hu-zijing/B2-DiffuRL
[CVPR 25] A framework named B^2-DiffuRL for RL-based diffusion model fine-tuning.
This framework helps AI researchers and practitioners fine-tune diffusion models to create images that better match text prompts. It takes a pre-trained diffusion model and desired image generation criteria, then outputs an improved model that generates images more aligned with prompts while maintaining diversity. This is for machine learning engineers and researchers working on advanced image generation.
No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer aiming to improve the alignment and diversity of images generated by your diffusion models, especially when dealing with sparse reward signals during fine-tuning.
Not ideal if you are looking for an out-of-the-box solution for general image generation without needing to fine-tune model internals or if you are not familiar with reinforcement learning concepts.
Stars
53
Forks
4
Language
Python
License
MIT
Category
Last pushed
Mar 31, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/hu-zijing/B2-DiffuRL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
FlorianFuerrutter/genQC
Generative Quantum Circuits
horseee/DeepCache
[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free
Gen-Verse/MMaDA
MMaDA - Open-Sourced Multimodal Large Diffusion Language Models (dLLMs with block diffusion,...
kuleshov-group/mdlm
[NeurIPS 2024] Simple and Effective Masked Diffusion Language Model
Shark-NLP/DiffuSeq
[ICLR'23] DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models