This model uses a novel slow-fast architecture to balance temporal resolution and spatial detail in video understanding, overcoming the sequence length limitations of traditional LLMs. It employs a dual-token strategy: "fast" tokens provide a quick overview, while "slow" tokens allow instruction-aware extraction of details via cross-attention.

Usage

python

import torch
import os
import numpy as np
from decord import VideoReader, cpu

from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.mm_utils import tokenizer_image_token, get_model_name_from_path
from llava.utils import disable_torch_init

def load_video(video_path, max_frames_num):
        vr = VideoReader(video_path, num_threads=4)
        fps = round(vr.get_avg_fps())
        frame_idx = [i for i in range(0, len(vr), fps)]

        uniform_sampled_frames = np.linspace(0, len(vr) - 1, max_frames_num, dtype=int)
        frame_idx = uniform_sampled_frames.tolist()
        spare_frames = vr.get_batch(frame_idx).asnumpy()

        return spare_frames

# Model
# Ensure you have cloned the code repository: git clone https://github.com/SHI-Labs/Slow-Fast-Video-Multimodal-LLM.git
model_path = "shi-labs/slowfast-video-mllm-qwen2-7b-convnext-576-frame64-s1t4" # Or other checkpoint
video_path = "Slow-Fast-Video-Multimodal-LLM/assets/catinterrupt.mp4" # Example video path from cloned repo
question = "Please describe this video in detail."
max_frames=64 # Set according to the specific checkpoint

disable_torch_init()
model_path = os.path.expanduser(model_path)
model_name = get_model_name_from_path(model_path)
# Make sure to pass trust_remote_code=True
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, use_flash_attn=True, trust_remote_code=True)

if model.config.mm_use_im_start_end:
    prompt = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "
" + question
else:
    prompt = DEFAULT_IMAGE_TOKEN + "
" + question

conv = conv_templates["qwen_1_5"].copy()
conv.append_message(conv.roles[0], prompt)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()

# read and process video
video = load_video(video_path, max_frames_num=max_frames)
video_tensor = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].half().cuda()
videos = [video_tensor]

input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
input_ids = input_ids.to(device='cuda', non_blocking=True).unsqueeze(dim=0)

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images=videos,
        do_sample=True,
        max_new_tokens=1024,
        num_beams=1,
        temperature=0.2,
        top_p=1.0,
        use_cache=True)

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(f"User input: {question}
")
print(outputs)

Citation

bibtex

@misc{wang2025slowfast,
      title={Slow-Fast Architecture for Video Multi-Modal Large Language Models},
      author={Haotian Wang and Zhengyuan Yang and Yue Zhao and Bin Lin and Zhe Chen and Yue Cao and Hongxia Yang},
      year={2025},
      eprint={2504.01328},\
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.01328v1},
}

shi-labs/slowfast-video-mllm-qwen2-7b-convnext-576-frame96-s1t6

Slow-Fast Architecture for Video Multi-Modal Large Language Models

Introduction

Usage

Citation

shi-labs/
slowfast-video-mllm-qwen2-7b-convnext-576-frame96-s1t6