Descriptions:
ByteDance’s Lens model is a 3-billion parameter unified multimodal system capable of image generation, image editing, video generation, video editing, and video understanding — all within a single checkpoint trained from scratch. In this walkthrough, Fahd Mirza demonstrates how to install and run the model locally on an Ubuntu system with an NVIDIA H100 GPU, covering Conda environment setup, dependency installation via the provided setup file, and Hugging Face authentication for model download.
VRAM usage during inference sits around 30GB — roughly on par with Flux and similar models — which Mirza notes makes it feasible on high-end workstation GPUs even if not the full 80GB H100. He runs the provided inference scripts for text-to-image across 11 prompts, sharing live commentary on output quality: watercolor and stylized renders come out well, while photorealistic subjects like human hair in sunlight fall short of what Flux or CogImage deliver.
According to ByteDance’s published benchmarks, Lens outperforms Janus Pro, OmniGen 2, and Intern VL on image generation, and beats Hunyuan Video and Wan 2.1 on video generation. It trails CogImage, Tuna, and Tuna 2 on image quality, and sits behind GPT Image 1 and CogImage Edit on image editing tasks. The video also covers switching between task modes (text-to-image, text-to-video, image edit, video edit) by changing a single parameter in the launch script.
📺 Source: Fahd Mirza · Published May 21, 2026
🏷️ Format: Tutorial Demo
