This ComfyUI workflow is designed for Qwen Image Edit 2511 local inpainting, object transfer, reference-based editing, and redraw-then-paste-back image production. The main goal of this workflow is to let users modify only a selected area of the main image, transfer visual elements from a reference image, generate a clean edited result, and then paste the edited region back into the original image layout. This makes it much more practical than full-frame image editing, especially when the original image is already good and only one object, region, clothing area, product element, background section, or visual style needs to be changed.
The workflow is built around Qwen Image Edit 2511, using qwen_image_edit_2511_fp8mixed.safetensors as the main editing model, Qwen-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors as the acceleration LoRA, qwen_2.5_vl_7b_fp8_scaled.safetensors as the vision-language text encoder, and qwen_image_vae.safetensors as the VAE. This combination gives the workflow strong image understanding, instruction-following ability, reference-image support, and fast local editing performance.
The core concept is “redraw and paste back.” In many image editing workflows, a model edits the whole image directly. That can be convenient, but it also creates a common problem: the face changes, the background shifts, the original composition drifts, the camera angle changes, or unrelated parts of the image are modified. This workflow is designed to reduce that problem. It crops or isolates the target region, sends that region into Qwen 2511 for local editing, then restores the edited result back into the original image using crop-box restoration logic.
This makes the workflow suitable for “万物迁移” style editing. A reference image can provide an object, style, texture, material, design, color, or visual identity, while the main image provides the target scene and composition. The workflow can then transfer the reference element into the masked region of the main image. For example, it can transfer a product, clothing style, accessory, logo, material texture, decorative pattern, prop, or object identity into another image while keeping the original scene more stable.
The first stage of the workflow prepares the input images and mask. The main image is loaded, resized, and prepared for editing. The mask defines the region that should be changed. The workflow includes mask processing tools such as MaskGrow, mask preview, and mask-to-image conversion. These tools help users check whether the selected area is correct before sending it into the model. A good mask is critical for local editing: if the mask is too small, the new element may not blend naturally; if it is too large, the edit may affect too much of the image.
The workflow also includes background removal and object isolation logic through Image Rembg. This is useful when the reference object needs to be separated from its original background before being transferred. For example, if you want to migrate a product or subject from one image into another, background removal can help isolate the key object and reduce unwanted reference contamination. This makes the reference cleaner and easier for Qwen 2511 to interpret.
TTP_Expand_And_Mask is used to expand and prepare the object region. This is useful when the edited area needs extra surrounding space for blending or when a transparent object/reference needs to be placed into a larger canvas. It helps create a more controlled input for local editing and avoids harsh boundary problems.
The workflow uses QwenEditConfigPreparer to prepare reference configuration. This node manages how images are passed into Qwen Image Edit as reference images and vision-language images. It supports reference resizing, longest-edge control, crop behavior, and visual-language resizing. This is important because Qwen Image Edit 2511 needs to understand both the main image and the reference image. If the reference size or crop is poorly prepared, the model may misunderstand what should be transferred.
The QwenEditAdaptiveLongestEdge node helps adapt the reference or main image size according to the image dimensions. This makes the workflow more flexible across different input resolutions. Instead of forcing every image into the same static size, it can adapt the longest edge to a controlled value, which is useful for high-resolution edits and different aspect ratios.
The central text-conditioning node is TextEncodeQwenImageEditPlusCustom_lrzjason. It receives the Qwen text encoder, VAE, prepared image configs, and the user instruction. The instruction describes how the image should be edited. In this workflow, the instruction logic is designed to analyze the input image, describe key visual features such as color, shape, size, texture, objects, and background, then apply the user’s modification instruction while keeping consistency with the original image where appropriate. This is very suitable for reference-based migration and local transformation tasks.
The workflow also uses ConditioningZeroOut for negative conditioning. This can help keep the generation process cleaner and reduce unwanted negative interference when using the Qwen edit conditioning structure. The model route then passes through ModelSamplingAuraFlow and CFGNorm. CFGNorm helps stabilize the guidance behavior, which is especially useful in image editing workflows where too much guidance can cause over-editing or identity drift.
The sampling stage uses Qwen Image Edit 2511 with Lightning 4-step acceleration. The workflow is designed for fast local editing rather than long slow sampling. The included KSampler route uses a short-step setup with Euler-style sampling and a low CFG-style setting. This makes the workflow suitable for quick iteration. Users can test masks, references, and instructions quickly before finalizing the best result.
After the Qwen edit result is generated, QwenEditOutputExtractor extracts the main image, mask, reference outputs, VAE images, and related edit outputs. QwenEditListExtractor and QwenEditAny2Image are used to extract reference-related images from the output list and convert them back into image format for preview or comparison. This makes the workflow transparent: users can inspect the main image, reference, mask, and generated result rather than only seeing the final output.
The paste-back stage is one of the most important parts of the workflow. CropWithPadInfo and RestoreCropBox allow the workflow to return the edited crop back to the original image position. This means the model can work on a focused local area, while the final output still preserves the original full image. This is the practical advantage of a redraw-and-paste-back pipeline: it gives the model enough room to edit locally, but avoids unnecessary changes to the whole picture.
The workflow also includes ImageReel and ImageReelComposit nodes. These create a visual comparison layout showing the main image, reference image, main image mask, and final result. This is very useful for Civitai, RunningHub, Bilibili, and YouTube demonstrations because the viewer can immediately understand what the workflow is doing. Instead of only showing a final result, the workflow can show the full logic: source image, reference, mask, and output.
Image Comparer is also included for before-and-after checking. This helps users inspect whether the paste-back result is aligned, whether the edited region blends well, whether the object migration succeeded, and whether unrelated areas were preserved. This is important for practical editing because a visually impressive edit is not enough if it damages the original image.
This workflow is especially useful for commercial and creator workflows. It can be used for product replacement, clothing transfer, accessory transfer, hairstyle modification, object migration, logo placement, prop transfer, material replacement, local style conversion, character detail correction, and social media cover editing. It is also useful for AI image post-production, where a generated image is mostly good but one section needs to be repaired or redesigned.
Main features:
- Qwen Image Edit 2511 local inpainting workflow
- Redraw-and-paste-back editing structure
- Uses qwen_image_edit_2511_fp8mixed.safetensors
- Uses Qwen-Edit-2511 Lightning 4-step LoRA
- Qwen 2.5 VL 7B FP8 text encoder support
- Qwen image VAE support
- Reference-based object and style transfer
- Mask-based local editing
- Image Rembg background removal for object isolation
- MaskGrow for stronger local selection control
- TTP_Expand_And_Mask for expanded object preparation
- QwenEditConfigPreparer for reference and VL image configuration
- TextEncodeQwenImageEditPlusCustom for instruction-based editing
- QwenEditOutputExtractor for output management
- CropWithPadInfo and RestoreCropBox for paste-back restoration
- ImageReel comparison layout
- Image Comparer before/after inspection
- Suitable for “万物迁移”, object migration, and local image post-production
Recommended use cases:
Local inpainting, object transfer, product replacement, clothing transfer, accessory migration, logo transfer, material transfer, reference-based redesign, masked region editing, AI image repair, background object replacement, character detail correction, product mockup creation, fashion image editing, commercial visual testing, social media cover correction, Civitai workflow demonstration, RunningHub online workflow publishing, and before/after editing showcases.
Suggested workflow:
Start by preparing the main image. This is the image you want to edit. Choose a source image with a clear composition and a visible target region. The workflow works best when the original image is already acceptable and only a local part needs to be changed.
Next, prepare the reference image. The reference image should clearly show the object, texture, material, style, logo, or visual element that you want to transfer. If the reference contains too much background noise, the workflow can use background removal to isolate the subject. A clean reference usually gives better migration results.
Create or load the mask on the main image. The mask should cover the area where the new element should appear. For object transfer, the mask should be slightly larger than the object area so the model has enough space to blend edges naturally. For precise edits, keep the mask tight but not too narrow.
Use the mask preview and mask grow tools to check the selected area. If the mask edge is too hard, increase blur or grow the mask slightly. If the mask covers unrelated parts, reduce it. Mask quality strongly affects final quality.
Write a clear editing instruction. A good instruction should explain what should be transferred and what should be preserved. For example: “Transfer the object style from the reference image into the masked area of the main image, while preserving the original background, camera angle, lighting, and subject identity.” For product edits, include material, placement, color, and scale. For clothing edits, include garment type, fabric, fit, and style.
Keep preservation language in the prompt. Since this is a local editing workflow, it is important to tell the model what should not change. Mention that the original composition, face, pose, background, lighting direction, and non-masked regions should remain unchanged where appropriate.
Run the Qwen 2511 edit stage. The Lightning 4-step route is designed for fast iteration, so it is practical to test several prompts and masks quickly. If the result is too weak, make the instruction more direct or expand the mask. If the result changes too much, simplify the instruction, improve the reference image, or reduce the edited region.
After generation, inspect the output before paste-back. Check whether the transferred object or style matches the reference, whether the object fits the target region, and…
