Instructions to use LLanv/AssetDropper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LLanv/AssetDropper 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("LLanv/AssetDropper", 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
LLJ commited on
Upload model_index.json
Browse files- model_index.json +2 -2
model_index.json
CHANGED
|
@@ -33,11 +33,11 @@
|
|
| 33 |
"CLIPTokenizer"
|
| 34 |
],
|
| 35 |
"unet": [
|
| 36 |
-
"
|
| 37 |
"UNet2DConditionModel"
|
| 38 |
],
|
| 39 |
"unet_encoder": [
|
| 40 |
-
"
|
| 41 |
"UNet2DConditionModel"
|
| 42 |
],
|
| 43 |
"vae": [
|
|
|
|
| 33 |
"CLIPTokenizer"
|
| 34 |
],
|
| 35 |
"unet": [
|
| 36 |
+
"diffusers",
|
| 37 |
"UNet2DConditionModel"
|
| 38 |
],
|
| 39 |
"unet_encoder": [
|
| 40 |
+
"diffusers",
|
| 41 |
"UNet2DConditionModel"
|
| 42 |
],
|
| 43 |
"vae": [
|