sglang/python/sglang/multimodal_gen/docs/cli.md

sgl-diffusion CLI Inference

The sgl-diffusion CLI provides a quick way to access the sgl-diffusion inference pipeline for image and video generation.

Prerequisites

• A working sgl-diffusion installation and the sgl-diffusion CLI available in $PATH.
• Python 3.10+ if you plan to use the OpenAI Python SDK.

Supported Arguments

Server Arguments

• --model-path {MODEL_PATH}: Path to the model or model ID
• --num-gpus {NUM_GPUS}: Number of GPUs to use
• --tp-size {TP_SIZE}: Tensor parallelism size (only for the encoder; should not be larger than 1 if text encoder offload is enabled, as layer-wise offload plus prefetch is faster)
• --sp-size {SP_SIZE}: Sequence parallelism size (typically should match the number of GPUs)
• --ulysses-degree {ULYSSES_DEGREE}: The degree of DeepSpeed-Ulysses-style SP in USP
• --ring-degree {RING_DEGREE}: The degree of ring attention-style SP in USP

Sampling Parameters

• --prompt {PROMPT}: Text description for the video you want to generate
• --num-inference-steps {STEPS}: Number of denoising steps
• --negative-prompt {PROMPT}: Negative prompt to guide generation away from certain concepts
• --seed {SEED}: Random seed for reproducible generation

Image/Video Configuration

• --height {HEIGHT}: Height of the generated output
• --width {WIDTH}: Width of the generated output
• --num-frames {NUM_FRAMES}: Number of frames to generate
• --fps {FPS}: Frames per second for the saved output, if this is a video-generation task

Output Options

• --output-path {PATH}: Directory to save the generated video
• --save-output: Whether to save the image/video to disk
• --return-frames: Whether to return the raw frames

Using Configuration Files

Instead of specifying all parameters on the command line, you can use a configuration file:

sglang generate --config {CONFIG_FILE_PATH}

The configuration file should be in JSON or YAML format with the same parameter names as the CLI options. Command-line arguments take precedence over settings in the configuration file, allowing you to override specific values while keeping the rest from the configuration file.

Example configuration file (config.json):

{
    "model_path": "FastVideo/FastHunyuan-diffusers",
    "prompt": "A beautiful woman in a red dress walking down a street",
    "output_path": "outputs/",
    "num_gpus": 2,
    "sp_size": 2,
    "tp_size": 1,
    "num_frames": 45,
    "height": 720,
    "width": 1280,
    "num_inference_steps": 6,
    "seed": 1024,
    "fps": 24,
    "precision": "bf16",
    "vae_precision": "fp16",
    "vae_tiling": true,
    "vae_sp": true,
    "vae_config": {
        "load_encoder": false,
        "load_decoder": true,
        "tile_sample_min_height": 256,
        "tile_sample_min_width": 256
    },
    "text_encoder_precisions": [
        "fp16",
        "fp16"
    ],
    "mask_strategy_file_path": null,
    "enable_torch_compile": false
}

Or using YAML format (config.yaml):

model_path: "FastVideo/FastHunyuan-diffusers"
prompt: "A beautiful woman in a red dress walking down a street"
output_path: "outputs/"
num_gpus: 2
sp_size: 2
tp_size: 1
num_frames: 45
height: 720
width: 1280
num_inference_steps: 6
seed: 1024
fps: 24
precision: "bf16"
vae_precision: "fp16"
vae_tiling: true
vae_sp: true
vae_config:
  load_encoder: false
  load_decoder: true
  tile_sample_min_height: 256
  tile_sample_min_width: 256
text_encoder_precisions:
  - "fp16"
  - "fp16"
mask_strategy_file_path: null
enable_torch_compile: false

To see all the options, you can use the --help flag:

sglang generate --help

Serve

Launch the sgl-diffusion HTTP server and interact with it using the OpenAI SDK and curl. The server implements an OpenAI-compatible subset for Videos under the /v1/videos namespace.

Start the server

Use the following command to launch the server:

SERVER_ARGS=(
  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers
  --text-encoder-cpu-offload
  --pin-cpu-memory
  --num-gpus 4
  --ulysses-degree=2
  --ring-degree=2
)

sglang serve"${SERVER_ARGS[@]}"

• --model-path: Which model to load. The example uses Wan-AI/Wan2.1-T2V-1.3B-Diffusers.
• --port: HTTP port to listen on (the default here is 30010).

Wait until the port is listening. In CI, the tests probe 127.0.0.1:30010 before sending requests.

OpenAI Python SDK usage

Initialize the client with a dummy API key and point base_url to your local server:

from openai import OpenAI

client = OpenAI(api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1")

• Create a video

video = client.videos.create(prompt="A calico cat playing a piano on stage", size="1280x720")
print(video.id, video.status)

Response example fields include id, status (e.g., queued → completed), size, and seconds.

• List videos

videos = client.videos.list()
for item in videos.data:
    print(item.id, item.status)

• Poll for completion and download content

import time

video = client.videos.create(prompt="A calico cat playing a piano on stage", size="1280x720")
video_id = video.id

# Simple polling loop
while True:
    page = client.videos.list()
    item = next((v for v in page.data if v.id == video_id), None)
    if item and item.status == "completed":
        break
    time.sleep(5)

# Download binary content (MP4)
resp = client.videos.download_content(video_id=video_id)
content = resp.read()  # bytes
with open("output.mp4", "wb") as f:
    f.write(content)

curl examples

• Create a video

curl -sS -X POST "http://localhost:30010/v1/videos" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-proj-1234567890" \
  -d '{
        "prompt": "A calico cat playing a piano on stage",
        "size": "1280x720"
      }'

• List videos

curl -sS -X GET "http://localhost:30010/v1/videos" \
  -H "Authorization: Bearer sk-proj-1234567890"

• Download video content

curl -sS -L "http://localhost:30010/v1/videos/<VIDEO_ID>/content" \
  -H "Authorization: Bearer sk-proj-1234567890" \
  -o output.mp4

API surface implemented here

The server exposes these endpoints (OpenAPI tag videos):

• POST /v1/videos — Create a generation job and return a queued video object.
• GET /v1/videos — List jobs.
• GET /v1/videos/{video_id}/content — Download binary content when ready (e.g., MP4).

Reference

• OpenAI Videos API reference: https://platform.openai.com/docs/api-reference/videos

Generate

Run a one-off generation task without launching a persistent server.

To use it, pass both server arguments and sampling parameters in one command, after the generate subcommand, for example:

SERVER_ARGS=(
  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
  --text-encoder-cpu-offload
  --pin-cpu-memory
  --num-gpus 4
  --ulysses-degree=2
  --ring-degree=2
)

SAMPLING_ARGS=(
  --prompt "A curious raccoon"
  --save-output
  --output-path outputs
  --output-file-name "A curious raccoon.mp4"
)

sglang generate "${SERVER_ARGS[@]}" "${SAMPLING_ARGS[@]}"

Once the generation task has finished, the server will shut down automatically.

Note

The HTTP server-related arguments are ignored in this subcommand.