vllm-project--vllm-omni/docs/user_guide/examples/offline_inference/glm_image.md

GLM-Image Offline Inference

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 live in vllm_omni/deploy/glm_image.yaml.

Architecture

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

Text-to-Image

from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(model="zai-org/GLM-Image")
    outputs = omni.generate(
        "A photorealistic mountain landscape at sunset",
        sampling_params={
            "height": 1024,
            "width": 1024,
            "num_inference_steps": 50,
            "guidance_scale": 1.5,
            "seed": 42,
        },
    )
    outputs[0].request_output.images[0].save("output.png")

Image-to-Image (Image Editing)

from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(model="zai-org/GLM-Image")
    outputs = omni.generate(
        {
            "prompt": "Convert this image to watercolor style",
            "multi_modal_data": {
                "image": "input.png",
            },
        },
        sampling_params={
            "height": 1024,
            "width": 1024,
            "num_inference_steps": 50,
            "guidance_scale": 1.5,
            "seed": 42,
        },
    )
    outputs[0].request_output.images[0].save("output.png")

Generation Parameters

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

VRAM Requirements

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