AI Image Metadata Viewer

Extract and display embedded generation parameters from SD and Midjourney images

Client-side viewer that reads PNG text chunks and JPEG metadata from AI-generated images to surface embedded generation parameters — prompt, negative prompt, model, seed, steps, CFG scale, and sampler — without uploading the file anywhere. It runs free in your browser on Gera Tools, with nothing uploaded.

Last updated Source: Gera Tools

Where does the metadata come from?

Stable Diffusion tools like AUTOMATIC1111 write generation parameters into a PNG tEXt chunk (usually keyed "parameters"). Other tools use EXIF UserComment or ImageDescription fields. This viewer reads those embedded strings directly in your browser.

AI image metadata viewer

Most AI image tools quietly embed the exact recipe used to create a picture inside the file itself. Stable Diffusion writes the full prompt, seed, steps, CFG, and sampler into a PNG text chunk; some tools use JPEG EXIF fields. That means you can often recover the precise parameters of any AI image you saved — to reproduce it, remix it, or learn from it. This viewer reads those fields locally, so even private images stay on your device.

What the parameters mean

When the viewer extracts generation metadata, here is what each field tells you:

Prompt — the full positive text instruction sent to the model. This is the most valuable thing to recover if you want to reproduce or extend an image.

Negative prompt — the terms the model was asked to avoid. Common negatives like blurry, lowres, extra limbs, bad anatomy suppress typical artifacts; custom negatives refine the style further.

Model / checkpoint — the base model used. The same prompt can produce dramatically different results across models, so this is essential context for reproduction. Names like v1-5-pruned-emaonly.ckpt or dreamshaper_8.safetensors identify the exact checkpoint.

Seed — the random initialisation number. Running the same seed with the same other parameters in the same model will produce a nearly identical image. Changing seed by even one digit will produce a completely different composition.

Steps — how many denoising iterations were run. Typical values are 20–50. More steps generally improve quality up to a point, then plateau.

CFG scale — how strongly the model adheres to the prompt. Values around 7 are a common default; higher values push the model to follow the prompt more strictly but can introduce artifacts; lower values give the model more creative latitude.

Sampler — the algorithm used for the denoising process. Common samplers include Euler a, DPM++ 2M Karras, and DDIM. Different samplers produce visibly different textures and convergence speeds.

How it works

When you select a file, the browser reads its bytes with FileReader. For PNGs the tool walks the chunk structure looking for tEXt and iTXt chunks — the one keyed parameters holds the Stable Diffusion string. For JPEGs it scans the EXIF block for UserComment and ImageDescription. The raw string is then parsed into labeled fields: everything before “Negative prompt:” is the prompt, and the trailing key/value list yields steps, sampler, CFG scale, seed, and model. Nothing is uploaded — all parsing happens in your browser.

Tips and limitations

  • Use originals. Re-encoding an image (screenshots, social uploads, format conversion) usually strips the metadata chunk — keep the file your tool produced.
  • Midjourney embeds little. Unlike SD, Midjourney does not write full parameters into the file, so expect sparse or empty results.
  • Seed is the key to reproduction. With the seed plus the other parameters you can regenerate a near-identical image in the same tool.
  • No metadata is normal. Many tools and exports simply do not embed parameters; an empty result does not mean the image is not AI-generated.