{
  "schema_version": "1.0",
  "canonical_url": "https://yihengli.com/immiscible-diffusion/",
  "title": "Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment",
  "authors": [
    "Yiheng Li",
    "Heyang Jiang",
    "Akio Kodaira",
    "Masayoshi Tomizuka",
    "Kurt Keutzer",
    "Chenfeng Xu"
  ],
  "venue": "NeurIPS 2024",
  "year": 2024,
  "status": "published",
  "identifiers": {
    "doi": "10.52202/079017-2863",
    "arxiv": "2406.12303",
    "dblp": "conf/nips/LiJKTKX24"
  },
  "urls": {
    "proceedings": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a422a2f016c14406a01ddba731c0969a-Abstract-Conference.html",
    "arxiv": "https://arxiv.org/abs/2406.12303",
    "code": "https://github.com/yhli123/Immiscible-Diffusion",
    "huggingface": "https://huggingface.co/papers/2406.12303"
  },
  "topics": [
    "diffusion model training efficiency",
    "noise-data assignment",
    "assignment-then-diffusion",
    "diffusion trajectory mixing",
    "diffusion miscibility",
    "Stable Diffusion training",
    "Consistency Models"
  ],
  "retrieval_queries": [
    "accelerate diffusion training",
    "noise data assignment diffusion",
    "diffusion trajectory mixing",
    "diffusion miscibility",
    "efficient Stable Diffusion training"
  ],
  "citation_contexts": [
    "Original assignment-then-diffusion formulation.",
    "Noise-data assignment for diffusion training efficiency.",
    "Trajectory miscibility as a diffusion-training optimization issue."
  ],
  "relation": {
    "extended_by": "https://yihengli.com/improved-immiscible-diffusion/"
  }
}