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title: TiM
emoji: π
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sdk: gradio
sdk_version: 5.44.1
app_file: app.py
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python_version: 3.1
Transition Models: Rethinking the Generative Learning Objective
1 MMLab CUHK β 2Shanghai AI Lab β 3USYD
*Equal Contribution β β‘Project Lead β β Corresponding Authors β
[arXiv]β [Model]β [Dataset]β
Highlights: We propose Transition Models (TiM), a novel generative model that learns to navigate the entire generative trajectory with unprecedented flexibility.
- Our Transition Models (TiM) are trained to master arbitrary state-to-state transitions. This approach allows TiM to learn the entire solution manifold of the generative process, unifying the few-step and many-step regimes within a single, powerful model.
- Despite having only 865M parameters, TiM achieves state-of-the-art performance, surpassing leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across all evaluated step counts on GenEval benchmark. Importantly, unlike previous few-step generators, TiM demonstrates monotonic quality improvement as the sampling budget increases.
- Additionally, when employing our native-resolution strategy, TiM delivers exceptional fidelity at resolutions up to $4096\times4096$.
π¨ News
2025-9-5
We are delighted to introduce TiM, which is the first text-to-image generator support any-step generation, entirely trained from scratch. We have released the codes and pretrained models of TiM.
1. Setup
First, clone the repo:
git clone https://github.com/WZDTHU/TiM.git && cd TiM
1.1 Environment Setup
conda create -n tim_env python=3.10
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu118
pip install flash-attn
pip install -r requirements.txt
pip install -e .
1.2 Model Zoo (WIP)
Text-to-Image Generation
A single TiM model can perform any-step generation (one-step, few-step, and multi-step) and demonstrate monotonic quality improvement as the sampling budget increases.
Model | Model Zoo | Model Size | VAE | 1-NFE GenEval | 8-NFE GenEval | 128-NFE GenEval |
---|---|---|---|---|---|---|
TiM-T2I | π€ HF | 865M | DC-AE | 0.67 | 0.76 | 0.83 |
mkdir checkpoints
wget -c "https://huggingface.co/GoodEnough/TiM-T2I/resolve/main/t2i_model.bin" -O checkpoints/t2i_model.bin
Class-guided Image Generation:
Model | Model Zoo | Model Size | VAE | 2-NFE FID | 500-NFE FID |
---|---|---|---|---|---|
TiM-C2I-256 | π€ HF | 664M | SD-VAE | 6.14 | 1.65 |
TiM-C2I-512 | π€ HF | 664M | DC-AE | 4.79 | 1.69 |
mkdir checkpoints
wget -c "https://huggingface.co/GoodEnough/TiM-C2I/resolve/main/c2i_model_256.safetensors" -O checkpoints/c2i_model_256.safetensors
wget -c "https://huggingface.co/GoodEnough/TiM-C2I/resolve/main/c2i_model_512.safetensors" -O checkpoints/c2i_model_512.safetensors
2. Sampling
Text-to-Image Generation
We provide the sampling scripts on three benchmarks: GenEval, DPGBench, and MJHQ30K. You can specify the sampling steps, resolutions, and CFG scale in the corresponding scripts.
Sampling with TiM-T2I model on GenEval benchmark:
bash scripts/sample/t2i/sample_t2i_geneval.sh
Sampling with TiM-T2I model on DPGBench benchmark:
bash scripts/sample/t2i/sample_t2i_dpgbench.sh
Sampling with TiM-T2I model on MJHQ30k benchmark:
bash scripts/sample/t2i/sample_t2i_mjhq30k.sh
Class-guided Image Generation
We provide the sampling scripts for ImageNet-256 and ImageNet-512.
Sampling with C2I model on $256\times256$ resolution:
bash scripts/sample/c2i/sample_256x256.sh
Sampling with C2I model on $512\times512$ resolution:
bash scripts/sample/c2i/sample_512x512.sh
3. Evaluation
Text-to-Image Generation
GenEval
Please follow the GenEval to setup the conda-environment.
Given the directory of the generated images SAMPLING_DIR
and folder of object dector OBJECT_DETECTOR_FOLDER
, run the following codes:
python projects/evaluate/geneval/evaluation/evaluate_images.py $SAMPLING_DIR --outfile geneval_results.jsonl --model-path $OBJECT_DETECTOR_FOLDER
This will result in a JSONL file with each line corresponding to an image. Run the following codes to obtain the GenEval Score:
python projects/evaluate/geneval/evaluation/summary_scores.py geneval_results.jsonl
DPGBench
Please follow the DPGBench to setup the conda-environment.
Given the directory of the generated images SAMPLING_DIR
, run the following codes:
python projects/evaluate/dpg_bench/compute_dpg_bench.py --image-root-path $SAMPLING_DIR --res-path dpgbench_results.txt --pic-num 4
MJHQ30K
Please download MJHQ30K as the reference-image.
Given the directory of the reference-image direcotry REFERENCE_DIR
and the directory of the generated images SAMPLING_DIR
, run the following codes to calculate the FID Score:
python projects/evaluate/mjhq30k/calculate_fid.py $REFERENCE_DIR $SAMPLING_DIR
For CLIP Score, first compute the text features and save it in MJHQ30K_TEXT_FEAT
:
python projects/evaluate/mjhq30k/calculate_clip.py projects/evaluate/mjhq30k/meta_data.json $MJHQ30K_TEXT_FEAT/clip_feat.safetensors --save-stats
Then run the following codes to calculate the CLIP Score:
python projects/evaluate/mjhq30k/calculate_clip.py $MJHQ30K_TEXT_FEAT/clip_feat.safetensors $SAMPLING_DIR
Class-guided Image Generation
The sampling generates a folder of samples to compute FID, Inception Score and other metrics.
Note that we do not pack the generate samples as a .npz
file, this does not affect the calculation of FID and other metrics.
Please follow the ADM's TensorFlow
evaluation suite
to setup the conda-environment and download the reference batch.
wget -c "https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/classify_image_graph_def.pb" -O checkpoints/classify_image_graph_def.pb
Given the directory of the reference batch REFERENCE_DIR
and the directory of the generated images SAMPLING_DIR
, run the following codes:
python projects/evaluate/adm_evaluator.py $REFERENCE_DIR $SAMPLING_DIR
4. Training
4.1 Dataset Setup
Currently, we provide all the preprocessed dataset for ImageNet1K. Please use the following commands to download the preprocessed latents.
bash tools/download_imagenet_256x256.sh
bash tools/download_imagenet_512x512.sh
For text-to-image generation, we provide a toy dataset. Please use the following command to download this dataset.
bash tools/download_toy_t2i_dataset.sh
4.2 Download Image Encoder
We use RADIO-v2.5-b as our image encoder for REPA-loss.
wget -c "https://huggingface.co/nvidia/RADIO/resolve/main/radio-v2.5-b_half.pth.tar" -O checkpoints/radio-v2.5-b_half.pth.tar
4.3 Training Scripts
Specify the image_dir
in configs/c2i/tim_b_p4.yaml
and train the base-model (131M) on ImageNet-256:
bash scripts/train/c2i/train_tim_c2i_b.sh
Specify the image_dir
in configs/c2i/tim_xl_p2_256.yaml
and train the XL-model (664M) on ImageNet-256:
bash scripts/train/c2i/train_tim_c2i_xl_256.sh
Specify the image_dir
in configs/c2i/tim_xl_p2_512.yaml
and train the XL-model (664M) on ImageNet-512:
bash scripts/train/c2i/train_tim_c2i_xl_512.sh
Specify the root_dir
in configs/t2i/tim_xl_p1_t2i.yaml
and train the T2I-model (865M) on Toy-T2I-Dataset:
bash scripts/train/t2i/train_tim_t2i.sh
Citations
If you find the project useful, please kindly cite:
@article{wang2025transition,
title={Transition Models: Rethinking the Generative Learning Objective},
author={Wang, Zidong and Zhang, Yiyuan and Yue, Xiaoyu and Yue, Xiangyu and Li, Yangguang and Ouyang, Wanli and Bai, Lei},
year={2025},
eprint={2509.04394},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
License
This project is licensed under the Apache-2.0 license.