---
base_model: PAIR/StreamingSVD
tags:
  - quantized
  - int8
  - image-to-video
---

# StreamingSVD — INT8 Quantized

This is an INT8 symmetric per-channel quantization of
[PAIR/StreamingSVD](https://huggingface.co/PAIR/StreamingSVD) by
[Picsart AI Research (PAIR)](https://huggingface.co/PAIR).

> **StreamingSVD: Consistent, Dynamic, and Extendable Image-Guided Long Video Generation**
> Roberto Henschel, Levon Khachatryan, Daniil Hayrapetyan, Hayk Poghosyan,
> Vahram Tadevosyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi
> [[arXiv 2403.14773]](https://arxiv.org/abs/2403.14773) · [[Project page]](https://streamingt2v.github.io) · [[Code]](https://github.com/Picsart-AI-Research/StreamingT2V)

## Quantization

| | Size | Dtype |
|---|---|---|
| Original | ~12.5 GB | F32 |
| **This repo** | **~3.2 GB** | **INT8** |

Large weight tensors are quantized with symmetric per-channel INT8.
Norms, biases, and positional embeddings are kept in FP32 for accuracy.

## Loading

```python
from safetensors.torch import load_file
import torch

tensors = load_file("model.int8.safetensors")
weights = {}
for k, v in tensors.items():
    if k.endswith(".scale"):
        continue
    scale_key = k + ".scale"
    if scale_key in tensors:
        scale = tensors[scale_key]
        shape = [-1] + [1] * (v.dim() - 1)
        weights[k] = v.float() * scale.view(shape)
    else:
        weights[k] = v
```

## Citation

```bibtex
@article{henschel2024streamingt2v,
  title={StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text},
  author={Henschel, Roberto and Khachatryan, Levon and Hayrapetyan, Daniil and Poghosyan, Hayk
          and Tadevosyan, Vahram and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey},
  journal={arXiv preprint arXiv:2403.14773},
  year={2024}
}
```