IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning
  using Instruct Prompts
Diffusion models continuously push the boundary of state-of-the-art image
generation, but the process is hard to control with any nuance: practice proves
that textual prompts are inadequate for accurately describing image style or
fine structural details (such as faces). ControlNet and IPAdapter address this
shortcoming by conditioning the generative process on imagery instead, but each
individual instance is limited to modeling a single conditional posterior: for
practical use-cases, where multiple different posteriors are desired within the
same workflow, training and using multiple adapters is cumbersome. We propose
IPAdapter-Instruct, which combines natural-image conditioning with ``Instruct''
prompts to swap between interpretations for the same conditioning image: style
transfer, object extraction, both, or something else still? IPAdapterInstruct
efficiently learns multiple tasks with minimal loss in quality compared to
dedicated per-task models.
66b2dda28d020e4f130b897b
Ciara Rowles
extracted_confirmed
2024-08-07T06:53:22.714Z
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Ciara
user
CiaraRowles
66b2dda28d020e4f130b897c
Shimon Vainer
claimed_verified
2024-08-08T07:40:34.591Z
63f9e95db31627d24009de1a
/avatars/53e6782c38bd1babca03389ae15a7d2e.svg
Shimon Vainer
user
esx2ve
66b2dda28d020e4f130b897d
Dante De Nigris
66b2dda28d020e4f130b897e
Slava Elizarov
admin_assigned
2024-08-08T12:16:32.421Z
639b56244bbcd176745a6e6d
https://cdn-avatars.huggingface.co/v1/production/uploads/639b56244bbcd176745a6e6d/a1DRanMtKUNJNcrt3ZmOg.jpeg
Slava Elizarov
user
SlavaElizarov
66b2dda28d020e4f130b897f
Konstantin Kutsy
admin_assigned
2024-08-08T12:16:38.957Z
64636800d4d34b01f45cc40d
/avatars/f423e37b8461f3bda11021b3abf80ee1.svg
Konstantin Kutsy
user
bostadynamics
66b2dda28d020e4f130b8980
Simon Donné
claimed_verified
2024-08-08T07:40:32.941Z
634d06167f1e86614c0e8e1e
/avatars/c1d119f42f35e0e2d7e1a655d193478b.svg
Simon Donné
user
simondonn
IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning
  using Instruct Prompts
Diffusion models continuously push the boundary of state-of-the-art image
generation, but the process is hard to control with any nuance: practice proves
that textual prompts are inadequate for accurately describing image style or
fine structural details (such as faces). ControlNet and IPAdapter address this
shortcoming by conditioning the generative process on imagery instead, but each
individual instance is limited to modeling a single conditional posterior: for
practical use-cases, where multiple different posteriors are desired within the
same workflow, training and using multiple adapters is cumbersome. We propose
IPAdapter-Instruct, which combines natural-image conditioning with ``Instruct''
prompts to swap between interpretations for the same conditioning image: style
transfer, object extraction, both, or something else still? IPAdapterInstruct
efficiently learns multiple tasks with minimal loss in quality compared to
dedicated per-task models.
IPAdapter-Instruct integrates natural-image conditioning with instruction prompts to efficiently handle multiple tasks in image generation without significant quality degradation.
diffusion models image generation textual prompts ControlNet IPAdapter conditional posterior style transfer object extraction Instruct prompts