Stable Diffusion Users Report Black Screen Issues with Forge Neo on High-End GPUs

Stable Diffusion Users Report Black Screen Issues with Forge Neo on High-End GPUs

Users of the popular Stable Diffusion interface Forge Neo are encountering a perplexing issue: sudden black screens with "no display signal" errors, even when the system remains fully operational. The problem, reported by a user on the r/StableDiffusion subreddit, affects high-end hardware configurations including the NVIDIA RTX 5070 Ti with 16GB VRAM, 32GB DDR RAM, and a 1000W power supply — hardware typically considered more than sufficient for AI image generation workloads.

The user, who previously ran Automatic1111 without issue on older hardware, upgraded to a custom-built system specifically to enhance generation speed and stability. However, since switching to Forge Neo, they’ve experienced intermittent display failures — occurring both during active image generation and while the application is idle. The system’s NVIDIA driver (version 591.86) is confirmed as up-to-date, ruling out basic driver obsolescence as the cause.

This issue highlights a growing tension between rapidly evolving AI software ecosystems and hardware stability expectations. Forge Neo, a performance-optimized fork of Automatic1111, has gained traction among power users for its faster inference times and improved memory efficiency. Yet, its aggressive memory allocation and GPU scheduling optimizations may be incompatible with certain hardware configurations, particularly newer or non-standard builds.

According to multiple responses in the original Reddit thread, similar black screen events have been reported by others using RTX 40-series and 50-series GPUs with Forge Neo, suggesting a pattern rather than an isolated anomaly. Some users have traced the issue to VRAM overflow during large batch generations, while others suspect conflicts between the CUDA runtime environment and the Windows Display Driver Model (WDDM) under heavy compute loads.

Hardware manufacturers and system integrators are often unaware of these niche software-hardware interactions, especially when custom PCs are assembled for AI workloads without standardized testing protocols. The user’s system, built by a third-party vendor, remains under warranty — raising the question of whether such failures constitute a hardware defect or a software-induced instability.

Technical experts recommend several troubleshooting steps: downgrading to a stable NVIDIA driver (such as 551.76 or 566.78), which have shown better compatibility with Forge Neo in community tests; reducing VRAM allocation in Forge Neo’s settings to prevent overflow; and disabling GPU overclocking or enabling "TCC Mode" (Tesla Compute Cluster) on supported cards to bypass Windows display driver interference. Additionally, running Forge Neo in headless mode (without a display output) via remote desktop or SSH has proven effective for some users as a workaround.

As AI image generation becomes more mainstream, the divide between consumer-grade hardware marketing and professional software requirements is widening. While manufacturers tout raw specs — VRAM, wattage, core counts — software like Forge Neo demands nuanced compatibility, not just power. Developers of AI frameworks must prioritize stability testing across a broader range of hardware, while users need clearer guidance on validated configurations.

For now, the affected user is advised to contact their system builder for diagnostics, as the issue may stem from an unstable power delivery circuit or faulty VRAM modules under sustained load. Meanwhile, the broader Stable Diffusion community is urging Forge Neo developers to release a patch addressing display driver conflicts, especially for newer NVIDIA architectures. Until then, users are advised to proceed with caution when upgrading hardware for AI workflows — even the most powerful systems are not immune to software-induced instability.

Source: Reddit user /u/Unlucky_reel, r/StableDiffusion, https://www.reddit.com/r/StableDiffusion/comments/1razzs5/from_automatic1111_to_forge_neo/