Watch the full video first if you want to understand how this Bernini-R V2V video editing workflow works in practice. The video shows how an existing source video can be edited through text instructions while preserving the original motion, camera structure, subject position, lighting, and scene rhythm.

This ComfyUI workflow is designed for Bernini-R video-to-video editing. Its main purpose is to take a source video and apply a controlled visual edit without rebuilding the whole clip from scratch. Instead of using pure text-to-video generation, this workflow starts from real video frames, extracts the source video components, and then uses BerniniConditioning to guide the editing process around the original motion and composition.

The workflow is built around the Bernini-R high-noise and low-noise model structure. It uses Bernini_HIGH_fp8_e4m3fn_scaled.safetensors and Bernini_LOW_fp8_e4m3fn_scaled.safetensors as the dual model branches. It also uses UMT5 XXL fp8 text encoding, Wan 2.1 VAE, LoadVideo, GetVideoComponents, BerniniConditioning, KSamplerAdvanced, VAEDecode, CreateVideo, SaveVideo, and PathchSageAttentionKJ. The model route includes LightX2V LoRA and UnifiedReward-Flex LoRA for both high-noise and low-noise stages, helping the workflow improve speed, stability, and final visual quality.

The source video is the foundation of the workflow. LoadVideo imports the original clip, and GetVideoComponents separates the frame sequence, audio, and FPS. The extracted frames are then passed into BerniniConditioning as the source video condition. This means the original performance, body movement, camera angle, timing, and background relationship can remain stable while the edit instruction changes the visual result.

The prompt system is also an important part of the graph. BerniniPromptEnhancer builds a Bernini-specific V2V instruction from a short user task. In the uploaded workflow, the example task is to make the woman in the video wear Lolita clothing. RHLLMChatNode then rewrites the task into a more detailed edit prompt. The output is cleaned through StringReplace nodes, removing the JSON wrapper before sending the final instruction into CLIPTextEncode. This allows the user to start with a simple edit request and let the workflow turn it into a more precise video editing prompt.

The generation route uses BerniniConditioning with a vertical 480×848 setup and 129 frames. The first KSamplerAdvanced stage performs the high-noise transformation, where the main edit is introduced. The second KSamplerAdvanced stage performs low-noise refinement, helping the edited video retain detail, stability, and consistency. The final latent is decoded through Wan 2.1 VAE and exported through CreateVideo and SaveVideo.

Compared with ordinary V2V workflows, this Bernini-R setup is more specialized for controlled video editing. It is useful when you want to change clothing, style, texture, atmosphere, lighting, or visual quality while keeping the original subject identity and motion stable. It is suitable for outfit replacement, fashion video edits, character restyling, dance video enhancement, cinematic cleanup, short-form video editing, advertising previews, Bilibili showcases, YouTube tutorials, RunningHub releases, and Civitai workflow publishing.

Main features:

• Bernini-R video-to-video editing workflow

• Source video driven editing

• Preserves original motion, pose, camera, and scene structure

• Bernini HIGH / LOW fp8 dual-model route

• UMT5 XXL fp8 text encoder

• Wan 2.1 VAE decoding

• BerniniPromptEnhancer V2V prompt creation

• RHLLMChatNode automatic prompt rewriting

• JSON cleanup chain for LLM output

• BerniniConditioning V2V control

• PathchSageAttentionKJ optimization

• LightX2V high / low noise LoRA support

• UnifiedReward-Flex high / low noise LoRA support

• KSamplerAdvanced two-stage generation

• Vertical 480×848 / 129-frame video setup

• CreateVideo and SaveVideo final output

Suggested workflow:

Prepare a clean source video first. The subject should be visible, the movement should be readable, and the camera should not shake too aggressively. Load the video into the workflow, then write a direct editing instruction. For example, describe the clothing change, style change, lighting adjustment, texture upgrade, or cinematic enhancement you want. Let BerniniPromptEnhancer and RHLLMChatNode expand the task into a more complete Bernini editing prompt. Check the cleaned prompt before rendering. If the subject identity changes too much, strengthen preservation rules for face, hair, body shape, motion, background, lighting, and camera framing. If the edit is too weak, make the target visual change more explicit. Start with a short test clip first, then increase the final output length after the edit is stable.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2062503672382320641?inviteCode=rh-v1111

If the results meet your expectations, you can later deploy it locally for customization.

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📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1yLEc6dEJc/

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⚙️打开下方链接即可在线体验，无需安装。
👉 工作流： https://www.runninghub.ai/post/2062503672382320641?inviteCode=rh-v1111
如果觉得效果理想，你也可以在本地进行自定义部署。

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📺 Bilibili 更新（中国大陆及南亚太地区）

如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频： https://www.bilibili.com/video/BV1yLEc6dEJc/

我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。