## Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation [![arXiv](https://img.shields.io/badge/arXiv%20paper-2602.11401-b31b1b.svg)](https://arxiv.org/abs/2602.11401)  Code for Latent Forcing ``` @article{baade2026latentforcing, title={Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation}, author={Alan Baade and Eric Ryan Chan and Kyle Sargent and Changan Chen and Justin Johnson and Ehsan Adeli and Li Fei-Fei}, journal={arXiv preprint arXiv:2602.11401}, year={2026}, } ``` Our code is based on JiT: https://github.com/LTH14/JiT.git

### Dataset We use [ImageNet](http://image-net.org/download) dataset, and Webdataset. ### Installation Download the code: ``` git clone https://github.com/AlanBaade/LatentForcing.git cd LatentForcing ``` Create the conda environment. uv is recommended, but not required. ```bash conda create -n latentforcing python=3.10 conda activate latentforcing uv pip install opencv-python==4.11.0.86 numpy==1.23 timm==0.9.12 tensorboard==2.10.0 scipy==1.9.1 einops==0.8.1 gdown==5.2.0 matplotlib==3.10.8 transformers==4.57.3 webdataset==1.0.2 uv pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu124 uv pip install "torch-fidelity @ git+https://github.com/LTH14/torch-fidelity.git@master" ``` ### Training Example script for training LatentForcing-L on ImageNet 200 epochs: ``` torchrun --nproc_per_node=8 --standalone \ main_jit.py \ --model JiTCoT-LM/16 \ --D_mean -1.2 --D_std 1.0 \ --P_mean -0.4 --P_std 0.8 \ --batch_size 128 --blr 5e-5 \ --epochs 200 --warmup_epochs 5 \ --gen_bsz 256 --num_images 10000 \ --cfg 1.0 --cfg_dino 1.0 \ --interval_min 0.0 --interval_max 1.0 \ --dino_weight 0.333 --choose_dino_p 0.4 \ --sample_mode dino_first_cascaded_noised \ --dh_depth 2 --dh_hidden_size 1024 \ --output_dir ${OUTPUT_DIR} \ --resume ${OUTPUT_DIR} \ --data_path ${DATA_PATH} \ --online_eval ``` For unconditional training and generation, set ```--label_drop_prob 1.0``` To train a Multi-Schedule model set ```--sample_mode shifted_independent_uniform``` ### Evaluation PyTorch pre-trained models are WIP Evaluate LatentForcing-L with Autoguidance (Default Evaluation Setting) ``` torchrun --nproc_per_node=8 --standalone \ main_jit.py \ --model JiTCoT-LM/16 \ --dh_depth 2 --dh_hidden_size 1024 \ --gen_bsz 1536 --num_images 50000 \ --cfg 1.5 --cfg_dino 1.5 \ --interval_min 0.0 --interval_max 1.0 \ --interval_min_dino 0.0 --interval_max_dino 1.0 \ --sample_mode dino_first_cascaded_noised \ --output_dir ${OUTPUT_DIR_EVAL} \ --resume ${OUTPUT_DIR} \ --data_path ${DATA_PATH} \ --evaluate_gen --num_sampling_steps 50 \ --sampling_method heun \ --guidance_method autoguidance \ --autoguidance_ckpt ${AUTOGUIDANCE_CKPT}$ ``` Evaluate LatentForcing-L with Interval CFG (Used in the System-Level comparison only) ``` torchrun --nproc_per_node=8 --standalone \ main_jit.py \ --model JiTCoT-LM/16 \ --dh_depth 2 --dh_hidden_size 1024 \ --gen_bsz 1536 --num_images 50000 \ --cfg 1.5 --cfg_dino 2.9 \ --interval_min 0.0 --interval_max 1.0 \ --interval_min_dino 0.06 --interval_max_dino 1.0 \ --sample_mode dino_first_cascaded_noised \ --output_dir ${OUTPUT_DIR_EVAL} \ --resume ${OUTPUT_DIR} \ --data_path ${DATA_PATH} \ --evaluate_gen --num_sampling_steps 50 \ --gen_shift_dino 0.575 --sampling_method heun \ --guidance_method cfg_interval \ --autoguidance_ckpt ${AUTOGUIDANCE_CKPT}$ ``` We use the same customized FID eval as JiT: [```torch-fidelity```](https://github.com/LTH14/torch-fidelity) ### Contact You can contact me at baade@stanford.edu for questions.