vllm-omni/examples/offline_inference/glm_image

GLM-Image Multistage End-to-End Inference

This example demonstrates how to run GLM-Image with the vLLM-Omni multistage architecture.

Architecture

GLM-Image uses a 2-stage pipeline:

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

Features

• vLLM-optimized AR: Uses PagedAttention and tensor parallelism for faster prior token generation
• Flexible deployment: AR and Diffusion stages can run on different GPUs
• Text-to-Image: Generate images from text descriptions
• Image-to-Image: Edit existing images with text prompts

Usage

Text-to-Image

python end2end.py \
    --model-path /path/to/glm-image \
    --config-path ../../vllm_omni/model_executor/stage_configs/glm_image.yaml \
    --prompt "A beautiful sunset over the ocean with sailing boats" \
    --height 1024 \
    --width 1024 \
    --output output_t2i.png

Image-to-Image (Image Editing)

python end2end.py \
    --model-path /path/to/glm-image \
    --config-path ../../vllm_omni/model_executor/stage_configs/glm_image.yaml \
    --prompt "Transform this scene into a winter wonderland" \
    --image input.png \
    --output output_i2i.png

With Custom Parameters

python end2end.py \
    --model-path /path/to/glm-image \
    --config-path ../../vllm_omni/model_executor/stage_configs/glm_image.yaml \
    --prompt "A photorealistic cat sitting on a window sill" \
    --height 1024 \
    --width 1024 \
    --num-inference-steps 50 \
    --guidance-scale 1.5 \
    --seed 42 \
    --output output.png

Shell Scripts

Run Text-to-Image

./run_t2i.sh

Run Image-to-Image

./run_i2i.sh --image /path/to/input.png

Stage Configuration

The stage config (glm_image.yaml) defines:

• Stage 0 (AR): Uses GPUARWorker with vLLM engine

  • Model: GlmImageForConditionalGeneration
  • Output: token_ids (prior tokens)

• Stage 1 (Diffusion): Uses diffusion engine

  • Model: GlmImagePipeline
  • Output: Generated image

See vllm_omni/model_executor/stage_configs/glm_image.yaml for full configuration.

Comparison with Single-Stage

Aspect	Single-Stage (transformers)	Multistage (vLLM)
AR Model	transformers native	vLLM PagedAttention
Memory	Higher (no KV cache opt)	Lower (optimized)
Throughput	Lower	Higher
Flexibility	Single GPU	Multi-GPU support

Troubleshooting

OOM Error

Try reducing memory usage:

# In glm_image.yaml, adjust:
gpu_memory_utilization: 0.5  # Reduce from 0.6

Slow Initialization

The first run loads model weights. Subsequent runs are faster:

--stage-init-timeout 900  # Increase timeout for slow storage

Requirements

• vLLM-Omni with GLM-Image support
• CUDA-capable GPU (recommended: H100/A100 with 80GB)
• GLM-Image model weights