dreevelleandClaude Opus 4.8
11ae9a3718
Multithreaded 8/16-bit PNG sequence saver for ComfyUI. Encodes the frames of a batch in parallel (PyAV releases the GIL during compression), giving a large speedup over the built-in Save Image (Advanced) node for video/frame dumps with byte-identical output. Embeds prompt/workflow metadata in the first frame only by default. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
3.0 KiB
ComfyUI-FastImageSequence
A fast PNG image-sequence saver for ComfyUI.
The built-in Save Image (Advanced) node encodes each frame of a batch one at a time. For long sequences (video / frame dumps) that's slow — almost all the time goes into single-threaded zlib PNG compression, not metadata.
This node encodes the frames of a batch in parallel. PyAV releases the GIL during compression, so encoding scales with your CPU cores. The output bytes are identical to the built-in node — same pixels, same compression, same file size — it's just much faster.
It also embeds the prompt/workflow metadata in only the first frame by default, so the workflow is still recoverable from the sequence without bloating every single frame.
Node
Save Image Sequence (Fast 16-bit) (category: image)
| Input | Default | Notes |
|---|---|---|
images |
— | The image batch to save. |
filename_prefix |
ComfyUI |
Supports the usual tokens (%date:yyyy-MM-dd%, etc.). |
bit_depth |
16-bit |
8-bit or 16-bit PNG. |
metadata (advanced) |
first_frame |
first_frame / all / none. |
threads (advanced) |
0 (auto) |
Encoder threads. 0 = min(8, CPU count). |
Drop it in wherever you'd use Save Image (Advanced). It handles the whole batch itself, including the first-frame metadata, so wire the entire sequence into this one node (don't split it across two save nodes — that would collide on the sequential file numbering).
Performance
Measured on a 16-core CPU, 457 frames at 1920×1088, 16-bit RGB (worst-case noisy content; real frames are faster):
| threads | ms/frame | 457 frames |
|---|---|---|
| 1 (built-in, serial) | 372 | ~170 s |
| 4 | 101 | ~46 s |
| 8 (auto default) | 60 | ~27 s |
| 16 | 43 | ~20 s |
| 24 | 44 | ~20 s (no gain) |
Scaling flattens at your core count — there's no benefit to setting threads
higher than the number of CPU cores. 0 (auto = 8) already captures most of the
win; set it to your core count for the last few seconds.
For reference, metadata injection costs ~0.9 ms/frame (~0.2% of the total), so
the metadata option is about file size / cleanliness, not speed.
Installation
Clone into your ComfyUI custom_nodes directory and restart:
cd ComfyUI/custom_nodes
git clone https://github.com/dreevelle/ComfyUI-FastImageSequence.git
av, numpy and torch ship with ComfyUI, so there are no extra dependencies
to install.
Notes
- Output is byte-identical to Save Image (Advanced) for the same input — this node only changes how fast the frames are written, not the result.
- True 16-bit PNGs (IHDR bit depth 16). Note that Pillow downsamples 16-bit
color PNGs to 8-bit when reading them, so a
PIL.Image.open(...).modecheck will reportRGBeven though the file on disk is genuinely 16-bit. - Self-contained: it copies the small PNG encode/metadata helpers rather than importing from ComfyUI internals, so a core update won't break it.