Instructions to use zai-org/GLM-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zai-org/GLM-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zai-org/GLM-Image", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
Today
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Fix VDEBench evaluation date compatibility
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Add VDEBench evaluation results
Verified signatureThis PR adds **GLM-Image** results for [VDEBench](https://huggingface.co/datasets/hongzhuyi/VDEBench), evaluated on all **3,506 examples** of dense English and Chinese document editing. | Task | Score ↑ | |---|---:| | Local (edited region, headline) | 0.459 | | Global (full page) | 0.453 | Each score is the equally weighted mean of IoU, CDM, BLEU-4 and TEDS on a 0–1 scale. The result file links to the full metric record from the VDEBench team. [Detailed results](https://huggingface.co/datasets/hongzhuyi/VDEBench/blob/main/results/2026-11-05/glm_image/results.json).c0795d5
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