Instructions to use yhx12/DiffThinker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yhx12/DiffThinker 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("yhx12/DiffThinker", 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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# DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
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<a href="https://diffthinker-project.github.io/"><img src="https://img.shields.io/badge/%F0%9F%8C%90%20Project-Page-2563eb" alt="Project Page"></a>
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### Inference & Evaluation
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The test datasets used in our experiments is provided within each task's directory. We recommend using the same data to ensure the reproducibility of our results and to facilitate comparison with other models. If you wish to generate your own test data, please refer to the ```gen.txt``` file in each task directory.
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```code
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# DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
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<a href="https://diffthinker-project.github.io/"><img src="https://img.shields.io/badge/%F0%9F%8C%90%20Project-Page-2563eb" alt="Project Page"></a>
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This model is based on the paper: https://arxiv.org/abs/2512.24165
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### Inference & Evaluation
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The test datasets used in our experiments is provided within each task's directory. We recommend using the same data to ensure the reproducibility of our results and to facilitate comparison with other models. If you wish to generate your own test data, please refer to the ```gen.txt``` file in each task directory.
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```code
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