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
- Draw Things
- DiffusionBee
Clarification on X-Omni VQ Tokenizer Licensing in GLM-Image
#4
by JosephusCheung - opened
Hi,
I noticed the VQ tokenizer weights in this repo are seemingly identical to X-Omni/X-Omni-En, which is licensed under Apache 2.0.
Since your model is released under MIT, could you clarify if the VQ weights were explicitly re-licensed? Or should the project license/model card be updated to reflect the Apache 2.0 terms for these weights?
Thanks!