/config.py` to set:
 
 - Paths: `model_path`, `vq_path`
-- Task template: `task_type in {t2i, x2i, howto, story, explore, vla}`, `use_image` controls `<|IMAGE|>` usage (set to true when reference images are provided)
+- Task template: `task_type in {t2i, x2i, howto, story, explore, vla}`
+- Input image: `use_image` (True to provide reference images, controls <|IMAGE|> token); set `reference_image` in each prompt to specify the image path. For x2i task, we recommand using `reference_image` as a list containing single/multiple image paths to be compatible with multi-image input.
 - Sampling: `sampling_params` (classifier_free_guidance, temperature, top_k/top_p, etc.)
+- Aspect Ratio (for t2i task): `aspect_ratio` ("4:3", "21:9", "1:1", "auto" etc..)
 
 ### Run Inference
 
@@ -73,24 +100,158 @@ Edit `configs/config.py` to set:
 python inference.py --cfg configs/config.py
 ```
 
+
+#### Example Configurations by Task
+Below are example commands for different tasks.
+Make sure to set CUDA_VISIBLE_DEVICES according to your available GPUs.
+
+
+```bash
+# 🖼️ Text-to-Image (T2I) task
+CUDA_VISIBLE_DEVICES=0 python inference.py --cfg configs/example_config_t2i.py
+
+# 🔄 Any-to-Image (X2I) task
+CUDA_VISIBLE_DEVICES=0,1 python inference.py --cfg configs/example_config_x2i.py
+
+# 🎯 Visual Guidance task
+CUDA_VISIBLE_DEVICES=0,1 python inference.py --cfg configs/example_config_visual_guidance.py
+
+# 📖 Visual Narrative task
+CUDA_VISIBLE_DEVICES=0,1 python inference.py --cfg configs/example_config_visual_narrative.py
+
+
+# After running inference, the model will generate results in protobuf format (.pb files) for each input prompt.
+```
+
+
 Protobuf outputs are written to `outputs/<exp_name>/proto/`. For better throughput, we recommend ≥2 GPUs.
 
+
+### Run Inference with vLLM
+
+#### vLLM Enviroment Setup
+
+1. [Optional Recommendation] Use a new virtual environment for vLLM backend.
+```bash
+conda create -n Emu3p5 python=3.12
+```
+
+2. Install vLLM and apply the patch files.
+```bash
+# Requires Python 3.12 or higher.
+# Recommended: CUDA 12.8.
+pip install -r requirements/vllm.txt
+pip install flash_attn==2.8.3 --no-build-isolation
+
+cd Emu3.5
+python src/patch/apply.py
+```
+
+#### Example Configurations by Task
+
+```bash
+# 🖼️ Text-to-Image (T2I) task
+CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_t2i.py
+
+# 🔄 Any-to-Image (X2I) task
+CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_x2i.py
+
+# 🎯 Visual Guidance task
+CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_visual_guidance.py
+
+# 📖 Visual Narrative task
+CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_visual_narrative.py
+```
+
+
 ### Visualize Protobuf Outputs
 
-To visualize generated protobuf files:
+To visualize generated protobuf files (--video: Generate video visualizations for interleaved output):
 
 ```bash
-python src/utils/vis_proto.py --input <input_proto_file> --output <output_dir>
+python src/utils/vis_proto.py --input <input_proto_path> [--output <output_dir>] [--video]
 ```
 
-## 3. Schedule
+- `--input`: supports a single `.pb` file or a directory; directories are scanned recursively.
+- `--output`: optional; defaults to `<input_dir>/results/<file_stem>` for files, or `<parent_dir_of_input>/results` for directories.
+
+Expected output directory layout (example):
+
+```text
+results/<pb_name>/
+├── 000_question.txt
+├── 000_global_cot.txt
+├── 001_text.txt
+├── 001_00_image.png
+├── 001_00_image_cot.txt
+├── 002_text.txt
+├── 002_00_image.png
+├── ...
+└── video.mp4              # only when --video is enabled
+```
+
+Each `*_text.txt` stores decoded segments, `*_image.png` stores generated frames, and matching `*_image_cot.txt` keeps image-level chain-of-thought notes when available.
+
+## 3. Gradio Demo
+
+We provide two Gradio Demos for different application scenarios:
+
+ Emu3.5-Image Demo —— Interactive interface optimized for Text-to-Image (T2I) and Any-to-Image (X2I) tasks:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 python gradio_demo_image.py --host 0.0.0.0 --port 7860
+```
 
-- [x] Inference Code
+Emu3.5-Interleave Demo —— Launch Emu3.5 Interleave Tasks (Visual Guidance and Visual Narrate) Gradio Demo
+```bash
+CUDA_VISIBLE_DEVICES=0,1 python gradio_demo_interleave.py --host 0.0.0.0 --port 7860
+```
+
+### Features
+
+- Image Generation: Support Text-to-Image Generation and Multimodal Image Generation
+- Interleaved Generation: Support long-sequence creation with alternating image and text generation
+- Multiple Aspect Ratios for T2I: 9 preset aspect ratios (4:3, 16:9, 1:1, etc.) plus auto mode
+- Chain-of-Thought Display: Automatically parse and format model's internal thinking process
+- Real-time Streaming: Stream text and image generation with live updates
+
+### Official Web & Mobile Apps
+
+- **Web**: Production-ready Emu3.5 experience is available at [zh.emu.world](https://zh.emu.world) (Mainland China) and [emu.world](https://emu.world) (global), featuring a curated homepage, “Create” workspace, inspiration feed, history, personal profile, and language switching.
+- **Mobile (Android APK & H5)**: Mobile clients provide the same core flows — prompt-based creation, “inspiration” gallery, personal center, and feedback & privacy entrypoints — with automatic UI language selection based on system settings.
+- **Docs**: For product usage details, see the **Emu3.5 AI 使用指南 (Chinese)** and **Emu3.5 AI User Guide (English)**:  
+  - CN: [Emu3.5 AI 使用指南](https://jwolpxeehx.feishu.cn/wiki/BKuKwkzZOi4pdRkVV13csI0FnIg?from=from_copylink)  
+  - EN: [Emu3.5 AI User Guide](https://jwolpxeehx.feishu.cn/wiki/Gcxtw9XHhisUu8kBEaac6s6xnhc?from=from_copylink)
+
+#### Mobile App Download (QR Codes)
+
+<div align='center'>
+  <table>
+    <tr>
+      <td align="center">
+        <img src="https://github.com/baaivision/Emu3.5/blob/main/assets/qr_zh.png?raw=True" alt="Emu3.5 Mobile App (Mainland China)" width="220" />
+        <br />
+        <sub><b>Emu3.5 Mobile · Mainland China</b></sub>
+      </td>
+      <td align="center">
+        <img src="https://github.com/baaivision/Emu3.5/blob/main/assets/qr.png?raw=True" alt="Emu3.5 Mobile App (Global)" width="220" />
+        <br />
+        <sub><b>Emu3.5 Mobile · Global</b></sub>
+      </td>
+    </tr>
+  </table>
+</div>
+
+
+
+## 4. Schedule
+
+- [x] Inference Code (NTP Version)
 - [ ] Advanced Image Decoder
-- [ ] Discrete Diffusion Adaptation(DiDA)
+- [ ] Discrete Diffusion Adaptation (DiDA) Inference & Weights
 
 
-## 4. Citation
+## 5. Citation
 
 ```bibtex
 @misc{cui2025emu35nativemultimodalmodels,
@@ -102,5 +263,4 @@ python src/utils/vis_proto.py --input <input_proto_file> --output <output_dir>
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2510.26583}, 
 }
-```
-
+```