Text-to-Image
Diffusers
StableDiffusionPipeline
stable-diffusion
sygil-diffusion
sygil-devs
finetune
stable-diffusion-1.5
Instructions to use Sygil/Sygil-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Sygil/Sygil-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("Sygil/Sygil-Diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "environment art, realistic" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Commit ·
0604b03
1
Parent(s): e36b0e9
Upload 2 files
Browse files- unet/config.json +1 -0
- unet/diffusion_pytorch_model.bin +1 -1
unet/config.json
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.10.2",
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"act_fn": "silu",
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"attention_head_dim": 8,
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"block_out_channels": [
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.10.2",
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"_name_or_path": "D:\\Alejandro\\Projects\\Python\\AI\\ImageGen\\Automatic1111\\models\\dreambooth\\sygil-diffusion-v0.1\\working\\unet",
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"act_fn": "silu",
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"attention_head_dim": 8,
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"block_out_channels": [
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unet/diffusion_pytorch_model.bin
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size 3438354725
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