Generating an Image From 1,000 Words: Enhancing Text-to-Image With
  Structured Captions
Text-to-image models have rapidly evolved from casual creative tools to
professional-grade systems, achieving unprecedented levels of image quality and
realism. Yet, most models are trained to map short prompts into detailed
images, creating a gap between sparse textual input and rich visual outputs.
This mismatch reduces controllability, as models often fill in missing details
arbitrarily, biasing toward average user preferences and limiting precision for
professional use. We address this limitation by training the first open-source
text-to-image model on long structured captions, where every training sample is
annotated with the same set of fine-grained attributes. This design maximizes
expressive coverage and enables disentangled control over visual factors. To
process long captions efficiently, we propose DimFusion, a fusion mechanism
that integrates intermediate tokens from a lightweight LLM without increasing
token length. We also introduce the Text-as-a-Bottleneck Reconstruction (TaBR)
evaluation protocol. By assessing how well real images can be reconstructed
through a captioning-generation loop, TaBR directly measures controllability
and expressiveness, even for very long captions where existing evaluation
methods fail. Finally, we demonstrate our contributions by training the
large-scale model FIBO, achieving state-of-the-art prompt alignment among
open-source models. Model weights are publicly available at
https://huggingface.co/briaai/FIBO
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Eyal Gutflaish
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Eliran Kachlon
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Hezi Zisman
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2025-11-11T19:41:45.565Z
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Hezi Zisman
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user
H3ZI
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Tal Hacham
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Nimrod Sarid
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Alexander Visheratin
691305eca644ba07c499c87e
Saar Huberman
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Gal Davidi
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Guy Bukchin
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Kfir Goldberg
claimed_verified
2025-11-12T12:21:01.644Z
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kfir goldberg
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user
kfirgold99
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Ron Mokady
claimed_verified
2025-11-11T19:41:43.302Z
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/avatars/5111133a889ffe4a7f33f1cf391fe4a9.svg
Ron Mokady
rmokady
user
rmokady
Generating an Image From 1,000 Words: Enhancing Text-to-Image With
  Structured Captions
Text-to-image models have rapidly evolved from casual creative tools to
professional-grade systems, achieving unprecedented levels of image quality and
realism. Yet, most models are trained to map short prompts into detailed
images, creating a gap between sparse textual input and rich visual outputs.
This mismatch reduces controllability, as models often fill in missing details
arbitrarily, biasing toward average user preferences and limiting precision for
professional use. We address this limitation by training the first open-source
text-to-image model on long structured captions, where every training sample is
annotated with the same set of fine-grained attributes. This design maximizes
expressive coverage and enables disentangled control over visual factors. To
process long captions efficiently, we propose DimFusion, a fusion mechanism
that integrates intermediate tokens from a lightweight LLM without increasing
token length. We also introduce the Text-as-a-Bottleneck Reconstruction (TaBR)
evaluation protocol. By assessing how well real images can be reconstructed
through a captioning-generation loop, TaBR directly measures controllability
and expressiveness, even for very long captions where existing evaluation
methods fail. Finally, we demonstrate our contributions by training the
large-scale model FIBO, achieving state-of-the-art prompt alignment among
open-source models. Model weights are publicly available at
https://huggingface.co/briaai/FIBO
A text-to-image model trained on long structured captions with DimFusion fusion mechanism and TaBR evaluation protocol achieves state-of-the-art prompt alignment and improved controllability.
text-to-image models long structured captions fine-grained attributes disentangled control DimFusion fusion mechanism lightweight LLM Text-as-a-Bottleneck Reconstruction TaBR evaluation protocol prompt alignment FIBO