Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
Paper • 2608.29997 • Published • 2
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]Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
Paper: Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
Code: GitHub
🌟 Overview
DDB (Discrete Diffusion Bridges) is a framework for spatially and temporally aligned image translation and generation. It introduces:
- A hybrid absorption mechanism that mixes source-image and mask tokens, preserving source structure as spatial anchors.
- An information-guided noise schedule that aligns training corruption with the easy-first decoding process used at inference time.
DDB supports text-guided image editing, structural image translation, and text-to-image generation.
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