[diffusion] Postprocess: implement frame interpolation using RIFE (#19384)

docs/diffusion/api/cli.md

@@ -41,6 +41,34 @@ The SGLang-diffusion CLI provides a quick way to access the inference pipeline f
 - `--fps {FPS}`: Frames per second for the saved output, if this is a video-generation task
 
 
+**Frame Interpolation** (video only)
+
+Frame interpolation is a post-processing step that synthesizes new frames
+between each pair of consecutive generated frames, producing smoother
+motion without re-running the diffusion model. The `--frame-interpolation-exp`
+flag controls how many rounds of interpolation to apply: each round inserts one
+new frame into every gap between adjacent frames, so the output frame count
+follows the formula **(N − 1) × 2^exp + 1** (e.g. 5 original frames with
+`exp=1` → 4 gaps × 1 new frame + 5 originals = **9** frames; with `exp=2` →
+**17** frames).
+
+- `--enable-frame-interpolation`: Enable frame interpolation. Model weights are downloaded automatically on first use.
+- `--frame-interpolation-exp {EXP}`: Interpolation exponent — `1` = 2× temporal resolution, `2` = 4×, etc. (default: `1`)
+- `--frame-interpolation-scale {SCALE}`: RIFE inference scale; use `0.5` for high-resolution inputs to save memory (default: `1.0`)
+- `--frame-interpolation-model-path {PATH}`: Local directory or HuggingFace repo ID containing RIFE `flownet.pkl` weights (default: `elfgum/RIFE-4.22.lite`, downloaded automatically)
+
+Example — generate a 5-frame video and interpolate to 9 frames ((5 − 1) × 2¹ + 1 = 9):
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --prompt "A dog running through a park" \
+  --num-frames 5 \
+  --enable-frame-interpolation \
+  --frame-interpolation-exp 1 \
+  --save-output
+```
+
 **Output Options**
 
 - `--output-path {PATH}`: Directory to save the generated video