[Model] MiniMax-H3: accept raw video masks and resolve the latent grid server-side (#7947)

Signed-off-by: chen hongwei <1792043268@qq.com>
Signed-off-by: Wang Zhipeng <wangzhipeng628@gmail.com>
Co-authored-by: Wang Zhipeng <wangzhipeng628@gmail.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>

docs/serving/videos_api.md

@@ -237,13 +237,22 @@ At least one mask is required. A nontrivial `video_noise_mask` requires
 `source_audio` or a `source_video` with an audio stream. Mask values are in
 `[0, 1]`: `0` preserves the source, `1` regenerates it, and fractional values
 blend the two behaviors. Exact all-one masks are no-ops and do not require a
-source. Source uploads without a mask are rejected. Masks may be a JSON scalar
-or arrays matching the H3 latent/token grid; pixel-resolution masks must be
-resized or pooled by the client before upload.
+source. Source uploads without a mask are rejected. Masks may be a JSON scalar,
+an array matching the H3 latent/token grid, or (video only) a frame-space
+array that the server resizes to the latent grid.
 
 For an aligned output of `F` frames at `W x H`, the video latent grid is
 `[Tv, H/16, W/16]`, where `Tv = 2 + 5 * ((F - 5) / 17)`. The video mask may be
-a scalar, a flat token vector, `[Tv, H/32, W/32]`, or the full latent grid. For
+a scalar, a flat token vector, `[Tv, H/32, W/32]`, the full latent grid, a
+spatial `[h, w]` mask applied to every frame, or a frame-space `[T, h, w]` mask
+with one slice per 24 fps output frame. A shape that matches the token or full
+latent grid is always read as that grid. Frame-space masks are area-resized to
+`[H/16, W/16]`; along time, a mask shorter than `F` is padded with its last
+slice (as the source video is) and a longer one is truncated, then each latent
+takes the maximum over the frames the causal video VAE folds into it, so any
+regenerated frame regenerates its latent. Because the 8 MiB limit below applies
+to the JSON file, clients should area-downsample frame-space masks by 16
+spatially before upload. For
 the model input, timestep, and velocity, a full-grid mask is max-pooled over
 each 2x2 spatial token and fractional values are rounded upward to 1/256
 levels. The final x0 restore uses the original, unquantized mask, so full-grid