CIVITAI / Workflows

Bernini-R Image Editing Image-to-Image Workflow

Watch the full video first if you want to understand how this Bernini-R image editing workflow works in practice. The video shows how one source image can be edited through a text instruction, how the workflow expands a simple idea into a stronger Bernini prompt, and how the final result can be exported as a clean single-frame image. This ComfyUI workflow is designed for Bernini-R image-to-image editing. Its main purpose is to take an existing image, preserve the important visual identity of the original subject, and apply a controlled transformation through text. Compared with pure text-to-image generation, this workflow starts from a real source image, so it can maintain the subject’s face, clothing, composition, visual direction, and key scene structure while changing the pose, background, object interaction, lighting, or atmosphere. The workflow is built around the Bernini-R high-noise and low-noise dual-model route. It uses Bernini_HIGH_fp8_e4m3fn_scaled.safetensors and Bernini_LOW_fp8_e4m3fn_scaled.safetensors as the two model branches. It also uses UMT5 XXL fp8 text encoding, Wan 2.1 VAE, BerniniConditioning, KSamplerAdvanced, VAEDecode, SaveImage, and PathchSageAttentionKJ. The generation chain also includes LightX2V LoRA and UnifiedReward-Flex LoRA for both high-noise and low-noise stages, helping the workflow improve speed, image quality, and final visual coherence. The source image section is the foundation of the workflow. LoadImage imports the original image, then image_scale_pixel_v2 prepares the image size and alignment before it enters BerniniConditioning. This makes the workflow suitable for controlled editing tasks such as changing a character’s pose, replacing a background, adding an object, changing the environment, converting the scene style, or creating a more cinematic version of an existing image. The prompt creation section is also important. BerniniPromptEnhancer is set to the i2i task type, meaning the workflow is optimized for image editing rather than pure generation. The user can write a short edit instruction, and the prompt enhancer builds a Bernini-specific system prompt. RHLLMChatNode then rewrites the task into a more detailed editing prompt. The output is cleaned through StringReplace nodes, removing the JSON wrapper before the final prompt is sent into CLIPTextEncode. In the uploaded example, the edit instruction cha