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/.
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