title: RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling
summary: Score Distillation Sampling (SDS) has emerged as an effective technique for
  leveraging 2D diffusion priors for tasks such as text-to-3D generation. While
  powerful, SDS struggles with achieving fine-grained alignment to user intent.
  To overcome this, we introduce RewardSDS, a novel approach that weights noise
  samples based on alignment scores from a reward model, producing a weighted SDS
  loss. This loss prioritizes gradients from noise samples that yield aligned
  high-reward output. Our approach is broadly applicable and can extend SDS-based
  methods. In particular, we demonstrate its applicability to Variational Score
  Distillation (VSD) by introducing RewardVSD. We evaluate RewardSDS and
  RewardVSD on text-to-image, 2D editing, and text-to-3D generation tasks,
  showing significant improvements over SDS and VSD on a diverse set of metrics
  measuring generation quality and alignment to desired reward models, enabling
  state-of-the-art performance. Project page is available at https://itaychachy.
  github.io/reward-sds/.
paper_authors._id: 67d29b617d0fc37e673c7e65
paper_authors.name: Itay Chachy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:58:43.179Z
paper_authors._id: 65744a2fe09de6aa74026d80
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65744a2fe09de6aa74026d80/kCxIKdeBJwAPKmvlm7fDP.jpeg
paper_authors.fullname: Itay Chachy
paper_authors.type: user
paper_authors.user: ItayChachy
paper_authors._id: 67d29b617d0fc37e673c7e66
paper_authors.name: Guy Yariv
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:58:45.687Z
paper_authors._id: 646d239f4220471ca0c6471c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/646d239f4220471ca0c6471c/sRwzko8XEUVCkeD7jXceH.jpeg
paper_authors.fullname: Guy Yariv
paper_authors.type: user
paper_authors.user: GuyYariv
paper_authors._id: 67d29b617d0fc37e673c7e67
paper_authors.name: Sagie Benaim
paper_title: RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling
paper_summary: Score Distillation Sampling (SDS) has emerged as an effective technique for
  leveraging 2D diffusion priors for tasks such as text-to-3D generation. While
  powerful, SDS struggles with achieving fine-grained alignment to user intent.
  To overcome this, we introduce RewardSDS, a novel approach that weights noise
  samples based on alignment scores from a reward model, producing a weighted SDS
  loss. This loss prioritizes gradients from noise samples that yield aligned
  high-reward output. Our approach is broadly applicable and can extend SDS-based
  methods. In particular, we demonstrate its applicability to Variational Score
  Distillation (VSD) by introducing RewardVSD. We evaluate RewardSDS and
  RewardVSD on text-to-image, 2D editing, and text-to-3D generation tasks,
  showing significant improvements over SDS and VSD on a diverse set of metrics
  measuring generation quality and alignment to desired reward models, enabling
  state-of-the-art performance. Project page is available at https://itaychachy.
  github.io/reward-sds/.
paper_ai_keywords: score distillation sampling (SDS) 2D diffusion priors text-to-3D generation reward model weighted SDS loss gradients high-reward output variational score distillation (VSD) RewardVSD text-to-image 2D editing generation quality alignment to desired reward models