ICML 2025 Spotlight
FID=1.26 on ImageNet 512x512, without any guidance.
Direct Discriminative Optimization (DDO) enables GAN-style finetuning of likelihood-based generative models, such as diffusion and autoregressive models, without requiring an explicit discriminator network. By incorporating reverse KL divergence and self-generated negative samples—similar in spirit to reinforcement learning methods used in large language models—DDO overcomes the limitations of traditional maximum likelihood training, which relies on forward KL and often leads to mode-covering behavior. As a result, DDO can substantially improve the generation quality without changing the network architecture or inference protocol.
We release our finetuned checkpoints in nvidia/DirectDiscriminativeOptimization · Hugging Face.
| Model Files | Base Repository |
|---|---|
edm-cifar10-uncond-vp-ddo.pkl,edm-cifar10-cond-vp-ddo.pkl |
EDM |
edm2-img64-s-ddo.pkl,edm2-img512-l-ddo.pkl |
EDM2 |
var_d16-ddo.pth,var_d30-ddo.pth |
VAR |
| Model | #Parameters | FID w/o guidance | FID w/ guidance |
|---|---|---|---|
edm-cifar10-uncond-vp-ddo.pkl |
56M | 1.38 | - |
edm-cifar10-cond-vp-ddo.pkl |
56M | 1.30 | - |
edm2-img64-s-ddo.pkl |
280M | 0.97 | - |
edm2-img512-l-ddo.pkl |
777M | 1.26 | 1.21 |
var_d16-ddo.pth |
310M | 3.12 | 2.54 |
var_d30-ddo.pth |
2.0B | 1.79 | 1.73 |
Each model type (EDM-based, EDM2-based, and VAR-based) should be used within its corresponding base repository and environment. Please ensure you have cloned the appropriate repository and set up its environment.
We use the EDM's original inference code.
- Generate samples
# Generate 50000 images and save them as fid-tmp/*/*.png
# For CIFAR-10 (unconditional)
torchrun --nproc_per_node=8 generate.py --outdir=fid-tmp --seeds=0-49999 --subdirs --network=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm-cifar10-uncond-vp-ddo.pkl
# For CIFAR-10 (conditional)
torchrun --nproc_per_node=8 generate.py --outdir=fid-tmp --seeds=0-49999 --subdirs --network=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm-cifar10-cond-vp-ddo.pkl- Calculate FID
torchrun --nproc_per_node=8 fid.py calc --images=fid-tmp --ref=https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/cifar10-32x32.npzWe provide EDM2_sample.py, which performs class rebalancing and integrates DPM-Solver-v3 for accelerated sampling. Please place this script inside the EDM2 repository.
- Generate samples
# Generate 50000 images and save them as fid-tmp/*/*.png
# For ImageNet-64
torchrun --nproc_per_node=8 EDM2_sample.py --outdir=fid-tmp --seeds=0-49999 --subdirs --net=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm2-img64-s-ddo.pkl
# For ImageNet 512x512
torchrun --nproc_per_node=8 EDM2_sample.py --outdir=fid-tmp --seeds=0-49999 --subdirs --net=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm2-img512-l-ddo.pkl
# For ImageNet 512x512 (with guidance)
torchrun --nproc_per_node=8 EDM2_sample.py --outdir=fid-tmp --seeds=0-49999 --subdirs --net=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm2-img512-l-ddo.pkl --gnet=https://nvlabs-fi-cdn.nvidia.com/edm2/posthoc-reconstructions/edm2-img512-xs-0134217-0.165.pkl --guidance=1.1
# For ImageNet 512x512 (DPM-Solver-v3)
torchrun --nproc_per_node=8 EDM2_sample.py --dpmv3 --steps=25 --outdir=fid-tmp --seeds=0-49999 --subdirs --net=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm2-img512-l-ddo.pkl
# For ImageNet 512x512 (with guidance, DPM-Solver-v3)
torchrun --nproc_per_node=8 EDM2_sample.py --dpmv3 --steps=25 --outdir=fid-tmp --seeds=0-49999 --subdirs --net=https://huggingface.co/nvidia/DirectDiscriminativeOptimization/resolve/main/edm2-img512-l-ddo.pkl --gnet=https://nvlabs-fi-cdn.nvidia.com/edm2/posthoc-reconstructions/edm2-img512-xs-0134217-0.165.pkl --guidance=1.1- Calculate FID
# For ImageNet-64
torchrun --nproc_per_node=8 calculate_metrics.py calc --metrics=fid --images=fid-tmp --ref=https://nvlabs-fi-cdn.nvidia.com/edm2/dataset-refs/img64.pkl
# For ImageNet 512x512
torchrun --nproc_per_node=8 calculate_metrics.py calc --metrics=fid --images=fid-tmp --ref=https://nvlabs-fi-cdn.nvidia.com/edm2/dataset-refs/img512.pklWe provide VAR_sample.py, an extended version of VAR's original sampling demo. Please place this script in the VAR repository.
- Generate samples
# Generate 50000 images, save both .png and compressed .npz files in samples/
# For ImageNet 256x256
# $DEPTH in {16,30} is supported
# $CFG can be set to 1.0 (guidance-free) or 1.4 (best FID)
torchrun --nproc_per_node=8 VAR_sample.py --depth $DEPTH --cfg $CFG- Calculate FID
Please use the OpenAI's FID evaluation toolkit and reference stats file VIRTUAL_imagenet256_labeled.npz to evaluate FID, IS, precision, and recall.
Copyright © 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
All materials, including source code and pre-trained models, are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
If our work assists your research, feel free to give us a star ⭐ or cite us using:
@article{zheng2025direct,
title={Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator},
author={Zheng, Kaiwen and Chen, Yongxin and Chen, Huayu and He, Guande and Liu, Ming-Yu and Zhu, Jun and Zhang, Qinsheng},
journal={arXiv preprint arXiv:2503.01103},
year={2025}
}