RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling
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/.
67d29b617d0fc37e673c7e65
Itay Chachy
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2025-03-14T08:58:43.179Z
65744a2fe09de6aa74026d80
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Itay Chachy
user
ItayChachy
67d29b617d0fc37e673c7e66
Guy Yariv
claimed_verified
2025-03-14T08:58:45.687Z
646d239f4220471ca0c6471c
https://cdn-avatars.huggingface.co/v1/production/uploads/646d239f4220471ca0c6471c/sRwzko8XEUVCkeD7jXceH.jpeg
Guy Yariv
user
GuyYariv
67d29b617d0fc37e673c7e67
Sagie Benaim
RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling
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/.
RewardSDS and RewardVSD enhance text-to-3D, text-to-image, and 2D editing by weighting noise samples based on alignment scores, improving generation quality and alignment.
Score Distillation Sampling SDS diffusion priors user intent reward model weighted SDS loss gradients Variational Score Distillation VSD text-to-image 2D editing text-to-3D generation generation quality alignment