TsinghuaC3I/Efficient-Diffusion-Models
TPAMI 2025 Survey Paper
This is a research survey paper compiling extensive knowledge on 'Efficient Diffusion Models' for anyone working with advanced generative AI. It brings together existing methods for creating high-quality images, videos, 3D models, and audio more efficiently, including how to adapt them for specific tasks. Researchers and practitioners in fields like computer vision, graphics, and audio processing would use this as a comprehensive guide.
No commits in the last 6 months.
Use this if you need a thorough overview of current techniques and advancements in making diffusion models faster and more effective for various generative tasks, helping you understand their core principles and practical applications.
Not ideal if you are looking for a ready-to-use software library or tool to implement diffusion models directly, as this is a survey paper primarily focused on research and conceptual understanding.
Stars
26
Forks
1
Language
Python
License
—
Category
Last pushed
Mar 31, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/TsinghuaC3I/Efficient-Diffusion-Models"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
UCSC-VLAA/story-iter
[ICLR 2026] A Training-free Iterative Framework for Long Story Visualization
PaddlePaddle/PaddleMIX
Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks,...
keivalya/mini-vla
a minimal, beginner-friendly VLA to show how robot policies can fuse images, text, and states to...
adobe-research/custom-diffusion
Custom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)
byliutao/1Prompt1Story
🔥ICLR 2025 (Spotlight) One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation...