/docs/user_guide/examples/online_serving/glm_image.md

GLM-Image Online Serving

GLM-Image is a 2-stage image generation model (AR + Diffusion) supported by vLLM-Omni's declarative config system. The pipeline topology and stage structure are declared in vllm_omni/model_executor/models/glm_image/pipeline.py; deployment knobs (GPU placement, memory, sampling params) live in vllm_omni/deploy/glm_image.yaml.

Start Server

vllm serve zai-org/GLM-Image --omni --port 8091

The config system auto-detects the pipeline from the model's model_index.json — no manual --stage-configs-path or --deploy-config needed.

By default, stage 0 (AR) runs on GPU 0 and stage 1 (Diffusion) on GPU 1. To colocate both stages on a single GPU, override per stage:

vllm serve zai-org/GLM-Image --omni --port 8091 \
    --stage-overrides '{"0": {"devices": "0"}, "1": {"devices": "0"}}'

API Calls

Text-to-Image

curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "A photorealistic mountain landscape at sunset"}
    ],
    "extra_body": {
      "height": 1024,
      "width": 1024,
      "num_inference_steps": 50,
      "guidance_scale": 1.5,
      "seed": 42
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png

Image-to-Image (Image Editing)

curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "Convert this image to watercolor style"},
          {"type": "image_url", "image_url": {"url": "data:image/png;base64,$(base64 -w0 input.png)}"}
        ]
      }
    ],
    "extra_body": {
      "height": 1024,
      "width": 1024,
      "num_inference_steps": 50,
      "guidance_scale": 1.5
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png

Using the OpenAI Python SDK

from openai import OpenAI
import base64

client = OpenAI(base_url="http://localhost:8091/v1", api_key="none")

response = client.chat.completions.create(
    model="zai-org/GLM-Image",
    messages=[{"role": "user", "content": "A beautiful sunset over the ocean"}],
    extra_body={
        "height": 1024,
        "width": 1024,
        "num_inference_steps": 50,
        "guidance_scale": 1.5,
        "seed": 42,
    },
)

img_url = response.choices[0].message.content[0].image_url.url
_, b64_data = img_url.split(",", 1)
with open("output.png", "wb") as f:
    f.write(base64.b64decode(b64_data))

For general-purpose request methods (curl, OpenAI SDK, Python requests), see the Text-to-Image and Image-to-Image guides.

Generation Parameters

When using /v1/chat/completions, pass these inside extra_body in the curl JSON, or via the extra_body keyword argument in the OpenAI Python SDK (see the Chat Completions API guide). When using the dedicated /v1/images/generations or /v1/images/edits endpoints, pass the supported generation controls as top-level fields directly. For image dimensions and count, use size and n rather than height or width.

Parameter	Type	Default	Description
height	int	1024	Image height in pixels
width	int	1024	Image width in pixels
num_inference_steps	int	50	Number of diffusion denoising steps
guidance_scale	float	1.5	Classifier-free guidance scale
seed	int	None	Optional random seed
negative_prompt	str	None	Negative prompt

Response Format

{
  "id": "chatcmpl-xxx",
  "created": 1234567890,
  "model": "zai-org/GLM-Image",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": [
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/png;base64,..."
            }
          }
        ]
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {}
}

Extract Image

cat response.json | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png

Architecture

GLM-Image uses a 2-stage pipeline:

Stage 0 (AR Model)                Stage 1 (Diffusion)
┌───────────────────┐            ┌─────────────────────┐
│ vLLM-optimized    │  prior     │  GlmImagePipeline   │
│ GlmImageFor       │──tokens──►│  ┌───────────────┐  │
│ Conditional       │            │  │ DiT Denoiser  │  │
│ Generation        │            │  └───────┬───────┘  │
│ (9B AR model)     │            │          ▼          │
└───────────────────┘            │  ┌───────────────┐  │
        ▲                        │  │  VAE Decode   │──┼──► Image
        │                        │  └───────────────┘  │
   Text / Image                  └─────────────────────┘
     Input

VRAM Requirements

Stage	VRAM
Stage-0 (AR)	~18 GiB + KV Cache
Stage-1 (DiT+VAE)	~20 GiB
Total	~38 GiB + KV Cache

FAQ

• If you encounter OOM errors, adjust gpu_memory_utilization in the deploy config:

# In vllm_omni/deploy/glm_image.yaml, reduce from default 0.6:
gpu_memory_utilization: 0.5

• The first request may be slow due to model warmup. Subsequent requests will be faster.