CIVITAI / Workflows

FireRed-Image-Edit-1.0 Dual-Image Editing Workflow

This workflow is designed for FireRed-Image-Edit-1.0 dual-image editing, mainly focused on transferring visual information from one image to another while keeping the target subject stable. A typical use case is: Image 1 provides the main person, pose, face, body structure, and composition, while Image 2 provides clothing, style, texture, or a reference object. The workflow then edits Image 1 according to Image 2 and the text instruction, making it suitable for outfit transfer, style replacement, character restyling, product reference editing, and controlled visual fusion. The workflow is built around FireRed-Image-Edit-1.0_fp8_e4m3fn.safetensors as the main editing model. It uses qwen_2.5_vl_7b_fp8_scaled.safetensors as the Qwen image text encoder and qwen_image_vae.safetensors as the VAE. It also applies Qwen-Image-Lightning-8steps-V2.0 as a model-only LoRA acceleration route, with the LoRA strength set lower than a full-force generation path. This makes the workflow practical for fast dual-image editing while still keeping enough model control for reference-based transformation. The core editing logic uses TextEncodeQwenImageEditPlus, which allows the prompt to be conditioned together with multiple image references. In this workflow, the prompt example is simple and direct: “the woman in Image 1 wears the clothes from Image 2.” This structure is very useful because the user does not need to describe every clothing detail manually. Image 2 provides the visual reference, while the prompt defines the editing relationship between the two images. The workflow also uses ImageScaleToTotalPixels to normalize both input images before editing. This helps keep the image references aligned and prevents unstable size differences from damaging the result. GetImageSize and EmptySD3LatentImage are used to match the latent canvas to the processed image dimensions, so the output remains consistent with the target image structure. A key part of this workflow is reference control. FluxKontextMultiReferenceLatentMethod is used with the index_timestep_zero method, helping the model understand how to use the visual references during generation. CFGNorm and ModelSamplingAuraFlow help stabilize the FireRed edit model, while KSampler runs an 8-step, low-CFG editing pass with Euler sampling and a simple scheduler. This setup is fast, direct, and suitable for repeated testing.