title: IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning
    using Instruct Prompts
summary: 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.
paper_authors._id: 66b2dda28d020e4f130b897b
paper_authors.name: Ciara Rowles
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-08-07T06:53:22.714Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63357214eb6132ca653020e7/A_imoyFDx30wgi0guG_s8.png
paper_authors.fullname: Ciara
paper_authors.type: user
paper_authors.user: CiaraRowles
paper_authors._id: 66b2dda28d020e4f130b897c
paper_authors.name: Shimon Vainer
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/53e6782c38bd1babca03389ae15a7d2e.svg
paper_authors.fullname: Shimon Vainer
paper_authors.type: user
paper_authors.user: esx2ve
paper_authors._id: 66b2dda28d020e4f130b897d
paper_authors.name: Dante De Nigris
paper_authors._id: 66b2dda28d020e4f130b897e
paper_authors.name: Slava Elizarov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T12:16:32.421Z
paper_authors._id: 639b56244bbcd176745a6e6d
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paper_authors.fullname: Slava Elizarov
paper_authors.type: user
paper_authors.user: SlavaElizarov
paper_authors._id: 66b2dda28d020e4f130b897f
paper_authors.name: Konstantin Kutsy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T12:16:38.957Z
paper_authors._id: 64636800d4d34b01f45cc40d
paper_authors.avatarUrl: /avatars/f423e37b8461f3bda11021b3abf80ee1.svg
paper_authors.fullname: Konstantin Kutsy
paper_authors.type: user
paper_authors.user: bostadynamics
paper_authors._id: 66b2dda28d020e4f130b8980
paper_authors.name: Simon Donné
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-08T07:40:32.941Z
paper_authors._id: 634d06167f1e86614c0e8e1e
paper_authors.avatarUrl: /avatars/c1d119f42f35e0e2d7e1a655d193478b.svg
paper_authors.fullname: Simon Donné
paper_authors.type: user
paper_authors.user: simondonn
paper_title: IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning
    using Instruct Prompts
paper_summary: 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.
paper_ai_summary: IPAdapter-Instruct integrates natural-image conditioning with instruction prompts to efficiently handle multiple tasks in image generation without significant quality degradation.
paper_ai_keywords: diffusion models image generation textual prompts ControlNet IPAdapter conditional posterior style transfer object extraction Instruct prompts