Updated: Jun 6, 2026
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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:
Bernini-R text-to-image workflow
Pure text input, no reference media required
Bernini HIGH / LOW fp8 dual-model route
UMT5 XXL fp8 text encoder
Wan 2.1 VAE decoding
BerniniPromptEnhancer T2I prompt creation
RHLLMChatNode automatic prompt rewriting
JSON cleanup chain for LLM output
BerniniConditioning image-style generation control
PathchSageAttentionKJ optimization
LightX2V high / low noise LoRA support
UnifiedReward-Flex high / low noise LoRA support
KSamplerAdvanced two-stage generation
1280×720 visual output setup
VAEDecode and final SaveVideo output route
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 Workflow
Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2062533470706036738?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/2062533470706036738?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。