Text-to-Image
Diffusers
Trained with AutoTrain
stable-diffusion
stable-diffusion-diffusers
lora
template:sd-lora
Instructions to use stablediffusionapi/my-stablediffusion-lora-5478 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stablediffusionapi/my-stablediffusion-lora-5478 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lykon/DreamShaper", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("stablediffusionapi/my-stablediffusion-lora-5478") prompt = "material, mahogany, floor, interior, living room," image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 561 Bytes
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---
tags:
- autotrain
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: Lykon/DreamShaper
instance_prompt: material, mahogany, floor, interior, living room,
license: openrail++
---
# AutoTrain LoRA DreamBooth - stablediffusionapi/my-stablediffusion-lora-5478
These are LoRA adaption weights for Lykon/DreamShaper. The weights were trained on material, mahogany, floor, interior, living room, using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
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