---
license: other
license_name: picsart-flowdis-model-license
license_link: LICENSE
base_model: PAIR/FlowDIS
tags:
- image-segmentation
- dichotomous-image-segmentation
- quantization
- int8
- flux
---

# FlowDIS transformer — INT8 ConvRot

INT8 quantization of the [FlowDIS](https://huggingface.co/PAIR/FlowDIS) DiT
(12B, FLUX.1-schnell architecture) with group-wise Hadamard rotations
([ConvRot, arXiv:2512.03673](https://arxiv.org/abs/2512.03673)), in the
ComfyUI-native quantized checkpoint format produced by
[convert_to_quant](https://pypi.org/project/convert-to-quant/).

Made for [Any Prompt DIS](https://github.com/Albertchen98/any-prompt-dis), whose
low-VRAM mode (`--int8 --t5-int4`) runs the full segmentation pipeline in
**~21 GiB peak VRAM at 1024²** (~18.4 GiB at 512²) — it fits a 24 GB card, which the
bf16 pipeline (~35 GiB peak) does not. Quantizing this checkpoint yourself requires
loading the bf16 transformer, so a 24 GB card also cannot produce it locally —
hence this pre-quantized upload.

## Details

- 228 `double_blocks`/`single_blocks` linear layers quantized to INT8
  (row-wise scales, ConvRot group size 256, rotations pre-applied to weights);
  input/modulation/final layers kept in bf16.
- End-to-end relative error vs bf16: ~1.2% on mask latents; visually
  indistinguishable masks in our tests.
- Runtime: `flowdis/quant.py` in the repo above — uses the fused
  `comfy-kitchen` INT8 kernel when available (quantized linears ~1.5× faster
  than bf16), otherwise a `torch.compile` fallback.

Reproduce from the bf16 checkpoint:

```bash
pip install convert-to-quant
ctq -i flowdis-transformer.safetensors \
    -o flowdis-transformer-int8-convrot.safetensors \
    --comfy_quant --int8 --convrot --convrot-group-size 256 \
    --exclude-layers "img_in|txt_in|time_in|vector_in|mod|final_layer" \
    --save-quant-metadata
```

## Usage

```bash
# place it next to the other FlowDIS weights:
hf download Albertchen96/FlowDIS-int8-convrot flowdis-transformer-int8-convrot.safetensors \
    --local-dir <root_model_dir>

# then, in the any-prompt-dis repo:
python inference_si.py --root-model-dir <root_model_dir> --int8 \
    --image-path input.jpg --prompt "dog" --output-path mask.png
```

## License

Derivative of the FlowDIS weights by Picsart AI Research; distributed under the
same [PicsArt Inc. FlowDIS Model License](LICENSE) (non-commercial). Review it
before redistribution or commercial use.