Instructions to use nitrosocke/redshift-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/redshift-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nitrosocke/redshift-diffusion", 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
Commit ·
7302e41
1
Parent(s): 4d5357f
Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default (#10)
Browse files- Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default (76e73f69b058b06ca53399af827820ed18250d59)
Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>
- unet/config.json +1 -1
unet/config.json
CHANGED
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@@ -26,7 +26,7 @@
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"norm_eps": 1e-05,
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"norm_num_groups": 32,
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"out_channels": 4,
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-
"sample_size":
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"norm_eps": 1e-05,
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"norm_num_groups": 32,
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"out_channels": 4,
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+
"sample_size": 64,
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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