Papers › Unified Continuous Generative Models

Unified Continuous Generative Models

12 May 2025arXiv:2505.07447archive 2025-07-28

Peng Sun, Yi Jiang, Tao Lin

Recent advances in continuous generative models, including multi-step approaches like diffusion and flow-matching (typically requiring 8-1000 sampling steps) and few-step methods such as consistency models (typically 1-8 steps), have demonstrated impressive generative performance. However, existing work often treats these approaches as distinct paradigms, resulting in separate training and sampling methodologies. We introduce a unified framework for training, sampling, and analyzing these models. Our implementation, the Unified Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a 675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID in 20 steps and a few-step model reaching 1.42 FID in just 2 steps. Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at 250 steps) improves performance to 1.06 FID in only 40 steps. Code is available at: https://github.com/LINs-lab/UCGM.

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Normalize LINs-Lab/UCGM/autoencs/autoencoder.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 9fcdaa6e423e8aa7 · report
apply_rotary_emb LINs-Lab/UCGM/networks/rmsnorm.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · d7b6dcfe63bfe59b · report
broadcat LINs-Lab/UCGM/networks/pos_embed.py official repository ran · honoured contract Apache-2.0 (permissive) · f50a8d20efb35c68 · report
center_crop_arr LINs-Lab/UCGM/autoencs/autoencoder.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 1712a07966b542ee · report
const_like LINs-Lab/UCGM/networks/edm2.py official repository ran Apache-2.0 (permissive) · f2514403e004d82b · report
constant LINs-Lab/UCGM/networks/edm2.py official repository ran Apache-2.0 (permissive) · 6d32f9cf6f29b386 · report
get_2d_sincos_pos_embed LINs-Lab/UCGM/networks/lightningdit.py official repository ran · honoured contract Apache-2.0 (permissive) · c92c27c924b517e8 · report
mean_flat LINs-Lab/UCGM/methodes/unigen.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · dd96bc6c07c85dae · report
modulate LINs-Lab/UCGM/networks/sit.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 03310bba324ae4fb · report
modulate LINs-Lab/UCGM/networks/ddt.py official repository ran · honoured contract fingerprinted Apache-2.0 (permissive) · 62fcb3912a967a50 · report
nonlinearity LINs-Lab/UCGM/autoencs/autoencoder.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 3137073275f8c21a · report
normalize LINs-Lab/UCGM/networks/edm2.py official repository ran Apache-2.0 (permissive) · ebfd171dc25ba0ce · report
precompute_freqs_cis LINs-Lab/UCGM/networks/rmsnorm.py official repository ran · violated contract Apache-2.0 (permissive) · 04a1fa63d6d4b8e4 · report
reshape_for_broadcast LINs-Lab/UCGM/networks/rmsnorm.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · 5a639d78ada17fee · report
rotate_half LINs-Lab/UCGM/networks/pos_embed.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 58823d9435a8751b · report
weight_init LINs-Lab/UCGM/networks/unetplus.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · d41a4250066bce93 · report
apply_rotary_emb LINs-Lab/UCGM/networks/ddt.py official repository unverified Apache-2.0 (permissive) · d0b336b17472a69b · report
get_2d_sincos_pos_embed_from_grid LINs-Lab/UCGM/networks/lightningdit.py official repository unverified Apache-2.0 (permissive) · 665d8a4e8f673a4c · report
get_attr_from_nested_module LINs-Lab/UCGM/methodes/unigen.py official repository unverified Apache-2.0 (permissive) · f18f5056c31115fc · report
modulate LINs-Lab/UCGM/networks/lightningdit.py official repository unverified Apache-2.0 (permissive) · 5ea6b9bd142d4069 · report
precompute_freqs_cis_2d LINs-Lab/UCGM/networks/ddt.py official repository unverified Apache-2.0 (permissive) · 48b378bed8fc0083 · report

Tasks

Image Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation ImageNet 256x256 SiT-XL/2 + UCGM-S (E2E-VAE + 40 sampling steps + CFG) FID 1.06 #1 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 SiT-XL/2 + UCGM-S (E2E-VAE + 40 sampling steps + CFG) NFE 80 #1 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 UCGM-XL/2 (VA-VAE + 30 sampling steps, without guidance) FID 1.21 #2 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 UCGM-XL/2 (VA-VAE + 30 sampling steps, without guidance) NFE 30 #2 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 UCGM-XL/2 (E2E-VAE + 40 sampling steps, without guidance) FID 1.21 #3 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 UCGM-XL/2 (E2E-VAE + 40 sampling steps, without guidance) NFE 40 #3 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 LightningDiT + UCGM-S (VA-VAE + 50 sampling steps + CFG) FID 1.21 #5 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 LightningDiT + UCGM-S (VA-VAE + 50 sampling steps + CFG) NFE 100 #5 of 94 Archive leaderboard report
Image Generation ImageNet 512x512 DDT-XL/2 + UCGM-S (SD-VAE + 150 sampling steps + CFG) FID 1.24 #2 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 DDT-XL/2 + UCGM-S (SD-VAE + 150 sampling steps + CFG) NFE 300 #2 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 DDT-XL/2 + UCGM-S (SD-VAE + 100 sampling steps + CFG) FID 1.25 #3 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 DDT-XL/2 + UCGM-S (SD-VAE + 100 sampling steps + CFG) NFE 200 #3 of 52 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Consistency ModelsDiffusion

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