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
zRzRzRzRzRzRzR commited on
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README.md
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@@ -165,7 +165,7 @@ curl -s -X POST "http://localhost:30000/v1/images/edits" \
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+ Please ensure that all text intended to be rendered in the image is enclosed in quotation marks in the model input and We strongly recommend using GLM-4.7 to enhance prompts for higher image quality. Please check [our github script](https://raw.githubusercontent.com/zai-org/GLM-Image/refs/heads/main/examples/prompt_utils.py) for more details.
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+ The AR model used in GLM‑Image is configured with `do_sample=True`, a temperature of `0.9`, and a topp of `0.75` by default. A higher temperature results in more diverse and rich outputs, but it can also lead to a certain decrease in output stability.
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+ The target image resolution must be divisible by 32. Otherwise, it will throw an error.
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+ vLLM-Omni and SGLang (with AR speedup) support is currently being integrated — stay tuned. For inference cost, you can check in our github.
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## Model Performance
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+ Please ensure that all text intended to be rendered in the image is enclosed in quotation marks in the model input and We strongly recommend using GLM-4.7 to enhance prompts for higher image quality. Please check [our github script](https://raw.githubusercontent.com/zai-org/GLM-Image/refs/heads/main/examples/prompt_utils.py) for more details.
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+ The AR model used in GLM‑Image is configured with `do_sample=True`, a temperature of `0.9`, and a topp of `0.75` by default. A higher temperature results in more diverse and rich outputs, but it can also lead to a certain decrease in output stability.
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+ The target image resolution must be divisible by 32. Otherwise, it will throw an error.
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+ Because inference optimizations for this architecture are currently limited, the runtime cost is still relatively high. You can set `enable_model_cpu_offload=True` to run it with `~23GB` of GPU memory, at the cost of slower inference.
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+ vLLM-Omni and SGLang (with AR speedup) support is currently being integrated — stay tuned. For inference cost, you can check in our github.
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## Model Performance
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