<div data-youtube-video><iframe width="640" height="480" allowfullscreen="true" autoplay="false" disablekbcontrols="false" enableiframeapi="false" endtime="0" ivloadpolicy="0" loop="false" modestbranding="false" origin playlist src="https://www.youtube.com/embed/mX1OIrjj6hk" start="0"></iframe></div>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:<ul><li>Bernini-R image-to-video workflow</li><li>One source image + text motion instruction</li><li>Preserves source image identity, composition, and visual context</li><li>Bernini HIGH / LOW fp8 dual-model route</li><li>UMT5 XXL fp8 text encoder</li><li>Wan 2.1 VAE decoding</li><li>LoadImage source image input</li><li>image_scale_pixel_v2 image preparation</li><li>BerniniPromptEnhancer I2V prompt creation</li><li>RHLLMChatNode automatic prompt rewriting</li><li>JSON cleanup chain for LLM output</li><li>BerniniConditioning I2V control</li><li>PathchSageAttentionKJ optimization</li><li>LightX2V high / low noise LoRA support</li><li>UnifiedReward-Flex high / low noise LoRA support</li><li>KSamplerAdvanced two-stage generation</li><li>Vertical 480×848 / 129-frame video setup</li><li>CreateVideo and SaveVideo final output</li></ul>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 WorkflowTry the workflow online right now — no installation required. 👉 Workflow: <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2062533786738446337?inviteCode=rh-v1111">https://www.runninghub.ai/post/2062533786738446337?inviteCode=rh-v1111</a>If the results meet your expectations, you can later deploy it locally for customization.🎁 Fan Benefits: Register to get 1000 points + daily login 100 points — enjoy 4090 performance and 48 GB super power!📺 Bilibili Updates (Mainland China &amp; 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: <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV1yLEc6dEJc/">https://www.bilibili.com/video/BV1yLEc6dEJc/</a>☕ Support Me on Ko-fiIf you find my content helpful and want to support future creations, you can buy me a coffee ☕. Every bit of support helps me keep creating — just like a spark that can ignite a blazing flame. 👉 Ko-fi: <a target="_blank" rel="ugc" href="https://ko-fi.com/aiksk">https://ko-fi.com/aiksk</a>💼 Business ContactFor collaboration or inquiries, please contact aiksk95 on WeChat.⚙️打开下方链接即可在线体验，无需安装。 👉 工作流： <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2062533786738446337?inviteCode=rh-v1111">https://www.runninghub.ai/post/2062533786738446337?inviteCode=rh-v1111</a> 如果觉得效果理想，你也可以在本地进行自定义部署。🎁 粉丝福利： 注册即送 1000 积分，每日登录 100 积分，畅玩 4090 体验 48 G 超级性能！📺 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： <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV1yLEc6dEJc/">https://www.bilibili.com/video/BV1yLEc6dEJc/</a>我会在 夸克网盘 持续更新模型资源： 👉 <a target="_blank" rel="ugc" href="https://pan.quark.cn/s/20c6f6f8d87b">https://pan.quark.cn/s/20c6f6f8d87b</a> 这些资源主要面向本地用户，方便进行创作与学习。

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