Instructions to use refabric/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use refabric/outputs with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SG161222/RealVisXL_V3.0_Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("refabric/outputs") prompt = "photo of rfbrc_xl_vbm_ks" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - refabric/outputs
Model description
These are refabric/outputs LoRA adaption weights for SG161222/RealVisXL_V3.0_Turbo.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
Trigger words
You should use photo of rfbrc_xl_vbm_ks to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for refabric/outputs
Base model
SG161222/RealVisXL_V3.0_Turbo