ORES: Open-vocabulary Responsible Visual Synthesis
Avoiding synthesizing specific visual concepts is an essential challenge in
responsible visual synthesis. However, the visual concept that needs to be
avoided for responsible visual synthesis tends to be diverse, depending on the
region, context, and usage scenarios. In this work, we formalize a new task,
Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model
is able to avoid forbidden visual concepts while allowing users to input any
desired content. To address this problem, we present a Two-stage Intervention
(TIN) framework. By introducing 1) rewriting with learnable instruction through
a large-scale language model (LLM) and 2) synthesizing with prompt intervention
on a diffusion synthesis model, it can effectively synthesize images avoiding
any concepts but following the user's query as much as possible. To evaluate on
ORES, we provide a publicly available dataset, baseline models, and benchmark.
Experimental results demonstrate the effectiveness of our method in reducing
risks of image generation. Our work highlights the potential of LLMs in
responsible visual synthesis. Our code and dataset is public available.
64ede02a74d9b58eabd573d0
Minheng Ni
claimed_verified
2023-09-08T15:51:52.084Z
6322952915b7beab57c9f439
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Minheng Ni
user
kodenii
64ede02a74d9b58eabd573d1
Chenfei Wu
admin_assigned
2023-08-29T13:43:37.741Z
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Chenfei Wu
user
Chenfei
64ede02a74d9b58eabd573d2
Xiaodong Wang
64ede02a74d9b58eabd573d3
Shengming Yin
admin_assigned
2023-08-29T13:45:41.436Z
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LanHarmony
user
LanHarmony
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Lijuan Wang
admin_assigned
2023-08-29T13:46:50.338Z
6413521d4e5305c14f22e110
/avatars/a6f8d0573e678f79bc3c0b7897b818ce.svg
Lijuan Wang
user
Lijuan
64ede02a74d9b58eabd573d5
Zicheng Liu
64ede02a74d9b58eabd573d6
Nan Duan
ORES: Open-vocabulary Responsible Visual Synthesis
Avoiding synthesizing specific visual concepts is an essential challenge in
responsible visual synthesis. However, the visual concept that needs to be
avoided for responsible visual synthesis tends to be diverse, depending on the
region, context, and usage scenarios. In this work, we formalize a new task,
Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model
is able to avoid forbidden visual concepts while allowing users to input any
desired content. To address this problem, we present a Two-stage Intervention
(TIN) framework. By introducing 1) rewriting with learnable instruction through
a large-scale language model (LLM) and 2) synthesizing with prompt intervention
on a diffusion synthesis model, it can effectively synthesize images avoiding
any concepts but following the user's query as much as possible. To evaluate on
ORES, we provide a publicly available dataset, baseline models, and benchmark.
Experimental results demonstrate the effectiveness of our method in reducing
risks of image generation. Our work highlights the potential of LLMs in
responsible visual synthesis. Our code and dataset is public available.
The Two-stage Intervention (TIN) framework uses a large-scale language model and diffusion synthesis model to enable responsible visual synthesis by avoiding forbidden visual concepts while following user queries.
Open-vocabulary Responsible Visual Synthesis Two-stage Intervention rewriting with learnable instruction large-scale language model synthesizing with prompt intervention diffusion synthesis model responsible visual synthesis