<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/luvedSu-t_I?rel=1" start="0"></iframe></div>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.The result is decoded through VAEDecodeTiled, which helps handle larger images more safely and reduces memory pressure. Image Comparer is included for before-and-after inspection, making it easy to compare the original target image and the edited output. This is especially important for dual-image editing, because the best result should preserve the identity and pose from Image 1 while accurately borrowing the clothing, style, or reference features from Image 2.This workflow is ideal for creators who want more control than ordinary text-only editing. It can be used for outfit transfer, fashion try-on concepts, character costume replacement, product-style reference transfer, visual identity remixing, social media cover creation, and Civitai example demonstrations. If you want to see how the dual-image references are connected, how the prompt should be written, and how FireRed handles reference fusion, watch the full tutorial from the YouTube link above.⚙️ Try the Workflow Online👉 Workflow: <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2024517003498754050?inviteCode=rh-v1111">https://www.runninghub.ai/post/2024517003498754050?inviteCode=rh-v1111</a>Open the link above to run the workflow directly online and view the generation results in real time.If the results meet your expectations, you can also deploy it locally for further customization.🎁 Fan Benefits: Register now to get 1000 points, plus 100 daily login points — enjoy 4090-level performance and 48 GB of powerful compute!📺 Bilibili Updates (Mainland China &amp; Asia-Pacific)If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.📺 Bilibili Video: <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV1R3ZBBrEik/">https://www.bilibili.com/video/BV1R3ZBBrEik/</a>I will continue updating model resources on Quark Drive:👉 <a target="_blank" rel="ugc" href="https://pan.quark.cn/s/20c6f6f8d87b">https://pan.quark.cn/s/20c6f6f8d87b</a>These resources are mainly prepared for local users, making creation and learning more convenient.⚙️ 在线体验工作流👉 工作流： <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2024517003498754050?inviteCode=rh-v1111">https://www.runninghub.ai/post/2024517003498754050?inviteCode=rh-v1111</a>打开上方链接即可直接运行该工作流，实时查看生成效果。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/BV1R3ZBBrEik/">https://www.bilibili.com/video/BV1R3ZBBrEik/</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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