Instructions to use xing0916/DDB_Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xing0916/DDB_Edit 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("xing0916/DDB_Edit", 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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by nielsr HF Staff - opened
README.md
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# Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
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## 🌟 Overview
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- A **hybrid absorption mechanism** that mixes source-image and mask tokens, preserving source structure as spatial anchors.
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- An **information-guided noise schedule** that aligns training corruption with the easy-first decoding process used at inference time.
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DDB supports text-guided image editing, structural image translation, and text-to-image generation.
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pipeline_tag: image-to-image
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library_name: diffusers
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# Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
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**Paper**: [Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation](https://huggingface.co/papers/2608.29997)
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**Code**: [GitHub](https://github.com/HKU-HealthAI/DDB)
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## 🌟 Overview
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- A **hybrid absorption mechanism** that mixes source-image and mask tokens, preserving source structure as spatial anchors.
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- An **information-guided noise schedule** that aligns training corruption with the easy-first decoding process used at inference time.
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DDB supports text-guided image editing, structural image translation, and text-to-image generation.
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