PersonaLive Checkpoint Loader

Not your usual checkpoint loader

Most loaders in ComfyUI pull a single .safetensors out of models/checkpoints and hand you a model. PersonaLiveCheckpointLoader is a different animal: it assembles an entire portrait-animation pipeline - a base SD 1.5 image-variations UNet plus CLIP image encoder, a VAE, a pose guider, a motion encoder, and PersonaLive's own weights - and hands the whole thing to the sampler as one PERSONALIVE_PIPE object. It's the front door to okdalto/ComfyUI-PersonaLive, the ComfyUI wrapper for the PersonaLive paper (arXiv 2512.11253), the "expressive portrait image animation for live streaming" work from GVCLab that made the rounds on r/StableDiffusion in December 2025.

You have exactly one decision to make here, and it's which folder to point at.

The one input and the one output

• model_dir - a dropdown of folders inside ComfyUI/models/. The shipped default (and the only option in the fresh install) is persona_live.
• pipe (PERSONALIVE_PIPE) - the assembled pipeline, wired straight into PersonaLivePhotoSampler.

Everything else is automatic. The loader expects ComfyUI/models/persona_live/ to hold three things: sd-image-variations-diffusers (base model), sd-vae-ft-mse (VAE), and persona_live/pretrained_weights/personalive/ containing the six .pth weight files (denoising_unet, reference_unet, temporal_module, motion_encoder, motion_extractor, pose_guider).

What actually happens when you run it

On first use, if the models are missing, the loader auto-downloads them from Hugging Face - roughly 15–20 GB - and organizes them into that structure for you. It's a snapshot_download under the hood, so the first run is a long wait and every run after is effectively instant because everything is cached. The "stuck at 0%" feeling is real; check your network, not the node.

Then it builds the pipeline: the SD 1.5 UNet gets inflated into a 3D UNet with AnimateDiff-style temporal and motion modules, a LivePortrait-style motion extractor (21 keypoints) and pose guider are loaded, and the six weight files are dropped into place. On CUDA it loads in fp16; on CPU it falls back to fp32 - technically supported, practically unusable, since this is a diffusion pipeline rendering frame by frame. Realistically you want an NVIDIA GPU with a good chunk of VRAM.

Install

Via ComfyUI Manager (search "ComfyUI-PersonaLive") or:

cd ComfyUI/custom_nodes
git clone https://github.com/okdalto/ComfyUI-PersonaLive
cd ComfyUI-PersonaLive
pip install -r requirements.txt

Watch two things. The pack wants torch>=2.6, so pip may try to upgrade the torch inside your ComfyUI venv - the single most likely way this pack breaks other nodes, so snapshot your environment first. And requirements.txt lists a lot of TensorRT-era bloat (tensorrt, pycuda, polygraphy, onnx) that only the optional TensorRT optimization path uses; if those fail to install, the loader and sampler still work. For a manual install, just replicate the folder structure above and drop the model files in - the loader's auto-download is convenience, not requirement.

Reality check

The paper markets this as running on a single 12 GB GPU, and the community is skeptical: the demos were H100s, and people running it on 4090s and 3090s describe it as working but nowhere near real-time. Fine as an offline tool - just don't build your live stream around it yet. The other thing nobody mentions: 15–20 GB is a lot of disk. Make sure you have the space before you queue up that first run.