patrickvonplaten/markup2im

Diffusion-based markup-to-image generation

Markup-to-Image Diffusion Models with Scheduled Sampling

We provide code to reproduce our paper on markup-to-image generation. Our code is built on top of HuggingFace diffusers and transformers.

Online Demo: https://huggingface.co/spaces/yuntian-deng/latex2im.

Example Generations

Scheduled Sampling	Baseline	Ground Truth

Prerequisites

• Pytorch

pip install transformers
pip install accelerate
pip install -qU git+https://github.com/da03/diffusers

Datasets & Pretrained Models

All datasets have been uploaded to Huggingface datasets.

• Math: data model
• Simple Tables: data model
• Sheet Music: data model
• Molecules: data model

Usage

Training

Generation

Visualizations

Math

To visualize the generation process, we need to first use the following command to save the intermediate images during generation:

python scripts/visualize_intermediate_steps.py --model_path models/math/scheduled_sampling/model_e100_lr0.0001.pt.100 --output_dir outputs/math/scheduled_sampling_visualization --save_intermediate_every 1

Next, we put together a gif image from the generated images:

python scripts/make_gif.py --input_dir outputs/math/scheduled_sampling_visualization/ --output_filename imgs/math_rendering.gif --select_filename 433d71b530.png --show_every 10

We can similarly visualize results from the baseline.

python scripts/visualize_intermediate_steps.py --model_path models/math/baseline/model_e100_lr0.0001.pt.100 --output_dir outputs/math/baseline_visualization --save_intermediate_every 1

python scripts/make_gif.py --input_dir outputs/math/baseline_visualization/ --output_filename imgs/math_rendering_baseline.gif --select_filename 433d71b530.png --show_every 10

Tables

To visualize the generation process, we need to first use the following command to save the intermediate images during generation:

python scripts/visualize_intermediate_steps.py --dataset_name yuntian-deng/im2html-100k --model_path models/tables/scheduled_sampling/model_e100_lr0.0001.pt.100 --output_dir outputs/tables/scheduled_sampling_visualization --save_intermediate_every 1

Next, we put together a gif image from the generated images:

python scripts/make_gif.py --input_dir outputs/tables/scheduled_sampling_visualization/ --output_filename imgs/tables_rendering.gif --select_filename 42725-full.png --show_every 10

We can similarly visualize results from the baseline.

python scripts/visualize_intermediate_steps.py --dataset_name yuntian-deng/im2html-100k --model_path models/tables/baseline/model_e100_lr0.0001.pt.100 --output_dir outputs/tables/baseline_visualization --save_intermediate_every 1

python scripts/make_gif.py --input_dir outputs/tables/baseline_visualization/ --output_filename imgs/tables_rendering_baseline.gif --select_filename 42725-full.png --show_every 10

Music

To visualize the generation process, we need to first use the following command to save the intermediate images during generation:

python scripts/visualize_intermediate_steps.py --dataset_name yuntian-deng/im2ly-35k-syn --model_path models/music/scheduled_sampling/model_e100_lr0.0001.pt.100 --output_dir outputs/music/scheduled_sampling_visualization --save_intermediate_every 1

Next, we put together a gif image from the generated images:

python scripts/make_gif.py --input_dir outputs/music/scheduled_sampling_visualization/ --output_filename imgs/music_rendering.gif --select_filename comp.17342.png --show_every 10

We can similarly visualize results from the baseline.

python scripts/visualize_intermediate_steps.py --dataset_name yuntian-deng/im2ly-35k-syn --model_path models/music/baseline/model_e100_lr0.0001.pt.100 --output_dir outputs/music/baseline_visualization --save_intermediate_every 1

python scripts/make_gif.py --input_dir outputs/music/baseline_visualization/ --output_filename imgs/music_rendering_baseline.gif --select_filename comp.17342.png --show_every 10

Molecules

To visualize the generation process, we need to first use the following command to save the intermediate images during generation:

python scripts/visualize_intermediate_steps.py --dataset_name yuntian-deng/im2smiles-20k --model_path models/molecules/scheduled_sampling/model_e100_lr0.0001.pt.100 --output_dir outputs/molecules/scheduled_sampling_visualization --save_intermediate_every 1

Next, we put together a gif image from the generated images:

python scripts/make_gif.py --input_dir outputs/molecules/scheduled_sampling_visualization/ --output_filename imgs/molecules_rendering.gif --select_filename B-1173.png --show_every 10

We can similarly visualize results from the baseline.

python scripts/visualize_intermediate_steps.py --dataset_name yuntian-deng/im2smiles-20k --model_path models/molecules/baseline/model_e100_lr0.0001.pt.100 --output_dir outputs/molecules/baseline_visualization --save_intermediate_every 1

python scripts/make_gif.py --input_dir outputs/molecules/baseline_visualization/ --output_filename imgs/molecules_rendering_baseline.gif --select_filename B-1173.png --show_every 10

Citation

@misc{deng2022markuptoimage,
      title={Markup-to-Image Diffusion Models with Scheduled Sampling}, 
      author={Yuntian Deng and Noriyuki Kojima and Alexander M. Rush},
      year={2022},
      eprint={2210.05147},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}