Text-to-Image
Diffusers
Trained with AutoTrain
stable-diffusion
stable-diffusion-diffusers
lora
template:sd-lora
Instructions to use stablediffusionapi/my-stablediffusion-lora-2548 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stablediffusionapi/my-stablediffusion-lora-2548 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", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("stablediffusionapi/my-stablediffusion-lora-2548") prompt = "best quality, aqua eyes, baseball cap, closed mouth, green background, hat, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
AutoTrain LoRA DreamBooth - stablediffusionapi/my-stablediffusion-lora-2548
These are LoRA adaption weights for Lykon/DreamShaper. The weights were trained on best quality, aqua eyes, baseball cap, closed mouth, green background, hat, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt using DreamBooth. LoRA for the text encoder was enabled: False.
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Model tree for stablediffusionapi/my-stablediffusion-lora-2548
Base model
Lykon/DreamShaper