<div align="center"> <img src="https://s.krea.ai/krea_realtime_14b_cover.webp" alt="Krea Realtime 14B" width="800"/>

Krea Realtime 14B

Real-time video generation with 14B parameter diffusion model

Distilled from Wan 2.1 14B using Self-Forcing technique

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Overview

This repository contains inference code for Krea-Realtime-14B, a real-time video diffusion model distilled from Wan 2.1 14B using the Self-Forcing distillation technique.

Self-Forcing converts traditional video diffusion models into autoregressive models, enabling real-time video generation. Scaling this technique to 14B parameters—over 10× larger than the original work—required significant memory optimizations and engineering breakthroughs.

This implementation is based on the Self-Forcing repository, starting from the LightX2V timestep distilled checkpoint.

📖 Technical Details: For a deep dive into the Self-Forcing technique and scaling challenges, see the official blog post.

Performance

• 11 fps text-to-video generation on NVIDIA B200 with 4 inference steps
• Optimized KV cache management (up to 25GB per GPU)
• Supports streaming and batch inference modes

Key Features

• Real-time video generation with 14B parameters
• WebSocket-based streaming server for live generation
• Offline batch sampling for high-quality outputs
• Multiple attention backends (Flash Attention 4, SageAttention)
• Video-to-video transformation capabilities
• Long-form video generation support

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System Requirements

• GPU: NVIDIA GPU with 40GB+ VRAM recommended
  • NVIDIA B200: 11 fps with 4 inference steps
  • H100, RTX 5xxx series also supported
• OS: Linux (Ubuntu recommended)
• Python: 3.11+
• Storage: ~30GB for model checkpoints

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Setup

1. Create Virtual Environment

uv sync

2. Install Attention Backend

For NVIDIA B200 GPUs (recommended):

uv pip install flash_attn --no-build-isolation

For H100/RTX 5xxx and other GPUs:

uv pip install libs/sageattention-2.2.1-cp311-cp311-linux_x86_64.whl
# Or alternatively:
bash install_sage.sh

Note: SageAttention 2++ and 3 have not been tested and may cause quality degradation.

3. Install FFmpeg

sudo apt update && sudo apt install ffmpeg

4. Download Model Checkpoints

# Base model
huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B \
  --local-dir-use-symlinks False \
  --local-dir wan_models/Wan2.1-T2V-1.3B

# Krea Realtime model
huggingface-cli download krea/krea-realtime-video \
  krea-realtime-video-14b.safetensors \
  --local-dir checkpoints

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Usage

Option 1: Real-time Server (release_server.py)

Launch a WebSocket server for real-time video generation with streaming output.

1. Configure Environment

export MODEL_FOLDER=wan_models
export CONFIG=configs/self_forcing_server_14b.yaml  # optional
export CUDA_VISIBLE_DEVICES=0
export DO_COMPILE=true  # Use torch.compile for better performance

2. Start Server

uvicorn release_server:app --host 0.0.0.0 --port 8000

3. Access Demo

• Health check: curl http://localhost:8000/health
• Web UI: Open http://localhost:8000/ in your browser
• The demo interface (templates/release_demo.html) allows you to:
  • Enter prompts
  • Adjust generation parameters
  • Stream frames in real-time over WebSocket

4. Configuration Options

• DO_COMPILE=false - Disable torch.compile for faster startup but slower inference
• CONFIG - Path to custom config file

Stop the server with Ctrl+C.

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Option 2: Offline Sampling (sample.py)

Generate videos offline without the WebSocket layer.

Basic Example

Create a script to generate videos:

# sample_run.py
from pathlib import Path
from release_server import GenerateParams
from sample import sample_videos

# Configure generation parameters
params = GenerateParams(
    prompt="",  # Will be overwritten per prompt
    width=832,
    height=480,
    num_blocks=9,
    seed=42,
    kv_cache_num_frames=3,
)

# Define prompts
prompts = [
    "A hyperrealistic close-up of ocean waves shimmering at sunset.",
    "A bustling neon-drenched alleyway with rain-soaked pavement.",
]

# Generate videos
sample_videos(
    prompts_list=prompts,
    config_path="configs/self_forcing_dmd_will_optims.yaml",
    output_dir="outputs/samples",
    params=params,
    save_videos=True,  # Requires ffmpeg
    fps=24,
)

Run

python sample_run.py

Key Details

• Model loading: Models are loaded lazily when models=None. Reuse the returned models object for multiple calls to avoid reloading.
• Output structure: Frames are saved to output_dir/prompt_XXX/. Videos (if save_videos=True) are saved as MP4 files.
• Additional helpers: Check sample.py for create_grid() and sample_single_video() utilities.

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Repository Structure

├── release_server.py          # WebSocket server for real-time generation
├── sample.py                  # Offline batch sampling
├── v2v.py                     # Video-to-video utilities
├── configs/                   # Configuration files
├── demo_utils/                # VAE and utility functions
├── model/                     # Model implementations
├── pipeline/                  # Inference pipelines
├── utils/                     # Helper utilities
├── wan/                       # Wan model components
└── templates/                 # Web UI templates

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Credits

This work is based on:

• Self-Forcing - Original distillation technique
• Wan 2.1 14B - Base text-to-video model
• LightX2V - Timestep distilled checkpoint

For technical details on the Self-Forcing scaling and optimization process, see our blog post.

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License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License - see the LICENSE.md file for details.

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Citation

If you use this work, please cite:

@software{krea_realtime_14b,
  title={Krea Realtime 14B: Real-time Video Generation},
  author={Erwann Millon},
  year={2025},
  url={https://github.com/krea-ai/realtime-video}
}