Abstract

Long video generation still suffers from error accumulation, weak temporal coherence, and prohibitive latency, limiting its applicability to interactive scenarios. We present JoyAI-Echo, a framework that breaks these barriers through four key advances. Central to its performance, a cross-modal audio-visual memory bank preserves character appearance and voice timbre consistently over five-minute videos, while a post-training pipeline combines memory-based reinforcement learning with distribution matching distillation for a 7.5× speedup to substantially boost visual quality and alignment. Empowered by these two components, JoyAI-Echo decisively outperforms HappyOyster (directing mode) on long-form generation and even surpasses the short-video specialist Wan 2.6 on human-centric tasks. Beyond raw generation quality, an interactive agent enables real-time user editing through conversational instructions, and a lightweight super-resolution module maintains high definition under streaming latency, further elevating the overall experience and delivering instantly editable, conversation-speed video creation. For the first time, JoyAI-Echo simultaneously achieves long-range cross-modal consistency, real-time inference for minute-long video, conversational interactivity, and high-resolution output — without compromise, inaugurating a new era of interactive video generation. Codes and weights will be open-sourced.

Highlights

• 🎞️ Minute-level multi-shot stories: generate a sequence of coherent shots from one prompt JSON.
• ⚡ DMD-distilled few-step inference: ~7.5x faster than the original pipeline.
• 🔊 Joint audio-video generation: one pipeline produces synchronized video and audio.
• 🧠 Paired cross-modal memory bank: conditions each new shot on prior visual identity and voice context for story-level consistency.

Current Release Scope

JoyAI-Echo currently focuses on text-to-video (T2V) and multi-shot long-video generation with paired audio-video memory. The memory used in our official pipeline is built from generated T2V shots.

Please note that image-to-video (I2V) is not supported in the current release.

We are actively working on I2V support and plan to release it in a future version.

Demo Gallery

Explore long-form and short-form JoyAI-Echo cases on the Project Page. 🍿

Results

Reported Scale

Human Evaluation

GSB user study on long- and short-video generation. The numbers denote the percentage of user preferences.

Repository Layout

.
+-- configs/
|   `-- inference.yaml                # all inference parameters (YAML)
+-- checkpoints/                      # model weights (download separately)
|   +-- echo-longvideo-release.safetensors
|   `-- gemma-3-12b/
+-- prompts/                          # multi-shot prompt JSON files
|   +-- example_single_shot.json
|   `-- example_multi_shot.json
+-- ltx-core/src/ltx_core/            # transformer, VAE, text-encoder building blocks
+-- ltx-pipelines/src/ltx_pipelines/  # sampler and pipeline utilities
+-- ltx-distillation/
|   +-- src/ltx_distillation/         # DMD wrappers, AV pipelines, memory bank, utils
|   `-- scripts/multishot_inference_dmd.py
+-- inference.py                      # main entrypoint (load once, infer all)
+-- requirements.txt
`-- environment.yml

Quickstart

1. Clone

git clone https://github.com/jd-opensource/JoyAI-Echo.git
cd JoyAI-Echo

2. Create the environment

The reference environment is Python 3.11 + PyTorch 2.8 + CUDA 12.8.

With conda:

conda env create -f environment.yml
conda activate echo-long

With uv:

uv venv --python 3.11 .venv
source .venv/bin/activate
uv pip install --extra-index-url https://download.pytorch.org/whl/cu128 -r requirements.txt

ffmpeg must be available on PATH for shot concatenation. The conda recipe includes it. If you use uv, install it with your system package manager:

sudo apt install ffmpeg
brew install ffmpeg

3. Download checkpoint

Download the JoyAI-Echo release checkpoint and Gemma text encoder:

Place them under checkpoints/:

checkpoints/
+-- echo-longvideo-release.safetensors
`-- gemma-3-12b/

4. Write a story prompt

Enhance your prompt first. We provide prompt enhancers — system prompts that expand a short story or idea into well-formed shot prompts: prompts/long_story_writer_system_prompt.md for long, multi-shot video, and prompts/short_story_writer_system_prompt.md for single-shot short video. We strongly recommend running your input through the matching enhancer before inference; un-enhanced prompts tend to produce noticeably weaker results.

Create a JSON file under prompts/. Each file is a single object with a prompts list, where every string is one complete shot. A single string produces one shot; multiple strings produce a multi-shot story, with each new shot conditioned on the previous ones through the paired audio-video memory bank.

Inside each string, write these parts in order:

A more convenient prompt-writing workflow will be released as a director agent for everyone to use.

5. Run inference

python inference.py

This loads the model once and processes all prompt files under prompts/.

💡 Note: The inference pipeline is optimized to run on lower-VRAM GPUs. Peak GPU usage is around 46–50 GB, at the cost of slightly longer per-shot inference time.

Outputs are written to:

inference_result/outputs/<prompt-name>/inference_<timestamp>/

Configuration

All inference parameters are managed in configs/inference.yaml. The file is organized into sections:

Override via CLI

Any YAML parameter can be overridden from the command line:

python inference.py --seed 42 --num-frames 121 --video-height 480 --video-width 832

Use a custom config file:

python inference.py --config configs/my_experiment.yaml

The Python entrypoint exposes the full configuration surface:

python inference.py --help

Hardware

Peak GPU usage is around 46–50 GB for the default 25 fps x 241 frames x 1280 x 736 setting, so a single H100/A100-class (80 GB) or 48 GB GPU is sufficient.

For smaller GPUs, reduce resolution/frames:

python inference.py --num-frames 121 --video-height 480 --video-width 832

TODO List

• Release inference code
• Release model checkpoints
• Add prompt examples
• Release Echo-SR (Super-resolution)
• Release Director Agent

Links

• Project page: https://echo-team-joy-future-academy-jd.github.io/Echo-LongVideo-Page/
• Repository: https://github.com/jd-opensource/JoyAI-Echo
• huggingface: https://huggingface.co/jdopensource/JoyAI-Echo

Citation

If JoyAI-Echo helps your research or products, please cite:

@techreport{echo2026longvideo,
  title        = {JoyAI-Echo: Pushing the Frontier of Long Video Generation},
  author       = {{Echo Team @ Joy Future Academy, JD}},
  institution  = {Joy Future Academy, JD},
  year         = {2026},
  month        = {May}
}