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
| { | |
| "_class_name": "GlmImagePipeline", | |
| "_diffusers_version": "0.37.0.dev0", | |
| "_name_or_path": "zai-org/GLM-Image-Decoder", | |
| "text_encoder": [ | |
| "transformers", | |
| "T5EncoderModel" | |
| ], | |
| "vision_language_encoder": [ | |
| "transformers", | |
| "GlmImageForConditionalGeneration" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "ByT5Tokenizer" | |
| ], | |
| "processor":[ | |
| "transformers", | |
| "GlmImageProcessor" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "GlmImageTransformer2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |