Stable Diffusion 5060 Ti Fixes for 2026

You just slotted in a brand-new RTX 5060 Ti, booted up, and nothing works the way it should. Stable Diffusion falls back to CPU, ComfyUI crashes before the first node runs, and you’re staring at a card you spent serious money on — sitting completely idle. I’ve been troubleshooting AI workstations for over three decades, and I want to tell you upfront: the GPU is almost certainly fine. The problem is almost always the software stack underneath it. This guide covers every Stable Diffusion GPU upgrade 5060 Ti migration issues I’ve encountered, tested, and resolved — so you can stop guessing and start generating.

📌 Definition Block Stable Diffusion GPU upgrade 5060 Ti migration issues is the set of compatibility failures that occur when a Stable Diffusion, ComfyUI, or A1111 setup is moved to an RTX 5060 Ti and the existing CUDA/PyTorch stack cannot execute workloads on the new Blackwell architecture. A typical example: the app detects the card and lists it by name, but throws CUDA: no kernel image is available for execution on the device the moment you try to generate an image.

What Is the Direct Fix for RTX 5060 Ti Migration Issues?

Most users I see hit this wall for one reason: they treat the GPU swap as a hardware-only event. You swap the card, update the driver, and expect everything else to carry over. It doesn’t. The old environment was built against an older CUDA compute target, and NVIDIA‘s new Blackwell architecture uses sm_120 — a compute capability that simply didn’t exist in older PyTorch wheels. NVIDIA Documentation

The fix has three pillars, and skipping any one of them will leave you stuck:

• Reinstall PyTorch with CUDA 12.8 or newer
• Rebuild the virtual environment instead of patching it
• Disable old custom nodes or extensions temporarily

Reinstall PyTorch with CUDA 12.8 or Newer

Go to the official PyTorch installer selector at PyTorch and choose the build that targets CUDA 12.8 or above. In my tests, installing the nightly or the latest stable release with --index-url https://download.pytorch.org/whl/cu128 is the fastest path. Once that wheel is in place, the 5060 Ti is recognized at the kernel level, not just at the device enumeration level.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

After installation, confirm the GPU is fully operational before launching any Stable Diffusion front-end:

python -c "import torch; print(torch.cuda.get_device_name(0)); print(torch.version.cuda)"

If the output shows NVIDIA GeForce RTX 5060 Ti alongside a CUDA version of 12.8 or higher, you are good to proceed.

Rebuild the Virtual Environment Instead of Patching It

I used to try patching old environments — upgrading torch in place, forcing package reinstalls, hoping nothing breaks. In my experience it almost always leads to a half-working setup where some nodes use the old binaries and others use the new ones. The only clean solution is to delete the venv or env folder entirely and recreate it from scratch.

# From inside your Stable Diffusion WebUI or ComfyUI root folder:
rm -rf venv # Linux/macOS
rd /s /q venv # Windows Command Prompt

Then recreate:
python -m venv venv
venv\Scripts\activate # Windows
source venv/bin/activate # Linux/macOS

Then reinstall requirements:
pip install -r requirements.txt

This step alone resolves the migration issue for the majority of users I’ve seen post about Stable Diffusion GPU upgrade 5060 Ti migration issues in community forums.

Disable Old Custom Nodes or Extensions Temporarily

If you are on ComfyUI with a large set of custom nodes, some of those nodes may pin specific older torch or CUDA versions in their own requirements.txt. Those secondary pins can silently downgrade your torch build after installation.

Before you diagnose further, move your custom_nodes folder out temporarily and test generation with a minimal base install. If it works, bring nodes back one by one to identify the conflict.

Why Does Stable Diffusion Detect the GPU But Not Use It?

This is the question that throws people off the most. Task Manager shows the 5060 Ti, NVIDIA Control Panel lists it, and the Stable Diffusion launch log even prints its name — but the moment inference starts, it falls back to CPU or crashes entirely.

The reason is that GPU detection and GPU execution are two completely separate operations. Detection is handled by the operating system and the display driver. Execution is handled by the CUDA runtime and the compiled kernel inside your PyTorch wheel. If the wheel was compiled before the 5060 Ti’s sm_120 compute target was added to the supported list, it will find the card and then refuse to run kernels on it. PyTorch

sm_120 Is the Key Compatibility Point

The RTX 5060 Ti belongs to NVIDIA’s Blackwell architecture, which uses the sm_120 CUDA compute capability. Many PyTorch builds shipped before mid-2025 target up to sm_90 (Hopper/Ada) at most. When the CUDA runtime encounters an sm_120 device and cannot find a matching compiled kernel, it throws the error shown below.

This is the verbatim error reported in community migration threads after upgrading to the RTX 5060 Ti:

NVIDIA GeForce RTX 5060 Ti with CUDA capability sm_120 is not compatible
with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60
sm_70 sm_75 sm_80 sm_86 sm_90.
If you want to use the NVIDIA GeForce RTX 5060 Ti GPU with PyTorch,
please check the instructions at https://pytorch.org/get-started/locally/

And in some A1111 and Fooocus setups, the second error form appears:

RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,
so the stacktrace below might be incorrect.

Both messages mean the same thing: your torch wheel does not contain a kernel binary for sm_120. The fix is a torch reinstall — not a driver update, not a Windows reinstall.

Bundled Installers Often Ship Outdated Torch Wheels

One-click packages — whether for A1111, ComfyUI portable, or EasyDiffusion — bundle a specific PyTorch version at the time of their last release. If the package was built before Blackwell support landed in the official torch distribution, it will ship with an older wheel and will not work on the RTX 5060 Ti out of the box.

The mistake I see most is users updating the one-click launcher itself (the .exe or batch file), thinking that automatically updates torch. It does not. The embedded Python environment and its packages are separate.

One-Click Packages May Hide the Real Dependency Problem

Portable packages often use embedded Python runtimes that are isolated from your system Python. That means even if you install the correct torch on your system, the packaged app keeps using its own bundled version.

To fix this correctly in a portable setup, you need to locate the embedded python_embeded (or similar) folder inside the package, activate that specific Python, and reinstall torch into it explicitly. Refer to the package maintainer’s GitHub issues for the exact path — the ComfyUI team has an active Blackwell support thread that details this step. NVIDIA Documentation

Which Errors Point to a CUDA Mismatch?

Knowing which error message you’re seeing helps you triage quickly. Here is a breakdown of the three most common error patterns I encounter with Stable Diffusion GPU upgrade 5060 Ti migration issues:

Error Text	Root Cause	Resolution
sm_120 is not compatible with the current PyTorch installation	Torch wheel compiled before Blackwell support	Reinstall torch with CUDA 12.8+
no kernel image is available for execution on the device	CUDA runtime found no matching kernel binary for the GPU	Rebuild venv + reinstall torch
GPU detected in logs, but inference runs on CPU	Torch falls back silently; --use-cpu may be auto-applied	Remove CPU fallback flags; reinstall torch

no kernel image is available

This error is the most common post-migration error I’ve seen for any architecture jump — from Maxwell to Turing, from Turing to Ampere, and now from Ada to Blackwell. It always means the compiled CUDA kernel inside your Torch installation does not include the binary for your specific GPU compute capability. The card is present; the code to run on it is simply missing from your current wheel.

sm_120 is not compatible

This is the cleaner, more informative version of the same root problem. PyTorch lists the compute capabilities it was compiled for (e.g., sm_37 through sm_90), and your GPU’s sm_120 is not in that list. The error message itself points you to PyTorch for the fix, which is exactly the right place to go.

GPU Present But Inference Stays on CPU

This is the silent failure mode, and in some ways it’s the most frustrating because there’s no obvious red error. The model loads, the generation starts — but it takes five minutes per image instead of five seconds. Check your launch logs for lines like Using CPU, No CUDA device found, or Torch device: cpu. If you see those, your torch still doesn’t see the GPU as execution-ready, and the same torch rebuild applies.

How Do You Migrate Safely After a GPU Upgrade?

The cleanest migrations I’ve done follow a strict sequence. Swapping the card is step one of seven — not step one of one. Here is the checklist I apply every time I upgrade an AI workstation GPU:

1. Install the latest NVIDIA driver — use DDU (Display Driver Uninstaller) to clean the old driver first, then install fresh from NVIDIA’s website. Do not use Windows automatic driver update for this.
2. Verify the new GPU is visible — run nvidia-smi in a terminal. It should show GeForce RTX 5060 Ti and the driver version.
3. Delete the old virtual environment — rm -rf venv or the equivalent. Do not patch; rebuild.
4. Reinstall Python dependencies — run pip install -r requirements.txt fresh.
5. Install Blackwell-compatible PyTorch — use the official selector at PyTorch and select CUDA 12.8 or newer.
6. Test with a minimal CUDA command — python -c "import torch; print(torch.cuda.get_device_name(0))" must print the GPU name without errors.
7. Launch Stable Diffusion — only after step 6 succeeds.

For a full overview of GPU troubleshooting across all Stable Diffusion toolchains, see the complete guide.

Confirm NVIDIA Driver Compatibility First

The NVIDIA driver is the foundation. Even with the correct torch wheel, a missing or corrupted driver can prevent CUDA from functioning. Run nvidia-smi and confirm the output shows your card, a current driver version, and a CUDA version of 12.8 or higher in the top right of the table. If the CUDA version shown is lower than 12.8, update the driver before doing anything else. NVIDIA CUDA Docs

Recreate the Environment From Scratch

I want to reinforce this point because it runs counter to the instinct most users have. When something is broken, the reflex is to fix it rather than replace it. With Python virtual environments tied to specific CUDA targets, replacement is almost always faster and more reliable than repair. The environment is small — rebuilding takes five minutes. Debugging a partially upgraded one can take five hours.

Test With a Minimal Torch CUDA Command

Before you open any Stable Diffusion front-end, run this three-line test:

import torch
print(torch.cuda.is_available()) # Must return True
print(torch.cuda.get_device_name(0)) # Must print your GPU name
print(torch.version.cuda) # Must show 12.8 or higher

If any of those lines fail or return unexpected values, stop there and fix the environment before proceeding. Launching a full SD stack on a broken torch base will produce misleading errors that are harder to trace back to the real cause.

Comparison: Old GPU vs. RTX 5060 Ti Setup Requirements

Setting	Pre-Blackwell GPU (e.g. RTX 3080)	RTX 5060 Ti (Blackwell)
CUDA Compute Capability	sm_86	sm_120
Minimum PyTorch Build	CUDA 11.x builds acceptable	CUDA 12.8+ required
One-Click Bundle Compatibility	Usually works out of the box	Requires manual torch update
Driver Version Requirement	Older drivers often sufficient	Current driver strongly recommended
Virtual Environment Reuse	Generally safe	Rebuild recommended
Custom Node Compatibility	Broad support	Verify each node individually

Frequently Asked Questions

Does the RTX 5060 Ti require a special Stable Diffusion build?

Often yes. Because the RTX 5060 Ti uses the Blackwell architecture with sm_120 compute capability, it needs a PyTorch build compiled with support for that target. Many older one-click bundles for A1111 or ComfyUI were built before that support was added. In practice, this means manually reinstalling torch with a CUDA 12.8+ build even if the rest of the package looks up to date. The official guidance from PyTorch walks through the correct installation command for your OS and package manager.

Is updating the NVIDIA driver enough to fix the migration issues?

No — and this is the most common misunderstanding I encounter. The NVIDIA driver controls hardware access at the OS level, but it does not change what CUDA kernels are compiled into your PyTorch wheel. You can have the very latest driver installed and still get sm_120 is not compatible errors if your torch build is old. The driver and the Python environment are two independent layers, and both must support Blackwell for the 5060 Ti to run inference correctly. NVIDIA Documentation

Why did Stable Diffusion work on my old GPU but fail on the 5060 Ti with the same setup?

Your old GPU — whether an RTX 3080, 3090, or even a 40-series card — uses a compute capability that was already included in the PyTorch wheel you installed at the time. The 5060 Ti’s sm_120 was not yet part of that wheel. It’s the same codebase, but the compiled binary for your new GPU’s instruction set simply isn’t there. It’s functionally similar to running a 64-bit application on a 32-bit OS: the software exists, the hardware exists, but the binary format doesn’t match.

Should I keep my old virtual environment after swapping to the RTX 5060 Ti?

Not if it was built before Blackwell support was available in torch. Keeping it introduces a high risk of partial upgrades where some packages use the new torch wheel and others still reference old cached binaries. The cleanest path is to delete the environment folder, recreate it, and reinstall from requirements.txt plus a fresh Blackwell-compatible torch. Most users who rebuild from scratch resolve their Stable Diffusion GPU upgrade 5060 Ti migration issues in under 30 minutes.

What if I’m using a portable ComfyUI package and can’t find the venv?

Portable packages typically use a folder named python_embeded or python inside the package root rather than a standard venv. You need to call that specific Python binary directly to install the correct torch:

# From the ComfyUI portable root:
python_embeded\python.exe -m pip install torch torchvision torchaudio ^
--index-url https://download.pytorch.org/whl/cu128

After this, relaunch ComfyUI using its normal startup script. The bundled Python will now have a Blackwell-compatible torch wheel.

How do I confirm the fix actually worked before running a full generation?

Run the three-line test in the terminal Python of your environment:

import torch
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0))
print(torch.version.cuda)

All three lines should return True, your GPU name, and 12.8 or higher respectively. If they do, your stack is clean and the 5060 Ti is fully ready for inference. For additional troubleshooting paths not covered here, visit the complete guide.

Written by Ice Gan — AI Tools Researcher with 33 years of IT infrastructure and GPU workstation experience. All steps were validated against community-reported migration logs and official documentation from PyTorch and NVIDIA Documentation.