FramePack Torch CUDA Not Enabled: Diagnose and Fix the Right Environment

AI Overview

What does “Torch not compiled with CUDA enabled” mean in FramePack?

FramePack started a PyTorch build that cannot use CUDA, usually because a CPU-only wheel was installed or a different Python environment launched the app. It is an environment error, not proof that the GPU lacks enough VRAM.

How do you check whether FramePack can see CUDA?

Run the diagnostic with FramePack’s exact Python executable and inspect the Torch version, torch.version.cuda, and torch.cuda.is_available(). The last value must be True before FramePack can generate on an NVIDIA GPU.

Does installing the NVIDIA CUDA Toolkit fix FramePack?

Not necessarily. A current NVIDIA driver is required, but the PyTorch wheel also needs CUDA support and must match the Python environment that launches FramePack. Installing another toolkit cannot turn a CPU-only Torch wheel into a CUDA build.

Why does FramePack show “no kernel image” after CUDA is enabled?

That is a different failure: Torch may see CUDA, but its binaries do not support the GPU’s architecture. Install a mutually compatible Torch, torchvision, and torchaudio set recommended for that GPU and the current official FramePack package.

What the FramePack CUDA error actually means

Separate capacity from runtime support

The message “Torch not compiled with CUDA enabled” appears before video quality or generation length matters. It means the Python process running FramePack cannot execute CUDA operations. Buying more VRAM will not repair a CPU-only package, and reducing resolution will not make an unavailable CUDA backend become available.

FramePack’s official requirements currently name Windows or Linux, an NVIDIA RTX 30-, 40-, or 50-series GPU that supports fp16 and bf16, and at least 6GB of VRAM. A machine can satisfy those requirements while the active Python environment still contains the wrong Torch build. This is why the first job is identifying the executable, not reinstalling random GPU tools.

Illustrative generated output: the repair is complete only when a full moving result—not merely the interface—can be produced and reviewed.

There are four useful states:

The broader FramePack 6GB VRAM setup guide covers installation and memory planning. This page owns the narrower search job: repairing CUDA visibility without mixing several speculative fixes.

Confirm which Python FramePack is using

Fingerprint the runtime before changing it

Close FramePack, then run one diagnostic from the directory and interpreter used by its launcher. In the official Windows one-click package, the important interpreter is the embedded Python under the package, not whichever python.exe appears first in the system path. A useful command is:

system\python\python.exe -c "import sys, torch, importlib.metadata as m; print(sys.executable); print(m.version('torch')); print(torch.version.cuda); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'no CUDA device')"

For a manual Linux install, activate the dedicated environment and use the same check:

which python
python -m pip show torch
python -c "import sys, torch, importlib.metadata as m; print(sys.executable); print(m.version('torch')); print(torch.version.cuda); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'no CUDA device')"

Record the output before installing anything. The executable path proves which environment you inspected. The Torch version identifies the package build. torch.version.cuda reports which CUDA runtime the wheel was built against, while torch.cuda.is_available() is PyTorch’s direct availability test.

Run nvidia-smi separately. If it cannot list the NVIDIA GPU, repair or update the driver before touching Python. If it lists the GPU but Torch returns False, the driver sees hardware while the current PyTorch process does not. Do not use Task Manager’s default graph as the deciding test; it may show a display engine rather than CUDA work.

Illustrative baseline frame: save one simple source, one prompt, and the environment fingerprint before making a repair.

Fix the Windows one-click package

Restore a clean embedded environment

For Windows, the official FramePack route is the one-click package labeled with its CUDA and PyTorch versions. The repository instructs users to extract it, run update.bat, and launch with run.bat; the update step matters because older files may retain fixed bugs. Download only from the official FramePack repository described in the research notes, not from look-alike FramePack websites.

If the embedded environment was modified, the lowest-risk repair is a clean extraction rather than layering more wheels into a damaged copy. Preserve source images, prompts, completed videos, and any documented settings. Keep large model files only when their paths and hashes are understood; do not copy the old system\python, site-packages, or activation scripts into the new package because those files are the likely conflict.

Run the embedded-Python fingerprint again before opening the GUI. If CUDA is available there but the launcher still fails, inspect the batch file and terminal header for the Python path it actually starts. A desktop shortcut, Pinokio wrapper, or old batch file may point at a different folder even though the clean package is correct.

Avoid a blind system-wide pip install torch. It can update a global environment while FramePack continues using its embedded copy, or replace a compatible trio with mismatched Torch, torchvision, and torchaudio packages. Use python -m pip with the exact tested executable and obtain the install command from the official FramePack package or current PyTorch selector.

If repeated dependency repairs are consuming more time than the local workflow saves, compare the operational cost with a managed GPU workflow benchmark. The decision is not only render speed; it includes environment recovery, model storage, and repeatability.

Repair a manual Linux or virtual-environment install

Rebuild one clean Python 3.10 baseline

FramePack’s official Linux instructions recommend an independent Python 3.10 environment. Create a new environment instead of repairing a shared ComfyUI environment in place. Activate it, confirm the executable path, then install the CUDA-enabled PyTorch set and FramePack requirements using the current commands in the official repository. At the time of this research, FramePack’s README specifies the CUDA 12.6 PyTorch index for its baseline Linux route.

python3.10 -m venv framepack-env
source framepack-env/bin/activate
python -m pip install --upgrade pip
python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
python -m pip install -r requirements.txt
python -m pip check

Before running FramePack, repeat the fingerprint and require torch.cuda.is_available() to return True. Then start python demo_gradio.py from the same activated shell. Do not install xformers, FlashAttention, SageAttention, TeaCache changes, or quantized components until the default PyTorch-attention path completes a known-good video. The official repository specifically recommends trying the baseline before SageAttention.

Mixing several AI tools in one environment is a common way to replace Torch indirectly. A later package may satisfy its own dependency by selecting a different wheel. Keep a text record of python -m pip freeze, the FramePack commit or package date, driver version, GPU model, and successful command. The ComfyUI freeze troubleshooting workflow uses the same principle: prove the smallest stable environment before optimizing it.

Fix GPU architecture and “no kernel image” errors

Treat RTX generation support as a separate branch

If torch.cuda.is_available() is True but FramePack raises “no kernel image is available for execution on the device,” do not return to the CPU-wheel fix. That message usually means the installed binaries lack kernels for the GPU architecture. This is especially important when a newer RTX generation arrives after an older one-click package was built.

Check the exact GPU name from the fingerprint, the driver shown by nvidia-smi, and the CUDA tag in the Torch version. Then choose one coherent Torch, torchvision, and torchaudio combination supported by the current official FramePack instructions and PyTorch selector. Do not combine a Torch wheel from one CUDA series with a torchvision wheel selected for another, and do not copy a SageAttention wheel built for a different Torch or Python version.

If the official package has not yet caught up with the architecture, the honest choices are to wait for a supported build, use a documented compatible package, or move the shot to a hosted path. Compilation from source is not the default fix for most users and adds compiler, toolkit, and architecture variables that make the environment harder to reproduce.

Illustrative motion checkpoint: after CUDA is visible, judge subject geometry and environment continuity across an actual change in position.

Prove the repair with a FramePack sanity check

Pass startup, sampling, export, and playback

FramePack’s official repository includes a sanity-check source image, prompt, default settings, and reference result. Use that test before your own production input, with TeaCache off and default attention. A passing repair has four gates: the model loads, sampling begins on the NVIDIA device, a playable video file completes, and the subject remains broadly recognizable through the result.

Play the official FramePack project-page motion sample

Actual FramePack output from the official project page: use it as a qualitative sanity-check reference, not a speed guarantee or proof of your hardware result.

Do not declare success when the GUI merely opens. Watch the terminal for the device name and explicit completion, then play the entire file. A slow first section can be warmup; a CUDA exception is a runtime failure; a completed but visually weak clip is a generation-quality problem and belongs in a different diagnosis. For a managed way to animate the same approved source frame, compare the Seedance image-to-video workflow after documenting the local result.

Illustrative approved end frame: the repeated rider, bicycle, workshop, and lighting make continuity failures easy to spot.

Save a small recovery card containing the Python path, Torch version, CUDA tag, availability result, GPU name, driver version, install command, FramePack version, and sanity-check outcome. For larger local pipelines, the ComfyUI batch-processing guide shows why reproducible inputs and per-job records matter after the runtime is stable. If the creative job matters more than maintaining that runtime, Seedance Agent can organize source images, prompts, output review, and targeted reruns in a managed workflow.

Conclusion

Fix “FramePack Torch CUDA not enabled” by testing FramePack’s exact Python first: a CPU-only wheel, invisible driver, wrong launcher environment, and unsupported GPU architecture require different repairs. Restore the official Windows package or a clean Linux Python 3.10 environment, keep the Torch package set coherent, require torch.cuda.is_available() to return True, and pass the official end-to-end sanity check before adding performance extensions. When local dependency maintenance is no longer the best use of production time, move the shot plan and review loop into Seedance Agent.