Instructions to use kindlytree/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kindlytree/demo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Linaqruf/anything-v3.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kindlytree/demo") prompt = "shanshui" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Linaqruf/anything-v3.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("kindlytree/demo")
prompt = "shanshui"
image = pipe(prompt).images[0]LoRA DreamBooth - kindlytree/lora-outputs
These are LoRA adaption weights for Linaqruf/anything-v3.0. The weights were trained on shanshui using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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Model tree for kindlytree/demo
Base model
Linaqruf/anything-v3.0


