Generation errors surface raw, un-actionable messages (e.g. "Cannot copy out of meta tensor")

Problem

When generation fails inside the Generate Image (Diffusion Pipeline) node, the user is shown the raw underlying exception with no context about what went wrong or how to fix it. A user reported this example:

Node 'Generate Image (Diffusion Pipeline)' execution failed: Attempted to execute node 'Generate Image (Diffusion Pipeline)'. Failed with error: Cannot copy out of meta tensor; no data!

Cannot copy out of meta tensor; no data! is a low-level PyTorch/accelerate error (typically about model weights still being on the meta device / a device-placement or offloading misconfiguration). To a user it is opaque — it gives no indication of:

• what they did wrong (or whether it's a bug vs. a config/environment issue),
• which step failed (model load, device placement, the actual diffusion run),
• what they could change to fix it.

The user is new to Griptape Nodes and called this out specifically as the main rough edge: "my only downside is that some error messages are hard to debug … sounds blurry to me."

Origin

Reported by a user in the community Discord, evaluating the diffusers node library (~5 hours of usage). Surface: Generate Image (Diffusion Pipeline) node in griptape-nodes-library-diffusers. Reproducibility unknown — reported as a representative example of a broader pain point rather than a single repro.

Impact

Hurts the new-user experience for the diffusers library. Opaque, un-actionable error text makes failures hard to self-diagnose, increases support load, and discourages adoption. Likely affects more than just this one error string — the underlying issue is that raw exceptions propagate to the UI without being caught and translated into something a user can act on.

Possible direction

One option, not a prescribed fix:

• Catch known failure modes (e.g. meta-tensor / device-placement errors, OOM, missing model files, unsupported dtype/device combos) and wrap them in messages that name the likely cause and a suggested remedy.
• For unrecognized errors, still add context (which node/step failed, the model/pipeline involved) rather than surfacing the bare exception.
• Consider a short troubleshooting section in the README mapping common errors to fixes.