Post-Processing

Post-Processing#

SGLang diffusion supports optional post-processing steps that run after generation to improve temporal smoothness (frame interpolation) or spatial resolution (upscaling). These steps are independent of the diffusion model and can be combined in a single run.

When both are enabled, frame interpolation runs first (increasing the frame count), then upscaling runs on every frame (increasing the spatial resolution).

Frame Interpolation (video only)#

Frame interpolation 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.

CLI Arguments#

Argument

Description

--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)

Supported Models#

Frame interpolation uses the RIFE (Real-Time Intermediate Flow Estimation) architecture. Only RIFE 4.22.lite (IFNet with 4-scale IFBlock backbone) is supported. The network topology is hard-coded, so custom weights provided via --frame-interpolation-model-path must be a flownet.pkl checkpoint that is compatible with this architecture.

Other RIFE versions (e.g., older v4.x variants with different block counts) or entirely different frame interpolation methods (FILM, AMT, etc.) are not supported.

Weight

HuggingFace Repo

Description

RIFE 4.22.lite (default)

elfgum/RIFE-4.22.lite

Lightweight model, downloaded automatically on first use

Example#

Generate a 5-frame video and interpolate to 9 frames ((5 − 1) × 2¹ + 1 = 9):

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

Upscaling (image and video)#

Upscaling increases the spatial resolution of generated images or video frames using Real-ESRGAN. The model weights are downloaded automatically on first use and cached for subsequent runs.

CLI Arguments#

Argument

Description

--enable-upscaling

Enable post-generation upscaling using Real-ESRGAN.

--upscaling-scale {SCALE}

Desired upscaling factor (default: 4). The 4× model is used internally; if a different scale is requested, a bicubic resize is applied after the network output.

--upscaling-model-path {PATH}

Local .pth file, HuggingFace repo ID, or repo_id:filename for Real-ESRGAN weights (default: ai-forever/Real-ESRGAN with RealESRGAN_x4.pth, downloaded automatically). Use the repo_id:filename format to specify a custom weight file from a HuggingFace repo (e.g. my-org/my-esrgan:weights.pth).

Supported Models#

Upscaling supports two Real-ESRGAN network architectures. The correct architecture is auto-detected from the checkpoint keys, so you only need to point --upscaling-model-path at a valid .pth file:

Architecture

Example Weights

Description

RRDBNet

RealESRGAN_x4plus.pth

Heavier model with higher quality; best for photos

SRVGGNetCompact

RealESRGAN_x4.pth (default), realesr-animevideov3.pth, realesr-general-x4v3.pth

Lightweight model; faster inference, good for video

The default weight file is ai-forever/Real-ESRGAN with RealESRGAN_x4.pth (SRVGGNetCompact, 4× native scale).

Other super-resolution models (e.g., SwinIR, HAT, BSRGAN) are not supported — only Real-ESRGAN checkpoints using the two architectures above are compatible.

Examples#

Generate a 1024×1024 image and upscale to 4096×4096:

sglang generate \
  --model-path black-forest-labs/FLUX.2-dev \
  --prompt "A cat sitting on a windowsill" \
  --output-size 1024x1024 \
  --enable-upscaling \
  --save-output

Generate a video and upscale each frame by 4×:

sglang generate \
  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
  --prompt "A curious raccoon" \
  --enable-upscaling \
  --upscaling-scale 4 \
  --save-output

Combining Frame Interpolation and Upscaling#

Frame interpolation and upscaling can be combined in a single run. Interpolation is applied first (increasing the frame count), then upscaling is applied to every frame (increasing the spatial resolution).

Example — generate 5 frames, interpolate to 9 frames, and upscale each frame by 4×:

sglang generate \
  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
  --prompt "A curious raccoon" \
  --num-frames 5 \
  --enable-frame-interpolation \
  --frame-interpolation-exp 1 \
  --enable-upscaling \
  --upscaling-scale 4 \
  --save-output
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