Same burning-ships prompt from Disco Diffusion to Flux and LTX-2.5, images and clips, all on one rented GPU.

Yeah. Quite a few projects still ship **official** builds with an older CUDA/PyTorch stack aimed at pre-Blackwell GPUs. In many cases, updating PyTorch may be enough to make the core app usable. If I were choosing a GUI for Blackwell today, I’d probably use Forge Neo or ComfyUI… * * * I would not generalize this to “the RTX 5060 is incompatible with image-generation software” as a whole. There **is** a real compatibility problem with some older packaged environments. Fooocus is a good example: its current launcher still defaults to `torch==2.1.0` and `xformers==0.0.23`, while RTX 5060 users have reported the bundled PyTorch rejecting the card’s `sm_120` compute capability and then failing with `no kernel image is available for execution on the device`. * Fooocus launcher / default PyTorch version * Fooocus RTX 5060 / sm_120 report #4053 * Fooocus RTX 5060 / sm_120 report #4123 But that is different from the GPU itself being unusable. PyTorch added Blackwell support with CUDA 12.8 builds in PyTorch 2.7, and current image-generation environments can use much newer CUDA/PyTorch stacks. * PyTorch 2.7 release: Blackwell + CUDA 12.8 support * ComfyUI * Forge Neo So for a new RTX 5060 installation, my default route would be: 1. **Start with a clean current ComfyUI or Forge Neo installation.** 2. **First test a basic generation without third-party custom nodes/extensions or optional attention packages.** 3. Only after that works, add xFormers / FlashAttention / SageAttention / ControlNet preprocessors / custom nodes one at a time. That separates “does Blackwell work at all?” from “does this particular extension/kernel work on Blackwell?” ComfyUI is especially straightforward as a baseline at the moment: its current Windows portable uses a modern PyTorch/CUDA stack, and its maintained stack manifest explicitly includes CUDA builds covering compute capability 12.0. * ComfyUI installation notes * ComfyUI PyTorch stack manifest Forge Neo is also actively targeting current PyTorch/CUDA versions. One useful design choice there is that it can fall back to native PyTorch scaled-dot-product attention rather than requiring every optional acceleration library. Its own README specifically warns against blindly installing every attention backend. * Forge Neo README Why this can be confusing on RTX 50-series (click for more details) A simple decision tree (click for more details) What about RuinedFooocus? (click for more details) So I think the practical picture is closer to: RTX 5060 itself: usable for current PyTorch/image-generation stacks old packaged PyTorch: can definitely be a problem current ComfyUI / Forge Neo: good places to establish a clean Blackwell baseline Fooocus official portable: old bundled runtime is a known problem RuinedFooocus: newer runtime management; should be evaluated separately xFormers / Flash / Sage / custom nodes: test separately from core GPU support 8 GB VRAM: still a real constraint, but a different problem For someone who just wants to generate images rather than debug Python packaging, I would probably start with a clean current **ComfyUI** or **Forge Neo** installation, verify one ordinary generation, and only then recreate the desired Fooocus/extensions workflow. That should tell you very quickly whether you are dealing with the RTX 5060 itself, an old packaged runtime, or one particular optional component.

The Nvidia RTX 5060 is incompatible with all image processing programs, such as Fooocus, RuinedFooocus, etc. Don’t drive yourselves crazy; until future updates or new programs arrive, don’t waste your time.

Aooo · Fooocus · Song · 2025