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Bria

FIBO 1.5

fibo-gen-1.5/text-to-image

BRIA's 8B flow-matching DiT distilled image model generates images with just 4 to 6 sampling steps and supports JSON-structured prompts with attribute-decoupled editing

Try fibo-gen-1.5/text-to-image

Input

Output

image
Fill in the parameters and run — results show up here.

Pricing

fibo-gen-1.5/text-to-image per image

Unit Price $0.0575

Positioning Overview

fibo-gen-1.5 is the text-to-image line in the fibo-1.5 family, aimed at developers who need professional-grade controllability and reproducibility. It is built on an 8B-parameter DiT with a flow-matching architecture and uses few-step distilled sampling, requiring only 4–6 steps at CFG=1.0 to complete inference. It also continues the structured long-prompt paradigm and follows attributes such as lighting, composition, color, and camera relatively well. It is suited to image-production scenarios that bring batch generation, attribute-level iteration, and result reproducibility into engineering workflows.

Core Highlights

  • Few-step sampling dramatically reduces per-image inference cost — after distillation, only 4–6 steps are needed with no classifier-free guidance, making calls cheaper than traditional high-CFG multi-step pipelines.
  • Structured long prompts deliver strong prompt adherence — generation is driven by long descriptions that separately specify the subject, lighting, composition, color, camera, and other attributes, giving clearer controllability in professional scenarios.
  • Reproducible generation results — the seed defaults to a fixed value of 5555; the same seed and prompt reproduce the same image, which facilitates batch pipelines and regression verification.
  • Division of labor between text-to-image and image editing within the family — the same family also includes the fibo-edit-1.5 image-editing line; switch to that line when you need to edit images based on reference images.

Capability Details

Few-step distilled sampling: images can be generated with the recommended settings of 4–6 steps and CFG=1.0, eliminating the tuning effort and compute consumption of traditional high-CFG workflows and returning usable results faster per request.

Structured prompt adherence: training and inference are based on structured long descriptions of roughly a thousand words; when developers spell out attributes such as subject, lighting, composition, and camera item by item, the resulting image more closely matches expectations.

Reproducible generation: the seed parameter and the prompt together determine the output; fixing both reproduces the same image, making it suitable for version comparison and result tracing in batch production.

Multiple aspect ratios: nine ratios are built in — 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, and 16:9 — so you can select one directly according to the placement or layout, reducing post-processing crops.

Long prompt input: the prompt supports up to 10,000 characters, so structured long descriptions can be written in full without being compressed into short sentences, allowing fuller attribute expression.

Use Cases

  • Commercial visual designers: image generation with structured long prompts — write lighting, composition, color, and camera into the prompt item by item to produce commercial-grade images directly usable for advertising and e-commerce materials.
  • Creative teams: attribute-level iterative refinement — fix the seed and change only a single attribute to regenerate repeatedly, producing a series of variants for option comparison and reducing rework from rewriting entire prompts.
  • Engineering teams: batch reproducible image generation — call in batches with a fixed seed and templated long prompts to produce style-consistent, traceable image assets for content-production pipelines.
  • Application developers: multi-ratio adapted generation — select the aspect ratio according to the target layout and generate images directly embeddable in apps, web pages, and video thumbnails.

Limitations and Selection Advice

Negative prompts have no effect in this version, so the image must be constrained entirely through positive descriptions; per the official documentation, weights do not apply to fine-tuning. Continuing to use legacy high-CFG multi-step workflows will deviate from the expected results; use CFG=1.0 with 4–6 steps. The controllability advantage depends on structured long prompts, and ordinary short prompts yield limited benefit. The 8B scale is demanding for local consumer-grade hardware, and local deployment requires a quantized version; calling it on HiAPI.ai avoids this issue.

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