Instructions to use 1038lab/SDMatte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 1038lab/SDMatte 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("1038lab/SDMatte", 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 README.md
Browse files
README.md
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
---
|
| 2 |
-
license:
|
| 3 |
pipeline_tag: image-to-image
|
| 4 |
library_name: diffusers
|
| 5 |
base_model:
|
|
@@ -59,5 +59,4 @@ This custom node integrates SDMatte into ComfyUI workflows, enabling high-qualit
|
|
| 59 |
|
| 60 |
> Recent interactive matting methods have shown satisfactory performance in capturing the primary regions of objects, but they fall short in extracting fine-grained details in edge regions. Diffusion models trained on billions of image-text pairs demonstrate exceptional capability in modeling highly complex data distributions and synthesizing realistic texture details, while exhibiting robust text-driven interaction capabilities — making them an attractive solution for interactive matting.
|
| 61 |
|
| 62 |
-
---
|
| 63 |
-
|
|
|
|
| 1 |
---
|
| 2 |
+
license: openrail++
|
| 3 |
pipeline_tag: image-to-image
|
| 4 |
library_name: diffusers
|
| 5 |
base_model:
|
|
|
|
| 59 |
|
| 60 |
> Recent interactive matting methods have shown satisfactory performance in capturing the primary regions of objects, but they fall short in extracting fine-grained details in edge regions. Diffusion models trained on billions of image-text pairs demonstrate exceptional capability in modeling highly complex data distributions and synthesizing realistic texture details, while exhibiting robust text-driven interaction capabilities — making them an attractive solution for interactive matting.
|
| 61 |
|
| 62 |
+
---
|
|
|