CUDA out of memory during AutoencoderKL tiling decode intermediate latents

AutoencoderKL ran out of VRAM while decoding tiled latent intermediates. Tiling reduces the peak for the main VAE operation, but overlap buffers, output assembly, dtype, and concurrent model residency still consume memory. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent multimodal failures.

Symptom

CUDA out of memory during AutoencoderKL tiling decode intermediate latents

Root cause

AutoencoderKL ran out of VRAM while decoding tiled latent intermediates. Tiling reduces the peak for the main VAE operation, but overlap buffers, output assembly, dtype, and concurrent model residency still consume memory. The decisive evidence is the first log line that precedes "CUDA out of memory during AutoencoderKL tiling decode intermediate latents" and differs from a healthy run.

Recommended fix

decode one image at a time, keep the VAE in a reduced precision supported by the model, and remove unrelated GPU residents before retrying.

How Denpex helps

Denpex investigates CUDA out of memory during AutoencoderKL tiling decode intermediate latents using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

The literal signature is "CUDA out of memory during AutoencoderKL tiling decode intermediate latents". It is a environment failure associated with diffusion, multimodal, cuDNN, and mixed precision. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

AutoencoderKL ran out of VRAM while decoding tiled latent intermediates. Tiling reduces the peak for the main VAE operation, but overlap buffers, output assembly, dtype, and concurrent model residency still consume memory. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

• The workload stops or loses forward progress after emitting "CUDA out of memory during AutoencoderKL tiling decode intermediate latents".
• A retry on the same configuration reproduces the failure because the causal state has not changed.
• The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
• Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

• diffusion, multimodal, cuDNN, and mixed precision
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

• ✓Find the first occurrence of "CUDA out of memory during AutoencoderKL tiling decode intermediate latents" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓measure allocated and reserved memory before each tile and during output assembly. Confirm the tile and overlap settings actually reduce the failing intermediate.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from image decode after the VAE memory budget is corrected only after the control passes.

• AutoencoderKL ran out of VRAM while decoding tiled latent intermediates. Tiling reduces the peak for the main VAE operation, but overlap buffers, output assembly, dtype, and concurrent model residency still consume memory.
• The decisive evidence is the first log line that precedes "CUDA out of memory during AutoencoderKL tiling decode intermediate latents" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

CUDA out of memory during AutoencoderKL tiling decode intermediate latents

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

decode one image at a time, keep the VAE in a reduced precision supported by the model, and remove unrelated GPU residents before retrying. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from image decode after the VAE memory budget is corrected.

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "CUDA out of memory during AutoencoderKL tiling decode intermediate latents" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.