CIVITAI / Workflows

Bernini-R Reference Background Replacement Video Editing Workflow

Watch the full video first if you want to understand how this Bernini-R reference background replacement workflow works in practice. The video shows how a source video and a reference background image can be combined into a controlled video editing pipeline, where the original subject, motion, pose, clothing, and camera framing are preserved while the surrounding environment is replaced with a new reference-based background. This ComfyUI workflow is designed for Bernini-R reference background video replacement. Its main purpose is to take an existing video, keep the main person or subject unchanged, and replace the background with a new scene provided by a reference image. Instead of generating a completely new video or changing the entire frame, this workflow focuses on controlled background transformation: the dancer or foreground subject remains consistent, while the environment, furniture, lighting atmosphere, and spatial style are rebuilt around them. The workflow is built around the Bernini-R video editing model route. It uses Bernini_HIGH_fp8_e4m3fn_scaled.safetensors and Bernini_LOW_fp8_e4m3fn_scaled.safetensors as the high-noise and low-noise model branches. It also uses UMT5 XXL fp8 text encoding, Wan 2.1 VAE, BerniniConditioning, LoadVideo, GetVideoComponents, BatchImagesNode, KSamplerAdvanced, VAEDecode, CreateVideo, SaveVideo, and VHS_VideoCombine. The graph also includes LightX2V-style LoRA acceleration support, helping the workflow run through a more practical video editing path. The key node is BerniniConditioning. This node receives the source video, reference images, positive and negative text conditioning, VAE, width, height, video length, and reference maximum size. In this workflow, the source video provides the foreground motion and timing, while the reference image provides the new background design. This is the central logic behind background replacement: the video decides what must stay unchanged, and the reference image decides what the new environment should look like. A major advantage of this workflow is the built-in prompt creation chain. The graph uses RHLLMChatNode to analyze the source video and reference background image, then generate a detailed Bernini editing prompt. The LLM output is passed through a JSON cleanup chain using StringReplace nodes, then automatically connected into the positive prompt encoder. This help