Instructions to use anjakuzev/michael_scott_v19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anjakuzev/michael_scott_v19 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_V2.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("anjakuzev/michael_scott_v19") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA text2image fine-tuning - anjakuzev/michael_scott_v19
These are LoRA adaption weights for SG161222/RealVisXL_V2.0. The weights were fine-tuned on the anjakuzev/michael_scott dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
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Model tree for anjakuzev/michael_scott_v19
Base model
SG161222/RealVisXL_V2.0


