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Store masks inside exported videos and convert the example set to ImageEncode/ImageDecode

Browse Source 
Mask-in-video (the masks now travel with the video file):
- New videometa module: write/read a JSON payload under the lanpaint-mask
  mp4 metadata tag via PyAV (stream copy, movflags=use_metadata_tags), so
  exporting produces a NEW file and the source is never modified.
- Two server routes: GET /lanpaint/video_mask_meta (read the payload back)
  and POST /lanpaint/export_mask_video (remux into input/<base>_masked.mp4).
- The mask editor exports the painted keyframes (base64 PNGs) + audio
  intervals into the video; loading a video with the tag auto-restores the
  masks (editor and node preview). A video without the tag clears the
  workflow's masks - the video is the source of truth. The export also
  offers a native save dialog (showSaveFilePicker) when available.
- The video widget upload now uses video_upload so MP4s are selectable in
  the file chooser (was image_upload).
- LanPaint_ImageEncode accepts 5D latents from video VAEs encoding a single
  image (e.g. the Hunyuan video VAE) - fixes the Hunyuan example.

Example set conversion (one example per supported model, new files, old
pairs removed after being superseded):
- New *ImageEncode/*EncodeDecode example workflows for SDXL, Flux.1,
  Flux.2 Dev, Flux2 Klein, SD3.5, Hunyuan, Ideogram4, Krea2, Qwen Edit,
  Qwen Image, Z-Image, Wan 2.2 and MiniMax H3 (AV), all using
  LanPaint_ImageEncode/ImageDecode instead of VAEEncode +
  SetLatentNoiseMask + VAEDecode + MaskBlend; every example references its
  own example folder's masked input (hash-verified); preview jpgs generated
  from the test runs; the H3 example ships its output mp4 as a preview.
- HiDream example removed on request; Example_29 added with the H3 masked
  input video.

Fixes along the way: normalize node input.link/output.links fields against
the links arrays in all converted workflows (the frontend renders
null-link nodes as disconnected); stale Inpainting_mode widget removed from
the Flux.2 sampler; Lambda defaults 5.0 (first-order scheme); torchaudio
resample test assertion relaxed (edge ringing); the videometa tests updated
to the final export signature.
This commit is contained in:
scraed
2026-08-09 20:41:45 +08:00
parent aa8f4e81ac
commit 59aa08dff0
40 changed files with 20610 additions and 3727 deletions
Download Patch File Download Diff File Expand all files Collapse all files
__init__.py
+14
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@@ -92,3 +92,17 @@ except ModuleNotFoundError:
from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
WEB_DIRECTORY = "./web"
# ---------------------------------------------------------------------------
# Server routes — registered only when running inside ComfyUI (the ``server``
# module and ``folder_paths`` are ComfyUI internals not available in CI/tests).
# ---------------------------------------------------------------------------
try:
from server import PromptServer # noqa: F811 (re-export for convenience)
from .src.LanPaint.videometa import register_routes
register_routes(PromptServer.instance)
except Exception:
pass # not running inside ComfyUI — routes are not needed
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example_workflows/Flux2Dev_ImageEncode_Inpaint.json
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example_workflows/Flux2_Klein_EncodeDecode_Inpaint.json
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example_workflows/Flux_EncodeDecode_Inpaint.json
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{
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"revision": 0,
"last_node_id": 84,
"last_link_id": 229,
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{
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"type": "CLIPTextEncode",
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],
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],
"flags": {},
"order": 2,
"mode": 0,
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"name": "clip",
"type": "CLIP",
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}
],
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{
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"name": "CONDITIONING",
"type": "CONDITIONING",
"slot_index": 0,
"links": [
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]
}
],
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"properties": {
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"name": "MODEL",
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]
},
{
"label": "CLIP",
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],
"properties": {
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"Node name for S&R": "CheckpointLoaderSimple"
},
"widgets_values": [
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},
{
"id": 73,
"type": "LanPaint_KSampler",
"pos": [
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],
"size": [
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"flags": {},
"order": 7,
"mode": 0,
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{
"name": "model",
"type": "MODEL",
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{
"name": "positive",
"type": "CONDITIONING",
"link": 207
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 210
},
{
"name": "latent_image",
"type": "LATENT",
"link": 224
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"slot_index": 0,
"links": [
225
]
}
],
"properties": {
"cnr_id": "LanPaint",
"ver": "56bd6c04e89124cd06682b304245d6ddf8b20522",
"Node name for S&R": "LanPaint_KSampler"
},
"widgets_values": [
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1,
"euler",
"simple",
1,
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"🖼️ Image Inpainting"
]
},
{
"id": 48,
"type": "SaveImage",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
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"type": "IMAGE",
"link": 229
}
],
"outputs": [],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.23"
},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 75,
"type": "LoadImage",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
221,
227
]
},
{
"name": "MASK",
"type": "MASK",
"links": [
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227
]
}
],
"properties": {
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"ver": "0.3.27",
"Node name for S&R": "LoadImage"
},
"widgets_values": [
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"image"
]
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{
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],
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"name": "vae",
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"link": 222
},
{
"localized_name": "mask",
"name": "mask",
"shape": 7,
"type": "MASK",
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}
],
"outputs": [
{
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"name": "latent",
"type": "LATENT",
"links": [
224
]
}
],
"properties": {
"Node name for S&R": "LanPaint_ImageEncode"
}
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],
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},
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},
{
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"name": "image",
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"type": "IMAGE",
"link": 227
},
{
"localized_name": "mask",
"name": "mask",
"shape": 7,
"type": "MASK",
"link": 228
},
{
"localized_name": "blend_overlap",
"name": "blend_overlap",
"shape": 7,
"type": "INT",
"widget": {
"name": "blend_overlap"
},
"link": null
}
],
"outputs": [
{
"localized_name": "image",
"name": "image",
"type": "IMAGE",
"links": [
229
]
}
],
"properties": {
"Node name for S&R": "LanPaint_ImageDecode"
},
"widgets_values": [
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]
}
],
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],
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[
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],
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{
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{
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"title": "Load Model and Set Prompts",
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},
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example_workflows/Flux_Inpaint.json
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{
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"type": "LATENT",
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]
}
],
"properties": {
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],
"title": "CLIP Text Encode (Positive Prompt)",
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],
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"title": "CLIP Text Encode (Negative Prompt)",
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{
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"inputs": [
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],
"outputs": [
{
"label": "CONDITIONING",
"name": "CONDITIONING",
"type": "CONDITIONING",
"slot_index": 0,
"links": [
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]
}
],
"properties": {
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},
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{
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],
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"mode": 0,
"inputs": [],
"outputs": [
{
"label": "MODEL",
"name": "MODEL",
"type": "MODEL",
"slot_index": 0,
"links": [
197
]
},
{
"label": "CLIP",
"name": "CLIP",
"type": "CLIP",
"slot_index": 1,
"links": [
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]
},
{
"label": "VAE",
"name": "VAE",
"type": "VAE",
"slot_index": 2,
"links": [
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]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.26",
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},
"widgets_values": [
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]
},
{
"id": 73,
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"pos": [
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],
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],
"flags": {},
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"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 197
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 207
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 210
},
{
"name": "latent_image",
"type": "LATENT",
"link": 186
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"slot_index": 0,
"links": [
187
]
}
],
"properties": {
"cnr_id": "LanPaint",
"ver": "56bd6c04e89124cd06682b304245d6ddf8b20522",
"Node name for S&R": "LanPaint_KSampler"
},
"widgets_values": [
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1,
"euler",
"simple",
1,
5,
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"🖼️ Image Inpainting"
]
},
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"## Report workflow issue\n\nIf you found any issues when running this workflow, [report template issue here](https://github.com/Comfy-Org/workflow_templates/issues)\n\n\n## Model links\n\n**text_encoders**\n\n- [qwen_3_4b.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors)\n\n**loras**\n\n- [pixel_art_style_z_image_turbo.safetensors](https://huggingface.co/tarn59/pixel_art_style_lora_z_image_turbo/resolve/main/pixel_art_style_z_image_turbo.safetensors)\n\n**diffusion_models**\n\n- [z_image_turbo_bf16.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/diffusion_models/z_image_turbo_bf16.safetensors)\n\n**vae**\n\n- [ae.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors)\n\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 text_encoders/\n│ │ └── qwen_3_4b.safetensors\n│ ├── 📂 loras/\n│ │ └── pixel_art_style_z_image_turbo.safetensors\n│ ├── 📂 diffusion_models/\n│ │ └── z_image_turbo_bf16.safetensors\n│ └── 📂 vae/\n│ └── ae.safetensors\n```\n"
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}
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example_workflows/wan2_2_T2I_ImageEncode_Inpaint.json
+1318
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examples/Example_29/Masked_LoadMe.mp4
BIN
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src/LanPaint/nodes.py
+23 -12
View File
@@ -865,7 +865,7 @@ class LanPaint_VideoMaskEditor:
files = []
return {
"required": {
"video": (files, {"image_upload": True, "tooltip": "Source video file. Returned as the video output and used by the mask editor preview."}),
"video": (files, {"video_upload": True, "tooltip": "Source video file. Returned as the video output and used by the mask editor preview."}),
"keyframes": ("STRING", {"default": "{}", "multiline": True, "tooltip": "Hidden: keyframe mask files {\"frame\": \"file.png\"}. Written by the mask editor."}),
"audio_mask": ("STRING", {"default": "[]", "multiline": True, "tooltip": "Hidden: audio inpainting intervals [{\"start\": s, \"end\": e}] in seconds. Written by the mask editor."}),
},
@@ -1170,8 +1170,11 @@ class LanPaint_ImageEncode:
Replaces the chain VAEEncode -> SetLatentNoiseMask (plus the manual
ImageScale of the mask) with one node. The mask is snapped to the latent's
actual spatial size (nearest-exact, the LanPaint mask convention), so
whatever the VAE's padding does, the mask always matches. Without a mask
this is a plain encode.
whatever the VAE's padding does, the mask always matches. Works with
image VAEs (4D latent) and video VAEs encoding a single image (5D latent
[B, C, T, H, W], e.g. the Hunyuan video VAE): the mask is attached as
[1, 1, T, H, W] and the sampler's prep handles it. Without a mask this is
a plain encode.
"""
@classmethod
@@ -1194,10 +1197,10 @@ class LanPaint_ImageEncode:
def encode(self, image, vae, mask=None):
z = vae.encode(image)
if len(z.shape) != 4:
ndim = len(z.shape)
if ndim not in (4, 5):
raise ValueError(
"LanPaint_ImageEncode expects an image VAE (latent [B, C, H, W]); "
f"got a {len(z.shape)}D latent — use LanPaint_AVEncode for video."
f"LanPaint_ImageEncode expects a 4D or 5D latent, got {ndim}D"
)
latent = {"samples": z}
if mask is not None:
@@ -1206,12 +1209,20 @@ class LanPaint_ImageEncode:
m = m[0, 0]
elif m.ndim == 3:
m = m[0]
target = z.shape[-2:]
if tuple(m.shape) != target:
m = torch.nn.functional.interpolate(
m.unsqueeze(0).unsqueeze(0), size=target, mode="nearest-exact"
)[0, 0]
latent["noise_mask"] = m.unsqueeze(0).unsqueeze(0)
if ndim == 4:
target = z.shape[-2:]
if tuple(m.shape) != target:
m = torch.nn.functional.interpolate(
m.unsqueeze(0).unsqueeze(0), size=target, mode="nearest-exact"
)[0, 0]
latent["noise_mask"] = m.unsqueeze(0).unsqueeze(0)
else: # 5D video-style VAE: snap to (T, H, W), one mask frame per token
t, h, w = z.shape[-3:]
if tuple(m.shape) != (h, w):
m = torch.nn.functional.interpolate(
m.unsqueeze(0).unsqueeze(0), size=(h, w), mode="nearest-exact"
)[0, 0]
latent["noise_mask"] = m.unsqueeze(0).unsqueeze(0).unsqueeze(2).expand(1, 1, t, h, w)
return (latent,)
src/LanPaint/videometa.py
+291
View File
@@ -0,0 +1,291 @@
"""Read and write LanPaint mask metadata in MP4 files via PyAV.
The metadata tag ``lanpaint-mask`` holds a UTF-8 JSON payload:
{
"version": 1,
"video": "<source video filename>",
"fps": <number>,
"keyframes": {"<frame_idx>": "<base64 PNG (no data: prefix)>", ...},
"audio_intervals": [{"start": <float>, "end": <float>}, ...]
}
Keyframes are base64-encoded PNG masks (RGBA, alpha = mask).
"No mask" = tag absent (None); "empty mask" = tag present with ``{}`` keyframes.
PyAV is import-guarded: the module-level ``av`` is ``None`` when PyAV is
unavailable, and the read/write functions raise ``RuntimeError`` in that case.
"""
import json
import os
from typing import Any, Dict, Optional
try:
import av
_HAS_AV = True
except ImportError:
av = None # type: ignore[assignment]
_HAS_AV = False
# ---------------------------------------------------------------------------
# Payload helpers
# ---------------------------------------------------------------------------
_METADATA_KEY = "lanpaint-mask"
_PAYLOAD_VERSION = 1
def encode_payload(payload: dict) -> str:
"""Encode a payload dict as a compact JSON string (UTF-8)."""
return json.dumps(payload, ensure_ascii=False, separators=(",", ":"))
def decode_payload(raw: Optional[str]) -> Optional[dict]:
"""Decode a metadata value to a payload dict, or None on any error.
``raw`` may be ``None`` (tag absent), a JSON string, or something
unexpected. Malformed / missing input returns ``None`` gracefully.
"""
if raw is None:
return None
if not isinstance(raw, str):
return None
try:
data = json.loads(raw)
except (TypeError, ValueError):
return None
if not isinstance(data, dict):
return None
return data
# ---------------------------------------------------------------------------
# Core read / write
# ---------------------------------------------------------------------------
def read_mask_metadata(path: str) -> Optional[dict]:
"""Read the ``lanpaint-mask`` metadata tag from an MP4 file.
Returns the decoded payload dict, or ``None`` when the tag is absent or
PyAV is unavailable.
"""
if not _HAS_AV:
raise RuntimeError(
"PyAV (av) is required to read mask metadata but is not installed"
)
with av.open(path, "r") as container:
raw = container.metadata.get(_METADATA_KEY, None)
return decode_payload(raw)
def write_mask_metadata(
input_path: str,
output_path: str,
payload: dict,
) -> None:
"""Remux *input_path* to *output_path*, attaching a ``lanpaint-mask``
metadata tag.
The video track is stream-copied (no re-encode) so the video content is
preserved byte-for-byte. Audio and other tracks are also preserved.
The source file at *input_path* is **never** modified.
Raises ``RuntimeError`` when PyAV is unavailable.
"""
if not _HAS_AV:
raise RuntimeError(
"PyAV (av) is required to write mask metadata but is not installed"
)
json_str = encode_payload(payload)
with av.open(input_path, "r") as in_container:
# ``movflags=use_metadata_tags`` is MANDATORY — without it ffmpeg
# silently drops custom metadata keys from the output.
with av.open(
output_path,
"w",
format="mp4",
options={"movflags": "use_metadata_tags"},
) as out_container:
# Copy any existing metadata (except our own key) from the source.
for key, value in in_container.metadata.items():
if key != _METADATA_KEY:
out_container.metadata[key] = value
# Write the LanPaint payload.
out_container.metadata[_METADATA_KEY] = json_str
# Stream-copy every stream from the source.
stream_map: Dict[int, Any] = {}
for in_stream in in_container.streams:
out_stream = out_container.add_stream_from_template(in_stream)
stream_map[in_stream.index] = out_stream
# Demux → mux every packet.
for packet in in_container.demux():
if packet.dts is None:
continue
out_stream = stream_map[packet.stream.index]
packet.stream = out_stream
out_container.mux(packet)
# The caller is responsible for the output file on disk.
# ---------------------------------------------------------------------------
# Helpers for server routes
# ---------------------------------------------------------------------------
def _unique_output_path(input_dir: str, base_name: str) -> str:
"""Return a non-clobbering output filename in *input_dir*.
Appends ``_masked``, then ``_masked_2``, ``_masked_3``, ... until a name
that does not already exist is found.
Returns the bare filename (not the full path).
"""
stem, ext = os.path.splitext(base_name)
candidate = f"{stem}_masked{ext}"
if not os.path.exists(os.path.join(input_dir, candidate)):
return candidate
n = 2
while True:
candidate = f"{stem}_masked_{n}{ext}"
if not os.path.exists(os.path.join(input_dir, candidate)):
return candidate
n += 1
def export_mask_video_from_request(
input_dir: str,
filename: str,
keyframes: dict,
audio_intervals: list,
fps: float,
) -> str:
"""Remux a source video with mask metadata, returning the new filename.
Parameters
----------
input_dir:
The ComfyUI input directory (``folder_paths.get_input_directory()``).
filename:
The source video filename (relative to *input_dir*).
keyframes:
Dict of ``{frame_idx: base64_png_string}``.
audio_intervals:
List of ``{"start": float, "end": float}`` dicts.
fps:
The video frame rate.
Returns
-------
The bare filename of the exported MP4 (in *input_dir*).
"""
payload: Dict[str, Any] = {
"version": _PAYLOAD_VERSION,
"video": filename,
"fps": fps,
"keyframes": keyframes,
"audio_intervals": audio_intervals,
}
src = os.path.join(input_dir, filename)
if not os.path.isfile(src):
raise FileNotFoundError(f"source video not found: {src}")
out_name = _unique_output_path(input_dir, filename)
out_path = os.path.join(input_dir, out_name)
write_mask_metadata(src, out_path, payload)
return out_name
def register_routes(server) -> None:
"""Register the LanPaint video-mask metadata routes on a ComfyUI
PromptServer instance.
Call this from ``__init__.py`` when running inside ComfyUI (the ``server``
module is only importable in that environment). Safe to call multiple
times — routes are registered once.
"""
import aiohttp
import folder_paths
@server.routes.get("/lanpaint/video_mask_meta")
async def video_mask_meta(request: aiohttp.web.Request) -> aiohttp.web.Response:
filename = request.query.get("filename", "")
if not filename:
return aiohttp.web.json_response({"found": False})
input_dir = folder_paths.get_input_directory()
path = os.path.join(input_dir, filename)
if not os.path.isfile(path):
return aiohttp.web.json_response({"found": False})
try:
payload = read_mask_metadata(path)
except Exception:
payload = None
if payload is None:
return aiohttp.web.json_response({"found": False})
return aiohttp.web.json_response({"found": True, "payload": payload})
@server.routes.post("/lanpaint/export_mask_video")
async def export_mask_video(request: aiohttp.web.Request) -> aiohttp.web.Response:
try:
body = await request.json()
except Exception:
return aiohttp.web.json_response(
{"error": "invalid JSON body"}, status=400
)
filename = body.get("filename", "")
if not filename:
return aiohttp.web.json_response(
{"error": "missing filename"}, status=400
)
keyframes = body.get("keyframes", {})
if not isinstance(keyframes, dict):
return aiohttp.web.json_response(
{"error": "keyframes must be a dict"}, status=400
)
audio_intervals = body.get("audio_intervals", [])
if not isinstance(audio_intervals, list):
return aiohttp.web.json_response(
{"error": "audio_intervals must be a list"}, status=400
)
fps = body.get("fps", 30.0)
try:
fps = float(fps)
except (TypeError, ValueError):
return aiohttp.web.json_response(
{"error": "fps must be a number"}, status=400
)
input_dir = folder_paths.get_input_directory()
try:
out_name = export_mask_video_from_request(
input_dir, filename, keyframes, audio_intervals, fps
)
except FileNotFoundError as e:
return aiohttp.web.json_response({"error": str(e)}, status=404)
except RuntimeError as e:
return aiohttp.web.json_response({"error": str(e)}, status=500)
except Exception as e:
return aiohttp.web.json_response(
{"error": f"export failed: {e}"}, status=500
)
return aiohttp.web.json_response(
{"filename": out_name, "path": os.path.join(input_dir, out_name)}
)
tests/test_videomask.py
+14 -5
View File
@@ -928,7 +928,10 @@ def test_merge_audio_resamples_to_original_rate(monkeypatch, tmp_path) -> None:
am = torch.ones(8)
out = nodes.merge_audio_with_mask(orig, inp, am, 0.0, 32000, 24000)
assert out.shape == (1, 2, 32000) # at the original sample rate
assert (out == 1.0).all() # fully masked: everything from the inpainted track
# fully masked: everything from the inpainted track. torchaudio's
# resampler rings at the signal edges (a constant signal is a step at the
# boundary), so assert the interior is exactly the inpainted track.
assert torch.allclose(out[..., 512:-512], torch.ones_like(out[..., 512:-512]), atol=1e-3)
def test_scipy_edt_matches_python_fallback(monkeypatch) -> None:
@@ -1043,15 +1046,21 @@ def test_image_encode_accepts_3d_and_4d_masks(monkeypatch, tmp_path) -> None:
assert torch.equal(l1["noise_mask"], l3["noise_mask"])
def test_image_encode_rejects_video_latent(monkeypatch, tmp_path) -> None:
def test_image_encode_accepts_video_vae_latent(monkeypatch, tmp_path) -> None:
nodes = _import_nodes(monkeypatch, tmp_path)
class VideoVAE:
def encode(self, x):
return torch.zeros(1, 24, 2, 2, 3) # 5D: video VAE
return torch.zeros(1, 24, 2, 2, 3) # 5D [B, C, T, H, W]: video VAE
with pytest.raises(ValueError, match="AVEncode"):
nodes.LanPaint_ImageEncode().encode(torch.zeros(1, 16, 24, 3), VideoVAE())
mask = torch.zeros(16, 24)
mask[8:, 12:] = 1.0
latent = nodes.LanPaint_ImageEncode().encode(
torch.zeros(1, 16, 24, 3), VideoVAE(), mask)[0]
# the mask rides as [1, 1, T, H, W], snapped to the latent spatial size
assert latent["noise_mask"].shape == (1, 1, 2, 2, 3)
assert latent["noise_mask"][0, 0, 1, 1, 1] == 1.0
assert latent["noise_mask"][0, 0, 0, 0, 0] == 0.0
def test_image_decode_resizes_and_merges(monkeypatch, tmp_path) -> None:
tests/test_videometa.py
+349
View File
@@ -0,0 +1,349 @@
"""Tests for the LanPaint video mask metadata read/write (videometa module).
Requires PyAV. All tests are skipped gracefully when PyAV is unavailable,
which is the case in CI environments (e.g., the system Python at C:\\Python314
does not have av installed, while the ComfyUI venv at E:\\ComfyUI\\.venv does).
"""
import os
import sys
import tempfile
import pytest
# Ensure the project root is on sys.path (tests may be run from any CWD).
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
try:
import av
import numpy as np
HAVE_AV = True
except ImportError:
av = None # type: ignore[assignment]
np = None # type: ignore[assignment]
HAVE_AV = False
pytestmark = pytest.mark.skipif(not HAVE_AV, reason="PyAV (av) not available")
if HAVE_AV:
from src.LanPaint.videometa import (
decode_payload,
encode_payload,
export_mask_video_from_request,
read_mask_metadata,
write_mask_metadata,
)
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
if HAVE_AV:
def _make_test_mp4(path: str, duration_frames: int = 10, fps: int = 10) -> None:
"""Create a tiny colour-bar MP4 via PyAV (no external tool required).
The video is 32x24 pixels, RGB frames encoded with libx264.
"""
container = av.open(path, "w", format="mp4")
stream = container.add_stream("libx264", rate=fps)
stream.width = 32
stream.height = 24
stream.pix_fmt = "yuv420p"
for i in range(duration_frames):
# simple colour bar: red channel varies per frame
arr = np.zeros((24, 32, 3), dtype=np.uint8)
r = (i * 25) % 256
for py in range(24):
for px in range(32):
arr[py, px] = [r, (px * 8) % 256, (py * 10) % 256]
frame = av.VideoFrame.from_ndarray(arr, format="rgb24")
for packet in stream.encode(frame):
container.mux(packet)
for packet in stream.encode():
container.mux(packet)
container.close()
# ---------------------------------------------------------------------------
# Payload encode / decode
# ---------------------------------------------------------------------------
class TestPayloadCodec:
def test_roundtrip_simple(self) -> None:
payload = {
"version": 1,
"video": "test.mp4",
"fps": 30.0,
"keyframes": {"0": "iVBORw0KGgoAAAA="},
"audio_intervals": [{"start": 1.0, "end": 2.5}],
}
encoded = encode_payload(payload)
assert isinstance(encoded, str)
decoded = decode_payload(encoded)
assert decoded == payload
def test_roundtrip_unicode(self) -> None:
payload = {
"version": 1,
"video": "テスト動画.mp4",
"fps": 24.0,
"keyframes": {
"0": "iVBORw0KGgoAAAA=",
"12": "iVBORw0KGgoAAAANSUhEUg==",
},
"audio_intervals": [
{"start": 0.5, "end": 1.25},
{"start": 3.0, "end": 4.75},
],
}
encoded = encode_payload(payload)
decoded = decode_payload(encoded)
assert decoded == payload
def test_empty_keyframes(self) -> None:
payload = {
"version": 1,
"video": "no_mask.mp4",
"fps": 30.0,
"keyframes": {},
"audio_intervals": [],
}
encoded = encode_payload(payload)
decoded = decode_payload(encoded)
assert decoded == payload
def test_decode_none(self) -> None:
assert decode_payload(None) is None
def test_decode_garbage(self) -> None:
assert decode_payload("not json") is None
assert decode_payload(42) is None # type: ignore[arg-type]
assert decode_payload("[]") is None # valid JSON but not a dict
def test_decode_missing_keyframes_ok(self) -> None:
# payloads missing optional fields still decode
assert decode_payload('{"version":1,"video":"v.mp4"}') == {
"version": 1,
"video": "v.mp4",
}
# ---------------------------------------------------------------------------
# Read / write round-trip
# ---------------------------------------------------------------------------
class TestWriteRead:
def test_roundtrip_with_keyframes(self) -> None:
payload = {
"version": 1,
"video": "src.mp4",
"fps": 30.0,
"keyframes": {
"0": "iVBORw0KGgoAAAA=",
"5": "iVBORw0KGgoAAAANSUhEUg==",
},
"audio_intervals": [{"start": 1.0, "end": 3.0}],
}
with tempfile.TemporaryDirectory() as td:
src = os.path.join(td, "src.mp4")
dst = os.path.join(td, "out.mp4")
_make_test_mp4(src, duration_frames=10, fps=30)
write_mask_metadata(src, dst, payload)
assert os.path.isfile(dst)
read_back = read_mask_metadata(dst)
assert read_back == payload
def test_tag_absent_on_plain_mp4(self) -> None:
with tempfile.TemporaryDirectory() as td:
src = os.path.join(td, "plain.mp4")
_make_test_mp4(src)
result = read_mask_metadata(src)
assert result is None
def test_source_is_never_modified(self) -> None:
payload = {
"version": 1,
"video": "src.mp4",
"fps": 25.0,
"keyframes": {},
"audio_intervals": [],
}
with tempfile.TemporaryDirectory() as td:
src = os.path.join(td, "src.mp4")
dst = os.path.join(td, "out.mp4")
_make_test_mp4(src, duration_frames=5, fps=25)
# hash the source bytes before writing
src_bytes_before = open(src, "rb").read()
write_mask_metadata(src, dst, payload)
src_bytes_after = open(src, "rb").read()
assert src_bytes_before == src_bytes_after, (
"write_mask_metadata modified the source file"
)
def test_unicode_in_keyframe_data(self) -> None:
# base64 strings can contain unicode context; metadata values are
# UTF-8 ― ensure round-trip with non-ASCII frame indices works.
payload = {
"version": 1,
"video": "видео.mp4",
"fps": 24.0,
"keyframes": {"0": "iVBORw0KGgoAAAA="},
"audio_intervals": [],
}
with tempfile.TemporaryDirectory() as td:
src = os.path.join(td, "src.mp4")
dst = os.path.join(td, "out.mp4")
_make_test_mp4(src, duration_frames=6, fps=24)
write_mask_metadata(src, dst, payload)
result = read_mask_metadata(dst)
assert result is not None
assert result["video"] == "видео.mp4"
# ---------------------------------------------------------------------------
# Filename suffixing (non-clobber)
# ---------------------------------------------------------------------------
class TestExportMaskVideo:
def test_basic_export(self) -> None:
with tempfile.TemporaryDirectory() as td:
src_name = "video.mp4"
src = os.path.join(td, src_name)
_make_test_mp4(src, duration_frames=10, fps=30)
out_name = export_mask_video_from_request(
input_dir=td,
filename=src_name,
keyframes={"0": "abc123"},
audio_intervals=[{"start": 0.0, "end": 2.0}],
fps=30.0,
)
assert out_name == "video_masked.mp4"
assert os.path.isfile(os.path.join(td, out_name))
def test_suffix_when_target_exists(self) -> None:
with tempfile.TemporaryDirectory() as td:
src_name = "video.mp4"
src = os.path.join(td, src_name)
_make_test_mp4(src, duration_frames=5, fps=25)
# Create a fake collision file
with open(os.path.join(td, "video_masked.mp4"), "wb") as f:
f.write(b"not an mp4")
out_name = export_mask_video_from_request(
input_dir=td,
filename=src_name,
keyframes={},
audio_intervals=[],
fps=25.0,
)
assert out_name == "video_masked_2.mp4"
assert os.path.isfile(os.path.join(td, out_name))
def test_multiple_suffixes(self) -> None:
with tempfile.TemporaryDirectory() as td:
src_name = "video.mp4"
src = os.path.join(td, src_name)
_make_test_mp4(src, duration_frames=5, fps=25)
# create several collisions
for name in ("video_masked.mp4", "video_masked_2.mp4", "video_masked_3.mp4"):
with open(os.path.join(td, name), "wb") as f:
f.write(b"not an mp4")
out_name = export_mask_video_from_request(
input_dir=td,
filename=src_name,
keyframes={},
audio_intervals=[],
fps=25.0,
)
assert out_name == "video_masked_4.mp4"
def test_source_not_found(self) -> None:
with tempfile.TemporaryDirectory() as td:
with pytest.raises(FileNotFoundError):
export_mask_video_from_request(
input_dir=td,
filename="nonexistent.mp4",
keyframes={},
audio_intervals=[],
fps=30.0,
)
def test_payload_fidelity(self) -> None:
"""The exported file must carry the exact payload that was given."""
payload_kf = {"0": "iVBORw0KGgoAAAA=", "7": "/9j/4AAQSkZJRgABAQ=="}
payload_ai = [
{"start": 0.5, "end": 1.0},
{"start": 3.25, "end": 5.75},
]
with tempfile.TemporaryDirectory() as td:
src_name = "vid.mp4"
_make_test_mp4(os.path.join(td, src_name), duration_frames=10, fps=30)
out_name = export_mask_video_from_request(
input_dir=td,
filename=src_name,
keyframes=payload_kf,
audio_intervals=payload_ai,
fps=30.0,
)
payload = read_mask_metadata(os.path.join(td, out_name))
assert payload is not None
assert payload["version"] == 1
assert payload["video"] == src_name
assert payload["fps"] == 30.0
assert payload["keyframes"] == payload_kf
assert payload["audio_intervals"] == payload_ai
# ---------------------------------------------------------------------------
# Metadata coexistence
# ---------------------------------------------------------------------------
class TestMetadataCoexistence:
def test_preexisting_metadata_is_preserved(self) -> None:
"""When the source MP4 already has metadata (e.g., title), it survives."""
with tempfile.TemporaryDirectory() as td:
src = os.path.join(td, "labelled.mp4")
# Create with a metadata tag
container = av.open(src, "w", format="mp4")
container.metadata["title"] = "Original Title"
container.metadata["comment"] = "Should survive"
stream = container.add_stream("libx264", rate=10)
stream.width = 32
stream.height = 24
stream.pix_fmt = "yuv420p"
frame = av.VideoFrame.from_ndarray(
np.zeros((24, 32, 3), dtype=np.uint8), format="rgb24"
)
for pkt in stream.encode(frame):
container.mux(pkt)
for pkt in stream.encode():
container.mux(pkt)
container.close()
dst = os.path.join(td, "labelled_masked.mp4")
payload = {
"version": 1,
"video": "labelled.mp4",
"fps": 10.0,
"keyframes": {},
"audio_intervals": [],
}
write_mask_metadata(src, dst, payload)
result = read_mask_metadata(dst)
assert result == payload
# Verify original metadata survived
container2 = av.open(dst, "r")
assert container2.metadata.get("title") == "Original Title"
assert container2.metadata.get("comment") == "Should survive"
container2.close()
web/lanpaint_video_mask_editor.js
+314 -2
View File
@@ -345,6 +345,7 @@ class VideoMaskEditor {
this.peaks = null; // decoded waveform peaks
this.waveCanvas = null;
this.waveDrag = null; // {start, end} while dragging a new interval
this._maskVideo = null; // last video for which masks were auto-loaded
}
/* ---------------- lifecycle ---------------- */
@@ -369,7 +370,9 @@ class VideoMaskEditor {
try {
await this._loadVideo(filename);
await this._loadSavedKeyframes();
if (!this._metaLoaded) {
await this._loadSavedKeyframes();
}
this.peaks = await loadAudioPeaks(this.video.currentSrc);
this._loadAudioIntervals();
await this.showFrame(0);
@@ -517,6 +520,114 @@ class VideoMaskEditor {
video.pause();
this.fps = await measureVideoFps(video);
this.frameCount = Math.max(1, Math.round(video.duration * this.fps));
this._metaLoaded = await this._autoLoadMaskMeta(filename);
}
/**
* Fetch `/lanpaint/video_mask_meta` for the video and, if metadata is
* present, upload the base64 keyframes as PNGs and populate the timeline
* and widget. Returns true when metadata was found and loaded.
*/
async _autoLoadMaskMeta(filename) {
try {
const url =
api.apiURL(
"/lanpaint/video_mask_meta?filename=" + encodeURIComponent(filename)
) + "&rand=" + Math.random();
const resp = await fetch(url);
if (!resp.ok) throw new Error("HTTP " + resp.status);
const json = await resp.json();
if (!json.found || !json.payload) {
// the video carries no mask metadata: the workflow's masks are
// meaningless for it, so clear them (the video is the source
// of truth for masks)
this.timeline.keyframes.clear();
this.timeline.sdfCache.clear();
this.undo.clear();
this.audioIntervals = [];
const kw = this.getKeyframesWidget();
if (kw) kw.value = "{}";
const aw = this.getAudioMaskWidget();
if (aw) aw.value = "[]";
return false;
}
const payload = json.payload;
// clear existing keyframes (the metadata is the source of truth)
this.timeline.keyframes.clear();
this.timeline.sdfCache.clear();
this.undo.clear();
const keyframeData = payload.keyframes || {};
const filenames = {};
const stamp = Date.now();
for (const [idxStr, b64png] of Object.entries(keyframeData)) {
const idx = parseInt(idxStr, 10);
if (isNaN(idx) || idx < 0 || idx >= this.frameCount) continue;
try {
const img = await new Promise((resolve, reject) => {
const im = new Image();
im.onload = () => resolve(im);
im.onerror = () => reject(new Error("decode failed"));
im.src = "data:image/png;base64," + b64png;
});
const c = this.timeline.newMaskCanvas();
c.getContext("2d").drawImage(img, 0, 0, c.width, c.height);
this.timeline.setKeyframe(idx, c);
// upload so the widget stores a server filename
const blob = await new Promise((r) => c.toBlob(r, "image/png"));
if (!blob) continue;
const name = "lanpaint_kf_" + stamp + "_" + idx + ".png";
const fd = new FormData();
fd.append("image", blob, name);
fd.append("type", "input");
const upResp = await api.fetchApi("/upload/image", {
method: "POST",
body: fd,
});
if (!upResp.ok) continue;
const upData = await upResp.json();
filenames[idx] = upData.name;
} catch (e) {
console.warn("[LanPaint] auto-load keyframe decode failed:", e);
}
}
const kw = this.getKeyframesWidget();
if (kw) kw.value = JSON.stringify(filenames);
const aw = this.getAudioMaskWidget();
if (aw) aw.value = JSON.stringify(payload.audio_intervals || "[]");
this._loadAudioIntervals();
this._maskVideo = filename;
// keep widget_values in sync
if (this.node.widgets_values && this.node.widgets) {
for (const ww of [kw, aw]) {
if (!ww) continue;
const wi = this.node.widgets.indexOf(ww);
if (wi >= 0) this.node.widgets_values[wi] = ww.value;
}
}
console.log(
"[LanPaint VideoMaskEditor] auto-loaded",
Object.keys(filenames).length,
"keyframes from mp4 metadata"
);
return true;
} catch (err) {
console.warn("[LanPaint] video_mask_meta fetch failed:", err);
// no metadata could be read: treat like not-found and clear
this.timeline.keyframes.clear();
this.timeline.sdfCache.clear();
this.undo.clear();
this.audioIntervals = [];
const kw = this.getKeyframesWidget();
if (kw) kw.value = "{}";
const aw = this.getAudioMaskWidget();
if (aw) aw.value = "[]";
return false;
}
}
/** Canvas for frame `index` (cached, LRU). */
@@ -690,6 +801,93 @@ class VideoMaskEditor {
}
}
async _exportMaskVideo() {
const filename = this.getVideoFilename();
if (!filename) {
alert("No video file selected.");
return;
}
if (this.timeline.keyframes.size === 0 && this.audioIntervals.length === 0) {
alert("No keyframes or audio intervals to export.");
return;
}
try {
const keyframes = {};
for (const [idx, canvas] of this.timeline.keyframes) {
keyframes[idx] = canvas
.toDataURL("image/png")
.replace(/^data:image\/png;base64,/, "");
}
const body = {
filename: filename,
keyframes: keyframes,
audio_intervals: this.audioIntervals,
fps: this.fps,
};
const resp = await api.fetchApi("/lanpaint/export_mask_video", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(body),
});
if (!resp.ok) {
const text = await resp.text().catch(() => "");
throw new Error("Export failed (" + resp.status + "): " + text);
}
const result = await resp.json();
this._maskVideo = filename;
// offer a native save dialog (choose location + name) when available;
// the input/ copy from the export stays as the ComfyUI-visible copy
if (typeof window.showSaveFilePicker === "function") {
try {
const viewUrl =
api.apiURL(
"/view?filename=" + encodeURIComponent(result.filename) + "&type=input"
) + app.getRandParam();
const buf = await (await fetch(viewUrl)).arrayBuffer();
const handle = await window.showSaveFilePicker({
suggestedName: result.filename,
types: [
{
description: "MP4 video",
accept: { "video/mp4": [".mp4"] },
},
],
});
const writable = await handle.createWritable();
await writable.write(new Uint8Array(buf));
await writable.close();
this._flash(
"Saved to " + handle.name + " (also in input: " + result.filename + ")"
);
} catch (pickErr) {
// user cancelled the picker or the API failed: the input/
// copy is still there and usable
this._flash("Exported to input: " + (result.path || result.filename));
}
} else {
this._flash("Exported: " + (result.path || result.filename));
}
// If the video widget points at the source file, refresh it to the
// newly exported masked file.
const vw = this.node.widgets?.find((x) => x.name === "video");
if (vw && vw.value === filename && vw.callback) {
vw.value = result.filename;
if (this.node.widgets_values) {
const vi = this.node.widgets.indexOf(vw);
if (vi >= 0) this.node.widgets_values[vi] = result.filename;
}
try {
vw.callback(result.filename);
} catch (_) {
/* ignore callback errors */
}
}
} catch (err) {
alert("Export failed: " + err.message);
}
}
async save() {
const w = this.getKeyframesWidget();
if (!w) return;
@@ -745,6 +943,7 @@ class VideoMaskEditor {
<span id="lpvme-frame-label">frame 0 / 0</span>
<span style="flex:1"></span>
<button id="lpvme-save" title="Upload keyframes and write them into the node">Save</button>
<button id="lpvme-export" title="Remux the source video with mask metadata">导出 mask 视频</button>
<button id="lpvme-close" title="Close (Escape)">Close</button>
</div>
<div id="lpvme-stage" style="flex:1;position:relative;overflow:hidden;background:#0f0f0f;">
@@ -859,6 +1058,9 @@ class VideoMaskEditor {
alert("Save failed: " + err.message);
}
});
this.el.querySelector("#lpvme-export").addEventListener("click", () => {
this._exportMaskVideo();
});
this.el.querySelector("#lpvme-close").addEventListener("click", () => this.close());
this.el.querySelector("#lpvme-add-kf").addEventListener("click", () => {
this.ensureKeyframe(this.current);
@@ -1030,9 +1232,15 @@ class NodeMaskPreview {
this.lastDrawnValue = null;
this.lastPlayheadX = null;
this._sizeWrapped = false;
this._maskVideo = null; // last video for which masks were auto-loaded
this._tick();
}
_getVideoFilename() {
const w = this.node.widgets?.find((x) => x.name === "video");
return w && typeof w.value === "string" ? w.value : null;
}
_keyframesWidget() {
return this.node.widgets?.find((w) => w.name === "keyframes");
}
@@ -1188,10 +1396,114 @@ class NodeMaskPreview {
this.timeline = new MaskTimeline();
this.timeline.setSize(video.videoWidth, video.videoHeight);
this.lastMaskIdx = null;
await this._reloadKeyframes();
const filename = this._getVideoFilename();
if (filename) {
const metaLoaded = await this._autoLoadMaskMeta(filename);
if (!metaLoaded) {
await this._reloadKeyframes();
}
} else {
await this._reloadKeyframes();
}
this.lastKeyframesValue = this._keyframesWidget()?.value;
}
/**
* Fetch `/lanpaint/video_mask_meta` for the video and, if metadata is
* present, upload the base64 keyframes as PNGs and populate the timeline
* and widget. Returns true when metadata was found and loaded.
*/
async _autoLoadMaskMeta(filename) {
try {
const url =
api.apiURL(
"/lanpaint/video_mask_meta?filename=" + encodeURIComponent(filename)
) + "&rand=" + Math.random();
const resp = await fetch(url);
if (!resp.ok) throw new Error("HTTP " + resp.status);
const json = await resp.json();
if (!json.found || !json.payload) {
// the video carries no mask metadata: clear the workflow's masks
this.timeline.keyframes.clear();
this.timeline.sdfCache.clear();
const kw = this._keyframesWidget();
if (kw) kw.value = "{}";
const aw = this.getAudioMaskWidget();
if (aw) aw.value = "[]";
return false;
}
const payload = json.payload;
this.timeline.keyframes.clear();
this.timeline.sdfCache.clear();
const keyframeData = payload.keyframes || {};
const filenames = {};
const stamp = Date.now();
for (const [idxStr, b64png] of Object.entries(keyframeData)) {
const idx = parseInt(idxStr, 10);
if (isNaN(idx) || idx < 0 || idx >= this.frameCount) continue;
try {
const img = await new Promise((resolve, reject) => {
const im = new Image();
im.onload = () => resolve(im);
im.onerror = () => reject(new Error("decode failed"));
im.src = "data:image/png;base64," + b64png;
});
const c = this.timeline.newMaskCanvas();
c.getContext("2d").drawImage(img, 0, 0, c.width, c.height);
this.timeline.setKeyframe(idx, c);
const blob = await new Promise((r) => c.toBlob(r, "image/png"));
if (!blob) continue;
const name = "lanpaint_kf_" + stamp + "_" + idx + ".png";
const fd = new FormData();
fd.append("image", blob, name);
fd.append("type", "input");
const upResp = await api.fetchApi("/upload/image", {
method: "POST",
body: fd,
});
if (!upResp.ok) continue;
const upData = await upResp.json();
filenames[idx] = upData.name;
} catch (e) {
console.warn("[LanPaint] preview auto-load keyframe decode failed:", e);
}
}
const kw = this._keyframesWidget();
if (kw) kw.value = JSON.stringify(filenames);
const aw = this.getAudioMaskWidget();
if (aw) aw.value = JSON.stringify(payload.audio_intervals || "[]");
this._maskVideo = filename;
if (this.node.widgets_values && this.node.widgets) {
for (const ww of [kw, aw]) {
if (!ww) continue;
const wi = this.node.widgets.indexOf(ww);
if (wi >= 0) this.node.widgets_values[wi] = ww.value;
}
}
console.log(
"[LanPaint NodeMaskPreview] auto-loaded",
Object.keys(filenames).length,
"keyframes from mp4 metadata"
);
return true;
} catch (err) {
console.warn("[LanPaint] preview video_mask_meta fetch failed:", err);
// no metadata could be read: treat like not-found and clear
this.timeline.keyframes.clear();
this.timeline.sdfCache.clear();
const kw = this._keyframesWidget();
if (kw) kw.value = "{}";
const aw = this.getAudioMaskWidget();
if (aw) aw.value = "[]";
return false;
}
}
async _reloadKeyframes() {
const w = this._keyframesWidget();
const timeline = this.timeline;