This node is designed specifically for FramePack/HunyuanVideo, aiming to replace text encoder module with LLama-3 Instruct model. Not entirely, just text encoder. Vision module stays the same, so you'll need original llava-llama-3 as well.

Inputs:

clip_model_name - your CLIP-L model that you usually use with Hunyuan/FramePack (e.g. clip-vit-large-patch14);
llama_instruct_model_name - your Llama-3 8B instruct model. Currently does not support loading as from_pretrained, expects .safetensors file;
llama3_model_name - your llava-llama-3 model you usually use with Hunyuan/FramePack (e.g. llava-llama-3-8b-v1_1)
type - select "hunyuan_video", left for compatibility;
pretrained_path - !MODEL WEIGHTS WILL NOT LOAD FROM THERE! Provide a HuggingFace path for config and tokenizer for your model;
system_prompt - your system prompt that Instruct model is going to be using.

Outputs: Just CLIP. Pass it through your nodes like you will do with regular DualCLIPLoader.

FunPack img2latent Interpolation Inputs: A batch of images (e.g. from Load Image, Load Video (Upload), Load Video (Path) et cetera.

Outputs:

img_batch_for_encode - latent output with interpolated image, resize if needed and put it into VAE Encode, pass as latent_image into your sampler;
img_for_start_images - takes exactly the last image from the batch. You can pass it to your CLIP Vision and encoders as start_image. Or end_image. Who am I to tell you.