Qwen Image 2512 Inpainting Troubles Spark Community Debate in AI Art Circles

Qwen Image 2512 Inpainting Troubles Spark Community Debate in AI Art Circles

A growing number of AI artists and developers are encountering baffling issues with the Qwen-Image-2512-Fun-Controlnet-Union model when attempting inpainting tasks within the ComfyUI framework. Despite the model’s inclusion in the latest ComfyUI update via Pull Request #12359, users report that masked regions remain untouched — with no error messages, no warnings, and no visible output. The problem has ignited a heated discussion on the r/StableDiffusion subreddit, where users are struggling to reconcile the model’s advertised capabilities with its real-world performance.

The Qwen-Image-2512-Fun-Controlnet-Union model, hosted on Hugging Face by Alibaba’s PAI team, was promoted as a unified control solution for text-to-image generation, offering enhanced inpainting, edge detection, and depth mapping in a single checkpoint. According to the model’s documentation, it supports the "ControlNet Inpainting Alimama Apply" node — a specialized ComfyUI node designed to integrate seamlessly with the Qwen-VL vision-language architecture. Yet, as user AetherworkCreations noted in a widely shared post, "nothing errors but no edits are done to the image." This silence from the system has left many uncertain whether the fault lies in user configuration, model corruption, or a deeper integration flaw.

Investigations reveal that the Qwen-VL model family — of which Qwen-Image-2512 is a derivative — was designed with advanced vision-language understanding, including fine-grained localization and text reading capabilities, as detailed in a peer-reviewed paper submitted to ICLR 2024 by researchers from Alibaba’s Tongyi Lab. The paper emphasizes the model’s ability to interpret spatial relationships and semantic context, suggesting that the underlying architecture should, in theory, support precise inpainting. However, the transition from vision-language reasoning to pixel-level inpainting in ComfyUI appears to have encountered unforeseen technical hurdles.

Several users have tested alternative configurations: swapping control masks, using different base models (SD 1.5, SDXL), and verifying the integrity of the downloaded checkpoint files. All yielded identical results — a static, unaltered output image. One developer speculated that the node may require a specific conditioning input format not documented in the official ComfyUI node descriptions. Others suspect that the model’s internal weights may not be properly mapped to the inpainting diffusion pipeline, causing the control signal to be ignored during inference.

Community members have begun reverse-engineering the node’s source code and comparing it with the original ControlNet implementation. Early findings suggest that the "controlnetinpaintingalimamaapply" node may be missing a critical conditioning layer that aligns the Qwen-Image-2512’s latent space with the diffusion model’s denoising steps. Without this alignment, the control signal remains inert — explaining the lack of visual changes despite the node appearing to execute successfully.

As of this report, neither Alibaba’s PAI team nor the ComfyUI maintainers have issued an official statement. However, the issue has been flagged in multiple GitHub repositories, and at least one contributor has begun drafting a patch to enforce explicit latent space normalization before control application. For now, users are advised to revert to legacy ControlNet models for critical inpainting tasks, while the community awaits either a fix or clarification from the model’s developers.

The incident underscores a broader challenge in the open-source AI ecosystem: the rapid deployment of complex, multi-modal models without sufficient documentation or testing infrastructure. As models like Qwen-Image-2512 become more powerful, the burden of integration falls increasingly on end-users — a trend that risks fragmenting the creative community if not addressed with better tooling and transparency.