Instructions to use rootlocalghost/LongCat-Image-Edit-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootlocalghost/LongCat-Image-Edit-Turbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="rootlocalghost/LongCat-Image-Edit-Turbo")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rootlocalghost/LongCat-Image-Edit-Turbo", dtype="auto") - Notebooks
- Google Colab
- Kaggle
clone transformer/config.json
Browse files- transformer/config.json +13 -0
transformer/config.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "LongCatImageTransformer2DModel",
|
| 3 |
+
"_diffusers_version": "0.30.0.dev0",
|
| 4 |
+
"attention_head_dim": 128,
|
| 5 |
+
"in_channels": 64,
|
| 6 |
+
"joint_attention_dim": 3584,
|
| 7 |
+
"num_attention_heads": 24,
|
| 8 |
+
"num_layers": 10,
|
| 9 |
+
"num_single_layers": 20,
|
| 10 |
+
"patch_size": 1,
|
| 11 |
+
"pooled_projection_dim": 3584
|
| 12 |
+
}
|
| 13 |
+
|