It's work in progress by Kijai:

Followed this method and it's working for me on Windows:

git clone https://github.com/kijai/ComfyUI-FramePackWrapper into Custom Nodes folder

cd ComfyUI-FramePackWrapper

pip install -r requirements.txt

Download:

BF16 or FP8

https://huggingface.co/Kijai/HunyuanVideo_comfy/tree/main

Workflow is included inside the ComfyUI-FramePackWrapper folder:

https://github.com/kijai/ComfyUI-FramePackWrapper/tree/main/example_workflows

Liquid, an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation by tokenizing images into discrete codes and learning these code embeddings alongside text tokens within a shared feature space for both vision and language. Unlike previous multimodal large language model (MLLM), Liquid achieves this integration using a single large language model (LLM), eliminating the need for external pretrained visual embeddings such as CLIP. For the first time, Liquid uncovers a scaling law that performance drop unavoidably brought by the unified training of visual and language tasks diminishes as the model size increases. Furthermore, the unified token space enables visual generation and comprehension tasks to mutually enhance each other, effectively removing the typical interference seen in earlier models. We show that existing LLMs can serve as strong foundations for Liquid, saving 100× in training costs while outperforming Chameleon in multimodal capabilities and maintaining language performance comparable to mainstream LLMs like LLAMA2. Liquid also outperforms models like SD v2.1 and SD-XL (FID of 5.47 on MJHQ-30K), excelling in both vision-language and text-only tasks. This work demonstrates that LLMs such as Qwen2.5 and GEMMA2 are powerful multimodal generators, offering a scalable solution for enhancing both vision-language understanding and generation.

Liquid has been open-sourced on 😊 Huggingface and 🌟 GitHub.
Demo: https://huggingface.co/spaces/Junfeng5/Liquid_demo

Stable Virtual Camera, currently in research preview. This multi-view diffusion model transforms 2D images into immersive 3D videos with realistic depth and perspective—without complex reconstruction or scene-specific optimization. We invite the research community to explore its capabilities and contribute to its development.

A virtual camera is a digital tool used in filmmaking and 3D animation to capture and navigate digital scenes in real-time. Stable Virtual Camera builds upon this concept, combining the familiar control of traditional virtual cameras with the power of generative AI to offer precise, intuitive control over 3D video outputs.

Unlike traditional 3D video models that rely on large sets of input images or complex preprocessing, Stable Virtual Camera generates novel views of a scene from one or more input images at user specified camera angles. The model produces consistent and smooth 3D video outputs, delivering seamless trajectory videos across dynamic camera paths.

The model is available for research use under a Non-Commercial License. You can read the paper here, download the weights on Hugging Face, and access the code on GitHub.

https://stability.ai/news/introducing-stable-virtual-camera-multi-view-video-generation-with-3d-camera-control

https://github.com/Stability-AI/stable-virtual-camera
https://huggingface.co/stabilityai/stable-virtual-camera