Instructions to use Intel/GLM-Image-int4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Intel/GLM-Image-int4-AutoRound with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Intel/GLM-Image-int4-AutoRound", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 602 Bytes
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"image_processor": {
"data_format": "channels_first",
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "GlmImageImageProcessorFast",
"image_std": [
0.5,
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],
"merge_size": 1,
"patch_size": 16,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 4194304,
"shortest_edge": 262144
},
"temporal_patch_size": 1
},
"processor_class": "GlmImageProcessor"
}
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