X-Prompt: Towards Universal In-Context Image Generation in
  Auto-Regressive Vision Language Foundation Models
In-context generation is a key component of large language models' (LLMs)
open-task generalization capability. By leveraging a few examples as context,
LLMs can perform both in-domain and out-of-domain tasks. Recent advancements in
auto-regressive vision-language models (VLMs) built upon LLMs have showcased
impressive performance in text-to-image generation. However, the potential of
in-context learning for general image generation tasks remains largely
unexplored. To address this, we introduce X-Prompt, a purely auto-regressive
large-vision language model designed to deliver competitive performance across
a wide range of both seen and unseen image generation tasks, all within a
unified in-context learning framework. X-Prompt incorporates a specialized
design that efficiently compresses valuable features from in-context examples,
supporting longer in-context token sequences and improving its ability to
generalize to unseen tasks. A unified training task for both text and image
prediction enables X-Prompt to handle general image generation with enhanced
task awareness from in-context examples. Extensive experiments validate the
model's performance across diverse seen image generation tasks and its capacity
to generalize to previously unseen tasks.
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Zeyi Sun
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2024-12-03T11:14:44.696Z
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Zeyi Sun
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Zery
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Ziyang Chu
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Pan Zhang
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Tong Wu
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2024-12-03T11:27:25.489Z
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Tong Wu
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Xiaoyi Dong
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Xiaoyi Dong
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Yuhang Zang
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Yuhang Zang
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Yuanjun Xiong
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2024-12-03T11:27:54.048Z
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Yuanjun Xiong
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Dahua Lin
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2024-12-03T11:28:00.539Z
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Dahua Lin
user
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Jiaqi Wang
claimed_verified
2024-12-11T09:21:04.208Z
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Jiaqi Wang
user
myownskyW7
X-Prompt: Towards Universal In-Context Image Generation in
  Auto-Regressive Vision Language Foundation Models
In-context generation is a key component of large language models' (LLMs)
open-task generalization capability. By leveraging a few examples as context,
LLMs can perform both in-domain and out-of-domain tasks. Recent advancements in
auto-regressive vision-language models (VLMs) built upon LLMs have showcased
impressive performance in text-to-image generation. However, the potential of
in-context learning for general image generation tasks remains largely
unexplored. To address this, we introduce X-Prompt, a purely auto-regressive
large-vision language model designed to deliver competitive performance across
a wide range of both seen and unseen image generation tasks, all within a
unified in-context learning framework. X-Prompt incorporates a specialized
design that efficiently compresses valuable features from in-context examples,
supporting longer in-context token sequences and improving its ability to
generalize to unseen tasks. A unified training task for both text and image
prediction enables X-Prompt to handle general image generation with enhanced
task awareness from in-context examples. Extensive experiments validate the
model's performance across diverse seen image generation tasks and its capacity
to generalize to previously unseen tasks.
X-Prompt, an auto-regressive large-vision language model, uses in-context learning to achieve competitive performance in seen and unseen image generation tasks.
in-context generation large language models LLMs auto-regressive vision-language models VLMs text-to-image generation in-context learning large-vision language model in-context token sequences unified in-context learning framework unified training task text prediction image prediction task awareness image generation