ComfyUI Freezes After Video Generation: Recovery Guide

AI Overview

Why does ComfyUI freeze after video generation?

The sampler may have finished while decoding, encoding, preview delivery, a custom node, or the browser is still blocked. Separate a slow final stage from a dead backend before restarting anything.

How do I tell whether ComfyUI is still working?

Read the terminal, GPU utilization, queue state, and output folder together. A changing log, active GPU, or growing file suggests work; an unchanged stack and idle device suggest a stall.

Should I clear VRAM when ComfyUI gets stuck?

Only after saving evidence and cancelling safely. Freeing models can recover a healthy process, but it will not repair a broken CUDA context, incompatible custom node, or failed encoder.

What is the fastest safe fix?

Save the workflow and log, stop the active prompt, restart ComfyUI, and run a tiny known-good video graph. Add the original nodes back in groups until the freeze returns.

Identify What Actually Froze

“ComfyUI freezes after video generation” can describe four different failures. The canvas may stop updating while the backend continues; the sampler may finish but VAE decoding or video encoding may still be running; the Python process may be alive but waiting on a custom node; or the GPU driver may have stopped responding. These cases look similar from the browser and require different fixes.

Start by recording the last visible node and the last terminal line. Do not immediately close every window. A useful diagnosis needs the workflow JSON, ComfyUI and frontend versions, custom-node versions, Python and PyTorch versions, GPU model, driver, launch arguments, dimensions, frame count, codec, and the last 30–50 log lines.

Frontend freeze or backend freeze?

Open the output folder and check whether a file is being created or its size is changing. Watch GPU utilization and memory without launching another prompt. If the terminal continues to print progress while the browser appears frozen, reload only the frontend after saving the workflow. If the terminal is static, GPU utilization is idle, and the queue never advances, treat it as a backend stall.

A small, visually clear baseline makes it easier to tell whether the pipeline completes before the heavy graph is restored.

Finished sampling is not finished delivery

Video graphs often perform expensive work after diffusion: decode latent frames, combine batches, move tensors between devices, interpolate, write audio, and encode the container. A progress bar at 100% may therefore mean the sampling stage is complete, not that the MP4 is finished. Check the exact last node rather than assuming “100%” equals a freeze.

Preserve Evidence Before You Restart

A restart is a recovery action, not a diagnosis. Before using it, export the workflow and copy the terminal text. Note whether the whole operating system froze, only the browser stopped, or only one queue item stalled. A system-wide lockup points more strongly toward a driver, CUDA backend, unstable overclock, power, or memory-pressure problem than a single unresponsive tab.

Create a compact incident record:

ComfyUI / frontend version:
Python / PyTorch / CUDA / driver:
GPU and system RAM:
Launch arguments:
Workflow and model:
Width × height, frames, batch size:
Last completed node:
Last terminal line:
Browser state / backend state / OS state:
First run or repeated-run failure:

Save partial output without trusting it

If an MP4 exists, inspect its duration and streams before treating it as complete. A truncated container may open but end early; an image sequence may contain fewer frames than requested. Keep the partial artifact under a failure-specific name so the next run cannot overwrite evidence.

The ComfyUI crop-video-with-audio guide explains why video and audio paths should remain explicit. That separation also helps isolate whether the freeze begins before muxing or inside the final encode.

Reduce Memory and Encoding Pressure

Video generation multiplies memory pressure across resolution, frame count, batch size, latent channels, VAE decode, interpolation, and encoding. Lowering one dimension can be more informative than randomly toggling memory flags. First halve the frame count. If the graph still stalls, reduce resolution. Then disable interpolation, upscaling, and optional previews while preserving the same base model and sampler.

Use a staged memory test

Run these tests in order and record whether each finishes:

1. One short clip at the workflow's smallest supported resolution.
2. The same clip with the intended VAE and decoder.
3. The same clip with the intended encoder but no interpolation.
4. The complete post-processing chain at short duration.
5. The intended duration only after all earlier stages pass.

Do not increase resolution and frame count in the same test. If a small clip succeeds and the long clip repeatedly stalls at decode, the graph is probably viable but the memory or decode plan is not. If even the minimum graph fails, investigate compatibility before tuning quality.

Use detail-rich but short material to test decode stability; length and resolution should be increased separately.

Avoid “fixes” that hide a driver failure

Clearing model memory, unloading previews, or restarting the frontend can help a healthy backend release resources. They cannot reset every failed GPU context. If the whole desktop locks, the terminal stops without a Python exception, or the driver resets, reproduce with a minimal official workflow and conservative hardware settings before blaming the prompt.

For model-file and decoder mismatches, follow the VAE loading checklist. A renamed or misplaced file is not converted into a compatible VAE simply because the loader can see it.

Isolate Custom Nodes and Frontend State

Custom nodes add Python packages, compiled extensions, UI code, encoders, and device-management behavior. Updating everything at once destroys the comparison that could reveal the cause. Save the working environment first, then disable third-party nodes and run the smallest core workflow available for the same media type.

If the core graph succeeds, re-enable custom-node groups by function: model loaders, conditioning tools, video utilities, interpolation, upscalers, then encoders. When the failure returns, split that group again. Record the node repository and commit rather than only its display name.

Test the browser separately

A frontend can stop showing progress even while the backend finishes. Reconnect from a fresh browser tab and inspect the queue or history before killing Python. Disable browser extensions for one test, avoid leaving several heavy workflow tabs open, and compare the frontend version with the backend version. A canvas refresh that restores status is evidence of a display-state problem, not proof that the GPU froze.

A completed delivery frame should retain believable skin, steam, food texture, and small hand detail after the full pipeline finishes.

Run a Known-Good Recovery Workflow

After a safe restart, do not queue the full production graph first. Use a tiny workflow that has completed on this machine before: one model, one short clip, no optional custom node, no interpolation, no upscale, and a standard encoder. This answers the most important question—whether the installation video stack is operational at all.

Short known-good motion baseline for ComfyUI recovery testing

A five-second moving output is enough to verify generation, decode, encoding, and playback before rebuilding the expensive graph.

Restore the graph by checkpoints

Make one saved JSON per checkpoint: 01-core, 02-loader, 03-conditioning, 04-post, and 05-final. Queue only one job while diagnosing. When a checkpoint fails, compare its terminal output with the previous successful checkpoint and remove half of the newly added nodes. This binary-search approach is faster than changing flags without a hypothesis.

If the queue itself is the problem, the ComfyUI batch-processing guide provides a safer pattern for independent jobs, explicit output names, and resumable batches. A failed item should not force you to repeat every approved clip.

Choose Local Recovery or a Hosted Workflow

Local ComfyUI is valuable when you need exact nodes, custom models, reproducible environment control, and access to the machine. It also makes you responsible for driver compatibility, Python dependencies, custom-node commits, memory planning, logging, and restarts. The right choice depends on whether those controls are part of the creative advantage or merely production overhead.

The local-versus-cloud AI video guide gives a practical decision framework. Stay local when the graph itself is a reusable asset and someone owns its maintenance. Use a hosted route when the team primarily needs approved shots, consistent references, predictable reruns, and delivery rather than continuous environment repair.

Where Seedance Agent fits

Seedance Agent can keep references, shot plans, approvals, and reruns attached to the production task without asking the editor to maintain a large local dependency graph. It does not make a broken local ComfyUI installation healthy. It provides an alternate production path when diagnosing drivers, nodes, and encoders costs more than rerunning the affected shot through a managed workflow.

The useful handoff is selective: preserve the approved prompt, source image, aspect ratio, duration, motion requirements, and failed-shot evidence. Regenerate only the rejected unit, compare it against the same acceptance checklist, then return the accepted clip to the edit.

Conclusion

When ComfyUI freezes after video generation, first identify whether the browser, backend, post-processing node, encoder, GPU driver, or entire operating system stopped. Save the workflow and logs, test a tiny known-good clip, reduce frames before resolution, isolate custom nodes in groups, and restore the graph through named checkpoints. If local maintenance is no longer creating useful control, move the next approved shot into a managed Seedance workflow →