Instructions to use Hishambarakat/checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Hishambarakat/checkpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Hishambarakat/checkpoint", dtype=torch.bfloat16, device_map="cuda") 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
Upload 2 files
Browse files- pretrained_ljs.pth +3 -0
- pretrained_vctk.pth +3 -0
pretrained_ljs.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c94fb49d08ba90c598de16e7d5dec8d26bf225c1cf193a4fba05eb2dbda5a561
|
| 3 |
+
size 145599717
|
pretrained_vctk.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ab981615c443d935fc3a89b08137df544a1175bad99bcbbc9f59e7c3d4930043
|
| 3 |
+
size 159123481
|