<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 text-to-image workflow works in practice. The video shows how a short text idea can be expanded into a detailed visual prompt, then processed through the Bernini-R dual-model route to generate a polished image-style result inside a clean ComfyUI pipeline.This ComfyUI workflow is designed for Bernini-R text-to-image generation. Its main purpose is to turn a simple creative idea into a finished visual result without requiring a source image, source video, reference image, or reference video. Unlike reference-based Bernini workflows, this version is focused on pure text input. The user writes the concept, and the workflow handles prompt enhancement, LLM rewriting, Bernini conditioning, sampling, decoding, and final output.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, BerniniConditioning, KSamplerAdvanced, VAEDecode, CreateVideo, SaveVideo, and PathchSageAttentionKJ. Even though this workflow is set up as a text-to-image task, it still uses the Bernini generation pipeline and video-compatible output structure, which makes it flexible for single-frame visual generation or short output testing.The prompt section is one of the strongest parts of the workflow. BerniniPromptEnhancer is set to the t2i task type. The user can enter a rough visual idea, and the enhancer builds a Bernini-specific prompt structure. In the uploaded example, the concept is a Van Gogh Sunflowers-inspired realistic fractured cubism image. RHLLMChatNode then rewrites the short idea into a much more detailed visual instruction. The output is cleaned through StringReplace nodes, removing the JSON wrapper before sending the final prompt into CLIPTextEncode.The generation section uses BerniniConditioning with a 1280×720 setup and very short length logic, making it suitable for image-style generation and concept testing. The first KSamplerAdvanced stage handles the high-noise construction phase, where the main composition, subject, geometry, color, and structure are created. The second KSamplerAdvanced stage handles low-noise refinement, improving visual polish, texture, detail consistency, and final image stability.The model chain also includes LightX2V LoRA and UnifiedReward-Flex LoRA on the high and low branches. These help improve generation efficiency and output quality. The Wan 2.1 VAE decodes the final latent into visible frames, and the workflow exports the result through the CreateVideo / SaveVideo output route.Compared with ordinary text-to-image workflows, this Bernini-R T2I graph is more structured. It does not only depend on a raw prompt. It combines LLM prompt rewriting, Bernini task conditioning, dual-stage sampling, SageAttention optimization, acceleration LoRA, reward-aligned LoRA, and final output assembly into one reusable creator workflow.Main features:<ul><li>Bernini-R text-to-image workflow</li><li>Pure text input, no reference media required</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>BerniniPromptEnhancer T2I prompt creation</li><li>RHLLMChatNode automatic prompt rewriting</li><li>JSON cleanup chain for LLM output</li><li>BerniniConditioning image-style generation 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>1280×720 visual output setup</li><li>VAEDecode and final SaveVideo output route</li></ul>Suggested workflow:Start with a clear visual concept first. Define the subject, style, composition, lighting, material, texture, color palette, and final image mood. Let BerniniPromptEnhancer and RHLLMChatNode expand the idea into a detailed Bernini prompt, then check the cleaned prompt before rendering. If the result is too vague, make the subject and visual structure more explicit. If the image becomes too busy, simplify the prompt and reduce the number of competing style directions. Use this workflow for concept art, painterly experiments, poster-style images, fractured visual styles, fantasy scenes, product concepts, Civitai previews, RunningHub demonstrations, and fast Bernini-R text-only visual testing.⚙️ RunningHub WorkflowTry the workflow online right now — no installation required. 👉 Workflow: <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2062533470706036738?inviteCode=rh-v1111">https://www.runninghub.ai/post/2062533470706036738?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/2062533470706036738?inviteCode=rh-v1111">https://www.runninghub.ai/post/2062533470706036738?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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