LLaDA 2.0-Uni In ComfyUI - The AI Model That Understands AND Generates Images

Original author：Benji’s AI Playground

Original source：https://www.youtube.com/embed/00F9Cr8ZTRQ

What this video is about: This video provides a hands-on walkthrough of LLaDA 2.0-Uni, a unified diffusion large language model from Inclusion AI that combines vision understanding, image generation, image editing, and reasoning in a single model. We cover the full setup process including model download options (official BF16 vs FP8 quantized), installation of the custom ComfyUI node (ComfyUI-LLaDA2-Uni), and step-by-step demonstrations of all four main features: text-to-image generation with thinking mode, image understanding with multi-task queries, instruction-based image editing, and the unique token decoder pipeline. The video also discusses the SPRING acceleration system for faster inference and provides honest performance analysis with stress-test prompts. This content is ideal for intermediate to advanced ComfyUI users, AI researchers, and developers who are interested in the emerging trend of unified multimodal models that combine understanding and generation. It is particularly valuable for anyone exploring alternatives to dedicated image generation models who wants to understand how diffusion-based LLMs work at the architecture level. ComfyUI workflow builders who want early access to cutting-edge models will benefit from the custom node installation guide. The video also serves AI content creators who want to stay ahead of the unified model trend and understand where image generation technology is heading. Basic familiarity with ComfyUI workflows, model quantization concepts, and Python environments is recommended. The emergence of unified diffusion LLMs represents a fundamental shift in how AI models are designed — moving from specialized single-task models to one model that can see, understand, reason, and create. LLaDA 2.0-Uni is one of the first open-source implementations of this concept, and its appearance on Hugging Face trending models signals growing research and industry interest. While the current performance is not yet competitive with dedicated image generation models in speed or editing quality, understanding this architecture now prepares creators and developers for the next generation of AI tools where conversational image creation and editing become the norm. The FP8 quantization approach demonstrated in this video also addresses a practical barrier — making a 60GB model accessible to users with more modest hardware. As models like DeepSeek V4 push down inference costs and unified models mature, the combination of cheap language model inference with on-device image generation could reshape how we interact with AI creatively. GitHub Official Repo: https://github.com/inclusionAI/LLaDA2.0-Uni ComfyUI custom node : https://github.com/benjiyaya/ComfyUI-LLaDA2-Uni/ (Workflow Included in the Repo) Official bf16 model: https://huggingface.co/inclusionAI/LLaDA2.0-Uni Your FP8 model: https://huggingface.co/benjiaiplayground/LLaDA2.0-Uni-FP8 Blog Post : https://www.patreon.com/posts/llada-2-0-uni-in-156883262?utm_source=youtube&utm_medium=video&utm_campaign=20260430 Timeline 00:00 - Introduction to LLaDA 2.0-Uni: Large Language Diffusion Analysis 01:30 - How the "Unified" architecture works (Understanding vs. Generation) 03:00 - Setup: Installing nodes and downloading model weights 04:30 - Text-to-Image Generation: Quality and prompt adherence 06:00 - Image Understanding: Asking the model questions about images 08:00 - Advanced Workflow: Combining vision and generation in one loop 11:00 - Performance tips and VRAM requirements 13:00 - Summary and final thoughts Local Workstation GPU : https://amzn.to/3XfXsAO -------------------------------------------------------------------------------------------------------------------------------- If You Like tutorial like this, You Can Support Our Work In Patreon: https://www.patreon.com/c/aifuturetech