Text-to-Image
Diffusers
Trained with AutoTrain
stable-diffusion
stable-diffusion-diffusers
lora
template:sd-lora
Instructions to use philipp-zettl/disco-elysium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use philipp-zettl/disco-elysium with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("philipp-zettl/disco-elysium") prompt = "in the style of DSYL" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from philipp-zettl/disco-elysium: direct link, hf CLI and curl.
- Browser
- Download file 534 Bytes
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https://huggingface.co/philipp-zettl/disco-elysium/resolve/main/README.md
- Command line
-
hf download hf://philipp-zettl/disco-elysium/README.md
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curl -L -o README.md https://huggingface.co/philipp-zettl/disco-elysium/resolve/main/README.md
534 Bytes
metadata
tags:
- autotrain
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
instance_prompt: in the style of DSYL
license: openrail++
AutoTrain LoRA DreamBooth - philipp-zettl/disco-elysium
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on in the style of DSYL using DreamBooth. LoRA for the text encoder was enabled: True.