Instructions to use slitrobo/infinite-passage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use slitrobo/infinite-passage with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("slitrobo/infinite-passage") prompt = "a beautiful infinite romapassage hallway in a building in Rome" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("slitrobo/infinite-passage")
prompt = "a beautiful infinite romapassage hallway in a building in Rome"
image = pipe(prompt).images[0]LoRA DreamBooth - infinite-passage
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on the instance prompt "a beautiful infinite romapassage hallway in a building in Rome" using DreamBooth. You can find some example images in the following.
Test prompt: a beautiful infinite romapassage hallway in a building in Rome

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Model tree for slitrobo/infinite-passage
Base model
stabilityai/stable-diffusion-2-1-base