Saltar al contenido
ModelPublicadoExterno
Descargar

CTFlow

ICCV 2025 Workshop on Vision-Language Models for 3D Understanding (VLM3D)

TipoOtherVersiónDiffusion pytorch modelCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
1,91 GB
Versión
vDiffusion pytorch model
Actualizado
10 oct 2026
Fuente
Hugging Face

Descripción de la fuente

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis

ICCV 2025 Workshop on Vision-Language Models for 3D Understanding (VLM3D)

[Paper] | [GitHub]


Overview

CTFlow is a 0.5B latent flow matching transformer for generating entire 3D CT volumes conditioned on clinical reports.

Key ideas:

  • Uses the FLUX A-VAE as the latent space encoder/decoder
  • Encodes clinical reports with the CT-CLIP text encoder
  • Generates CT volumes auto-regressively block-by-block, keeping memory tractable while maintaining temporal coherence across slices
  • Trained on CT-RATE, a large-scale dataset of 3D CT volumes paired with clinical reports

Checkpoint

This repository contains the pretrained STDiT-L2 checkpoint (512M parameters, trained for 680,000 steps):

checkpoint-680000/
└── denoiser_ema/     ← use this for inference

Usage

See the GitHub repository for full installation instructions, training configs, and inference scripts.

Quick inference:

git clone https://github.com/WongJiayi/CTFlow
cd CTFlow

python auto_regressive_generate/main.py \
    --config /path/to/config.yaml \
    --ckpt /path/to/checkpoint-680000/denoiser_ema \
    --embedding /path/to/ct_embedding.pt \
    --output output_frames/ \
    --type full-body

Citation

@InProceedings{Wang_2025_ICCVW,
    author    = {Wang, Jiayi and Reynaud, Hadrien and Erick, Franciskus Xaverius and Kainz, Bernhard},
    title     = {CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    year      = {2025},
}

Fuentes

1 fuente
Hugging FacePrincipalVersión f9945e2a7a867fbaaa5f67455571239b883e224e
huggingface.co/EnyaWoooo/CTFlow/tree/f9945e2a7a867fbaaa5f67455571239b883e224e

Verificado 10 oct

Artefactos del modelo

2 artefactos

checkpoint-680000/denoiser_ema/diffusion_pytorch_model.safetensors

safetensors · 1,91 GB · SHA-256 37a9e4289d53…a546 · Hugging Face

Descargar

checkpoint-680000/denoiser/diffusion_pytorch_model.safetensors

safetensors · 1,91 GB · SHA-256 1906d7815076…a123 · Hugging Face

Descargar

Extractos de fuentes

2 extractos

--- license: apache-2.0 tags: - medical-imaging - ct-generation - flow-matching - diffusion - text-to-3d - auto-regressive --- # CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis **ICCV 2025 Workshop on Vision-Language Models for 3D Understanding (VLM3D)** [[Paper]](https://openaccess.thecvf.com/content/ICCV2025W/VLM3D/papers/Wang_CTFlow_Video-Inspired_Latent_Flow_Matching_for_3D_CT_Synthesis_ICCVW_2025_paper.pdf) | [[GitHub]](https://github.com/WongJiayi/CTFlow) --- ## Overview CTFlow is a **0.5B latent flow matching transformer** for generating entire 3D CT volumes conditioned on clinical reports. Key ideas: - Uses the **FLUX A-VAE** as the latent space encoder/decoder - Encodes clinical reports with the **CT-CLIP text encoder** - Generates CT volumes **auto-regressively block-by-block**, keeping memory tractable while maintaining temporal coherence across slices - Trained on **CT-RATE**, a large-scale dataset of 3D CT volumes paired with clinical reports --- ## Checkpoint This repository contains the pretrained **STDiT-L2** checkpoint (512M parameters, trained for 680,000 steps): ``` checkpoint-680000/ └── denoiser_ema/ ← use this for inference ``` --- ## Usage See the [GitHub repository](https://github.com/WongJiayi/CTFlow) for full installation instructions, training configs, and inference scripts. **Quick inference:** ```bash git clone https://github.com/WongJiayi/CTFlow cd CTFlow python auto_regressive_generate/main.py \ --config /path/to/config.yaml \ --ckpt /path/to/checkpoint-680000/denoiser_ema \ --embedding /path/to/ct_embedding.pt \ --output output_frames/ \ --type full-body ``` --- ## Citation ```bibtex @InProceedings{Wang_2025_ICCVW, author = {Wang, Jiayi and Reynaud, Hadrien and Erick, Franciskus Xaverius and Kainz, Bernhard}, title = {CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, year = {2025}, } ```

Source context: 0 downloads · 0 likes · Pipeline text-to-3d · Library diffusers · Repo EnyaWoooo/CTFlow