Metadata Transfer v2
This workflow lets you copy readable generation metadata from one image and write it to another image. It is designed for cases where an image still looks correct, but the embedded metadata is missing, incomplete, or no longer matches your current setup.
This is especially useful when preparing images for Civitai, recovering metadata after external editing, or correcting details after renaming models or changing file paths. Instead of manually rebuilding the settings, the workflow can pull the available data from a source image and apply it to an identical target image, including key fields such as the model name and KSampler settings.
The goal is simple: keep your image metadata clean, accurate, and ready for upload without having to recreate the generation info by hand.
How to Use:
Load the image that contains the correct metadata into the “SD Prompt Reader” node. This is your source image.
Load the image you want to update into the “Load Image” node. This is the target image that will receive the metadata.
Enable “Manually Input Settings” only if you want to override the automatic metadata transfer and enter the values yourself. Leave it off if you want the workflow to pull the settings directly from the source image.
If the source image used any LoRAs, load those LoRA models into the “Wildcard Encode (Inspire)” node. This allows the workflow to include the LoRA information when writing the target image’s metadata.
Note:
When “Manually Input Settings” is disabled, the workflow will automatically read the available settings from the source image and write them into the target image’s metadata. LoRAs are the one exception: they must be loaded manually in “Wildcard Encode (Inspire)” if you want them included, even when automatic transfer is enabled.
Manual Input
If you want to enter the metadata yourself, first enable the “Manually Input Settings” boolean node. This tells the workflow to use the values you provide manually instead of reading them automatically from the source image.
Toggle “Manually Input Settings” on.
Load the checkpoint/model you want written into the metadata.
Enter the KSampler settings, including the sampler, scheduler, steps, CFG scale, and seed.
Enter the positive prompt .
Enter the negative prompt .
Optional: If the image used LoRAs, load the LoRA models into the “Wildcard Encode (Inspire)” node so they can be included in the final metadata. After the manual values are entered, run the workflow. The target image will be saved with the metadata values you provided.
