Abstrakti

High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composing iterative generative processes, such as GFlowNets and diffusion models, is that to realize the desired target distribution, all steps of the generative process need to be coordinated, and satisfy delicate balance conditions. In this work, we propose Compositional Sculpting: a general approach for defining compositions of iterative generative processes. We then introduce a method for sampling from these compositions built on classifier guidance. We showcase ways to accomplish compositional sculpting in both GFlowNets and diffusion models. We highlight two binary operations - the harmonic mean and the contast - between pairs, and the generalization of these operations to multiple component distributions. We offer empirical results on image and molecular generation tasks. Project codebase: https://github.com/timgaripov/compositional-sculpting.
AlkuperäiskieliEnglanti
OtsikkoAdvances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023
KustantajaCurran Associates Inc.
Sivut12665-12702
ISBN (elektroninen)978-1-7138-9992-1
TilaJulkaistu - 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaConference on Neural Information Processing Systems - Ernest N. Morial Convention Center, New Orleans, Yhdysvallat
Kesto: 10 jouluk. 202316 jouluk. 2023
Konferenssinumero: 37
https://nips.cc/

Julkaisusarja

NimiAdvances in Neural Information Processing Systems
KustantajaMorgan Kaufmann Publishers
Vuosikerta36
ISSN (painettu)1049-5258

Conference

ConferenceConference on Neural Information Processing Systems
LyhennettäNeurIPS
Maa/AlueYhdysvallat
KaupunkiNew Orleans
Ajanjakso10/12/202316/12/2023
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