Soft Inpainting Bug in Forge Neo Sparks Community Outcry Among AI Art Enthusiasts

Artists and developers using Forge Neo, a high-performance interface for Stable Diffusion models, are raising alarms over a persistent bug affecting the Soft Inpainting functionality. According to a detailed report posted on Reddit’s r/StableDiffusion, users are experiencing a critical failure where Soft Inpainting—designed to subtly blend new content into masked regions of an image—produces the original, unaltered image as output, even though the preview clearly shows the model processing changes in real time. The issue, first documented by user Aristeides92, has since been corroborated by dozens of other users, indicating a systemic problem rather than an isolated installation error.

Soft Inpainting is a nuanced technique that differs from standard inpainting by applying gradual, diffusion-based blending to preserve texture and lighting coherence. Unlike hard masks that overwrite pixels, soft inpainting uses alpha-weighted transitions to integrate generated content seamlessly. This makes it indispensable for professional digital artists working on concept art, photo restoration, and character design. The fact that the preview renders correctly but the final output reverts to the source image suggests a disconnect between the inference pipeline and the final image assembly stage within Forge Neo’s codebase.

While the Reddit thread has become a hub for troubleshooting—offering workarounds such as downgrading to older versions of Forge or switching to Automatic1111’s web UI—no definitive fix has been confirmed. Community members have speculated that the bug may stem from an incompatibility between the latest Stability Matrix integration and Forge Neo’s updated sampling scheduler. Some users have noted that the issue does not occur in the original Forge release, implying a regression introduced during recent optimizations for speed and memory efficiency.

Despite the proliferation of AI tools, software stability remains a persistent challenge in the open-source diffusion ecosystem. Unlike commercial platforms like Adobe Firefly or Midjourney, community-driven tools like Forge Neo rely on volunteer maintainers and fragmented documentation. This incident underscores the tension between rapid feature development and rigorous quality assurance in AI software. The absence of an official changelog or bug tracker from the Forge Neo team has further frustrated users, who are now calling for greater transparency and accountability.

Meanwhile, unrelated software updates from other domains highlight the broader context of software maintenance. For instance, CRAN, the Comprehensive R Archive Network, published several new packages on February 15, 2026—including causalOT and quickSentiment—demonstrating the maturity and rigor of established open-source ecosystems. These packages undergo extensive testing and version control, a standard that the AI image generation community is still striving to match. In contrast, the Iranian software portal soft98.ir, while offering free Windows utilities and system tools, operates in a completely different domain and serves as an example of how non-AI software distribution platforms handle user support through forums and localized documentation.

As of this reporting, the Forge Neo development team has not issued a public statement. However, the volume of reports and the specificity of the bug—particularly the discrepancy between preview and output—have drawn the attention of several open-source contributors on GitHub. One anonymous developer has begun reverse-engineering the inpainting pipeline to identify whether the issue lies in the latent space handling or the final image tensor aggregation. Early findings suggest that the soft mask weights are being applied correctly during sampling but are being overwritten during the post-processing phase.

For now, affected users are advised to use standard inpainting for critical projects or revert to earlier Forge Neo builds. The community remains hopeful that a patch will emerge soon, but the incident serves as a stark reminder: even the most powerful AI tools are only as reliable as the software beneath them. As the line between artist and engineer blurs, the demand for robust, well-maintained tools has never been greater.