[Doc] [skip ci]Sync. (#1363)
Signed-off-by: Alicia <115451386+congw729@users.noreply.github.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
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# GLM-Image Online Serving
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/glm_image>.
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This example demonstrates how to deploy GLM-Image for online image generation using vLLM-Omni.
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## 🛠️ Installation
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Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
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## Run examples (GLM-Image)
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**Note**: These examples work with the default configuration on **2× NVIDIA A100 (80GB)** or equivalent. Stage 0 (AR) and Stage 1 (Diffusion) each use one GPU by default. For single-GPU setups, modify the stage configuration to share the same device.
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### Launch the Server
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```bash
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# Use default configuration
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vllm serve zai-org/GLM-Image --omni --port 8091
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```
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Or use the convenience script:
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```bash
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cd examples/online_serving/glm_image
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bash run_server.sh
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```
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If you have a custom stage configs file:
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```bash
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vllm serve zai-org/GLM-Image --omni --port 8091 --stage-configs-path /path/to/glm_image.yaml
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```
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### Send Requests
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Get into the glm_image folder:
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```bash
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cd examples/online_serving/glm_image
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```
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Send request via Python:
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```bash
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python openai_chat_client.py --prompt "A cute cat sitting on a window sill"
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```
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The Python client supports the following command-line arguments:
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- `--prompt` (or `-p`): Text prompt for generation (default: `A beautiful sunset over the ocean with sailing boats`)
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- `--output` (or `-o`): Output file path (default: `glm_image_output.png`)
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- `--server` (or `-s`): Server URL (default: `http://localhost:8091`)
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- `--image` (or `-i`): Input image path (for image-to-image editing)
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- `--height`: Image height in pixels (default: 1024)
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- `--width`: Image width in pixels (default: 1024)
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- `--steps`: Number of inference steps (default: 50)
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- `--guidance-scale`: Classifier-free guidance scale (default: 1.5)
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- `--seed`: Random seed (default: 42)
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- `--negative`: Negative prompt
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## Modality Control
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GLM-Image supports **text-to-image** and **image-to-image** modes.
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The default yaml configuration deploys AR on GPU 0 and DiT on GPU 1. You can use the default configuration file: [`glm_image.yaml`](https://github.com/vllm-project/vllm-omni/tree/main/vllm_omni/model_executor/stage_configs/glm_image.yaml)
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| Mode | Input | Output | Description |
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| -------------- | ------------ | ------ | ---------------------------------- |
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| Text-to-Image | Text | Image | Generate images from text prompts |
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| Image-to-Image | Image + Text | Image | Edit images with text instructions |
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### Text-to-Image
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Generate images from text prompts:
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**Using Python client**
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```bash
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python openai_chat_client.py \
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--prompt "A photorealistic mountain landscape at sunset" \
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--height 1024 \
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--width 1024 \
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--output landscape.png
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```
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**Using curl**
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```bash
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curl -s http://localhost:8091/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "A beautiful sunset over the ocean with sailing boats"}
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],
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"extra_body": {
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"height": 1024,
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"width": 1024,
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"num_inference_steps": 50,
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"guidance_scale": 1.5,
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"seed": 42
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}
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}' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png
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```
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Or use the script:
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```bash
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bash run_curl_text_to_image.sh "A futuristic city skyline at night"
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```
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### Image-to-Image (Image Editing)
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Edit images with text instructions:
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**Using Python client**
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```bash
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python openai_chat_client.py \
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--prompt "Convert this image to watercolor style" \
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--image input.png \
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--output watercolor.png
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```
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**Using curl**
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```bash
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IMG_B64=$(base64 < input.png | tr -d '\n')
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curl -s http://localhost:8091/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d @- <<EOF | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png
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{
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"messages": [{
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"role": "user",
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"content": [
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{"type": "text", "text": "Convert this image to watercolor style"},
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{"type": "image_url", "image_url": {"url": "data:image/png;base64,'$IMG_B64'"}}
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]
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}],
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"extra_body": {
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"height": 1024,
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"width": 1024,
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"num_inference_steps": 50,
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"guidance_scale": 1.5,
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"seed": 42
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}
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}
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EOF
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```
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Or use the script:
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```bash
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bash run_curl_image_edit.sh input.png "Convert to watercolor style"
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```
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## Generation Parameters (extra_body)
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| Parameter | Type | Default | Description |
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| --------------------- | ----- | ------- | ----------------------------------- |
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| `height` | int | 1024 | Image height in pixels |
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| `width` | int | 1024 | Image width in pixels |
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| `num_inference_steps` | int | 50 | Number of diffusion denoising steps |
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| `guidance_scale` | float | 1.5 | Classifier-free guidance scale |
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| `seed` | int | 42 | Random seed for reproducibility |
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| `negative_prompt` | str | None | Negative prompt |
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## Response Format
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```json
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{
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"id": "chatcmpl-xxx",
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"created": 1234567890,
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"model": "zai-org/GLM-Image",
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/png;base64,..."
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}
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}
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]
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},
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"finish_reason": "stop"
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}
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],
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"usage": {}
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}
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```
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## Extract Image
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```bash
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# From a saved JSON response
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cat response.json | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png
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```
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## Architecture
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GLM-Image uses a 2-stage multistage pipeline:
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```
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Stage 0 (AR Model) Stage 1 (Diffusion)
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┌───────────────────┐ ┌─────────────────────┐
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│ vLLM-optimized │ prior │ GlmImagePipeline │
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│ GlmImageFor │──tokens──►│ ┌───────────────┐ │
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│ Conditional │ │ │ DiT Denoiser │ │
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│ Generation │ │ └───────┬───────┘ │
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│ (9B AR model) │ │ ▼ │
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└───────────────────┘ │ ┌───────────────┐ │
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▲ │ │ VAE Decode │──┼──► Image
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│ │ └───────────────┘ │
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Text / Image └─────────────────────┘
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Input
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```
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## VRAM Requirements
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| Stage | VRAM |
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| :---------------- | :--------------------- |
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| Stage-0 (AR) | **~18 GiB + KV Cache** |
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| Stage-1 (DiT+VAE) | **~20 GiB** |
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| Total | **~38 GiB + KV Cache** |
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## File Description
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| File | Description |
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| --------------------------- | ------------------------------------- |
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| `run_server.sh` | Server startup script |
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| `run_curl_text_to_image.sh` | Text-to-image curl example |
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| `run_curl_image_edit.sh` | Image-to-image (editing) curl example |
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| `openai_chat_client.py` | Python client (t2i + i2i) |
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## FAQ
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- If you encounter OOM errors, adjust `gpu_memory_utilization` in the stage config:
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```yaml
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# In glm_image.yaml, reduce from default 0.6:
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gpu_memory_utilization: 0.5
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```
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- The first request may be slow due to model warmup. Subsequent requests will be faster.
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## Example materials
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??? abstract "openai_chat_client.py"
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``````py
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--8<-- "examples/online_serving/glm_image/openai_chat_client.py"
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``````
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??? abstract "run_curl_image_edit.sh"
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``````sh
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--8<-- "examples/online_serving/glm_image/run_curl_image_edit.sh"
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``````
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??? abstract "run_curl_text_to_image.sh"
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``````sh
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--8<-- "examples/online_serving/glm_image/run_curl_text_to_image.sh"
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``````
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??? abstract "run_server.sh"
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``````sh
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--8<-- "examples/online_serving/glm_image/run_server.sh"
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``````
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