DreamID: High-Fidelity and Fast diffusion-based Face Swapping via
  Triplet ID Group Learning
In this paper, we introduce DreamID, a diffusion-based face swapping model
that achieves high levels of ID similarity, attribute preservation, image
fidelity, and fast inference speed. Unlike the typical face swapping training
process, which often relies on implicit supervision and struggles to achieve
satisfactory results. DreamID establishes explicit supervision for face
swapping by constructing Triplet ID Group data, significantly enhancing
identity similarity and attribute preservation. The iterative nature of
diffusion models poses challenges for utilizing efficient image-space loss
functions, as performing time-consuming multi-step sampling to obtain the
generated image during training is impractical. To address this issue, we
leverage the accelerated diffusion model SD Turbo, reducing the inference steps
to a single iteration, enabling efficient pixel-level end-to-end training with
explicit Triplet ID Group supervision. Additionally, we propose an improved
diffusion-based model architecture comprising SwapNet, FaceNet, and ID Adapter.
This robust architecture fully unlocks the power of the Triplet ID Group
explicit supervision. Finally, to further extend our method, we explicitly
modify the Triplet ID Group data during training to fine-tune and preserve
specific attributes, such as glasses and face shape. Extensive experiments
demonstrate that DreamID outperforms state-of-the-art methods in terms of
identity similarity, pose and expression preservation, and image fidelity.
Overall, DreamID achieves high-quality face swapping results at 512*512
resolution in just 0.6 seconds and performs exceptionally well in challenging
scenarios such as complex lighting, large angles, and occlusions.
6809dd092e04f68a3f5baa66
Fulong Ye
admin_assigned
2025-04-24T11:21:52.931Z
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fulong ye
user
Alon77777
6809dd092e04f68a3f5baa67
Miao Hua
admin_assigned
2025-04-24T11:21:59.688Z
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miaohua
user
miaohua
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Pengze Zhang
6809dd092e04f68a3f5baa69
Xinghui Li
admin_assigned
2025-04-24T11:23:09.434Z
6752cd83ffaeeb979db974ae
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Xinghui Li
user
Crayon-Shinchan
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Qichao Sun
admin_assigned
2025-04-24T11:22:46.844Z
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Qichao Sun
user
giruhc9gj
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Songtao Zhao
6809dd092e04f68a3f5baa6c
Qian He
admin_assigned
2025-04-24T11:22:32.939Z
645dcad7a19f3e64bbf35e6c
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rV1uHDSnZv7jAvFq4ftj4.jpeg
Qian He
user
heqian
6809dd092e04f68a3f5baa6d
Xinglong Wu
admin_assigned
2025-04-24T11:22:21.048Z
67bc6b515d9470ec64bdcc33
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dttL8Fb3bKCVG5zjg_02q.png
Xinglong Wu
user
Xingzhe-xlwu
DreamID: High-Fidelity and Fast diffusion-based Face Swapping via
  Triplet ID Group Learning
In this paper, we introduce DreamID, a diffusion-based face swapping model
that achieves high levels of ID similarity, attribute preservation, image
fidelity, and fast inference speed. Unlike the typical face swapping training
process, which often relies on implicit supervision and struggles to achieve
satisfactory results. DreamID establishes explicit supervision for face
swapping by constructing Triplet ID Group data, significantly enhancing
identity similarity and attribute preservation. The iterative nature of
diffusion models poses challenges for utilizing efficient image-space loss
functions, as performing time-consuming multi-step sampling to obtain the
generated image during training is impractical. To address this issue, we
leverage the accelerated diffusion model SD Turbo, reducing the inference steps
to a single iteration, enabling efficient pixel-level end-to-end training with
explicit Triplet ID Group supervision. Additionally, we propose an improved
diffusion-based model architecture comprising SwapNet, FaceNet, and ID Adapter.
This robust architecture fully unlocks the power of the Triplet ID Group
explicit supervision. Finally, to further extend our method, we explicitly
modify the Triplet ID Group data during training to fine-tune and preserve
specific attributes, such as glasses and face shape. Extensive experiments
demonstrate that DreamID outperforms state-of-the-art methods in terms of
identity similarity, pose and expression preservation, and image fidelity.
Overall, DreamID achieves high-quality face swapping results at 512*512
resolution in just 0.6 seconds and performs exceptionally well in challenging
scenarios such as complex lighting, large angles, and occlusions.
A diffusion-based face swapping model, DreamID, achieves high identity similarity, attribute preservation, and fast inference by using explicit Triplet ID Group supervision and an improved diffusion architecture.
diffusion-based face swapping model Triplet ID Group data diffusion models explicit supervision SD Turbo SwapNet FaceNet ID Adapter identity similarity attribute preservation image fidelity pose and expression preservation complex lighting large angles occlusions