docs: trim model card (deploy/quickstart moved to GitHub); fix figure to 30 FPS @ 720x1248; condense highlights

README.md

@@ -61,7 +61,7 @@ The framework combines:
 
 Through autoregressive diffusion modeling, aligned autoregressive distribution matching distillation, long-horizon optimization, bounded KV-state inference, and deployment-oriented scheduling, JoyAI-Video-Edit achieves stable long-range editing while substantially reducing inference latency.
 
-Our deployment system reaches **30.19 FPS end-to-end at 720 × 1280 resolution**, enabling real-time streaming video editing.
+Our deployment system reaches **30 FPS end-to-end at 720 × 1248 resolution**, enabling real-time streaming video editing.
 
 ---
 
@@ -78,9 +78,7 @@ Our deployment system reaches **30.19 FPS end-to-end at 720 × 1280 resolution**
 
 ### ⚡ Real-Time Streaming Editing
 
-JoyAI-Video-Edit edits frames continuously as they arrive instead of waiting for the entire video.
-
-It supports open-ended streams and does not require a predefined video duration.
+JoyAI-Video-Edit edits frames continuously as they arrive instead of waiting for the entire video. It supports open-ended streams and does not require a predefined video duration.
 
 ### 🎯 Instruction-Guided Video Editing
 
@@ -110,11 +108,7 @@ JoyAI-Video-Edit introduces an autoregressive diffusion framework consisting of:
 
 ### 🚀 Real-Time 720p Deployment
 
-The full deployment pipeline achieves:
-
-**30.19 FPS @ 720 × 1280**
-
-with end-to-end streaming inference.
+The full deployment pipeline achieves **30 FPS @ 720 × 1248** with end-to-end streaming inference.
 
 ---
 
@@ -134,144 +128,6 @@ Technical report:
 
 ---
 
-## 📦 Model Download
-
-The released JoyAI-Video-Edit checkpoints are hosted on Hugging Face.
-
-### Hugging Face CLI
-
-First install the Hugging Face CLI:
-
-```bash
-pip install -U huggingface_hub
-```
-
-Then download the complete model repository:
-
-```bash
-hf download jdopensource/JoyAI-Video-Edit \
-    --local-dir ./deploy/deps/checkpoints/JoyAI-Video-Edit
-```
-
-Alternatively, you can use:
-
-```bash
-huggingface-cli download jdopensource/JoyAI-Video-Edit \
-    --local-dir ./deploy/deps/checkpoints/JoyAI-Video-Edit
-```
-
-The downloaded checkpoint directory should look like:
-
-```text
-deploy/deps/checkpoints/JoyAI-Video-Edit/
-├── config.json
-├── dit/
-│   └── joyai_video_edit_dit_0804.pth
-└── vae/
-    ├── config.json
-    └── diffusion_pytorch_model.safetensors
-```
-
-> **Note**
->
-> JoyAI-Video-Edit currently uses its own deployment and inference pipeline.
-> The released checkpoint should **not** be loaded directly with
-> `diffusers.DiffusionPipeline.from_pretrained()`.
-
-MiMo-VL and the ONNX detector files are additional runtime dependencies. Please refer to the deployment documentation in the GitHub repository for details.
-
----
-
-## 🚀 Quick Start
-
-### 1. Clone the Repository
-
-```bash
-git clone https://github.com/jd-opensource/JoyAI-Video-Edit.git
-cd JoyAI-Video-Edit
-```
-
-### 2. Create the Environment
-
-```bash
-conda create -n joyai-video-edit python=3.10 -y
-conda activate joyai-video-edit
-
-python -m pip install -r requirements.txt
-```
-
-### 3. Download JoyAI-Video-Edit
-
-```bash
-hf download jdopensource/JoyAI-Video-Edit \
-    --local-dir ./deploy/deps/checkpoints/JoyAI-Video-Edit
-```
-
-### 4. Prepare Runtime Dependencies
-
-MiMo-VL and the required ONNX detector files are external runtime dependencies.
-
-Please follow:
-
-**[DEPLOYMENT.md](https://github.com/jd-opensource/JoyAI-Video-Edit/blob/main/DEPLOYMENT.md)**
-
-for detailed checkpoint preparation and deployment instructions.
-
-### 5. Launch
-
-```bash
-cd deploy
-bash run_server.sh
-```
-
-Then open:
-
-```text
-http://localhost:8080
-```
-
-For deployment on a remote server, bind the service to `0.0.0.0` and expose the selected port or use SSH port forwarding.
-
----
-
-## 🛠️ Custom Deployment
-
-For custom deployment settings, edit:
-
-```text
-deploy/run_server.sh
-```
-
-You can configure:
-
-- JoyAI-Video-Edit checkpoint paths
-- CUDA device placement
-- Server host
-- Server port
-- Runtime cache directories
-
-The default deployment script uses persistent TorchInductor, Triton, and CUDA cache directories so that compiled artifacts can be reused across launches.
-
-`TORCHINDUCTOR_AUTOGRAD_CACHE` is not required for inference-only serving.
-
----
-
-## 🚧 Roadmap
-
-- [ ] **Consumer GPU support**  
-  Optimize deployment for consumer GPUs such as NVIDIA GeForce RTX 5090.
-
-- [ ] **Stronger JoyAI-Video-Edit model**  
-  Improve overall editing quality with a particular focus on reference-image-guided video editing (RV2V).
-
-- [ ] **Training framework**  
-  Release the complete training pipeline.
-
-- [ ] **Data pipeline**  
-  Release the data generation and processing framework.
-
----
-
 ## 📚 Citation
 
 If JoyAI-Video-Edit is useful for your research or project, please cite: