Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
This paper presents Diffusion Forcing, a new training paradigm where a
diffusion model is trained to denoise a set of tokens with independent
per-token noise levels. We apply Diffusion Forcing to sequence generative
modeling by training a causal next-token prediction model to generate one or
several future tokens without fully diffusing past ones. Our approach is shown
to combine the strengths of next-token prediction models, such as
variable-length generation, with the strengths of full-sequence diffusion
models, such as the ability to guide sampling to desirable trajectories. Our
method offers a range of additional capabilities, such as (1) rolling-out
sequences of continuous tokens, such as video, with lengths past the training
horizon, where baselines diverge and (2) new sampling and guiding schemes that
uniquely profit from Diffusion Forcing's variable-horizon and causal
architecture, and which lead to marked performance gains in decision-making and
planning tasks. In addition to its empirical success, our method is proven to
optimize a variational lower bound on the likelihoods of all subsequences of
tokens drawn from the true joint distribution. Project website:
https://boyuan.space/diffusion-forcing/
6686505ab9a71fa518adabed
Boyuan Chen
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2024-07-08T08:51:04.275Z
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Boyuan Chen
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buoyancy99
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Diego Marti Monso
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Yilun Du
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2024-07-04T07:33:50.189Z
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Yilun Du
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yilundu
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Max Simchowitz
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2024-09-05T07:24:41.770Z
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Max Simchowitz
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Russ Tedrake
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2024-07-06T11:04:27.558Z
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Russ Tedrake
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RussTedrake
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Vincent Sitzmann
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2024-07-04T07:33:50.189Z
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Vincent Sitzmann
user
vsitzmann
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
This paper presents Diffusion Forcing, a new training paradigm where a
diffusion model is trained to denoise a set of tokens with independent
per-token noise levels. We apply Diffusion Forcing to sequence generative
modeling by training a causal next-token prediction model to generate one or
several future tokens without fully diffusing past ones. Our approach is shown
to combine the strengths of next-token prediction models, such as
variable-length generation, with the strengths of full-sequence diffusion
models, such as the ability to guide sampling to desirable trajectories. Our
method offers a range of additional capabilities, such as (1) rolling-out
sequences of continuous tokens, such as video, with lengths past the training
horizon, where baselines diverge and (2) new sampling and guiding schemes that
uniquely profit from Diffusion Forcing's variable-horizon and causal
architecture, and which lead to marked performance gains in decision-making and
planning tasks. In addition to its empirical success, our method is proven to
optimize a variational lower bound on the likelihoods of all subsequences of
tokens drawn from the true joint distribution. Project website:
https://boyuan.space/diffusion-forcing/
Diffusion Forcing trains diffusion models to denoise tokens with independent noise levels, combining next-token prediction and full-sequence diffusion strengths for sequence generation and decision-making tasks.
diffusion model denoise tokens independent noise levels sequence generative modeling causal next-token prediction model variable-length generation full-sequence diffusion models rolling-out sequences continuous tokens video variational lower bound true joint distribution