112 lines
3.9 KiB
Markdown
112 lines
3.9 KiB
Markdown
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# GLM-Image for text-to-image and image editing
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## Summary
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- Vendor: Z.ai
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- Model: `GLM/GLM-Image`
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- Task: Text-to-image (T2I) and image-to-image
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- Mode: Online serving with the OpenAI-compatible API
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- Maintainer: Community
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## When to use this recipe
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Use this recipe when you want a known-good starting point for serving
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`GLM/GLM-Image` with vLLM-Omni on **two 80 GB NVIDIA A800** GPUs (Ampere-class,
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same default layout as the upstream **2×A100 80GB** example: Stage 0 AR on GPU 0,
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Stage 1 diffusion on GPU 1) and validate the deployment with the existing
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`examples/online_serving/glm_image` clients.
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## References
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- Upstream or canonical docs:
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[`docs/user_guide/examples/online_serving/glm_image.md`](../../docs/user_guide/examples/online_serving/glm_image.md)
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- Related issue or discussion:
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[#2888](https://github.com/vllm-project/vllm-omni/pull/2888)
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## Hardware Support
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This recipe documents **dual-GPU** CUDA layouts on A800 80 GB
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for the same software stack. Add more platforms (for example ROCm / NPU) as
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community validation lands.
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## GPU
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### 2× A800 80GB
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#### Environment
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These versions were taken from a working **editable** install: activate `vllm-omni/.venv` (or your equivalent), then align `pip` / Git with the rows below when reproducing this recipe.
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- OS: Linux
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- Python: 3.12
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- Driver / runtime: NVIDIA CUDA stack with **two** A800 80 GB GPUs visible (set `CUDA_VISIBLE_DEVICES` on your host if needed)
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- vLLM: **0.19.0**
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- vLLM-Omni: **0.19.0rc2.dev138+g38d5f2d53** (editable install from this repo; Git **`38d5f2d5`**, `git describe` ≈ **`v0.19.0rc1-138-g38d5f2d5`**)
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- Transformers: **5.5.4** (same `.venv` as above; required so `glm_image` configs load for Stage 0)
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#### Command
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Start the server from the repository root:
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```bash
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vllm serve zai-org/GLM-Image --omni --port 8091
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```
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To use the bundled stage config explicitly (same default as above):
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```bash
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vllm serve zai-org/GLM-Image \
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--omni \
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--port 8091 \
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--deploy-config vllm_omni/deploy/glm_image.yaml
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```
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#### Verification
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Run one of the existing example clients after the server is ready:
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```bash
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curl -s http://172.18.69.133:8000/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 landscape painting"}
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],
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"extra_body": {
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"height": 1920,
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"width": 1920,
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"num_inference_steps": 50,
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"true_cfg_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 > land.png
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```
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After the command finishes, check for the output files:
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```bash
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ls output.png
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```
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#### Sample end-to-end metrics
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One representative **offline** GLM-Image E2E run on this recipe’s **2× A800 80GB**.
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Overall summary from the run’s metrics. Rough wall-time split: **Stage 0 (AR)** ~**25 s**,
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**Stage 1 (diffusion)** ~**34 s** (see `e2e_stage_*_wall_time_ms` below).
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| Field | Value |
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| --- | ---: |
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| e2e_requests | 1 |
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| e2e_wall_time_ms | 61,148.679 |
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| e2e_total_tokens | 1,300 |
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| e2e_avg_time_per_request_ms | 61,148.679 |
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| e2e_avg_tokens_per_s | 21.260 |
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| e2e_stage_0_wall_time_ms | 24,708.760 |
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| e2e_stage_1_wall_time_ms | 33,787.442 |
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#### Notes
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- Memory usage: Roughly **~38 GiB + KV** on Stage 0 (AR) and **~20 GiB** on Stage 1 (DiT+VAE) per the user guide; two 80 GB cards match the default split.
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- Key flags: `--omni` is required; `--stage-configs-path` is optional unless you use a custom YAML (for example single-GPU).
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- Keep **Transformers ≥ 5.5.1** (this recipe used **5.5.4**) so `glm_image` configs resolve; otherwise Stage 0 can fail at `ModelConfig` validation.
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- Known limitations: This starter recipe follows the dual-GPU online path documented under `examples/online_serving/glm_image`. The first request may be slower due to warmup.
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- Generation time: about **61 s** wall time end-to-end for the sample above (50 inference steps, 1024×1024).
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