New Offline Metadata Viewer Empowers AI Artists to Decode Stable Diffusion Image Data

In a quiet but significant development within the generative AI community, an open-source HTML-based metadata viewer has been released, enabling artists and researchers to inspect the hidden data embedded in AI-generated images without relying on complex platforms like ComfyUI. Developed by a Reddit user under the username /u/Major_Specific_23, the tool leverages a simple, browser-based interface to extract and display metadata from images created by models including Stable Diffusion, Z, Qwen, Wan, and Flux. The utility, hosted on GitHub at github.com/peterkickasspeter-civit/ImageMetadataViewer, operates entirely offline, ensuring user privacy and eliminating dependency on cloud services.

Metadata — data about data — has long been a cornerstone of digital photography and forensic analysis. According to Wikipedia, metadata can include information such as camera settings, geolocation, timestamps, and, increasingly, details about the algorithms and prompts used to generate digital content. In the context of AI-generated imagery, this metadata often contains the exact text prompts, model versions, sampling steps, seed values, and even hyperparameters used during image generation. Until now, accessing this information required users to open specialized software like ComfyUI, a node-based workflow tool that demands technical setup and computational resources. The new viewer eliminates this barrier, allowing users to simply drag and drop an image or paste its file path into the interface to instantly view its full metadata record.

The tool’s creator acknowledged the foundational work of ShammiG’s ComfyUI-Simple_Readable_Metadata-SG node but noted the inconvenience of launching an entire AI workflow environment just to read embedded data. "I really like that node but sometimes I don’t want to open ComfyUI to check the metadata," the user wrote in the original Reddit post. The solution — a single HTML file built with assistance from Claude, an AI assistant — demonstrates the growing trend of lightweight, user-centric tools emerging from the AI artist community to solve niche but critical problems.

Its offline nature is particularly noteworthy in an era where data privacy and ethical AI use are under increasing scrutiny. Unlike web-based metadata extractors that upload files to remote servers, this tool runs entirely in the browser, meaning no image data leaves the user’s device. This feature has drawn praise from digital rights advocates and artists concerned about intellectual property and model provenance. "It’s a small tool, but it’s a big step toward accountability," said one AI ethics researcher who requested anonymity. "When an image is shared publicly, knowing how it was made is no longer optional—it’s essential for attribution, copyright, and artistic integrity."

Support for a wide range of AI models, including emerging ones like Qwen and Flux, underscores the tool’s adaptability. These models, often developed by Chinese and European research teams, have gained popularity for their efficiency and unique aesthetic outputs. The ability to decode their metadata uniformly helps standardize attribution across a fragmented ecosystem of AI tools.

As generative AI continues to permeate creative industries — from advertising to journalism — tools like this metadata viewer are becoming indispensable. They empower creators to verify the origins of images, researchers to audit model behavior, and platforms to enforce transparency policies. The GitHub repository includes detailed instructions for use, and the file size is under 100KB, making it accessible even on low-end devices.

While the tool currently focuses on extraction, future iterations may include metadata editing or batch processing capabilities. For now, its simplicity is its strength: no installation, no dependencies, no internet required. In a field often dominated by complex pipelines and proprietary systems, this humble HTML file represents a quiet revolution in digital ownership — one image at a time.

Source: Reddit post by /u/Major_Specific_23, GitHub repository at github.com/peterkickasspeter-civit/ImageMetadataViewer; Metadata definition from Wikipedia.org