Explore FIBO

Provides the exact transcript the speech model will read aloud. Write the words, punctuation, and language you want spoken; punctuation can influence pauses and phrasing, while pronunciation still depends on the model’s training data.

Describes unwanted subjects, styles, defects, or artifacts such as blur or watermarks. It steers generation away from those concepts but is not a guaranteed content filter.

Sets the horizontal resolution of generated images or video frames in pixels. Larger values create more detail but require more memory and processing time; combine it with height to choose the aspect ratio.

Sets the vertical resolution of generated images or video frames in pixels. Larger values create more detail but require more memory and processing time; keep the height compatible with the model’s supported dimensions and desired aspect ratio.

Controls how many iterative denoising passes produce the final media. More steps can refine detail and consistency but increase generation time, with diminishing quality gains beyond the model’s useful range.

Sets the strength of classifier-free guidance toward the text prompt. Higher values usually improve literal prompt adherence but can reduce natural motion or create artifacts; lower values give the model more visual freedom.

Requests multiple independent image variations from the same prompt in one run. Each additional image increases compute and memory use; random generator state or seeds determine how much the results differ.

Initializes the pipeline’s random-number generator. Reusing the same seed, prompt, model, and settings usually reproduces the same output, while changing it creates a new variation.

Overrides the scheduler’s normal diffusion timestep sequence with a custom descending list. This is an advanced reproducibility and scheduler-tuning control; use a schedule supported by the selected scheduler and do not combine it with custom sigmas.

Caps the tokenized prompt length accepted by the model’s text encoder. Text beyond the limit is truncated, so increasing it can preserve long descriptions when supported but uses more memory.

Chooses the data representation returned by the pipeline. Image frames are convenient for viewing, NumPy arrays suit numerical processing, and latent tensors are compact intermediate data that still require decoding.

Enables the pipeline’s patch-based generation path, which divides processing into spatial regions to manage large outputs or specialized model behavior. It can change memory use and seam behavior, so keep it off unless the pipeline or target resolution benefits from patching.

Passes advanced JSON options directly to the model’s joint text-image attention processor. Use it only for supported processor arguments such as scaling controls, because unknown keys or incompatible values can cause inference errors.

Chooses the structure of the pipeline result. Enabled returns an object with named properties that is easier to read; disabled returns a positional tuple that may suit older or lower-level integrations.