[Perf] GLM Image

examples/offline_inference/glm_image/README.md

@@ -0,0 +1,138 @@
+# 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
+
+```bash
+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)
+
+```bash
+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
+
+```bash
+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
+
+```bash
+./run_t2i.sh
+```
+
+### Run Image-to-Image
+
+```bash
+./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:
+
+```bash
+# 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:
+
+```bash
+--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