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
Add VDEBench evaluation results
#31
by hongzhuyi - opened
This PR adds GLM-Image results for 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.