Instructions to use LinxiaoShi/Magicbokeh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LinxiaoShi/Magicbokeh with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LinxiaoShi/Magicbokeh", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit ·
f9b23ad
1
Parent(s): 0b5c3c1
Track pth files with LFS
Browse files- DAdepth.pth +3 -0
- lora.pkl +3 -0
DAdepth.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f1d97076e00b938f88c80c8bf5194dc9ddb7a6ccce30d2b0c4741e96466682ad
|
| 3 |
+
size 4014848159
|
lora.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3a1a8907aeca382e3756d7d8a4d3203cf6fdf65af45925f90cad412c81086491
|
| 3 |
+
size 1006916194
|