Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
wildcard
Instructions to use baruga/ancient-maps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use baruga/ancient-maps with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("baruga/ancient-maps", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of ancma map of beautiful flower garden." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Add `scale_factor` to vae config. (#1)
Browse files- Add `scale_factor` to vae config. (910450666dc3bf9ba84dc74e22d47487e69882a2)
Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>
- vae/config.json +1 -0
vae/config.json
CHANGED
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@@ -21,6 +21,7 @@
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"norm_num_groups": 32,
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"out_channels": 3,
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"sample_size": 512,
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"up_block_types": [
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"norm_num_groups": 32,
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"out_channels": 3,
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"sample_size": 512,
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+
"scaling_factor": 0.18215,
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"up_block_types": [
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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