Instructions to use PoolerSP/LogiLete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PoolerSP/LogiLete 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("PoolerSP/LogiLete", 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
Update model_index.json
Browse files- model_index.json +0 -8
model_index.json
CHANGED
|
@@ -5,10 +5,6 @@
|
|
| 5 |
"diffusers",
|
| 6 |
"DDIMScheduler"
|
| 7 |
],
|
| 8 |
-
"text_encoder": [
|
| 9 |
-
"transformers",
|
| 10 |
-
"CLIPTextModel"
|
| 11 |
-
],
|
| 12 |
"tokenizer": [
|
| 13 |
"transformers",
|
| 14 |
"CLIPTokenizer"
|
|
@@ -17,9 +13,5 @@
|
|
| 17 |
"diffusers",
|
| 18 |
"UNet2DConditionModel"
|
| 19 |
],
|
| 20 |
-
"vae": [
|
| 21 |
-
"diffusers",
|
| 22 |
-
"AutoencoderKL"
|
| 23 |
-
],
|
| 24 |
"safety_checker": null
|
| 25 |
}
|
|
|
|
| 5 |
"diffusers",
|
| 6 |
"DDIMScheduler"
|
| 7 |
],
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
"tokenizer": [
|
| 9 |
"transformers",
|
| 10 |
"CLIPTokenizer"
|
|
|
|
| 13 |
"diffusers",
|
| 14 |
"UNet2DConditionModel"
|
| 15 |
],
|
|
|
|
|
|
|
|
|
|
|
|
|
| 16 |
"safety_checker": null
|
| 17 |
}
|