---
title: "Bernini-R 1.3B"
description: "Bernini-R 1.3B from ByteDance: text-to-image and image-to-image; Runner parity pending; dedicated `worldfoundry-bernini-cu124`."
modelId: "bernini-r-1.3b"
pageSource: authored
---

Bernini-R 1.3B is the small renderer-only member of ByteDance's Bernini family — a 1.3B rendering checkpoint fine-tuned from Wan2.1 1.3B, covering the same six task types as its larger siblings. It is the practical single-GPU way to try the Bernini task surface. WorldFoundry vendors the renderer inference in-tree and loads ByteDance/Bernini-R-1.3B-Diffusers.

<ModelPaperFigures modelId="bernini-r-1.3b" locale="en" />

The recorded paper is [Bernini: Latent Semantic Planning for Video Diffusion](https://arxiv.org/abs/2605.22344) (2026).

Project page: [bernini-ai.github.io](https://bernini-ai.github.io/). Code: [bytedance/Bernini](https://github.com/bytedance/Bernini).

This is **not** the planner+renderer [Bernini](/docs/guides/supported-models/bernini) card and **not** [Bernini-R 14B](/docs/guides/supported-models/bernini-r-14b).

Same paper: [Bernini: Latent Semantic Planning for Video Diffusion](https://arxiv.org/abs/2605.22344). Project: [bernini-ai.github.io](https://bernini-ai.github.io/). Weights: `ByteDance/Bernini-R-1.3B-Diffusers` (**apache-2.0**). GPU artifact parity is **pending**. Status: **Runner parity pending**.

<ModelCommandBuilder modelId="bernini-r-1.3b" locale="en" />

## What it is for

Pick `bernini-r-1.3b` when you want this family's text-to-video line in WorldFoundry: a prompt in, a generated clip out. Use it as the catalog T2V start rather than an editor or a world-model stepper.

The manifest explicitly records this as a renderer-only 1.3B checkpoint fine-tuned from the Wan2.1 1.3B diffusion-transformer backbone; the 7B planner stage of the full Bernini design is absent.

Catalog tasks include text-to-image, image-to-image, text-to-video, and video-to-video. Catalog aliases include `bernini-r-1p3b` and `bernini-r-1.3b-diffusers`. Documented modalities: text, image, and video in, image and video out.

Recorded evaluation entry points include [VBench](/docs/evaluation/benchmark-hub/vbench) (Quick quality yardstick for the small renderer).

## Runtime contract

Optional `prompt`, `task_type`, `neg_prompt`, `num_frames`, and `height`; artifacts `generated_image`, `generated_video`, and `bernini-r-1.3b.mp4`.

CLI task profiles: `t2v`, `t2i`, `i2i`, `v2v`, `r2v`, and `rv2v`. WorldFoundry binds `BerniniWorldFoundryPipeline`.

Family status: **Runner parity pending**.

Runner parity is pending_gpu_validation, but the small footprint makes this the recommended first stop in the Bernini family. Select tasks via task_type; the checkpoint is apache-2.0 and must be staged locally.

Official renderer inference is vendored in-tree; no external Bernini checkout is used.

## Running in WorldFoundry

The catalog launch is `worldfoundry-eval run bernini-r-1.3b --pipeline.task-profile t2v`. The command builder above switches `--pipeline.task-profile` among recorded tasks and still emits `worldfoundry-eval run`.

It runs in the dedicated environment `worldfoundry-bernini-cu124` (Python 3.11, CUDA 12.4, torch==2.5.1+cu124).

Recorded weight repositories include `ByteDance/Bernini-R-1.3B-Diffusers` and `ByteDance/Bernini-R-1.3B-Diffusers`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh bernini-r-1.3b`. Add `--download` to fetch missing public checkpoints.

Recorded licenses include apache-2.0; status: **Runner parity pending**.

<ModelRelatedRecipes modelId="bernini-r-1.3b" locale="en" />