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The model was presented in the paper Unified Continuous Generative Models.
Modellquelle
Quellenbeschreibung
The model was presented in the paper Unified Continuous Generative Models.
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: .
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1 QuelleVerifiziert 15. Sept.
Modellartefakte
18 Artefaktebuffers/vaes/e2evavae_f16d32.ckpt
ckpt · 267 MB · SHA-256 011bfdabb2fd…6aaf · Hugging Face
HerunterladenQuellenauszüge
2 AuszügeThe code for this model is available on Github: https://github.com/LINs-lab/UCGM
buffers/vaes/stat/dcae_f32c32_512.pt
pt · 1,75 KB · SHA-256 1fb7f7b40877…ca7f · Hugging Face
buffers/vaes/stat/e2evavae_f16d32_256.pt
pt · 1,75 KB · SHA-256 bf237612d372…e186 · Hugging Face
Herunterladenbuffers/vaes/stat/edm2_sdvae_f8c4_512.pt
pt · 1,50 KB · SHA-256 79acb6778ab6…6cbc · Hugging Face
Herunterladenbuffers/vaes/stat/vavae_f16d32_256.pt
pt · 1,75 KB · SHA-256 fb96ba21f7f4…5ee7 · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k256_ddt_xl_sdvae_relinear/checkpoints/0000000.pt
pt · 2,51 GB · SHA-256 b5c9bba15f6f…2339 · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k256_lightningdit_xl_vavae_relinear/checkpoints/0000000.pt
pt · 2,52 GB · SHA-256 414e8389a374…1def · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k256_sit_xl_repae_linear/checkpoints/0000000.pt
pt · 2,52 GB · SHA-256 f24157ac9c7e…850f · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k512_ddt_xl_sdvae_relinear/checkpoints/0000000.pt
pt · 2,51 GB · SHA-256 9bc4a665acf1…41a7 · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k512_edm2_l_sdvae_edm/checkpoints/0000000.pt
pt · 1,45 GB · SHA-256 c6a85db972d3…8783 · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k512_edm2_s_sdvae_edm/checkpoints/0000000.pt
pt · 535 MB · SHA-256 515bf0a91213…c653 · Hugging Face
Herunterladenoutputs/sampling_multi_steps/in1k512_edm2_xxl_sdvae_edm/checkpoints/0000000.pt
pt · 2,84 GB · SHA-256 f00c1d4c8d11…4b85 · Hugging Face
Herunterladenoutputs/tuning_few_steps/in1k256_ddt_xl_sdvae_relinear/checkpoints/0000800.pt
pt · 2,51 GB · SHA-256 70e2743dac87…81ee · Hugging Face
Herunterladenoutputs/tuning_few_steps/in1k256_lightningdit_xl_vavae_relinear/checkpoints/0000800.pt
pt · 2,52 GB · SHA-256 572550a9c03f…f2eb · Hugging Face
Herunterladenoutputs/tuning_few_steps/in1k256_sit_xl_repae_linear/checkpoints/0000500.pt
pt · 2,52 GB · SHA-256 b281c2069903…949e · Hugging Face
Herunterladenoutputs/tuning_few_steps/in1k256_sit_xl_sdvae_linear/checkpoints/0000800.pt
pt · 2,52 GB · SHA-256 e5caa5cd4b7d…0e5f · Hugging Face
Herunterladen--- license: apache-2.0 pipeline_tag: unconditional-image-generation library_name: diffusers --- # Unified Continuous Generative Models The model was presented in the paper [Unified Continuous Generative Models](https://huggingface.co/papers/2505.07447). # Paper Abstract 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. # Code The code for this model is available on Github: https://github.com/LINs-lab/UCGM
Source context: 0 downloads · 6 likes · Pipeline unconditional-image-generation · Library diffusers · Repo sp12138sp/UCGM
