Instructions to use callgg/glm-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use callgg/glm-encoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("callgg/glm-encoder", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a66f81558f6936dd613961ec97face2aebda114b374a239c2de3b7a183eeee5
- Size of remote file:
- 20 MB
- SHA256:
- bcc742fc44db1f0870d7320b495e6240cc40e202565fc96786220fa0d9ddb41c
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