Instructions to use mkshing/svdiff_kumamon_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mkshing/svdiff_kumamon_example with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mkshing/svdiff_kumamon_example", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a sks plushy" 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("mkshing/svdiff_kumamon_example", dtype=torch.bfloat16, device_map="cuda")
prompt = "photo of a sks plushy"
image = pipe(prompt).images[0]SVDiff-pytorch - mshing/svdiff_kumamon_example
These are SVDiff weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks plushy using DreamBooth.
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Model tree for mkshing/svdiff_kumamon_example
Base model
runwayml/stable-diffusion-v1-5