Instructions to use pradhaansbhat/Thinking-In-Boxes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pradhaansbhat/Thinking-In-Boxes 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("pradhaansbhat/Thinking-In-Boxes", 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
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README.md
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@@ -45,7 +45,7 @@ This is the trained LoRA used in the paper **Thinking In Boxes: 3D Editing in Re
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Thinking-In-Boxes is an Image-to-Image Generative LoRA for `black-forest-labs/FLUX.1-Kontext-dev` trained for the task of Geometric Image Editing.
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This LoRA is trained on the dataset
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## Usage with Diffusers 🧨
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Thinking-In-Boxes is an Image-to-Image Generative LoRA for `black-forest-labs/FLUX.1-Kontext-dev` trained for the task of Geometric Image Editing.
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This LoRA is trained on the Thinking-In-Boxes dataset available [here](https://huggingface.co/datasets/pradhaansbhat/Thinking-In-Boxes). Details of training are available in the supplementary section of the paper.
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## Usage with Diffusers 🧨
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