Updated: Jun 6, 2026
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Watch the full video first if you want to understand how this Bernini-R image-to-video workflow works in practice. The video shows how one source image can be turned into a dynamic video, how the prompt enhancement chain expands the motion instruction, and how the final result can be generated online without rebuilding a local ComfyUI setup.
This ComfyUI workflow is designed for Bernini-R image-to-video generation. Its main purpose is to take a still image as the starting visual condition, then generate a short video based on a text instruction. Compared with pure text-to-video generation, this workflow gives the model a concrete visual anchor. The source image provides the subject, composition, framing, environment, and initial visual identity, while the prompt controls the action, reaction, camera behavior, atmosphere, and scene progression.
The workflow is built around the Bernini-R high-noise and low-noise model route. 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, BerniniConditioning, KSamplerAdvanced, VAEDecode, CreateVideo, SaveVideo, and PathchSageAttentionKJ. The model chain also includes LightX2V LoRA and UnifiedReward-Flex LoRA for both high-noise and low-noise stages, helping improve generation efficiency, motion coherence, and final visual quality.
The source image section is the foundation of the workflow. LoadImage imports the starting image, then image_scale_pixel_v2 prepares the image size and alignment before sending it into the Bernini-R conditioning structure. This makes the workflow suitable for animating portraits, character images, trackside scenes, product photos, concept images, and cinematic still frames.
The prompt creation section is also important. BerniniPromptEnhancer is set to the i2v task type. The user can write a simple instruction, and the workflow converts it into a Bernini-specific image-to-video prompt. RHLLMChatNode then rewrites the task into a more detailed cinematic instruction. The output is cleaned through StringReplace nodes, removing the JSON wrapper before sending the final prompt into CLIPTextEncode. In the uploaded example, the source image is animated into a trackside scene where a woman reacts with extreme surprise as an F1 car and a black truck race past her, creating smoke and dynamic motion.
The generation section uses BerniniConditioning in i2v mode with a vertical 480×848 setup and 129 frames. The first KSamplerAdvanced stage handles the high-noise construction phase, where the main motion, reaction, camera energy, and scene transformation are created. The second KSamplerAdvanced stage performs low-noise refinement, improving detail, stability, and temporal consistency. The final latent is decoded through Wan 2.1 VAE and exported through CreateVideo and SaveVideo.
Compared with ordinary image-to-video workflows, this Bernini-R setup is more structured. It combines source image anchoring, LLM prompt expansion, Bernini task conditioning, dual-stage sampling, SageAttention optimization, acceleration LoRA, reward-aligned LoRA, and final video output into one reusable creator pipeline.
Main features:
Bernini-R image-to-video workflow
One source image + text motion instruction
Preserves source image identity, composition, and visual context
Bernini HIGH / LOW fp8 dual-model route
UMT5 XXL fp8 text encoder
Wan 2.1 VAE decoding
LoadImage source image input
image_scale_pixel_v2 image preparation
BerniniPromptEnhancer I2V prompt creation
RHLLMChatNode automatic prompt rewriting
JSON cleanup chain for LLM output
BerniniConditioning I2V 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 one clean source image first. The subject should be clear, the composition should be readable, and the image should already contain the visual identity you want to preserve. Load the image into the workflow, then write a direct motion instruction describing what should happen in the video. Define the action, emotional reaction, camera movement, environment motion, lighting, and cinematic style. Let BerniniPromptEnhancer and RHLLMChatNode expand the task into a more detailed Bernini instruction, then check the cleaned prompt before rendering. If the output drifts too far from the source image, strengthen preservation rules for identity, background, camera framing, and visual style. If the video is too static, make the action and environmental motion more explicit. Start with the default vertical setup first, then adjust prompt and seed after the base result is stable.
⚙️ RunningHub Workflow
Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2062533786738446337?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/2062533786738446337?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。