Instructions to use nan2/lcbanner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nan2/lcbanner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nan2/lcbanner", 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
- Draw Things
- DiffusionBee
Update vae/config.json
#2
by bindy - opened
- vae/config.json +1 -1
vae/config.json
CHANGED
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@@ -20,7 +20,7 @@
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"layers_per_block": 2,
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"norm_num_groups": 32,
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"out_channels": 3,
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-
"sample_size":
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"scaling_factor": 0.18215,
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"up_block_types": [
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"UpDecoderBlock2D",
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"layers_per_block": 2,
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"norm_num_groups": 32,
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"out_channels": 3,
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+
"sample_size": 256,
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"scaling_factor": 0.18215,
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"up_block_types": [
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"UpDecoderBlock2D",
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